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Complex behavioral contagion in collective evasion of mobile animal groups can be predicted by reconstructing quantitative interaction networks. Based on the assumption of time-scale separation between a fast contagion process and a slower movement response, the underlying interaction networks have been previously assumed to be static, determined by the spatial structure at the onset of the collective escape response. This idealization does not account for the temporal evolution of the spatial network structure, which may have a major impact on the behavioral contagion dynamics. Here, we propose a spatially-explicit, agent-based model for the coupling between behavioral contagion and the network dynamics originating from the spreading movement response. We explore the impact of movement parameters (startle speed, initial directionality, and directional noise) on average cascade size. By conducting numerical simulations for different density levels, we show that increasing escape speed suppresses the cascade size in most cases, that the cascade size depends strongly on the movement direction of the initially startled individual, and that large variability in the direction of individual escape movements (rotational noise) will typically promote the spread of behavioral contagion through spatial groups. Our work highlights the importance of accounting for movement dynamics in behavioral contagion, and facilitates our understanding of rapid coordinated response and collective information processing in animal groups.

Gait analysis holds significant importance in monitoring daily health, particularly among older adults. Advancements in sensor technology enable the capture of movement in real-life environments and generate big data. Machine learning, notably deep learning (DL), shows promise to use these big data in gait analysis. However, the inherent black-box nature of these models poses challenges for their clinical application. This study aims to enhance transparency in DL-based gait classification for aged-related gait patterns using Explainable Artificial Intelligence, such as SHAP. A total of 244 subjects, comprising 129 adults and 115 older adults (age>65), were included. They performed a 3-minute walking task while accelerometers were affixed to the lumbar segment L3. DL models, convolutional neural network (CNN) and gated recurrent unit (GRU), were trained using 1-stride and 8-stride accelerations, respectively, to classify adult and older adult groups. SHAP was employed to explain the models' predictions. CNN achieved a satisfactory performance with an accuracy of 81.4% and an AUC of 0.89, and GRU demonstrated promising results with an accuracy of 84.5% and an AUC of 0.94. SHAP analysis revealed that both CNN and GRU assigned higher SHAP values to the data from vertical and walking directions, particularly emphasizing data around heel contact, spanning from the terminal swing to loading response phases. Furthermore, SHAP values indicated that GRU did not treat every stride equally. CNN accurately distinguished between adults and older adults based on the characteristics of a single stride's data. GRU achieved accurate classification by considering the relationships and subtle differences between strides. In both models, data around heel contact emerged as most critical, suggesting differences in acceleration and deceleration patterns during walking between different age groups.

Human Assumed Central Sensitization is involved in the development and maintenance of chronic low back pain (CLBP). The Central Sensitization Inventory (CSI) was developed to evaluate the presence of HACS, with a cut-off value of 40/100 based on patients with chronic pain. However, various factors including pain conditions (e.g., CLBP), and gender may influence this cut-off value. For chronic pain condition such as CLBP, unsupervised clustering approaches can take these factors into consideration and automatically learn the HACS-related patterns. Therefore, this study aimed to determine the cut-off values for a Dutch-speaking population with CLBP, considering the total group and stratified by gender based on unsupervised machine learning. In this study, questionnaire data covering pain, physical, and psychological aspects were collected from patients with CLBP and aged-matched pain-free adults (referred to as healthy controls, HC). Four clustering approaches were applied to identify HACS-related clusters based on the questionnaire data and gender. The clustering performance was assessed using internal and external indicators. Subsequently, receiver operating characteristic analysis was conducted on the best clustering results to determine the optimal cut-off values. The study included 151 subjects, consisting of 63 HCs and 88 patients with CLBP. Hierarchical clustering yielded the best results, identifying three clusters: healthy group, CLBP with low HACS level, and CLBP with high HACS level groups. Based on the low HACS levels group (including HC and CLBP with low HACS level) and high HACS level group, the cut-off value for the overall groups were 35, 34 for females, and 35 for. The findings suggest that the optimal cut-off values for CLBP is 35. The gender-related cut-off values should be interpreted with caution due to the unbalanced gender distribution in the sample., Comment: 31 pages, 5 tables, 3 figures

We proposed the boundary-integral type neural networks (BINN) for the boundary value problems in computational mechanics. The boundary integral equations are employed to transfer all the unknowns to the boundary, then the unknowns are approximated using neural networks and solved through a training process. The loss function is chosen as the residuals of the boundary integral equations. Regularization techniques are adopted to efficiently evaluate the weakly singular and Cauchy principle integrals in boundary integral equations. Potential problems and elastostatic problems are mainly concerned in this article as a demonstration. The proposed method has several outstanding advantages: First, the dimensions of the original problem are reduced by one, thus the freedoms are greatly reduced. Second, the proposed method does not require any extra treatment to introduce the boundary conditions, since they are naturally considered through the boundary integral equations. Therefore, the method is suitable for complex geometries. Third, BINN is suitable for problems on the infinite or semi-infinite domains. Moreover, BINN can easily handle heterogeneous problems with a single neural network without domain decomposition.

Pulsar glitches are the sudden increase in their spin frequency, most accompanied with a long timescale recovery process. A permanent shift would be remained in the first order derivative of spin frequency with time. Relevant data fitting research about this persistent shift was performed in this essay, we have found a more suitable fitting strategy by choosing a fitting function form $k\ln(ax + b)$ and demonstrated that would be more meaningful in pure mathematical aspect in describe the correlation between the time scale of Delayed-Spin-Up $\tau_{d}$ (so as the persistent shift $\Delta \dot{\nu}_{p}$) and glitch size $\Delta \nu$.Some possible physical ideas about this fitting also have been presented while the clear physical mechanism behind this strategy still remain unknown., Comment: The paper should be further modified and this version is too draft-like

Solitons are self-reinforcing localized wave packets arising from a balance of linear and nonlinear effects. This definition encompasses the interplay of nonlinear gain and loss, leading to the concept of dissipative solitons that has been instrumental in understanding the wide variety of mode locking phenomena in ultrafast optics. To date, most studies have involved the group velocity dispersion as a key ingredient for soliton generation. Here, we report on a novel kind of soliton, both theoretically and experimentally, which builds up in spectrally confined cavities when dispersion is practically absent. Precisely, the interplay between the Kerr nonlinearity and spectral filtering results in an infinite hierarchy of eigenfunctions which, combined with optical gain, allow for the generation of stable dispersion-less dissipative solitons in a previously uncharted regime. When the filter order tends to be infinite, we find an unexpected link between dissipative and conservative solitons, in the form of Nyquist-pulse-like solitons endowed with an ultra-flat spectrum. In contrast to the dispersion-enabled solitons, these dispersion-less Nyquist solitons build on a fully confined spectrum and their energy scaling is not constrained by the pulse duration. This study broadens the fundamental scope of dissipative soliton physics and opens new avenues for engineering optical solitons endowed with superior temporal and spectral features., Comment: 25 pages, 16 figures

Glitch is supposed to be a useful probe into pulsar's interior, but the underlying physics remains puzzling. The glitch activity may reflect a lower limit of the crustal moment of inertia in conventional neutron star models. Nevertheless, its statistical feature could also be reproduced in the strangeon star model, which is focused here. We formulate the glitch activity of normal radio pulsars under the framework of starquake of solid strangeon star model, the shear modulus of strangeon matter is constrained to be $\mu\simeq 3\times10^{34}~\rm erg/cm^{3}$, consistent with previous work. Nevertheless, about ten times the shift in oblateness accumulated during glitch interval is needed to fulfill the statistical observations. The fact that typical glitch sizes of two rapidly evolving pulsars (the Crab pulsar and PSR B0540-69) are about two orders of magnitude lower than that of the Vela pulsar, significantly lower than the oblateness change they can supply, indicates probably that only a part of oblateness change is relieved when a pulsar is young. The unreleased oblateness and stress may relax as compensation in the following evolution. The small glitch sizes and low glitch activity of the Crab pulsar can be explained simultaneously in this phenomenological model. Finally, we obtain energy release to be $\Delta E\sim 2.4\times 10^{40}~\rm erg$ and $\Delta E\sim 4.2\times 10^{41}~\rm erg$ for typical glitch size of $\Delta\nu/\nu\sim 10^{-6}$ (Vela-like) and $\sim 10^{-8}$ (Crab-like). The upcoming SKA may test this model through the energy release and the power-law relation between the reduced recovery coefficient $Q/|\dot\nu|^{1/2}$ and $\Delta\nu/\nu$., Comment: 16 pages, 5 figures, accepted by MNRAS

Fingerprinting is a popular indoor localization technique since it can utilize existing infrastructures (e.g., access points). However, its site survey process is a labor-intensive and time-consuming task, which limits the application of such systems in practice. In this paper, motivated by the availability of advanced sensing capabilities in smartphones, we propose a fast and reliable fingerprint collection method to reduce the time and labor required for site survey. The proposed method uses a landmark graph-based method to automatically associate the collected fingerprints, which does not require active user participation. We will show that besides fast fingerprint data collection, the proposed method results in accurate location estimate compared to the state-of-the-art methods. Experimental results show that the proposed method is an order of magnitude faster than the manual fingerprint collection method, and using the radio map generated by our method achieves a much better accuracy compared to the existing methods.

Pulsar glitches are sudden increase in their spin frequency, in most cases followed by the long timescale recovery process. As of this writing, about 546 glitches have been reported in 188 pulsars, the Crab pulsar is a special one with unique manifestations. This writing presents a statistic study on post-glitch observables of the Crab pulsar, especially the delayed spin-up in post-glitch phase and persistent shift in the slow-down rate of the star. By analyzing the radio data over 45 years, we find that two power law functions respectively fit the persistent shift and delayed spin-up timescales versus glitch size well, and we find a linear correlation between the persistent shift and delayed spin-up timescale from the consistency of the two fitting functions, probably indicating their same physical origin and may provide a new probe of interior physics of neutron stars., Comment: 6 pages, 7 figures, comments welcome

The digital correlator is a crucial element in a modern radio telescope. In this paper we describe a scalable design of the correlator system for the Tianlai pathfinder array, which is an experiment dedicated to test the key technologies for conducting 21cm intensity mapping survey. The correlator is of the FX design, which firstly performs Fast Fourier Transform (FFT) including Polyphase Filter Bank (PFB) computation using a Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Reconfigurable Open Architecture Computing Hardware-2 (ROACH2) board, then computes cross-correlations using Graphical Processing Units (GPUs). The design has been tested both in laboratory and in actual observation., Comment: 11 page, 10 figures

A coherently driven Kerr optical cavity is able to convert a continuous-wave laser to a sequence of ultrashort soliton pulses, enabling the generation of broadband and mode-locked frequency combs. Kerr cavity solitons are balanced through an energy exchange with the driving pump field. Improving the energy conversion efficiency from the pump to the soliton is of great significance for practical applications, but remains an outstanding challenge due to a limited temporal overlap between the soliton and the pump. Here, we report the discovery of temporal Kerr solitons in mutually coupled cavities instead of a traditional single cavity. We propose a strategy for breaking the limitation of pump-to-soliton energy conversion, and connect the underlying mechanism to impedance matching in radiofrequency electronic circuits. With macro optical fiber ring cavities which share the same physical model as miniature optical microresonators, we demonstrate nearly one-order improvement of the efficiency. The results pave the way towards super-efficient soliton microcombs based on optical microresonators with ultra-high quality factors., Comment: 30 pages, 13 figures; updated with experimental results

Microcomb generation with simultaneous $\chi^{(2)}$ and $\chi^{(3)}$ nonlinearities brings new possibilities for ultra-broadband and potentially self-referenced integrated comb sources. However, the evolution of the intracavity field involving multiple nonlinear processes shows complex dynamics that is still poorly understood. Here we report on strong soliton regulation induced by fundamental-second-harmonic (FD-SH) mode coupling. The formation of solitons from chaos is extensively investigated based on coupled Lugiato-Lefever equations. The soliton generation shows more deterministic behaviors in the presence of FD-SH mode interaction, in sharp contrast to the usual cases where the soliton number and relative locations are stochastic. Deterministic single soliton transition, soliton binding and prohibition are observed, depending on the phase matching condition and coupling coefficient between the fundamental and second-harmonic waves. Our finding provides important new insights into the soliton dynamics in microcavities with simultaneous $\chi^{(2)}$ and $\chi^{(3)}$ nonlinearities, and can be immediate guidance for broadband soliton comb generation with such platforms., Comment: 6 pages, 6 figures

Modal crosstalk is the main bottleneck in MMF-enabled optical datacenter networks with direct detection. A novel time-slicing-based crosstalk-mitigated MDM scheme is first proposed, then theoretically analyzed and experimentally demonstrated., Comment: Accepted by Optical Fiber Communications Conference (OFC), 2018

Microwave phased array antennas (PAAs) are very attractive to defense applications and high-speed wireless communications for their abilities of fast beam scanning and complex beam pattern control. However, traditional PAAs based on phase shifters suffer from the beam-squint problem and have limited bandwidths. True-time-delay (TTD) beamforming based on low-loss photonic delay lines can solve this problem. But it is still quite challenging to build large-scale photonic TTD beamformers due to their high hardware complexity. In this paper, we demonstrate a photonic TTD beamforming network based on a miniature microresonator frequency comb (microcomb) source and dispersive time delay. A method incorporating optical phase modulation and programmable spectral shaping is proposed for positive and negative apodization weighting to achieve arbitrary microwave beam pattern control. The experimentally demonstrated TTD beamforming network can support a PAA with 21 elements. The microwave frequency range is $\mathbf{8\sim20\ {GHz}}$, and the beam scanning range is $\mathbf{\pm 60.2^\circ}$. Detailed measurements of the microwave amplitudes and phases are performed. The beamforming performances of Gaussian, rectangular beams and beam notch steering are evaluated through simulations by assuming a uniform radiating antenna array. The scheme can potentially support larger PAAs with hundreds of elements by increasing the number of comb lines with broadband microcomb generation., Comment: 10 pages, 9 figures, Journal of Lightwave Technology, 2018

In this paper, we proposed a novel OTSS-assisted optical network architecture for smart-grid communication networks, which has unique requirements for low-latency connections. Illustrative results show that, OTSS can provide extremely better performance in latency and blocking probability than conventional flexi-grid optical networks., Comment: IEEE Photonics Society 1st Place Best Poster Award, on CLEO-PR/OECC/PGC 2017