mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder

In this paper we propose mmFall - a novel fall detection system, which comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect the human body's point cloud along with the body centroid, and (ii) a variational recurrent autoencoder (VRAE) to compute the anomaly level of the body motion based on the acquired point cloud. A fall is claimed to have occurred when the spike in anomaly level and the drop in centroid height occur simultaneously. The mmWave radar sensor provides several advantages, such as privacycompliance and high-sensitivity to motion, over the traditional sensing modalities. However, (i) randomness in radar point cloud data and (ii) difficulties in fall collection/labeling in the traditional supervised fall detection approaches are the two main challenges. To overcome the randomness in radar data, the proposed VRAE uses variational inference, a probabilistic approach rather than the traditional deterministic approach, to infer the posterior probability of the body's latent motion state at each frame, followed by a recurrent neural network (RNN) to learn the temporal features of the motion over multiple frames. Moreover, to circumvent the difficulties in fall data collection/labeling, the VRAE is built upon an autoencoder architecture in a semi-supervised approach, and trained on only normal activities of daily living (ADL) such that in the inference stage the VRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs. During the experiment, we implemented the VRAE along with two other baselines, and tested on the dataset collected in an apartment. The receiver operating characteristic (ROC) curve indicates that our proposed model outperforms the other two baselines, and achieves 98% detection out of 50 falls at the expense of just 2 false alarms.Comment: Preprint versio

mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs

In this paper, mm-Pose, a novel approach to detect and track human skeletons in real-time using an mmWave radar, is proposed. To the best of the authors' knowledge, this is the first method to detect >15 distinct skeletal joints using mmWave radar reflection signals. The proposed method would find several applications in traffic monitoring systems, autonomous vehicles, patient monitoring systems and defense forces to detect and track human skeleton for effective and preventive decision making in real-time. The use of radar makes the system operationally robust to scene lighting and adverse weather conditions. The reflected radar point cloud in range, azimuth and elevation are first resolved and projected in Range-Azimuth and Range-Elevation planes. A novel low-size high-resolution radar-to-image representation is also presented, that overcomes the sparsity in traditional point cloud data and offers significant reduction in the subsequent machine learning architecture. The RGB channels were assigned with the normalized values of range, elevation/azimuth and the power level of the reflection signals for each of the points. A forked CNN architecture was used to predict the real-world position of the skeletal joints in 3-D space, using the radar-to-image representation. The proposed method was tested for a single human scenario for four primary motions, (i) Walking, (ii) Swinging left arm, (iii) Swinging right arm, and (iv) Swinging both arms to validate accurate predictions for motion in range, azimuth and elevation. The detailed methodology, implementation, challenges, and validation results are presented.Comment: Submitted to IEEE Sensors Journa

Parenting styles as a moderator of the association between pubertal timing and chinese adolescents’ drinking behavior

Background: Pubertal timing refers to the timing of an individual regarding pubertal sexual maturation, both physiologically and psychologically. Existing research shows that pubertal timing is associated with behavioral problems. This study investigated the role of parenting style in the relationship between pubertal timing and Chinese adolescents’ smoking behavior. Methods: The study examined the association of pubertal timing, parenting style and adolescents’ smoking behavior, using the Pubertal Development Scale (Chinese version), Simplified Parenting Style Scale-Chinese version, and three items related to adolescents’ smoking situation. Participants were 1391 Chinese adolescents aged 11–16 years old (53.41% boys). Hierarchical linear regression analyses assessed the moderating role of parenting style on the association between pubertal timing and adolescent smoking behavior. Results: The results indicated that parenting style moderates the relationship between pubertal timing and adolescent smoking behavior. For male adolescents, father rejection moderated the relationship between early pubertal timing and smoking behavior. For female adolescents, father rejection, father emotional warmth, and mother emotional warmth moderated the relationship between pubertal timing and smoking behavior. Conclusions: Findings from the study highlight the importance of parenting style, which may influence the negative outcomes associated with early pubertal timing and can help improve interventions aimed at reducing these negative outcomes

Why Is Maternal Control Harmful? The Relation between Maternal Control, Insecure Attachment and Antisocial Personality Disorder Features in Chinese College Students:A Sequential Mediation Model

Background: Previous work has indicated that a negative parenting style is associated with antisocial personality disorder features in Chinese college students, yet few studies have explored the unique role of negative mothering in children’s antisocial personality disorder. Methods: The current study mainly examined the sequential mediation effect of parental antipathy and neglect (PAN) and mother negative loving (a form of insecure attachment) in the association between mother control and adulthood antisocial personality disorder features (ASPD features) in the framework of attachment theory and cognitive–behavioral theory. A community sample of 1547 Chinese college students filled in the Parental Bonding Instrument, the Childhood Experience of Care and Abuse Questionnaire, the Adult Attachment Questionnaire and the Personality Diagnostic Questionnaire-4+. Results: A sequential mediation model analysis showed that maternal control significantly predicted PAN, mother negative loving, as well as ASPD features. Conclusions: Mother control and mother negative loving appear to advance on the development and exacerbation of ASPD features in college students

Proactive caching placement for arbitrary topology with multi-hop forwarding in ICN

With the rapid growth of network traffic and the enhancement of the quality of experiences of users, Information-Centric Networking (ICN), which is a content-centric network architecture with named data caching and routing, is proposed to improve the multimedia content distribution efficiency. In arbitrary topology, cache nodes and users are randomly distributed and connected, hence it is challenging to achieve an optimal caching placement under this situation. In this paper, we propose a caching placement algorithm for arbitrary topology in ICN. We formulate an optimization problem of proactive caching placement for arbitrary topology combined with multi-hop forwarding, with an objective to optimize the user delay and the load balancing level of the nodes simultaneously. Since the original problem is NP-hard, we solve the formulated caching placement problem in two sub-problems, content replica allocation subproblem and content replica placement sub-problem. First, in the content replica allocation sub-problem, the replica number of each content is obtained by utilizing the auction theory. Second, the replica number of each content is used as a constraint for the content replica placement sub-problem, which is solved by matching theory. The caching placement algorithm combined with multi-hop NRR forwarding maximizes the utilization of cache resources in order to achieve better caching performance. The numerical results show that significant hop count savings and load balancing level improvement are attainable via the proposed algorithm

Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering point cloud of each one. For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature. A three-layer CNN model is created to classify the behavior for each patient. The tracking and point clouds detection algorithm was also implemented on an mmWave radar hardware platform with an embedded graphics processing unit (GPU) board to collect Doppler pattern and run the CNN model. A training dataset of six types of behavior were collected, over a long duration, to train the model using Adam optimizer with an objective to minimize cross-entropy loss function. Lastly, the system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient's behavior in a two-patient scenario.Comment: This paper has been submitted to IEEE Radar Conference 201

Boosting Few-Shot Text Classification via Distribution Estimation

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples to estimate the corresponding mean and covariance. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly.Comment: Accepted to AAAI 202

Tunable topological phase transition in soft Rayleigh beam system with imperfect interfaces

Acoustic metamaterials, particularly the topological insulators, exhibit exceptional wave characteristics that have sparked considerable research interest. The study of imperfect interfaces affect is of significant importance for the modeling of wave propagation behavior in topological insulators. This paper models a soft Rayleigh beam system with imperfect interfaces, and investigates its topological phase transition process tuned by mechanical loadings. The model reveals that the topological phase transition process can be observed by modifying the distance between imperfect interfaces in the system. When a uniaxial stretch is applied, the topological phase transition points for longitudinal waves decrease within a limited frequency range, while they increase within a larger frequency scope for transverse waves. Enhancing the rigidity of the imperfect interfaces also enables shifting of the topological phase transition point within a broader frequency range for longitudinal waves and a confined range for transverse waves. The transition of topologically protected interface modes in the transmission performance of a twenty-cell system is verified, which include altering frequencies, switching from interface mode to edge mode. Overall, this study provides a new approach and guideline for controlling topological phase transition in composite and soft phononic crystal systems.Comment: 39 pages,8 figure

Clinical analysis and functional characterization of KCNQ2-related developmental and epileptic encephalopathy

BackgroundDevelopmental and epileptic encephalopathy (DEE) is a condition characterized by severe seizures and a range of developmental impairments. Pathogenic variants in KCNQ2, encoding for potassium channel subunit, cause KCNQ2-related DEE. This study aimed to examine the relationships between genotype and phenotype in KCNQ2-related DEE.MethodsIn total, 12 patients were enrolled in this study for genetic testing, clinical analysis, and developmental evaluation. Pathogenic variants of KCNQ2 were characterized through a whole-cell electrophysiological recording expressed in Chinese hamster ovary (CHO) cells. The expression levels of the KCNQ2 subunit and its localization at the plasma membrane were determined using Western blot analysis.ResultsSeizures were detected in all patients. All DEE patients showed evidence of developmental delay. In total, 11 de novo KCNQ2 variants were identified, including 10 missense variants from DEE patients and one truncating variant from a patient with self-limited neonatal epilepsy (SeLNE). All variants were found to be loss of function through analysis of M-currents using patch-clamp recordings. The functional impact of variants on M-current in heteromericKCNQ2/3 channels may be associated with the severity of developmental disorders in DEE. The variants with dominant-negative effects in heteromeric channels may be responsible for the profound developmental phenotype.ConclusionThe mechanism underlying KCNQ2-related DEE involves a reduction of the M-current through dominant-negative effects, and the severity of developmental disorders in DEE may be predicted by the impact of variants on the M-current of heteromericKCNQ2/3 channels

Curved nanographenes: Multiple emission, thermally activated delayed fluorescence, and non-radiative decay

The intriguing and rich photophysical properties of three curved nanographenes (CNG 6, 7, and 8) are investigated by time-resolved and temperature-dependent photoluminescence (PL) spectroscopy. CNG 7 and 8 exhibit dual fluorescence, as well as dual phosphorescence at low temperature in the main PL bands. In addition, hot bands are detected in fluorescence as well as phosphorescence, and, in the narrow temperature range of 100–140 K, thermally activated delayed fluorescence (TADF) with lifetimes on the millisecond time-scale is observed. These findings are rationalized by quantum-chemical simulations, which predict a single minimum of the S1 potential of CNG 6, but two S1 minima for CNG 7 and CNG 8, with considerable geometric reorganization between them, in agreement with the experimental findings. Additionally, a higher-lying S2 minimum close to S1 is optimized for the three CNG, from where emission is also possible due to thermal activation and, hence, non-Kasha behavior. The presence of higher-lying dark triplet states close to the S1 minima provides mechanistic evidence for the TADF phenomena observed. Non-radiative decay of the T1 state appears to be thermally activated with activation energies of roughly 100 meV and leads to disappearance of phosphorescence and TADF at T > 140