Latent Action Research Articles

Nerolidol inhibits proliferation and triggers ROS-facilitated apoptosis in lung carcinoma cells via the suppression of MAPK/STAT3/NF-κB and P13K/AKT pathways.

Lung cancer (LC) is the leading cause of malignancy-related mortalities globally, and the existing treatment interventions are associated with harmful side effects. In the current study, we evaluated the anti-tumor efficiency of nerolidol (NRD) on human non-small cell lung cancer (NSCLC) cells. Nerolidol is a sesquiterpene alcohol extracted from the essential oils of aromatic flora with known anti-cancer activities. The latent action of NRD on antiproliferative and apoptotic effects in A549 cells is uncertain. Thus, our work is designed to explore the antiproliferative and apoptotic actions of NRD (20 and 25 μM/mL) against A549 cells. The activity of NRD on A549 cell cytotoxicity, intracellular reactive oxygen species (ROS), mitochondrial membrane potential (MMP), apoptosis, anti-apoptotic proteins, and MAPK/TAT3/NF-κB and P13K/AKT signaling pathways were assessed using MTT tests, dichlorodihydrofluorescein diacetate (DCFH-DA), dual acridine orange/ethidium bromide (AO/EB), DAPI, Rh-123, reverse transciption polymerase chain reaction (RT-PCR), and western blot analyses. We found that NRD could inhibit NSCLC cell viability through elevated intracellular ROS and MMP loss and elicited apoptosis in a quantity-dependent manner. Similarly, NRD can reduce inflammatory cytokines and anti-apoptotic elements, as well as trigger apoptotic signaling pathways. Our data established that NRD decreases A549 cell proliferation through ROS-mediated apoptosis, triggering the MAPK/STAT3/NF-κB and P13K/AKT pathways, suggesting that NRD is a possible protective remedy for NSCLC.

Discriminative Action Snippet Propagation Network for Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization (WTAL) aims to classify and localize actions in untrimmed videos with only video-level labels. Recent studies have attempted to obtain more accurate temporal boundaries by exploiting latent action instances in ambiguous snippets or propagating representative action features. However, empirically handcrafted ambiguous snippet extraction and the imprecise alignment of representative snippet propagation lead to challenges in modeling the completeness of actions for these methods. In this article, we propose a Discriminative Action Snippet Propagation Network (DASP-Net) to accurately discover ambiguous snippets in videos and propagate discriminative instance-level features throughout the video for improving action completeness. Specifically, we introduce a novel discriminative feature propagation module for capturing the global contextual attention and propagating the action concept across the whole video by perceiving the discriminative action snippets with instance information from the same video. Simultaneously, we incorporate denoised pseudo-labels as supervision, where we correct the controversial prediction based on the feature space distribution during training, thereby alleviating false detection caused by noise background features. Furthermore, we design an ambiguous feature mining module, which maximizes the feature affinity information of action and background in ambiguous snippets to generate more accurate latent action and background snippets and learns more precise action instance boundaries through contrastive learning of action and background snippets. Extensive experiments show that DASP-Net achieves state-of-the-art results on THUMOS14 and ActivityNet1.2 datasets.

Effects of KLF11 on Vascular Smooth Muscle Cells and its Underlying Mechanisms in Intracranial Aneurysm.

Vascular smooth muscle cells (VSMCs) affect the phenotypic changes in intracranial aneurysm (IA). They exhibit enhanced dissociation and migration and play a key role in IA pathogenesis. KLF transcription factor 11 (KLF11), a member of the KLF family, significantly affects the cancer cell proliferation, differentiation, and apoptosis. However, its expression, biological functions, and latent action mechanisms in IA remain unclear. This study aimed to analyze the effects of KLF11 on H2O2-induced human brain VSMCs (HBVSMCs) in IA. We determined the mRNA levels of KLF11 in 15 paired arterial wall tissues of patients with IA and healthy volunteers. HBVSMCs were stimulated with H2O2 for 6h to establish an IA model in vitro. Cell viability, apoptosis, and inflammatory cytokine (interleukin [IL-1β, tumor necrosis factor-α, and IL-6) levels were assessed using the 3-(4,5-dimethylthiazol-2-yl)-2-5-diphenyltetrazolium bromide, flow cytometry, and enzyme-linked immunosorbent assays, respectively. KLF11 expression was determined via quantitative reverse transcription-polymerase chain reaction, western blotting, and immunofluorescence analyses. Furthermore, p-p38, p38, cleaved-caspase 3, and caspase 3 levels were determined via western blotting. KLF11 levels were downregulated in the arterial wall tissues of patients with IA than in those of the control group. KLF11 upregulation by KLF11-plasmid promoted the cell viability, reduced apoptosis, decreased cleaved-caspase 3 expression, and inhibited the secretion of inflammatory factors in H2O2-induced HBVSMCs. KLF11-plasmid remarkably reduced p-p38 expression and p-p38/p-38 ratio; however, these effects were reversed by P79350 treatment. Overall, KLF11 upregulation improved the HBVSMC functions and exerted protective effects against IA, suggesting its potential for IA treatment.

Interdisciplinary approach to defining outdoor places of knowledge work: quantified photo analysis.

Working outdoors is an emerging, sparsely studied phenomenon in knowledge work. Office tasks have traditionally been considered to belong to indoor environments. The worldwide pandemic of COVID-19 has increased and changed attitudes toward multilocational working. The aim of this method study is 2-fold: first to define for interdisciplinary context outdoor environments when they are used as places of outdoor knowledge work and second to quantify the thematic photo analysis to support interdisciplinary understanding of the places of outdoor knowledge work. The review of literature has been one of the methods to support the interdisciplinary approach of this article. The photographs of outdoor knowledge workplaces and views from the workstations are studied through photo analysis customized from the existing press photograph analysis. First, we defined outdoor environments when used as places of outdoor knowledge work, as unconditioned outdoor or semi-outdoor places (opposite to closed indoor spaces with stable, conditioned indoor climate) providing favorable action possibilities as sources of comfort and mitigating unfavorable conditions, for example, by microclimatic solutions. Instead of defining all spaces as outdoor environments not fulfilling a stable indoor climate (conditioned) definition, adaptation to thermal and physical environments also brought semi-outdoor space into the definition. In this context, favorable latent action possibilities (affordance) in the built environment are often related to microclimate as a source of comfort. Second, we focussed on photo analysis. The proposed model is based on journalistic photo analysis PPSA and the pOKW model, which have been further developed in this study to pOKW2 model for analyzing mobile-based collected self-reported photographs by the occupants. In this pOKW2 model, the photographs would have time-location information enabling the combining of data from other datasets and thereby reducing the number of characteristics to be analyzed from the photograph. We proposed rating (in numeric form) to detect the favorable and unfavorable characteristics in the photographs most likely supporting or hindering conditions of outdoor knowledge work. This quantification would enable the use of machine vision analysis and would support handling large quantities of photographs and their combination with other datasets in interdisciplinary research. The quantification of the photo analysis (pOKW2) includes the readiness to combine the analysis results with other time-location-specific datasets in an interdisciplinary research collaboration to advance our understanding of latent action possibilities for outdoor knowledge work.

Temporal Action Segmentation With High-Level Complex Activity Labels

The temporal action segmentation task segments videos temporally and predicts action labels for all frames. Fully supervising such a segmentation model requires dense frame-wise action annotations, which are expensive and tedious to collect. This work is the first to propose a Constituent Action Discovery (CAD) framework that only requires the video-wise high-level complex activity label as supervision for temporal action segmentation. The proposed approach automatically discovers constituent video actions using an activity classification task. Specifically, we define a finite number of latent action prototypes to construct video-level dual representations with which these prototypes are learned collectively through the activity classification training. This setting endows our approach with the capability to discover potentially shared actions across multiple complex activities. Due to the lack of action-level supervision, we adopt the Hungarian matching algorithm to relate latent action prototypes to ground truth semantic classes for evaluation. We show that with the high-level supervision, the Hungarian matching can be extended from the existing video and activity levels to the global level. The global-level matching allows for action sharing across activities, which has never been considered in the literature before. Extensive experiments demonstrate that our discovered actions can help perform temporal action segmentation and activity recognition tasks.

Structured Cooperative Reinforcement Learning With Time-Varying Composite Action Space.

In recent years, reinforcement learning has achieved excellent results in low-dimensional static action spaces such as games and simple robotics. However, the action space is usually composite, composed of multiple sub-action with different functions, and time-varying for practical tasks. The existing sub-actions might be temporarily invalid due to the external environment, while unseen sub-actions can be added to the current system. To solve the robustness and transferability problems in time-varying composite action spaces, we propose a structured cooperative reinforcement learning algorithm based on the centralized critic and decentralized actor framework, called SCORE. We model the single-agent problem with composite action space as a fully cooperative partially observable stochastic game and further employ a graph attention network to capture the dependencies between heterogeneous sub-actions. To promote tighter cooperation between the decomposed heterogeneous agents, SCORE introduces a hierarchical variational autoencoder, which maps the heterogeneous sub-action space into a common latent action space. We also incorporate an implicit credit assignment structure into the SCORE to overcome the multi-agent credit assignment problem in the fully cooperative partially observable stochastic game. Performance experiments on the proof-of-concept task and precision agriculture task show that SCORE has significant advantages in robustness and transferability for time-varying composite action space.

Sparse Multi-Reference Alignment: Phase Retrieval, Uniform Uncertainty Principles and the Beltway Problem

Motivated by cutting-edge applications like cryo-electron microscopy (cryo-EM), the Multi-Reference Alignment (MRA) model entails the learning of an unknown signal from repeated measurements of its images under the latent action of a group of isometries and additive noise of magnitude \(\sigma \). Despite significant interest, a clear picture for understanding rates of estimation in this model has emerged only recently, particularly in the high-noise regime \(\sigma \gg 1\) that is highly relevant in applications. Recent investigations have revealed a remarkable asymptotic sample complexity of order \(\sigma ^6\) for certain signals whose Fourier transforms have full support, in stark contrast to the traditional \(\sigma ^2\) that arise in regular models. Often prohibitively large in practice, these results have prompted the investigation of variations around the MRA model where better sample complexity may be achieved. In this paper, we show that sparse signals exhibit an intermediate \(\sigma ^4\) sample complexity even in the classical MRA model. Further, we characterize the dependence of the estimation rate on the support size s as \(O_p(1)\) and \(O_p(s^{3.5})\) in the dilute and moderate regimes of sparsity respectively. Our techniques have implications for the problem of crystallographic phase retrieval, indicating a certain local uniqueness for the recovery of sparse signals from their power spectrum. Our results explore and exploit connections of the MRA estimation problem with two classical topics in applied mathematics: the beltway problem from combinatorial optimization, and uniform uncertainty principles from harmonic analysis. Our techniques include a certain enhanced form of the probabilistic method, which might be of general interest in its own right.

MPR-RL: Multi-Prior Regularized Reinforcement Learning for Knowledge Transfer

In manufacturing, assembly tasks have been a challenge for learning algorithms due to variant dynamics of different environments. Reinforcement learning (RL) is a promising framework to automatically learn these tasks, yet it is still not easy to apply a learned policy or skill, that is the ability of solving a task, to a similar environment even if the deployment conditions are only slightly different. In this letter, we address the challenge of transferring knowledge within a family of similar tasks by leveraging multiple skill priors. We propose to learn prior distribution over the specific skill required to accomplish each task and compose the family of skill priors to guide learning the policy for a new task by comparing the similarity between the target task and the prior ones. Our method learns a latent action space representing the skill embedding from demonstrated trajectories for each prior task. We have evaluated our method on a task in simulation and a set of peg-in-hole insertion tasks and demonstrate better generalization to new tasks that have never been encountered during training. Our Multi-Prior Regularized RL (MPR-RL) method is deployed directly on a real world Franka Panda arm, requiring only a set of demonstrated trajectories from similar, but crucially not identical, problem instances.

Multiview Actionable Knowledge Graph Generation From Wikipedia Biographies

<i>Actionable knowledge graph</i> (AKG), a specialized version of knowledge graph, was proposed recently to represent, analyze, and predict human action, thus facilitating deeper understanding of human action by robots. However, the automatic construction of AKGs from action-related corpora is still an unexplored problem. In this study, we first propose three unsupervised matrix factorization&#x2013;based frameworks for AKG generation from three different perspectives: <i>subject</i>, <i>context</i> and <i>functionality</i> of action, respectively. Further, we propose a hybrid model based on neural network matrix factorization (NNMF) that considers multi-source signals simultaneously. It not only learns the latent action representations, but also learns the optimal learning objective rather than assuming it to be fixed. To quantitatively verify the utility of the constructed AKGs, we introduce a novel application, that is, predicting the most likely missing action records in Wikipedia biographies. Experimental results on a large-scale Wikipedia biography dataset show that the proposed model brings significant improvement over the baselines, which demonstrates the strong expressiveness of our generated AKGs.

Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion

Hierarchical learning has been successful at learning generalizable locomotion skills on walking robots in a sample-efficient manner. However, the low-dimensional “latent” action used to communicate between two layers of the hierarchy is typically user-designed. In this letter, we present a fully-learned hierarchical framework, that is capable of jointly learning the low-level controller and the high-level latent action space. Once this latent space is learned, we plan over continuous latent actions in a model-predictive control fashion, using a learned high-level dynamics model. This framework generalizes to multiple robots, and we present results on a Daisy hexapod simulation, A1 quadruped simulation, and Daisy robot hardware. We compare a range of learned hierarchical approaches from literature, and show that our framework outperforms baselines on multiple tasks and two simulations. In addition to learning approaches, we also compare to inverse-kinematics (IK) acting on desired robot motion, and show that our fully-learned framework outperforms IK in adverse settings on both A1 and Daisy simulations. On hardware, we show the Daisy hexapod achieve multiple locomotion tasks, in an unstructured outdoor setting, with only 2000 hardware samples, reinforcing the robustness and sample-efficiency of our approach.

Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search.

Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy), leading to sample-inefficient explorations of architectures. To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics. During the search phase, as different action sequences lead to regions with different performance, the search efficiency can be significantly improved by biasing towards the good regions. On three NAS tasks, empirical results demonstrate that LaNAS is at least an order more sample efficient than baseline methods including evolutionary algorithms, Bayesian optimizations, and random search. When applied in practice, both one-shot and regular LaNAS consistently outperform existing results. Particularly, LaNAS achieves 99.0 percent accuracy on CIFAR-10 and 80.8 percent top1 accuracy at 600 MFLOPS on ImageNet in only 800 samples, significantly outperforming AmoebaNet with 33× fewer samples. Our code is publicly available at https://github.com/facebookresearch/LaMCTS.

MALA: Cross-Domain Dialogue Generation with Action Learning

Response generation for task-oriented dialogues involves two basic components: dialogue planning and surface realization. These two components, however, have a discrepancy in their objectives, i.e., task completion and language quality. To deal with such discrepancy, conditioned response generation has been introduced where the generation process is factorized into action decision and language generation via explicit action representations. To obtain action representations, recent studies learn latent actions in an unsupervised manner based on the utterance lexical similarity. Such an action learning approach is prone to diversities of language surfaces, which may impinge task completion and language quality. To address this issue, we propose multi-stage adaptive latent action learning (MALA) that learns semantic latent actions by distinguishing the effects of utterances on dialogue progress. We model the utterance effect using the transition of dialogue states caused by the utterance and develop a semantic similarity measurement that estimates whether utterances have similar effects. For learning semantic actions on domains without dialogue states, MALA extends the semantic similarity measurement across domains progressively, i.e., from aligning shared actions to learning domain-specific actions. Experiments using multi-domain datasets, SMD and MultiWOZ, show that our proposed model achieves consistent improvements over the baselines models in terms of both task completion and language quality.

The optimum marker for the detection of lymphatic vessels.

Podoplanin, lymphatic vessel endothelial hyaluronic acid receptor-1, prospero-related homeobox-1 and vascular endothelial growth factor receptor 3 have been demonstrated to have crucial roles in the development of the lymphatic system and lymphangiogenesis process by combining with their corresponding receptors. Thus, the four markers have been widely used in labelling lymphatic vessels for the detection of lymphangiogenesis and lymphatic vessel invasion. Numerous authors have aimed to identify the roles of these four markers in the lymphatic system and the mechanisms have been partly clarified at the molecular level. The aim of the present review was to comprehensively clarify the characteristics and latent action modes of the four markers in order to determine which is the best one for the detection of lymphangiogenesis and lymphatic vessel invasion.

Learning multi-level features for sensor-based human action recognition

This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor. The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information. Low-level features capture the time and frequency domain property while mid-level representations learn the composition of the action. The Max-margin Latent Pattern Learning (MLPL) method is proposed to learn high-level semantic descriptions of latent action patterns as the output of our framework. The proposed method achieves the state-of-the-art performances, 88.7%, 98.8% and 72.6% (weighted F1 score) respectively, on Skoda, WISDM and OPP datasets.

Unsupervised discovery of crowd activities by saliency-based clustering

Along with the rapid development of digital information technology, video surveillance systems have been widely used in numerous public places, such as squares, shopping malls and banks, to monitor crowd in case of anomalous events. Meanwhile, great challenges have been posed to worldwide researchers because the analysis of the exponentially growing crowd activity data is an arduous task. In this paper, we develop a novel unsupervised crowd activity discovery algorithm aiming to automatically explore latent action patterns among crowd activities and partition them into meaningful clusters. Inspired by the computational model of human vision system, we present a spatio-temporal saliency-based representation to simulate visual attention mechanism and encode human-focused components in an activity stream. Combining with feature pooling, we can obtain a more compact and robust activity representation. Based on affinity matrix of activities, N-cut is performed to generate clusters with meaningful activity patterns. We carry out experiments on our HIT-BJUT dataset and the UMN dataset. The experimental results demonstrate that the proposed unsupervised discovery method is fast and capable of automatically mining meaningful activities from large-scale and unbalanced video data with mixed crowd activities.

A MIMO R2R control using data-driven gain scheduling

This work presents a partial least square (PLS) based double exponentially weighted moving average controller (d-EWMA) of multiple-input and multiple-output (MIMO) processes subjected to constant or moderate gain shifts disturbance. This study is mainly focused on a new gain monitoring method and a PLS based model updating. It was demonstrated that under the circumstance the variance of the first order difference of the latent control action would be more effective in detecting the gain shifts. The performance improvements are illustrated using a simulation problem from literature and applications to thickness control in a diffusion furnace of a local semiconductor facility.

Resting membrane potential of diaphragm muscle fibers in rats under conditions of total ischemia

The dose-dependent action of the low-intensive He-Ne laser on the capillaries of the temporal area of the brain which were revealed by Mg ATPase has been determined. Laser radiation during 0.5-15 min provoked a complex of the activity of the enzymes in the vessels with density latent action of the factor. Subsequent increasing of the exposition (from 30 min to hours) bring to the opposite effect.

Destruction and reorganization of the receptor membrane in labellar chemosensory cells of the blowfly. Long-lasting latent action of colchicine.

Electron micrographs of sections of the labellar chemosensilla of the blowfly, Phormia regina, showed that treatment with sodium deoxycholate (DOC; 7.2 mM for 2 min) destroyed the distal processes of the receptors from up to 10 microns from the tip of the sensillum, but these processes regenerated almost completely within 0.5 h. However, when DOC treatment was preceded by colchicine treatment (25 mM for 2 min), greater than 10 h was required for complete regeneration. Sugar receptor responses supported these findings and disclosed a more detailed time course of regeneration after DOC treatment: without colchicine pretreatment, the destroyed distal process completely regenerated in 0.3-1.0 h, but with pretreatment, regeneration began at 3 h and reached the chemosensillar tip at 8 h at the earliest. Hardly any depression of the response was observed for 8 h after treatment with colchicine alone, but a transient depression was detected at 12 h. Based on these results, the role of microtubules in the maintenance of the receptor membrane is discussed.

Stimulating Action of Radio-active Deposits in the Body

EXPERIMENTS in our laboratory, soon to be reported in the “American Journal of Cancer,” indicate that there is no direct stimulation of tissue grown in vitro by radium which causes an uncontrolled increase in the rate of mitotic division. Our clinical observations point to some alternative explanation, namely, an over-response by the tissue to a destructive or irritating action. This is consistent with the history of malignancy which developed from an over-exposure among the early radiologists, and is further suggested by the bone necrosis which has been found to occur when radium seeds, improperly screened, have been placed in the oral cavity. This necrosis does not appear as a rule until several years after the exposure. In neither instance is there any radioactive deposit in the body. The action is local and indicates that some element in the cell has been injured, the effect of which does not become apparent for some time. This latent action due to radiation is still unexplained. The evidence so far p...

DANGEROUS DRUG REACTIONS

To the Editor:— Too little emphasis has been given to the possibility that a drug may have, besides its direct action, an indirect or latent and potential effect. Thus, cinchophen appears to cause liver injury only in sensitive individuals, in contradistinction to hydrazine, which produces liver damage in all subjects. The recent work of Watkins ( Proc. Staff Meet., Mayo Clin. 8: 713 [Nov. 22] 1933) and of Madison and Squier (The Journal, March 10, p. 755) indicates quite definitely that compounds such as amidopyrine and various barbituric acid derivatives can cause granulocytopenia; but unlike the action of benzene, which consistently attacks bone marrow, the condition is produced in only a small percentage of cases. There has been a growing tendency to look on this latent toxic action of drugs as allergic. In a recent article ( Am. J. M. Sc. 187: 155 [Jan.] 1934) I proposed the theory that drugs which