A Generalized Gradient Projection Filter Algorithm for Inequality Constrained Optimization

A generalized gradient projection filter algorithm for inequality constrained optimization is presented. It has three merits. The first is that the amount of computation is lower, since the gradient matrix only needs to be computed one time at each iterate. The second is that the paper uses the filter technique instead of any penalty function for constrained programming. The third is that the algorithm is of global convergence and locally superlinear convergence under some mild conditions

Modulation of 2-Point Discrimination Threshold Under Chemically Induced Global Stimulation

The two-point discrimination threshold(2PDT) serves as a critical indicator in the study of tactile acuity, representing the minimal distance at which an individual can differentiate two distinct points of contact on the skin. This measurement is instrumental in exploring the neural mechanisms underlying tactile perception. On the other hand, tactile acuity can be modulated by global stimulation. Prior research indicates that experimental inflammation induced by an application of capsaicin cream increases2PDT. In our study, we applied chemicals(oregano, menthol, and Sichuan pepper) to selectively activate receptors that usually respond to mild physical stimuli to investigate their influence on2PDT without inducing inflammation. The results unveiled a pronounced augmentation of2PDT following any form of global stimulation. Intriguingly, the cumulative effect of the chemical mix on2PDT appeared to be additive. These observations suggest that Wide Dynamic Range(WDR) neurons, functioning as relay nuclei with projections for touch, warmth, and cold sensations, play a pivotal role in this process. In lateral connection structures parallel to afferent nerve transmission pathways with WDR neurons as relay nuclei, global stimulation amplifies excitatory connections over inhibitory ones, thereby elevating the two-point discrimination threshold

How Do We Move: Modeling Human Movement with System Dynamics

Modeling how human moves in the space is useful for policy-making in transportation, public safety, and public health. Human movements can be viewed as a dynamic process that human transits between states (\eg, locations) over time. In the human world where intelligent agents like humans or vehicles with human drivers play an important role, the states of agents mostly describe human activities, and the state transition is influenced by both the human decisions and physical constraints from the real-world system (\eg, agents need to spend time to move over a certain distance). Therefore, the modeling of state transition should include the modeling of the agent's decision process and the physical system dynamics. In this paper, we propose \ours to model state transition in human movement from a novel perspective, by learning the decision model and integrating the system dynamics. \ours learns the human movement with Generative Adversarial Imitation Learning and integrates the stochastic constraints from system dynamics in the learning process. To the best of our knowledge, we are the first to learn to model the state transition of moving agents with system dynamics. In extensive experiments on real-world datasets, we demonstrate that the proposed method can generate trajectories similar to real-world ones, and outperform the state-of-the-art methods in predicting the next location and generating long-term future trajectories.Comment: Accepted by AAAI 2021, Appendices included. 12 pages, 8 figures. in Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI'21), Feb 202

Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks

Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusion strategies such as merging them at the input, concatenating multi-modality features inside models, or applying attention to each data modality. These fusion strategies are straightforward yet insufficient. In this paper, we propose a novel fusion method named Explicit Attention-Enhanced Fusion (EAEF) that fully takes advantage of each type of data. Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features. EAEF uses one branch to enhance feature extraction for i) and iii) and the other branch to remedy insufficient representations for ii). The outputs of two branches are fused to form complementary features. As a result, the proposed fusion method outperforms state-of-the-art by 1.6\% in mIoU on semantic segmentation, 3.1\% in MAE on salient object detection, 2.3\% in mAP on object detection, and 8.1\% in MAE on crowd counting. The code is available at https://github.com/FreeformRobotics/EAEFNet

Phase Structure of the Topological Anderson Insulator

We study the disordered topological Anderson insulator in a 2-D (square not strip) geometry. We first report the phase diagram of finite systems and then study the evolution of phase boundaries when the system size is increased to a very large $1120 \times 1120$ area. We establish that conductance quantization can occur without a bulk band gap, and that there are two distinct scaling regions with quantized conductance: TAI-I with a bulk band gap, and TAI-II with localized bulk states. We show that there is no intervening insulating phase between the bulk conduction phase and the TAI-I and TAI-II scaling regions, and that there is no metallic phase at the transition between the quantized and insulating phases. Centered near the quantized-insulating transition there are very broad peaks in the eigenstate size and fractal dimension $d_2$; in a large portion of the conductance plateau eigenstates grow when the disorder strength is increased. The fractal dimension at the peak maximum is $d_2 \approx 1.5$. Effective medium theory (CPA, SCBA) predicts well the boundaries and interior of the gapped TAI-I scaling region, but fails to predict all boundaries save one of the ungapped TAI-II scaling region. We report conductance distributions near several phase transitions and compare them with critical conductance distributions for well-known models.Comment: Minor changes only in this versio

Effects of different extracts of Cremastra appendiculata (D. Don) Makino Cremastra appendiculata (D. Don) Makino on apoptosis of A549 cells

Purpose: To investigate the effect of different extracts of Cremastra appendiculata (D. Don) Makino onapoptosis of A549 cells, and the underlying mechanism.Methods: The contents of colchicine in ethyl acetate and n-butanol extracts of Cremastra appendiculata(D. Don) Makino were determined using high performance liquid chromatography (HPLC). Lung cancerA549 cells cultured in vitro were divided into blank control, standard colchicine and Cremastra appendiculata (D. Don) Makino extract groups. The effect of different extract concentrations on proliferation of the cells was determined using methyl thiazolyl diphenyl-tetrazolium (MTT) assay, while apoptosis of A549 cells induced by the extracts was evaluated by flow cytometry (FC).Results: Compared with the standard colchicine group, there was no colchicine in the n-butanol and ethyl acetate extracts of Cremastra appendiculata. Results from MTT assay showed that the extract inhibited the proliferation of A549 cells (p < 0.05). Flow cytometry results showed that ethyl acetate extract significantly enhanced apoptosis in A549 cells (p < 0.05). However, n-butanol extract had no significant effect on the apoptosis of A549 cells (p < 0.05).Conclusion: The ethyl acetate extract of Cremastra appendiculata (D. Don) Makino induces apoptosis in lung cancer A549 cells. Therefore, there is a need for further research and development of antitumor drugs from the extract of Cremastra appendiculata (D. Don) Makino. Keywords: Cremastra appendiculata (D. Don) Makino, Colchicine, A549 cells, Apoptosi