Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems

As one of the central tasks in machine learning, regression finds lots of applications in different fields. An existing common practice for solving regression problems is the mean square error (MSE) minimization approach or its regularized variants which require prior knowledge about the models. Recently, Yi et al., proposed a mutual information based supervised learning framework where they introduced a label entropy regularization which does not require any prior knowledge. When applied to classification tasks and solved via a stochastic gradient descent (SGD) optimization algorithm, their approach achieved significant improvement over the commonly used cross entropy loss and its variants. However, they did not provide a theoretical convergence analysis of the SGD algorithm for the proposed formulation. Besides, applying the framework to regression tasks is nontrivial due to the potentially infinite support set of the label. In this paper, we investigate the regression under the mutual information based supervised learning framework. We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique. For the proposed formulation, we give the convergence analysis of the SGD algorithm for solving it in practice. Finally, we consider a multi-output regression data model where we derive the generalization performance lower bound in terms of the mutual information associated with the underlying data distribution. The result shows that the high dimensionality can be a bless instead of a curse, which is controlled by a threshold. We hope our work will serve as a good starting point for further research on the mutual information based regression.Comment: 28 pages, 2 figures, presubmitted to AISTATS2023 for reviewin

Multi-field coupling dynamic response analysis of pipelines with double corrosion defects under seismic loading in cold regions

Multi-field coupling system in the paper is composed of the corroded pipelines, fluid, heat preservation layers and frost -heaving soil, and the buried pipelines are inevitable to be affected by earthquakes, but few studies have been done on corroded pipelines in multi-field coupling under seismic loading in cold regions. The paper analyzes the dynamic response of the pipelines under seismic loading. Method by FEM (finite element method), the three-dimensional multi-field coupling mechanics model has been established for analysis, based on a thermal-fluid-solid multi-field coupling analysis theory, considering the actual stress-strain characteristics of the pipeline steel and the frost heaving force of soil. Meanwhile, the influences of fluid pressure, fluid temperature, corrosion defects and seismic waves on the mechanical properties of the pipelines are then discussed. The results show that: the relative corrosion depth, fluid pressure and fluid temperature have obvious influence on the mechanical properties of corroded pipelines; other factors are relatively weak; the properties of corroded pipelines do not change with different seismic loading. For the corroded pipelines in cold regions, the factors which have obvious influence on the mechanical properties of pipelines should be monitored intensely

The effect of multiple thermal cycles on Ti-6Al-4V deposits fabricated by wire-arc directed energy deposition:Microstructure evolution, mechanical properties, and corrosion resistance

Thermal cycles have an important effect on the microstructure and properties of the components fabricated by wire-arc directed energy deposition (wire-arc DED). In this study, a Gleeble thermal-mechanical simulator was adopted to create closer-to-reality thermal cycles with the assistance of a numerical simulation model and experimental Ti-6Al-4V deposition. Step-by-step microstructure evolution, including αm, retained β, and GB α, microhardness gradual variation, and the corrosion resistance change before and after the entire thermal cycle were investigated. Therefore, combining phase orientation and high-magnification morphology, transformed and untransformed α that occurred in low- and medium-temperature thermal cycles can be distinguished. After the entire thermal cycle, αm laths coarsened from ∼1 µm to ∼1.2 µm, and the content of retained β phase became more and more. The αm formed around grain boundaries partially disappeared and was occupied by α laths from the inner grain. GB α was more continuously distributed along prior β grain boundaries due to its lower formation temperature during the subsequent thermal cycles that were occurring incomplete α→β transformation. The severe preferential orientation of α phases formed after the deposition and high-temperature thermal cycle was also alleviated through the twice low-temperature thermal cycles. Besides, the microhardness decreased from 318.78 ± 7.5 HV to 285.17 ± 5.3 HV after the high-temperature thermal cycle but eventually increased significantly to 330.5 ± 6.4 HV after experiencing the final low-temperature thermal cycle. The corrosion resistance decreased after the entire thermal cycle, indicating a performance difference between the top and bottom regions of the Ti-6Al-4 V component fabricated by wire-arc DED.</p

Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness. Since there is a rigorous mapping between the Signed Distance Map (SDM) calculated from object boundary contours and the binary segmentation map, we exploit the feasibility of learning the SDM directly from medical scans. By converting the segmentation task into predicting an SDM, we show that our proposed method retains superior segmentation performance and has better smoothness and continuity in shape. To leverage the complementary information in traditional segmentation training, we introduce an approximated Heaviside function to train the model by predicting SDMs and segmentation maps simultaneously. We validate our proposed models by conducting extensive experiments on a hippocampus segmentation dataset and the public MICCAI 2015 Head and Neck Auto Segmentation Challenge dataset with multiple organs. While our carefully designed backbone 3D segmentation network improves the Dice coefficient by more than 5% compared to current state-of-the-arts, the proposed model with SDM learning produces smoother segmentation results with smaller Hausdorff distance and average surface distance, thus proving the effectiveness of our method.Comment: AAAI 202

Na+-induced Ca2+ influx through reverse mode of Na+-Ca2+ exchanger in mouse ventricular cardiomyocyte

BACKGROUND: Dobutamine is commonly used for clinical management of heart failure and its pharmacological effects have long been investigated as inotropics via β-receptor activation. However, there is no electrophysiological evidence if dobutamine contributes inotropic action due at least partially to the reverse mode of Na+-Ca2+ exchanger (NCX) activation. METHODS: Action potential (AP), voltage-gated Na+ (INa), Ca2+ (ICa), and K+ (Ito and IK1) currents were observed using whole-cell patch technique before and after dobutamine in ventricular cardiomyocytes isolated from adult mouse hearts. Another sets of observation were also performed with Kb-r7943 or in the solution without [Ca2+]o. RESULTS: Dobutamine (0.1-1.0 μM) significantly enhanced the AP depolarization with prolongation of AP duration (APD) in a concentration-dependent fashion. The density of INa was also increased concentration-dependently without alternation of voltage-dependent steady-status of activation and inactivation, reactivation as well. Whereas, the activities for ICa, Ito, and IK1 were not changed by dobutamine. Intriguingly, the dobutamine-mediated changes in AP repolarization were abolished by 3 μM Kb-r7943 pretreatment or by simply removing [Ca2+]o without affecting accelerated depolarization. Additionally, the ratio of APD50/APD90 was not significantly altered in the presence of dobutamine, implying that effective refractory period was remain unchanged. CONCLUSIONS: This novel finding provides evidence that dobutamine upregulates of voltage-gated Na+ channel function and Na+ influx-induced activation of the reverse mode of NCX, suggesting that dobutamine may not only accelerate ventricular contraction via fast depolarization but also cause Ca2+ influx, which contributes its positive inotropic effect synergistically with β-receptor activation without increasing the arrhythmogenetic risk

CanopyCAM – an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans

Canopy cover (CC) is an important indicator for crop development. Currently, CC can be estimated indirectly by measuring leaf area index (LAI) using commercially available hand-held meters. However, it does not capture the dynamics of CC. Continuous CC monitoring is essential for dry edible beans production since it can affect crop water use, weed, and disease control. It also helps growers to closely monitor “yellowness”, or senescence of dry beans to decide proper irrigation cutoff timing to allow the crop to dry down for harvest. Therefore, the goal of this study was to develop a device – CanopyCAM, containing software and hardware that can monitor dry bean CC continuously. CanopyCAM utilized an in-house developed image-based algorithm, edge-computing, and Internet of Things (IoT) telemetry to process and transmit CC in real-time. In the 2021 growing season, six CanopyCAMs were developed with three installed in fully irrigated dry edible beans research plots and three installed at commercial farm fields, respectively. CC measurements were recorded at 15 min interval from 7:00 am to 7:00 pm in each day. Initially, the overall trend of CC development increased over time but fluctuations in daily readings were noticed due to changing lighting conditions which caused some overexposed images. A simple filtering algorithm was developed to remove the “noisy images”. CanopyCAM measured CC (CCCanopyCAM) were compared with CC obtained from a LI-COR Plant Canopy Analyzer (CCLAI). The average error between CCCanopyCAM and CCLAI was 2.3 %, and RMSE and R2 were 2.95 % and 0.99, respectively. In addition, maximum CC (CCmax) and duration of the maximum CC (tmax_canopy) were identified at each installation location using the generalized reduced gradient (CRG) algorithm with nonlinear optimization. An improvement of correlation was found between dry bean yield and combination of CCmax and tmax_canopy (R2 = 0.77, Adjusted R2 = 0.62) as compared to yield versus CCmax (R2 = 0.58) or yield versus tmax_canopy (R2 = 0.45) only. This edge-computing, IoT enabled CanopyCAM, provided accurate and continuous CC readings for dry edible beans which could be used by growers and researchers for different purposes

Development an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans

Canopy cover (CC) is an important indicator for crop development. Currently, CC can be estimated indirectly by measuring leaf area index (LAI), using commercially available hand-held meters. However, it does not capture the dynamics of CC. Continuous CC monitoring is essential for dry edible beans production since it can affect crop water use, weed, and disease control. It also helps growers to closely monitor “yellowness”, or senescence of dry beans to decide proper irrigation cutoff to allow the crop to dry down for harvest. The goal of this study was to develop a device – CanopyCAM, containing software and hardware that can monitor dry bean CC continuously. CanopyCAM utilized an in-house developed image-based algorithm, edge-computing, and Internet of Things (IoT) telemetry to transmit and report CC in real-time. In the 2021 growing season, six CanopyCAMs were developed with three installed in fully irrigated dry edible beans research plots and three installed at commercial farms. CC measurements were recorded at 15 min interval from 7:00 am to 7:00 pm each day. Initially, the overall trend of CC development increased over time but there were many fluctuations in daily readings due to lighting conditions which caused some overexposed images. A simple filtering algorithm was developed to remove the “noisy images”. CanopyCAM measured CC (CCCanopyCAM) were compared with CC obtained from a Li-COR Plant Canopy Analyzer (CCLAI). The average error between CCCanopyCAM and CCLAI was 2.3%, and RMSE and R2 were 2.95% and 0.99, respectively. In addition, maximum CC (CCmax) and duration of the maximum CC (tmax_canopy) were identified at each installation location using the generalized reduced gradient (CRG) algorithm with nonlinear optimization. An improvement of correlation was found between dry bean yield and combination of CCmax and tmax_canopy (R2 = 0.77, Adjusted R2 = 0.62) as compared to yield vs. CCmax (R2 = 0.58) or yield vs. tmax_canopy (R2 = 0.45). This edge-computing, IoT enabled capability of CanopyCAM, provided accurate CC readings which could be used by growers and researchers for different purpose

Protective effects of ciliary neurotrophic factor on the retinal ganglion cells by injure of hydrogen peroxide

AIM: To explore the effect of ciliary neurotrophic factor (CNTF) on retinal ganglion cell (RGC)-5 induced by hydrogen peroxide (H2O2). METHODS: After cell adherence, RGC-5 culture medium was changed to contain different concentrations of H2O2 from 50 to 150 µmol/L at four time points (0.5, 1, 1.5 and 2h) to select the concentration and time point for H2O2 induced model. Two different ways of interventions for injured RGC-5 cells respectively were CNTF as an addition in the culture medium or recombinant lentiviral plasmid carrying CNTF gene transfecting bone mesenchymal stem cells (BMSCs) for co-culture with RGC-5. RESULTS: Compared to the control group, H2O2 led to RGC-5 death closely associated with concentrations and action time of H2O2 and we chose 125 µmol/L and 2h to establish the H2O2-induced model. While CNTF inhibited the loss of RGC-5 cells obviously with a dose-dependent survival rate. Nevertheless two administration routes had different survival rate yet higher rate in recombinant lentiviral plasmid group but there were no statistically significant differences. CONCLUSION: Both the two administration routes of CNTF have effects on RGC-5 cells induced by H2O2. If their own advantages were combined, there may be a better administration route