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To advance the circular economy (CE), it is crucial to gain insights into the evolution of public sentiments, cognitive pathways of the masses concerning circular products and digital technology, and recognise the primary concerns. To achieve this, we collected data related to the CE from diverse platforms including Twitter, Reddit, and The Guardian. This comprehensive data collection spanned across three distinct strata of the public: the general public, professionals, and official sources. Subsequently, we utilised three topic models on the collected data. Topic modelling represents a type of data-driven and machine learning approach for text mining, capable of automatically categorising a large number of documents into distinct semantic groups. Simultaneously, these groups are described by topics, and these topics can aid in understanding the semantic content of documents at a high level. However, the performance of topic modelling may vary depending on different hyperparameter values. Therefore, in this study, we proposed a framework for topic modelling with hyperparameter optimisation for CE and conducted a series of systematic experiments to ensure that topic models are set with appropriate hyperparameters and to gain insights into the correlations between the CE and public opinion based on well-established models. The results of this study indicate that concerns about sustainability and economic impact persist across all three datasets. Official sources demonstrate a higher level of engagement with the application and regulation of CE. To the best of our knowledge, this study is pioneering in investigating various levels of public opinions concerning CE through topic modelling with the exploration of hyperparameter optimisation.

In this paper, we perform the phase space analysis to investigate the evolution of the early universe in Einstein-Cartan theory. By studying the stability of critical points in dynamical system, it is found that there exist two stable critical points which represent an expanding solution and an Einstein static solution respectively. After analyzing the phase diagram of the dynamical system, we find that there may exist a bouncing universe, an oscillating universe or an Einstein static universe in the early time of universe. In addition, by assuming that the early universe filled by the radiation with $\omega= 1/3$ , the initial states of the early universe are Einstein static universe or oscillating universe. When the equation of state $\omega$ decreases with time, the universe can exit from the initial state and evolve into an expanding phase.

The search for exotic spin configurations and related novel transport properties continues to be fueled by the promise of new electronic states and outstanding candidate components for spintronic applications. In layered Cr$_{7-\delta}$Te$_8$, the applied field drives a before unreported magnetic transition revealed by the alternating current magnetic susceptibility measurements around room temperature. This observed magnetic transition results in a sign change for the anomalous Hall effect which exhibits non-monotonous temperature dependence. The prominent topological Hall effect (THE) with a large value of 1$\mu \Omega \cdot cm$ has been observed without breaking the inversion symmetry for Cr$_{7-\delta}$Te$_8$. This robust THE can persist up to room temperature attributed to the nonzero fluctuation-driven scalar spin chirality. The complicated interactions of long-range and short-range magnetic orders lead to rich exotic magnetic states with related novel transport properties in Cr$_{7-\delta}$Te$_8$.

We report on the charge dynamics of kagome FeGe, an antiferromagnet with a charge density wave (CDW) transition at $T_{\mathrm{CDW}} \simeq 105$ K, using polarized infrared spectroscopy and band structure calculations. We reveal a pronounced optical anisotropy, various excitations associated with flat bands and van Hove singularities (VHSs), and a moderate level of electronic correlations. Notably, there are two types of remarkable spectral weight (SW) redistributions for above and below $T_{\mathrm{CDW}}$. The former involves a transfer between incoherent and coherent excitations driven by the magnetic splitting-induced elevation of flat bands. The latter manifests itself as a sudden change of SW from low to high energies for both $a$ and $c$ directions, suggesting a first-order transition and the three-dimensional nature of CDW. These anomalies in SW significantly differ from those observed in other kagome metals like CsV$_3$Sb$_5$, where the nesting of VHSs results in a pronounced CDW gap feature. Instead, our findings can be accounted for by the jump of VHSs relative to the Fermi energy via a first-order structural transition involving large partial Ge1-dimerization. Our study thus unveils a complex interplay among structure, magnetism, electronic correlations, and charge order in FeGe, offering valuable insights for a comprehensive understanding of CDW order in kagome systems., Comment: 7 pages, 3 figures

We investigate the constant-roll inflation and the evolution of primordial black holes (PBHs) with Barrow holographic dark energy (BHDE). Using the modified Friedmann equation and the constant-roll condition in BHDE model, we calculate the constant-roll parameters, the scalar spectral index parameter and the tensor-to-scalar ratio with the chaotic potential $V_{0}\phi^{n}$. Then, we show that a suitable value of the power exponent is $n=1$ by using the Planck 2018 data. Considering the accretion process and the evaporation due to Hawking radiation, we discuss the evolution of PBHs in BHDE model and obtain that the PBHs mass is in the mass window of PBHs.

In this paper, using the latest Pantheon+ sample of Type Ia supernovae (SNe Ia), Baryon Acoustic Oscillation (BAO) measurements, and observational Hubble data (OHD), we carry out a joint constraint on the Hubble constant $H_0$, the spatial curvature $\Omega_{\rm K}$, and the sound horizon at the end of drag epoch $r_{\rm d}$. To be model-independent, four cosmography models, i.e., the Taylor series in terms of redshift $y_1=z/(1+z)$, $y_2=\arctan(z)$, $y_3=\ln(1+z)$, and the Pad\'e approximants, are used without the assumption of flat Universe. The results show that the $H_0$ is anti-correlated with $\Omega_{\rm K}$ and $r_{\rm d}$, indicating smaller $\Omega_{\rm K}$ or $r_{\rm d}$ would be helpful in alleviating the Hubble tension. And the values of $H_0$ and $r_{\rm d}$ are consistent with the estimate derived from the Planck Cosmic Microwave Background (CMB) data based on the flat $\Lambda$CDM model, but $H_0$ is in 2.3$\sim$3.0$\sigma$ tension with that obtained by \cite{Riess2022} in all these cosmographic approaches. Meanwhile, a flat Universe is preferred by the present observations under all approximations except the third order of $y_1$ and $y_2$ of the Taylor series. Furthermore, according to the values of the Bayesian evidence, we found that the flat $\Lambda$CDM remains to be the most favored model by the joint datasets, and the Pad\'e approximant of order (2,2), the third order of $y_3$ and $y_1$ are the top three cosmographic expansions that fit the datasets best, while the Taylor series in terms of $y_2$ are essentially ruled out., Comment: 18 pages, 4 figures

Magnetic skyrmions, the topological states possessing chiral magnetic structure with non-trivial topology, have been widely investigated as a promising candidate for spintronic devices. They can also couple with superconducting vortices to form skyrmion-vortex pairs, hosting Majorana zero mode which is a potential candidate for topological quantum computering. A lot of theoretical proposals have been put forward on constructing skyrmion-vortex pairs in heterostructures of chiral magnet and superconductor. Nevertheless, how to generate skyrmion-vortex pairs in a controllable way experimentally remains a significant challenge. We have designed a heterostructure of chiral magnet and superconductor [CoFeB/Ir/Ta]7/Nb in which zero field N\'eel-type skyrmions can be stabilized and the superconducting vortices can couple with the skyrmions when Nb is in the superconducting state. We have directly observed the formation of skyrmion-superconducting vortex pairs which is dependent on the direction of the applied magnetic field. Our results provide an effective method to manipulate the quantum states of skyrmions with the help of superconducting vortices, which can be used to explore the possible existence of Majorana zero mode for future quantum computation., Comment: 17 pages, 3 figures

The cosmological principle is one of the fundamental assumptions of the standard model of Cosmology (SCM), and it allow us to describe cosmic distances and clocks by using the Friedmann-Lema$\rm{\hat{{\i}}}$tre-Roberton-Walker (FLRW) metric. Thus, it is essential to test the FLRW metric with cosmological observations to verify the validity of the SCM. In this work, we perform tests of the FLRW metric by comparing the observational comoving angles between the Hubble $H(z)$ and angular Baryon Acoustic Oscillation (BAO) measurements. The Gaussian process is employed to reconstruct the Hubble $H(z)$ measurements and the angular diameter distance (ADD) from the transversal BAO data. A non-parametric method is adopted to probe the possible deviations from the FLRW metric at any redshift by comparing the comoving distances from the reconstructed Hubble $H(z)$ measurements with the ADD reconstructed from the transversal BAO data. Then, we propose two types of parameterizations for the deviations from the FLRW metric, and test the FLRW metric by using the priors of specific sound horizon scales. To avoid the bias caused by the prior of a specific sound horizon scale, we perform the consistency test with a flat prior of the sound horizon scale. We find that there a concordance between the FLRW metric and the observational data by using parametric and non-parametric methods, and the parameterizations can be employed to test the FLRW metric in a new way independent of the sound horizon scale., Comment: Accepted by PRD

We propose a set of techniques to efficiently importance sample the derivatives of several BRDF models. In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts which are real-valued and can have negative values. This leads to a new source of variance arising from their change in sign. Real-valued functions cannot be perfectly importance sampled by a positive-valued PDF and the direct application of BRDF sampling leads to high variance. Previous attempts at antithetic sampling only addressed the derivative with the roughness parameter of isotropic microfacet BRDFs. Our work generalizes BRDF derivative sampling to anisotropic microfacet models, mixture BRDFs, Oren-Nayar, Hanrahan-Krueger, among other analytic BRDFs. Our method first decomposes the real-valued differential BRDF into a sum of single-signed functions, eliminating variance from a change in sign. Next, we importance sample each of the resulting single-signed functions separately. The first decomposition, positivization, partitions the real-valued function based on its sign, and is effective at variance reduction when applicable. However, it requires analytic knowledge of the roots of the differential BRDF, and for it to be analytically integrable too. Our key insight is that the single-signed functions can have overlapping support, which significantly broadens the ways we can decompose a real-valued function. Our product and mixture decompositions exploit this property, and they allow us to support several BRDF derivatives that positivization could not handle. For a wide variety of BRDF derivatives, our method significantly reduces the variance (up to 58x in some cases) at equal computation cost and enables better recovery of spatially varying textures through gradient-descent-based inverse rendering.

We analyze the stability of the Einstein static universe in scalar-tensor theory of gravity, and find it can be stable against both scalar and tensor perturbations under certain conditions. By assuming the emergent scenario originating from an Einstein static state, followed by an instantaneous transition to an inflationary phase, we study and obtain the analytical approximations of the primordial power spectrum for the emergent scenario. Then, we plot the primordial power spectrum and CMB TT-spectrum of the emergent scenario and the slow expansion scenario. These figures show that both of these spectra for the slow expansion scenario are the same as that for $\Lambda$CDM, and the spectra of the emergent scenario are lower than that for $\Lambda$CDM at large scales.

In this paper, we carry out a new model-independent cosmological test for the cosmic distance duality relation~(CDDR) by combining the latest five baryon acoustic oscillations (BAO) measurements and the Pantheon type Ia supernova (SNIa) sample. Particularly, the BAO measurement from extended Baryon Oscillation Spectroscopic Survey~(eBOSS) data release~(DR) 16 quasar sample at effective redshift $z=1.48$ is used, and two methods, i.e. a compressed form of Pantheon sample and the Artificial Neural Network~(ANN) combined with the binning SNIa method, are applied to overcome the redshift-matching problem. Our results suggest that the CDDR is compatible with the observations, and the high-redshift BAO and SNIa data can effectively strengthen the constraints on the violation parameters of CDDR with the confidence interval decreasing by more than 20 percent. In addition, we find that the compressed form of observational data can provide a more rigorous constraint on the CDDR, and thus can be generalized to the applications of other actual observational data with limited sample size in the test for CDDR., Comment: 13 pages, 4 figures

The emergent universe provides a possible method to avoid the big bang singularity by considering that the universe stems from an stable Einstein static universe rather than the singularity. Since the Einstein static universe exists before inflation, it may leave some relics in the CMB power spectrum. In this paper, we analyze the stability condition for the Einstein static universe in general relativity with k-essence against both the scalar and tensor perturbations. And we find the emergent universe can be successfully realized by constructing a scalar potential and an equation of state parameter. Solving the curved Mukhanov-Sasaki equation, we obtain the analytical approximation for the primordial power spectrum, and then depict the TT-spectrum of the emergent universe. The results show that both the primordial power spectrum and CMB TT-spectrum are suppressed on large scales.

The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: the pre-trained graph representation learning model tested with full graphs underperforms the model tested with well-pruned graphs. This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence. To tackle this issue, we propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. RCGRL introduces an active approach to generate instrumental variables under unconditional moment restrictions, which empowers the graph representation learning model to eliminate confounders, thereby capturing discriminative information that is causally related to downstream predictions. We offer theorems and proofs to guarantee the theoretical effectiveness of the proposed approach. Empirically, we conduct extensive experiments on a synthetic dataset and multiple benchmark datasets. The results demonstrate that compared with state-of-the-art methods, RCGRL achieves better prediction performance and generalization ability., Comment: Accepted by AAAI 2023 as Oral Presentation

Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data. Previous research works rarely consider token-level feature fusion, and few works explore learning the common features related to sentiment in multimodal data to help the model fuse multimodal features. In this paper, we propose a Contrastive Learning and Multi-Layer Fusion (CLMLF) method for multimodal sentiment detection. Specifically, we first encode text and image to obtain hidden representations, and then use a multi-layer fusion module to align and fuse the token-level features of text and image. In addition to the sentiment analysis task, we also designed two contrastive learning tasks, label based contrastive learning and data based contrastive learning tasks, which will help the model learn common features related to sentiment in multimodal data. Extensive experiments conducted on three publicly available multimodal datasets demonstrate the effectiveness of our approach for multimodal sentiment detection compared with existing methods. The codes are available for use at https://github.com/Link-Li/CLMLF, Comment: Accepted to Findings of NAACL 2022

The frustrated magnet Nd$_2$Ir$_2$O$_7$, where strong correlations together with spin-orbit coupling play a crucial role, is predicted to be a Weyl semimetal and to host topological pairs of bulk Dirac-like valleys. Here we use an external magnetic field to manipulate the localized rare earth 4f moments coupled to the 5d electronic bands. Low energy optical spectroscopy reveals that a field of only a few teslas suffices to create charge compensating pockets of holes and electrons in different regions of momentum space, thus introducing a valley population shift that can be tuned with the field., Comment: 8 pages, 6 figures

The emergent scenario provides a possible way to avoid the big bang singularity by assuming that the universe originates from an Einstein static state. Therefore, an Einstein static universe stable under perturbations is crucial to a successful implementation of the emergent mechanism. In this paper, we analyze the stability of the Einstein static universe against the scalar perturbations in the mimetic theory and find that stable Einstein static solutions exist under certain conditions in this theory. In the original mimetic gravity, the Einstein static universe is unstable. Then, we find that the universe can naturally exit from the initial static state, evolve into an inflationary era and then exit from the inflationary era. Thus, the emergent scenario can be used to resolve the big bang singularity in the mimetic theory., Comment: Published in Classical and Quantum Gravity. arXiv admin note: text overlap with arXiv:1801.04358

Based on the holographic principle and the Barrow entropy, Barrow holographic dark energy had been proposed. In order to analyze the stability and the evolution of Barrow holographic dark energy, we, in this paper, apply the dynamical analysis and statefinder methods to Barrow holographic dark energy with different IR cutoff and interacting terms. In the case of using Hubble horizon as IR cutoff with the interacting term $Q=\frac{\lambda}{H}\rho_{m}\rho_{D}$, we find this model is stable and can be used to describe the whole evolution of the universe when the energy transfers from the pressureless matter to the Barrow holographic dark energy. When the dynamical analysis method is applied to this stable model, an attractor corresponding to an accelerated expansion epoch exists and this attractor can behave as the cosmological constant. Furthermore, the coincidence problem can be solved in this case. Then, after using the statefinder analysis method to this model, we find this model can be discriminated from the standard $\Lambda$CDM model. Finally, we have discussed the turning point of Hubble diagram in Barrow holographic dark energy and find the turning point does not exist in this model., Comment: Accepted for publication in EPJC

The highly mobile electrons at the interface of SrTiO3 with other oxide insulators, such as LaAlO3 or AlOx, are of great current interest. A vertical gate voltage allows controlling a metal/superconductor-to-insulator transition, as well as electrical modulation of the spin-orbit Rashba coupling for spin-charge conversion. These findings raise important questions about the origin of the confined electrons as well as the mechanisms that govern the interfacial electric field. Here we use infrared ellipsometry and confocal Raman spectroscopy to show that an anomalous polar moment is induced at the interface that is non-collinear, highly asymmetric and hysteretic with respect to the vertical gate electric field. Our data indicate that an important role is played by the electromigration of oxygen vacancies and their clustering at the antiferrodistortive domain boundaries of SrTiO3, which generates local electric and possibly also flexoelectric fields and subsequent polar moments with a large lateral component. Our results open new perspectives for the defect engineering of lateral devices with strongly enhanced and hysteretic local electric fields that can be manipulated with various other parameters, like strain, temperature, or photons.

Quantum key distribution (QKD) is an important branch of quantum information science as it holds promise for unconditionally secure communication. For QKD research, a central issue is to improve the final secure key rate (SKR) and the maximal transmission distance. To address this issue, most works focused on reducing the information leakage of QKD. In this paper, we propose a novel approach to further reduce the information leakage by specially considering the overlap between the information leakage of quantum part and post-processing part. The overlap means that the information leakage of post-processing part caused solely by multi-photon pulses is considered twice in previous studies, i.e., both in quantum part and post-processing part. Since the information carried by multi-photon pulses has been considered as completely known by Eve through the photon-number-splitting attack in quantum part, there is no need to consider it in post-processing part repetitively during the SKR calculation. Therefore, our approach can theoretically reduce the information leakage of a QKD protocol. Based on this idea, we derive the formulas to calculate the amount of information leakage for decoy-BB84 and sending-or-not-sending twin-field protocols. Simulation results for these two typical protocols also demonstrate that our approach evidently improves the SKR as well as the maximal transmission distance under practical experimental parameters., Comment: 9 pages, 3 figures

We present an investigation into the optical properties of Nd$_{4}$Ni$_{3}$O$_{8}$ at different temperatures from 300 down to 5~K over a broad frequency range. The optical conductivity at 5~K is decomposed into IR-active phonons, a far-infrared band $\alpha$, a mid-infrared band $\beta$, and a high-energy absorption edge. By comparing the measured optical conductivity to first-principles calculations and the optical response of other nickelates, we find that Nd$_{4}$Ni$_{3}$O$_{8}$ features evident charge-stripe fluctuations. The $\beta$ band is attributed to electronic transitions between the gapped Ni-$d_{x^2-y^2}$ bands due to fluctuating charge stripes, while the high-frequency absorption edge corresponds to the onset of transitions involving other high-energy bands. Furthermore, an analysis of the temperature-dependent optical spectral weight reveals a $T^{2}$ law, which is likely to originate from strong correlation effects., Comment: 8 pages, 6 figures

Acute lower respiratory infections caused by respiratory viruses are common and persistent infectious diseases worldwide and in China, which have pronounced seasonal patterns. Meteorological factors have important roles in the seasonality of some major viruses. Our aim was to identify the dominant meteorological factors and to model their effects on common respiratory viruses in different regions of China. We analysed monthly virus data on patients from 81 sentinel hospitals in 22 provinces in mainland China from 2009 to 2013. The geographical detector method was used to quantify the explanatory power of each meteorological factor, individually and interacting in pairs. 28369 hospitalised patients with ALRI were tested, 10387 were positive for at least one virus, including RSV, influenza virus, PIV, ADV, hBoV, hCoV and hMPV. RSV and influenza virus had annual peaks in the north and biannual peaks in the south. PIV and hBoV had higher positive rates in the spring summer months. hMPV had an annual peak in winter spring, especially in the north. ADV and hCoV exhibited no clear annual seasonality. Temperature, atmospheric pressure, vapour pressure, and rainfall had most explanatory power on most respiratory viruses in each region. Relative humidity was only dominant in the north, but had no significant explanatory power for most viruses in the south. Hours of sunlight had significant explanatory power for RSV and influenza virus in the north, and for most viruses in the south. Wind speed was the only factor with significant explanatory power for human coronavirus in the south. For all viruses, interactions between any two of the paired factors resulted in enhanced explanatory power, either bivariately or non-linearly., Comment: 6 figures and tables

Twin-Field quantum key distribution (TF-QKD) and its variants, e.g. Phase-Matching QKD, Sending-or-not-sending QKD, and No Phase Post-Selection TFQKD promise high key rates at long distance to beat the rate distance limit without a repeater. The security proof of these protocols are based on decoy-state method, which is usually performed by actively modulating a variable optical attenuator together with a random number generator in practical experiments, however, active-decoy schemes like this may lead to side channel and could open a security loophole. To enhance the source security of TF-QKD, in this paper, we propose passive-decoy based TF-QKD, in which we combine TF-QKD with the passive-decoy method. And we present a simulation comparing the key generation rate with that in active-decoy, the result shows our scheme performs as good as active decoy TF-QKD, and our scheme could reach satisfactory secret key rates with just a few photon detectors. This shows our work is meaningful in practice.

With infrared spectroscopy we studied the bulk electronic properties of the topological antiferromagnet MnBi$_2$Te$_4$ with $T_N \simeq 25~\mathrm{K}$. With the support of band structure calculations, we assign the intra- and interband excitations and determine the band gap of $E_g \approx$ 0.17 eV. We also obtain evidence for two types of conduction bands with light and very heavy carriers. The multiband free carrier response gives rise to an unusually strong increase of the combined plasma frequency, $\omega_{\mathrm{pl}}$, below 300 K. The band reconstruction below $T_N$, yields an additional increase of $\omega_{\mathrm{pl}}$ and a splitting of the transition between the two conduction bands by about 54 meV. Our study thus reveals a complex and strongly temperature dependent multi-band low-energy response that has important implications for the study of the surface states and device applications., Comment: 16 pages, 15 figures

The fleet management of mobile working machines with the help of connectivity can increase safety and productivity. Although in our previous study, we proposed a solution to use IEEE 802.11p to achieve the fleet management of construction machines, the shortcoming of WIFI may limit the usage of this technology in some cases. Alternatively, the fifth-generation mobile networks (5G) have shown great potential to solve the problems. Thus, as the world's first academic paper investigating 5G and construction machines' cooperation, we demonstrated the scenarios where 5G can have a significant effect on the construction machines industry. Also, based on the simulation we made in $ns-3$, we compared the performance of 4G and 5G for the most relevant construction machines scenarios. Last but not least, we showed the feasibility of remote-control and self-working construction machines with the help of 5G., Comment: 8 pages, 12 figures

Quantum key distribution (QKD) can help two distant peers to share secret key bits, whose security is guaranteed by the law of physics. In practice, the secret key rate of a QKD protocol is always lowered with the increasing of channel distance, which severely limits the applications of QKD. Recently, twin-field (TF) QKD has been proposed and intensively studied, since it can beat the rate-distance limit and greatly increase the achievable distance of QKD. Remarkalebly, K. Maeda et. al. proposed a simple finite-key analysis for TF-QKD based on operator dominance condition. Although they showed that their method is sufficient to beat the rate-distance limit, their operator dominance condition is not general, i.e. it can be only applied in three decoy states scenarios, which implies that its key rate cannot be increased by introducing more decoy states, and also cannot reach the asymptotic bound even in case of preparing infinite decoy states and optical pulses. Here, to bridge this gap, we propose an improved finite-key analysis of TF-QKD through devising new operator dominance condition. We show that by adding the number of decoy states, the secret key rate can be furtherly improved and approach the asymptotic bound. Our theory can be directly used in TF-QKD experiment to obtain higher secret key rate. Our results can be directly used in experiments to obtain higher key rates.

We propose a new building block, IdleBlock, which naturally prunes connections within the block. To fully utilize the IdleBlock we break the tradition of monotonic design in state-of-the-art networks, and introducing hybrid composition with IdleBlock. We study hybrid composition on MobileNet v3 and EfficientNet-B0, two of the most efficient networks. Without any neural architecture search, the deeper "MobileNet v3" with hybrid composition design surpasses possibly all state-of-the-art image recognition network designed by human experts or neural architecture search algorithms. Similarly, the hybridized EfficientNet-B0 networks are more efficient than previous state-of-the-art networks with similar computation budgets. These results suggest a new simpler and more efficient direction for network design and neural architecture search.

The intelligent vehicle (IV) has become a promising technology that could revolutionize our life in smart cities sooner or later. However, it yet suffers from many security vulnerabilities. Traditional security methods are incapable to secure the IV data sharing against malicious attacks. Blockchain, as expected by both research and industry communities, has emerged as a good solution to address these issues. The major issues in IV data sharing are trust, data accuracy and reliability of data sharing in the communication channel. Blockchain technology, previously working for the cryptocurrency, has recently applied to build trust and reliability in peer-to-peer networks with similar topologies of IV data sharing. In this chapter, we present a new framework, namely biometric blockchain (BBC), for secure IV data sharing. In our new scheme, biometric information is exploited as a cue to record who is responsible in the data sharing activities, while the proposed BBC technology serves as the backbone of the IV data-sharing architecture. Hence, the proposed BBC technology provides a more reliable trust environment between the vehicles while personal identities are traceable in the proposed new scheme.

The dynamical and static Rashba effects in hybrid methylammonium (MA) lead halide perovskites have recently been theoretically predicted. However, only the static effect was experimentally confirmed so far. Here we report on the dynamical Rashba effect observed using snapshot transient absorption spectral imaging with 400 nm pumping for a fully encapsulated film of 20-nm-sized 3D MAPbBr3 nanocrystals. The effect causes a 240 meV splitting of the lowest-energy absorption bleaching band, initially appearing over sub-ps timescale and progressively stabilizing to 60 meV during 500 ps. The integrated intensities of the split subbands demonstrate a photon-helicity-dependent asymmetry, thus proving the Rashba-type splitting and providing direct experimental evidence for the Rashba spin-split edge states in lead halide perovskite materials. The ultrafast dynamics is governed by the relaxation of two-photon-excited electrons in the Rashba spin-split system caused by a built-in electric field originating from dynamical charge separation in the entire MAPbBr3 nanocrystal., Comment: Under consideration at Nature Communications

Blockchain has been emerging as a promising technology that could totally change the landscape of data security in the coming years, particularly for data access over Internet-of-Things and cloud servers. However, blockchain itself, though secured by its protocol, does not identify who owns the data and who uses the data. Other than simply encrypting data into keys, in this paper, we proposed a protocol called Biometric Blockchain (BBC) that explicitly incorporate the biometric cues of individuals to unambiguously identify the creators and users in a blockchain-based system, particularly to address the increasing needs to secure the food logistics, following the recently widely reported incident on wrongly labelled foods that caused the death of a customer on a flight. The advantage of using BBC in the food logistics is clear: it can not only identify if the data or labels are authentic, but also clearly record who is responsible for the secured data or labels. As a result, such a BBC-based solution can great ease the difficulty to control the risks accompanying the food logistics, such as faked foods or wrong gradient labels.

This paper studies how rewards based on class rank affect student effort and performance using a game-theoretic classroom competition model and data from the resettlement of Southeast Asian refugees in the US. The paper finds that variation in the presence of strong or weak students changes the incentives and test scores of incumbent students depending on their ability group in accord with the competition model, with increases in the number of strong students lowering effort among strong and weak incumbents but raising the test scores of weak incumbents. The results suggest that competition induced by rank-based rewards within homogeneous ability groups lowers overall effort levels, while the presence of strong students directly augments the performance, but not the effort levels, of weak students despite the competition. The paper also rules out a number of alternative explanations for these school composition effects, including disruptions, teacher-initiated changes in curriculum in response to changing class composition, selective incumbent-student school exit, and endogenous responses of refugee location choices to school performance. [This report was funded by the China National Natural Science Foundation.]

Twin-field quantum key distribution (TF-QKD) and its variant protocols are highly attractive due to the advantage of overcoming the rate-loss limit for secret key rates of point-to-point QKD protocols. For variations of TF-QKD, the key point to ensure security is switching randomly between a code mode and a test mode. Among all TF-QKD protocols, their code modes are very different, e.g. modulating continuous phases, modulating only two opposite phases, and sending or not sending signal pulses. Here we show that, by discretizing the number of global phases in the code mode, we can give a unified view on the first two types of TF-QKD protocols, and demonstrate that increasing the number of discrete phases extends the achievable distance, and as a trade-off, lowers the secret key rate at short distances due to the phase post-selection.

In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with global structures. Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies. In this work, we propose to factorize the mixed feature maps by their frequencies, and design a novel Octave Convolution (OctConv) operation to store and process feature maps that vary spatially "slower" at a lower spatial resolution reducing both memory and computation cost. Unlike existing multi-scale methods, OctConv is formulated as a single, generic, plug-and-play convolutional unit that can be used as a direct replacement of (vanilla) convolutions without any adjustments in the network architecture. It is also orthogonal and complementary to methods that suggest better topologies or reduce channel-wise redundancy like group or depth-wise convolutions. We experimentally show that by simply replacing convolutions with OctConv, we can consistently boost accuracy for both image and video recognition tasks, while reducing memory and computational cost. An OctConv-equipped ResNet-152 can achieve 82.9% top-1 classification accuracy on ImageNet with merely 22.2 GFLOPs., Comment: Accepted to ICCV 2019

The optical properties of YbMnSb2 have been measured in a broad frequency range from room temperature down to 7 K. With decreasing temperature, a flat region develops in the optical conductivity spectra at about 300cm-1, which can not be described by the well-known Drude-Lorentz model. A frequency-independent component has to be introduced to model the measured optical conductivity. Our first-principles calculations show that YbMnSb2 possesses a Dirac nodal line near the Fermi level. A comparison between the measured optical properties and calculated electronic band structures suggests that the frequency-independent optical conductivity component arises from interband transitions near the Dirac nodal line, thus demonstrating that YbMnSb2 is a Dirac nodal line semimetal., Comment: 7 pages, 5 figures

Twin-Field Quantum Key Distribution(TF-QKD) protocol and its variants, such as Phase-Matching QKD(PM-QKD), sending or not QKD(SNS-QKD) and No Phase Post-Selection TF-QKD(NPP-TFQKD), are very promising for long-distance applications. However, there are still some gaps between theory and practice in these protocols. Concretely, a finite-key size analysis is still missing, and the intensity fluctuations are not taken into account. To address the finite-key size effect, we first give the key rate of NPP-TFQKD against collective attack in finite-key size region and then prove it can be against coherent attack. To deal with the intensity fluctuations, we present an analytical formula of 4-intensity decoy state NPP-TFQKD and a practical intensity fluctuation model. Finally, through detailed simulations, we show NPP-TFQKD can still keep its superiority of high key rate and long achievable distance., Comment: 16 pages, 9 figures

The parameters choosing (such as probabilities of choosing X-basis or Z-basis, intensity of signal state and decoy state, etc.) and system calibrating will be more challenging when the number of users of a measurement-device-independent quantum key distribution(MDI-QKD) network becomes larger. At present, people usually use optimization algorithms to search the best parameters. This method can find the optimized parameters accurately but may cost lots of time and hardware resources. It's a big problem in large scale MDI-QKD network. Here, we present a new method, using Back Propagation Artificial Neural Network(BPNN) to predict, rather than searching the optimized parameters. Compared with optimization algorithms, our BPNN is faster and more lightweight, it can save system resources. Another big problem brought by large scale MDI-QKD network is system recalibration. BPNN can support this work in real time, and it only needs to use some discarded data generated from communication process, rather than require us to add additional devices or scan the system, Comment: 7 pages, 8 figures

We present temperature-dependent infrared spectroscopic studies of CaMnSb2, a proposed threedimensional topological material. The low plasma edge in the reflectivity spectrum and small Drude component in the optical conductivity indicate a very low carrier density. The low-frequency optical conductivity is well described by the superposition of a narrow and a broad Drude terms. Several linear components have been observed in the low-temperature optical conductivity, but none of them extrapolates to the origin, at odds with the optical response expected for three-dimensional Dirac fermions. A series of absorption peaks have been resolved in the high-frequency optical conductivity. The energy of these peaks agrees well with the interband transitions expected for the band structures from first-principles calculations. Intriguingly, the lowest band gap increases with decreasing temperature, mimic the temperature evolution of inverted bands. Furthermore, our theoretical calculations demonstrate the existence of weak coupling between two Sb-chains layers results in the topological trivial surface states in CaMnSb2., Comment: 7 pages,4 figures, Physical Review B 2018

The optical properties of KFe$_2$As$_2$ have been measured for light polarized in the a-b planes over a wide temperature and frequency range. Below $T^\ast\simeq 155$ K, where this material undergoes an incoherent-coherent crossover, we observe a new coherent response emerging in the optical conductivity. A spectral weight analysis suggests that this new feature arises out of high-energy bound states. Below about $T_{\rm FL} \simeq 75$ K the scattering rate for this new feature is quadratic in temperature, indicating a Fermi-liquid response. Theory calculations suggest this crossover is dominated by the $d_{xy}$ orbital. Our results advocate for Kondo-type screening as the mechanism for the orbital-selective incoherent-coherent crossover in hole-overdoped KFe$_2$As$_2$., Comment: Manuscript: 5 pages with 3 figures. Supplementary material: 6 pages with 5 figures and 1 table

In cognitive radio networks (CRN), secondary users (SUs) can share spectrum with licensed primary users (PUs). Because an SU receiver (SU-Rx) does not always share the same view of spectrum availability as the corresponding SU transmitter (SU-Tx), spectrum sensing conducted only by an SU transmitter tends to be overly sensitive to guarantee safe spectrum access at the price of SU inefficiency. In this letter, we propose a joint spectrum sensing mechanism, named Full-Duplex Joint Sensing (FJDS), to relax sensitivity of SU detection and improve SU throughput. FDJS employs instantaneous feedback enabled by in-band full duplex communication to facilitate the sharing of spectral information from SU-Rx to SU-Tx. The joint detection problem in FDJS is modeled as non-linear optimization and solved by a binary searching algorithm. Simulation results show that FDJS could improve SU throughput as well as meeting PU interruption constraints in a wide range of parameter settings., Comment: 4 pages, 3 figures, submitted to IEEE Communications Letters

Optical vortices, as a kind of structured beam with helical phase wavefronts and doughnut shape intensity distribution, have been used for fabricating chiral structures in metal and spiral patterns in anisotropic polarization-dependent azobenzene polymer. However, in isotropic polymer, the fabricated microstructures are typically confined to non-chiral cylindrical geometry due to two-dimensional doughnut intensity profile of optical vortices. Here we develop a powerful strategy for realizing chiral microstructures in isotropic material by coaxial interference of a vortex beam and a plane wave, which produces three-dimensional (3D) spiral optical fields. This coaxial interference beams are creatively produced by designing the contrivable holograms consisting of azimuthal phase and equiphase loaded on liquid-crystal spatial light modulator. Then, in isotropic polymer, 3D chiral microstructures are achieved under illumination of the coaxial interference femtosecond laser beams with their chirality controlled by the topological charge. Our further investigation reveals that the spiral lobes and chirality are caused by the interfering patterns and helical phase wavefronts, respectively. This technique is simple, stable, and easy-operation, and offers broad applications in optical tweezers, optical communications and fast metamaterial fabrication., Comment: 17 pages, 7 figures

In Ca1-xRExFeAs2 (RE= rare earth), an antiferromagnetic (AFM) phase as well as a structural transition has been reported, even in the electron-overdoped regime. Here we investigated the temperature-dependent in-plane optical spectroscopy of overdoped Ca0.77Nd0.23FeAs2. Upon entering the AFM state, we found an abrupt reduction of low-frequency (500-2 000 cm-1) spectral weight in the optical conductivity. In sharp contrast to the parent compounds of 122 system, where spin-density-wave gaps have been clearly observed in the AFM state, a gap signature is absent in Ca0.77Nd0.23FeAs2. This may be a consequence of the poor nesting condition between hole and electron pockets. However, a spectral weight analysis shows that the reduced spectral weight at low frequency is transferred to the high frequency range (> 4 000 cm-1), pointing to a localization effect. These observations suggest that the AFM order in Ca0.77Nd0.23FeAs2 is most likely to originate from a localized nature rather than Fermi surface nesting., Comment: 6 pages, 6 figures

We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O(sqrt(n)) memory to train a n layer network, with only the computational cost of an extra forward pass per mini-batch. As many of the state-of-the-art models hit the upper bound of the GPU memory, our algorithm allows deeper and more complex models to be explored, and helps advance the innovations in deep learning research. We focus on reducing the memory cost to store the intermediate feature maps and gradients during training. Computation graph analysis is used for automatic in-place operation and memory sharing optimizations. We show that it is possible to trade computation for memory - giving a more memory efficient training algorithm with a little extra computation cost. In the extreme case, our analysis also shows that the memory consumption can be reduced to O(log n) with as little as O(n log n) extra cost for forward computation. Our experiments show that we can reduce the memory cost of a 1,000-layer deep residual network from 48G to 7G with only 30 percent additional running time cost on ImageNet problems. Similarly, significant memory cost reduction is observed in training complex recurrent neural networks on very long sequences.

In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh). We point out that, besides the well-known non-zero centered property, slope of the activation function near the origin is another possible reason making training deep networks with the logistic function difficult to train. We demonstrate that, with proper rescaling, the logistic sigmoid achieves comparable results with tanh. Then following the same argument, we improve tahn by penalizing in the negative part. We show that "penalized tanh" is comparable and even outperforms the state-of-the-art non-saturated functions including ReLU and leaky ReLU on deep convolution neural networks. Our results contradict to the conclusion of previous works that the saturation property causes the slow convergence. It suggests further investigation is necessary to better understand activation functions in deep architectures.

MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines., Comment: In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2016

The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, \emph{learning with a strong adversary}, that learns robust classifiers from supervised data. The proposed method takes finding adversarial examples as an intermediate step. A new and simple way of finding adversarial examples is presented and experimentally shown to be efficient. Experimental results demonstrate that resulting learning method greatly improves the robustness of the classification models produced.

We give negative answers to Lin-Ni's conjecture for any four and six dimensional domains. No condition on the symmetry, geometry nor topology of the domain is needed., Comment: 28 pages

The temperature dependence of in plane optical conductivity has been investigated for Ca0.86Pr0.14Fe2As2 which shows a structural transition from tetragonal (T) to collapsed tetragonal(cT) phase at TcT=73 K. Upon entering the cT phase, drastic change characterized by the formation of a midinfrared peak near 3200 cm-1(0.4 eV) in the optical conductivity is observed.Analysis of the spectral weight reveals reduced electron correlation after the cT phase transition.Based on the calculated band structure and simulated optical conductivity, we attribute the new feature around 0.4 eV to the formation of interlayer As-As bond. The As-As bond strongly affects the Fe-As hybridizations, and in turn, drastically changes the Ca0.86Pr0.14Fe2As2 into a nonmagnetic Fermi liquid system without bulk superconductivity in the cT phase.

In this paper we investigate the performance of different types of rectified activation functions in convolutional neural network: standard rectified linear unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU). We evaluate these activation function on standard image classification task. Our experiments suggest that incorporating a non-zero slope for negative part in rectified activation units could consistently improve the results. Thus our findings are negative on the common belief that sparsity is the key of good performance in ReLU. Moreover, on small scale dataset, using deterministic negative slope or learning it are both prone to overfitting. They are not as effective as using their randomized counterpart. By using RReLU, we achieved 75.68\% accuracy on CIFAR-100 test set without multiple test or ensemble.

In this work, based on quantum operator Hermite polynomials and Weyl's mapping rule, we find a generation function of the two-variable Hermite polynomials. And then, noting that the Weyl ordering is invariant under the similar transformations, we obtain another generalized differential expression related to the Hermite polynomials. Those identites can be applied to investigate the nonclasscial properties of quantum optical fields., Comment: 12 pages,1 figure

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.