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

Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex

Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain. Here, we recorded in vivo extracellular activity of the ACC and S1 simultaneously from male adult Sprague-Dale rats (n = 5), while repetitive noxious laser stimulations were delivered to animalÕs hindpaw during pain experiments. We identified spontaneous pain-like events based on stereotyped pain behaviors in rats. We further conducted systematic analyses of spike and local field potential (LFP) recordings from both ACC and S1 during evoked and spontaneous pain episodes. From LFP recordings, we found stronger phase-amplitude coupling (theta phase vs. gamma amplitude) in the S1 than the ACC (n = 10 sessions), in both evoked (p = 0.058) and spontaneous pain-like behaviors (p = 0.017, paired signed rank test). In addition, pain-modulated ACC and S1 neuronal firing correlated with the amplitude of stimulus-induced event-related potentials (ERPs) during evoked pain episodes. We further designed statistical and machine learning methods to detect pain signals by integrating ACC and S1 ensemble spikes and LFPs. Together, these results reveal differential coding roles between the ACC and S1 in cortical pain processing, as well as point to distinct neural mechanisms between evoked and putative spontaneous pain at both LFP and cellular levels

China's Culture

170 hal.;22 c

Cell-Free Massive MIMO with Energy-Efficient Downlink Operation in Industrial IoT

Cell-free massive Multi-input Multi-output (MIMO) can offer higher spectral efficiency compared with cellular massive MIMO by providing services to users through the collaboration of distributed APs, and cell-free massive MIMO systems with distributed operations are attracting a great deal of industry attention due to their simplicity and ease of deployment. This paper aims to find an optimal solution for energy efficiency in the downlink operation in the Industrial Internet based on cell-free massive MIMO systems with distributed operations. A system model is proposed, and a theoretical analysis on energy efficiency is presented. The optimization problem of efficient downlink operation is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is further decomposed into two sub-problems, i.e., maximizing the sum-rate of the downlink transmission and optimizing the total energy consumption. The two sub-problems are addressed via AP selection and power allocation, respectively. The simulation results demonstrate that our algorithms can significantly improve the energy efficiency with low computational complexity in comparison with traditional distributed cell-free massive MIMO. Even in the presence of pilot contamination, the proposed algorithms can still provide significant energy efficiency when a large number of IoTDs are connected

Variation in concentrations of major bioactive compounds in Prunella vulgaris L. related to plant parts and phenological stages

Prunella vulgaris L. (Labiatae) contains a variety of structurally diverse natural products, primarily rosmarinic acid (RA), ursolic acid (UA) and oleanolic acid (OA), which possess a wide array of biological properties. In the present study, P. vulgaris was harvested at three developmental stages (vegetative, full-flowering and mature-fruiting stages), dissected into stem and leaf tissues and assayed for chemical contents using high performance liquid chromatography. Significant changes in the concentrations of the major secondary metabolites (RA, UA and OA) were observed at the different development stages. The highest concentrations of RA, UA and OA were found at the full-flowering stage (15.83 mg/g dry weight (DW) RA, 1.77 mg/g DW UA and 0.65 mg/g DW OA). Among the different aerial parts of the plant, the concentrations of RA, UA and OA were higher in the leaves than in the stems at the different developmental stages. These results suggest that the full-flowering stage is characterized by the highest concentrations of bioactive compounds. Therefore, this stage may be the optimum point for harvesting P. vulgaris plants. In additional, the leaves of P. vulgaris demonstrated higher RA, UA and OA concentrations than the stems, suggesting higher utilization potential

Optimisation of potassium chloride nutrition for proper growth, physiological development and bioactive component production in Prunella vulgaris L.

Prunella vulgaris L. is an important medicinal plant with a variety of pharmacological activities, but limited information is available about its response to potassium chloride (KCl) supplementation. P. vulgaris seedlings were cultured in media with four different KCl levels (0, 1.00, 6.00 and 40.00 mM). Characteristics relating to the growth, foliar potassium, water and chlorophyll content, photosynthesis, transpiration, nitrogen metabolism, bioactive constituent concentrations and yield were determined after three months. The appropriate KCl concentration was 6.00 mM to result in the highest values for dry weight, shoot height, spica and root weight, spica length and number in P. vulgaris. The optimum KCl concentration resulted in a maximum net photosynthetic rate (Pn) that could be associated with the highest chlorophyll content and fully open stomata conductance. A supply of surplus KCl resulted in a higher concentration of foliar potassium and negatively correlated with the biomass. Plants that were treated with the appropriate KCl level showed a greater capacity for nitrate assimilation. The Pn was significantly and positively correlated with nitrate reductase (NR) and glutamine synthetase (GS) activities and was positively correlated with leaf-soluble protein and free amino acid (FAA) contents. Both KCl starvation (0 mM) and high KCl (40.00 mM) led to water loss through a high transpiration rate and low water absorption, respectively, and resulted in increased concentrations of ursolic acid (UA), oleanolic acid (OA) and flavonoids, with the exception of rosmarinic acid (RA). Moreover, the optimum concentration of KCl significantly increased the yields of RA, UA, OA and flavonoids. Our findings suggested that significantly higher plant biomass; chlorophyll content; Pn; stronger nitrogen anabolism; lower RA, UA, OA and flavonoid accumulation; and greater RA, UA, OA and flavonoid yields in P. vulgaris could be expected in the presence of the appropriate KCl concentration (6.00 mM)