Robust multi-clue face tracking system

In this paper we present a multi-clue face tracking system, based on the combination of a face detector and two independent trackers. The detector, a variant of the Viola-Jones algorithm, is set to generate very low false positive error rate. It initiates the tracking system and updates its state. The trackers, based on 3DRS and optical flow respectively, have been chosen to complement each other in different conditions. The main focus of this work is the integration of the two trackers and the design of a closed loop detector-tracker system, aiming at achieving superior robustness at real-time operation on a PC platform. Tests were carried out to assess the actual performance of the system. With an average of about 95% correct face location rate and no significant false positives, the proposed approach appears to be particularly robust to complex backgrounds, ambient light variation, face orientation and scale changes, partial occlusions, different\ud facial expressions and presence of other unwanted faces

On Training Traffic Predictors via Broad Learning Structures:A Benchmark Study

A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various traffic datasets by the California Department of Transportation, and employs a variety of standard algorithms (LASSO regression, shallow and deep neural networks, stacked autoencoders, convolutional, and recurrent neural networks) for comparison purposes: all algorithms are implemented in MATLAB on the same computing platform. The study demonstrates a BLS training process two-three orders of magnitude faster (tens of seconds against tens-hundreds of thousands of seconds), allowing unprecedented real-time capabilities. Additional comparisons with the extreme learning machine architecture, a learning algorithm sharing some features with BLS, confirm the fast training of least-square training as compared to gradient training

Gate-tunable antiferromagnetic Chern insulator in twisted bilayer transition metal dichalcogenides

A series of recent experimental works on twisted MoTe$_2$ homobilayers have unveiled an abundance of exotic states in this system. Valley-polarized quantum anomalous Hall states have been identified at hole doping of $\nu = -1$, and the fractional quantum anomalous Hall effect is observed at $\nu = -2/3$ and $\nu = -3/5$. In this work, we investigate the electronic properties of AA-stacked twisted bilayer MoTe$_2$ at $\nu=-2$ by $k$-space Hartree-Fock calculations. We find that the phase diagram is qualitatively similar to the phase diagram of a Kane-Mele-Hubbard with staggered onsite potential. A noteworthy phase within the diagram is the antiferromagnetic Chern insulator, stabilized by the external electric field. We attribute the existence of this Chern insulator to an antiferromagnetic instability at a topological phase transition between the quantum spin hall phase and a band insulator phase. We highlight that the antiferromagnetic Chern insulator phase is most evident at a twist angle of approximately $4^\circ$. Our research proposes the potential of realizing a Chern insulator beyond $\nu=-1$, and contributes fresh perspectives on the interplay between band topology and electron-electron correlations in moir\'e superlattices

Gate-tunable phonon magnetic moment in bilayer graphene

We develop a first-principles quantum scheme to calculate the phonon magnetic moment in solids. As a showcase example, we apply our method to study gated bilayer graphene, a material with strong covalent bonds. According to the classical theory based on the Born effective charge, the phonon magnetic moment in this system should vanish, yet our quantum mechanical calculations find significant phonon magnetic moments. Furthermore, the magnetic moment is highly tunable by changing the gate voltage. Our results firmly establish the necessity of the quantum mechanical treatment, and identify small-gap covalent materials as a promising platform for studying tunable phonon magnetic moment.Comment: 6 pages, 3 figure

Externalizing traits: Shared causalities for COVID-19 and Alzheimer\u27s dementia using Mendelian randomization analysis

Externalizing traits have been related with the outcomes of coronavirus disease 2019 (COVID-19) and Alzheimer\u27s dementia (AD); however, whether these associations are causal remains unknown. We used the two-sample Mendelian randomization (MR) approach with more than 200 single-nucleotide polymorphisms (SNPs) for externalizing traits to explore the causal associations of externalizing traits with the risk of COVID-19 (infected COVID-19, hospitalized COVID-19, and severe COVID-19) or AD based on the summary data. The inverse variance–weighted method (IVW) was used to estimate the main effect, followed by several sensitivity analyses. IVW analysis showed significant associations of externalizing traits with COVID-19 infection (odds ratio [OR] = 1.456, 95% confidence interval [95% CI] = 1.224–1.731), hospitalized COVID-19 (OR = 1.970, 95% CI = 1.374–2.826), and AD (OR = 1.077, 95% CI = 1.037–1.119). The results were consistent using weighted median (WM), penalized weighted median (PWM), MR-robust adjusted profile score (MR-RAPS), and leave-one-out sensitivity analyses. Our findings assist in exploring the causal effect of externalizing traits on the pathophysiology of infection and severe infection of COVID-19 and AD. Furthermore, our study provides evidence that shared externalizing traits underpin the two diseases

Effectiveness of Multiple Daily Injections or Continuous Subcutaneous Insulin Infusion for Children with Type 1 Diabetes Mellitus in Clinical Practice

Aims. To determine whether multiple daily injections (MDIs) or continuous subcutaneous insulin infusion (CSII) contributes to better glucose control in children with different type 1 diabetes duration. Methods. Subjects were grouped according to early (≤1 year after disease onset; 1A) or late (1–3 years after onset; 2A) MDIs/CSII treatment initiation. Corresponding control groups (1B, 2B) received insulin injections twice daily. Results. HbA1c levels were consistently lower in group 1A than in group 1B (6 months (T2): 7.37% versus 8.21%; 12 months (T3): 7.61% versus 8.41%; 24/36 months (T4/T5): 7.61% versus 8.72%; all P<0.05), but were lower in group 2A than in group 2B only at T2 (8.36% versus 9.19%; P=0.04). Levels were lower in group 1A than in group 2A when disease duration was matched (7.61% versus 8.49%; P<0.05). Logistic regression revealed no correlation between HbA1c level and MDIs/CSII therapy. HbA1c levels were only negatively related to insulin dosage. Conclusions. Blood glucose control was better in patients receiving MDIs/CSII than in those receiving conventional treatment. Early MDIs/CSII initiation resulted in prolonged maintenance of low HbA1c levels compared with late initiation. MDIs/CSII therapy should be combined with comprehensive management

Fractional Chern Insulator in Twisted Bilayer MoTe2_2

A recent experiment has reported the first observation of a zero-field fractional Chern insulator (FCI) phase in twisted bilayer MoTe$_2$ moir\'e superlattices [Nature 622, 63-68 (2023)]. The experimental observation is at an unexpected large twist angle 3.7$^\circ$ and calls for a better understanding of the FCI in real materials. In this work, we perform large-scale density functional theory calculation for the twisted bilayer MoTe$_2$, and find that lattice reconstruction is crucial for the appearance of an isolated flat Chern band. The existence of the FCI state at $\nu = -2/3$ are confirmed by exact diagonalization. We establish phase diagrams with respect to the twist angle and electron interaction, which reveal an optimal twist angle of $3.5^\circ$ for the observation of FCI. We further demonstrate that an external electric field can destroy the FCI state by changing band geometry and show evidence of the $\nu=-3/5$ FCI state in this system. Our research highlights the importance of accurate single particle band structure in the quest for strong correlated electronic states and provides insights into engineering fractional Chern insulator in moir\'e superlattices

The impact of resilience on the mental health of military personnel during the COVID-19 pandemic: coping styles and regulatory focus

Military personnel encountered multiple stressful events during the COVID-19 lockdown. Reducing non-combat attrition due to mental disorders is crucial for military morale and combat effectiveness. Grounded in stress theory and regulatory focus theory, this study investigates the influence of resilience on military personnel’s mental health; coping style and regulatory focus are considered potential mediators and moderators, respectively. We conducted a routine psychological assessment on 1,110 military personnel in China. The results indicate that: (1) resilience has a negative impact on the psychological symptoms of military groups; (2) mature and mixed coping styles in military personnel mediate the association between resilience and psychological symptoms; and (3) regulatory focus predominance has a negative moderating effect on mature coping styles’ effects on psychological symptoms. Furthermore, this study supports previous findings that resilience and mental health are interrelated; it demonstrates that military personnel can effectively reduce negative psychological symptoms by improving their resilience level and adopting mature coping styles under stressful situations. The current study presents interventional insights regarding coping styles and mental health from a self-regulatory perspective during the COVID-19 pandemic