Search Results (10)

Climate change mitigation depends on actions that affect the public interest and lead to widespread changes in public attitudes and behavior. With the global outbreak of the COVID-19 pandemic, humanity faced a more imminent threat to its well-being and viability. This retrospective cross-sectional study examines how public interest in climate change was attenuated by the severity of the COVID-19 pandemic using Google Trends Search Volume Index (SVI), weather, and climate data on a United States state-level basis during the first two years of the pandemic from 2020 to 2022. To identify channels through which the COVID-19 pandemic affected information demand on climate change, a novel fixed effect regression model of public climate change interest was developed. The measure captures changes in the climate change SVI independent of weather and climate conditions, comprising pandemic-related changes in living circumstances such as COVID-19-related cases and deaths, mask mandates, and the proportion of the vaccinated population. Our results indicate that public interest in climate change was systematically attenuated by the severity of the COVID-19 pandemic. In addition, this study provides an approach for identifying drivers of public interest in climate change. Full article

Recently, human action recognition has gained widespread use in fields such as human–robot interaction, healthcare, and sports. With the popularity of wearable devices, we can easily access sensor data of human actions for human action recognition. However, extracting spatio-temporal motion patterns from sensor data and capturing fine-grained action processes remain a challenge. To address this problem, we proposed a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM. The spatial information of sensor signals is extracted by hierarchical multi-scale convolution with finer-grained features, and the multi-channel features are fused by adaptive channel feature fusion to retain important information and improve the efficiency of the model. The dynamic channel-selection-LSTM based on the attention mechanism captures the temporal context information and long-term dependence of the sensor signals. Experimental results show that the proposed model achieves Macro F1-scores of 0.68, 0.91, 0.53, and 0.96 on four public datasets: Opportunity, PAMAP2, USC-HAD, and Skoda, respectively. Our model demonstrates competitive performance when compared to several state-of-the-art approaches. Full article

The recognition of human movements is a crucial aspect of AI-related research fields. Although methods using vision and sensors provide more valuable data, they come at the expense of inconvenience to users and social limitations including privacy issues. WiFi-based sensing methods are increasingly being used to collect data on human activity due to their ubiquity, versatility, and high performance. Channel state information (CSI), a characteristic of WiFi signals, can be employed to identify various human activities. Traditional machine learning approaches depend on manually designed features, so recent studies propose leveraging deep learning capabilities to automatically extract features from raw CSI data. This research introduces a versatile framework for recognizing human activities by utilizing CSI data and evaluates its effectiveness on different deep learning networks. A hybrid deep learning network called CNN-GRU-AttNet is proposed to automatically extract informative spatial-temporal features from raw CSI data and efficiently classify activities. The effectiveness of a hybrid model is assessed by comparing it with five conventional deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU) on two widely recognized benchmark datasets (CSI-HAR and StanWiFi). The experimental results demonstrate that the CNN-GRU-AttNet model surpasses previous state-of-the-art techniques, leading to an average accuracy improvement of up to 4.62%. Therefore, the proposed hybrid model is suitable for identifying human actions using CSI data. Full article

Graph convolutional networks (GCNs) have obtained remarkable performance in skeleton-based action recognition. However, previous approaches fail to capture the implicit correlations between joints and handle actions across varying time intervals. To address these problems, we propose an adaptive multi-scale difference graph convolution Network (AMD-GCN), which comprises an adaptive spatial graph convolution module (ASGC) and a multi-scale temporal difference convolution module (MTDC). The first module is capable of acquiring data-dependent and channel-wise graphs that are adaptable to both samples and channels. The second module employs the multi-scale approach to model temporal information across a range of time scales. Additionally, the MTDC incorporates an attention-enhanced module and difference convolution to accentuate significant channels and enhance temporal features, respectively. Finally, we propose a multi-stream framework for integrating diverse skeletal modalities to achieve superior performance. Our AMD-GCN approach was extensively tested and proven to outperform the current state-of-the-art methods on three widely recognized benchmarks: the NTU-RGB+D, NTU-RGB+D 120, and Kinetics Skeleton datasets. Full article

Gesture recognition, the basis of human–computer interaction (HCI), is a significant component for the development of smart home, VR, and senior care management. Most gesture recognition methods still depend on sensors worn by the user or video-based gestures for recognition, can be used for fine-grained gesture recognition. our paper implements a gesture recognition method that is independent of environment and gesture drawing direction, and it achieves gesture recognition classification by using small sample data. Wi-NN, proposed in this study, does not require the user to wear additional device. In this case, channel state information (CSI) extracted from Wi-Fi signal is used to capture the action information of the human body via CSI. After pre-processing to reduce the interference of environmental noise as much as possible, clear action information is extracted using the feature extraction method based on time domain to obtain the gesture action feature data. The gathered data are integrated with the weighted k-nearest neighbor (KNN) classification recognizer for classification task. The experiment outcomes revealed that the accuracy scores of the same gesture for different users and different gestures for the same user under the same environment were 93.1% and 89.6%, respectively. The experiments in different environments also achieved good recognition results, and by comparing with other experimental methods, the experiments in this paper have better recognition results. Evidently, good classification results were generated after the original data were processed and incorporated into the weighted KNN. Full article

A very challenging task for action recognition concerns how to effectively extract and utilize the temporal and spatial information of video (especially temporal information). To date, many researchers have proposed various spatial-temporal convolution structures. Despite their success, most models are limited in further performance especially on those datasets that are highly time-dependent due to their failure to identify the fusion relationship between the spatial and temporal features inside the convolution channel. In this paper, we proposed a lightweight and efficient spatial-temporal extractor, denoted as Channel-Wise Spatial-Temporal Aggregation block (CSTA block), which could be flexibly plugged in existing 2D CNNs (denoted by CSTANet). The CSTA Block utilizes two branches to model spatial-temporal information separately. In temporal branch, It is equipped with a Motion Attention Module (MA), which is used to enhance the motion regions in a given video. Then, we introduced a Spatial-Temporal Channel Attention (STCA) module, which could aggregate spatial-temporal features of each block channel-wisely in a self-adaptive and trainable way. The final experimental results demonstrate that the proposed CSTANet achieved the state-of-the-art results on EGTEA Gaze++ and Diving48 datasets, and obtained competitive results on Something-Something V1&V2 at the less computational cost. Full article

Solifenacin (Vesicare^®, SOL), known to be a member of isoquinolines, is a muscarinic antagonist that has anticholinergic effect, and it has been beneficial in treating urinary incontinence and neurogenic detrusor overactivity. However, the information regarding the effects of SOL on membrane ionic currents is largely uncertain, despite its clinically wide use in patients with those disorders. In this study, the whole-cell current recordings revealed that upon membrane depolarization in pituitary GH₃ cells, the exposure to SOL concentration-dependently increased the amplitude of M-type K⁺ current (I_K(M)) with effective EC₅₀ value of 0.34 μM. The activation time constant of I_K(M) was concurrently shortened in the SOL presence, hence yielding the K_D value of 0.55 μM based on minimal reaction scheme. As cells were exposed to SOL, the steady-state activation curve of I_K(M) was shifted along the voltage axis to the left with no change in the gating charge of the current. Upon an isosceles-triangular ramp pulse, the hysteretic area of I_K(M) was increased by adding SOL. As cells were continually exposed to SOL, further application of acetylcholine (1 μM) failed to modify SOL-stimulated I_K(M); however, subsequent addition of thyrotropin releasing hormone (TRH, 1 μM) was able to counteract SOL-induced increase in I_K(M) amplitude. In cell-attached single-channel current recordings, bath addition of SOL led to an increase in the activity of M-type K⁺ (K_M) channels with no change in the single channel conductance; the mean open time of the channel became lengthened. In whole-cell current-clamp recordings, the SOL application reduced the firing of action potentials (APs) in GH₃ cells; however, either subsequent addition of TRH or linopirdine was able to reverse SOL-mediated decrease in AP firing. In hippocampal mHippoE-14 neurons, the I_K(M) was also stimulated by adding SOL. Altogether, findings from this study disclosed for the first time the effectiveness of SOL in interacting with K_M channels and hence in stimulating I_K(M) in electrically excitable cells, and this noticeable action appears to be independent of its antagonistic activity on the canonical binding to muscarinic receptors expressed in GH₃ or mHippoE-14 cells. Full article

Action potentials (spikes) can trigger the release of a neurotransmitter at chemical synapses between neurons. Such release is uncertain, as it occurs only with a certain probability. Moreover, synaptic release can occur independently of an action potential (asynchronous release) and depends on the history of synaptic activity. We focus here on short-term synaptic facilitation, in which a sequence of action potentials can temporarily increase the release probability of the synapse. In contrast to the phenomenon of short-term depression, quantifying the information transmission in facilitating synapses remains to be done. We find rigorous lower and upper bounds for the rate of information transmission in a model of synaptic facilitation. We treat the synapse as a two-state binary asymmetric channel, in which the arrival of an action potential shifts the synapse to a facilitated state, while in the absence of a spike, the synapse returns to its baseline state. The information bounds are functions of both the asynchronous and synchronous release parameters. If synchronous release facilitates more than asynchronous release, the mutual information rate increases. In contrast, short-term facilitation degrades information transmission when the synchronous release probability is intrinsically high. As synaptic release is energetically expensive, we exploit the information bounds to determine the energy–information trade-off in facilitating synapses. We show that unlike information rate, the energy-normalized information rate is robust with respect to variations in the strength of facilitation. Full article

Information embedding on actions is a new channel model in which a specific decoder is used to observe the actions taken by the encoder and retrieve part of the message intended for the receiver. We revisit this model and consider a different scenario where a secrecy constraint is imposed. By adding a wiretapper in the model, we aim to send the confidential message to the receiver and keep it secret from the wiretapper as much as possible. We characterize the inner and outer bounds on the capacity-equivocation region of such a channel with noncausal (and causal) channel state information. Furthermore, the lower and upper bounds on the sum secrecy capacity are also obtained. Besides, by eliminating the specific decoder, we get a new outer bound on the capacity-equivocation region of the wiretap channel with action-dependent states and prove it is tighter than the existing outer bound. A binary example is presented to illustrate the tradeoff between the sum secrecy rate and the information embedding rate under the secrecy constraint. We find that the secrecy constraint and the communication requirements of information embedding have a negative impact on improving the secrecy transmission rate of the given communication link. Full article

In this paper, we investigate the model of wiretap channel with action-dependent channel state information. Given the message to be communicated, the transmitter chooses an action sequence that affects the formation of the channel states, and then generates the channel input sequence based on the state sequence and the message. The main channel and the wiretap channel are two discrete memoryless channels (DMCs), and they are connected with the legitimate receiver and the wiretapper, respectively. Moreover, the transition probability distribution of the main channel depends on the channel state. Measuring wiretapper’s uncertainty about the message by equivocation, inner and outer bounds on the capacity-equivocation region are provided both for the case where the channel inputs are allowed to depend non-causally on the state sequence and the case where they are restricted to causal dependence. Furthermore, the secrecy capacities for both cases are bounded, which provide the best transmission rate with perfect secrecy. The result is further explained via a binary example. Full article