arXiv subjects

Ning Jia

Publications and source records attributed to Ning Jia.

33 records · Page 2Linked to original sources

arXiv2023

Investigating day-to-day route choices based on multi-scenario laboratory experiments. Part I: Route-dependent attraction and its modeling

In the area of urban transportation networks, a growing number of day-to-day (DTD) traffic dynamic theories have been proposed to describe the network flow evolution, and an increasing amount of laboratory experiments have been conducted to observe travelers' behavior regularities. However, the "communication" between theorists and experimentalists has not been made well. This paper devotes to 1) detecting unanticipated behavior regularities by conducting a series of laboratory experiments, and 2) improving existing DTD dynamics theories by embedding the observed behavior regularities into a route choice model. First, 312 subjects participated in one of the eight decision-making scenarios and make route choices repeatedly in congestible parallel-route networks. Second, three route-switching behavior patterns that cannot be fully explained by the classic route-choice models are observed. Third, to enrich the explanation power of a discrete route-choice model, behavioral assumptions of route-dependent attractions, i.e., route-dependent inertia and preference, are introduced. An analytical DTD dynamic model is accordingly proposed and proven to steadily converge to a unique equilibrium state. Finally, the proposed DTD model could satisfactorily reproduce the observations in various datasets. The research results can help transportation science theorists to make the best use of laboratory experimentation and to build network equilibrium or DTD dynamic models with both real behavioral basis and neat mathematical properties.

physics.soc-ph↗

arXiv2022

Assessing the dynamic vulnerability of an urban rail transit system and a case study of Beijing, China

Urban rail transit is the backbone of urban transportation, and thus it is significant to understand its vulnerability, i.e., whether the system can still maintain normal operations when facing operational disturbances with different magnitudes. To this end, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. The method includes an accessibility-based identification of station importance with time-varying passenger demand and a new dynamic vulnerability evaluation index. An empirical analysis is carried out by taking the rail transit system of Beijing, China as an example. Results show that the distribution of high-importance stations varies with the time of day, affected by both static topology and hourly-changing passenger flow. Under the disturbance of operation delay, the impact of high-importance stations on the network vulnerability changes with the increase of delayed travel demand. It is also found that some stations that serve as bridges (i.e., reasonable paths link the origin station and destination) and are visited by large passenger flow have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main reason for system performance degradation, and some important stations within the line will act as catalysts to accelerate the performance degradation. This method provides a reference for measuring dynamic passenger flow-related network vulnerability and supplies the field with a new vulnerability evaluation index.

math.DS↗

arXiv2022

Ultraprecise off-axis atom localization with hybrid fields

Atom localization enables a high-precision imaging of the atomic position, which has provided vast applications in fundamental and applied science. In the present work, we propose a scheme for realizing two-dimensional off-axis atom localization in a three-level Lambda-type system. Benefiting from the use of a hybrid coupling field which consists of one Gaussian beam and one Laguerre-Gaussian beam, our scheme shows that the atoms can be localized at arbitrary position with a high spatial resolution. Taking account of realistic experimental parameters, our numerical simulation predicts that the atoms can be precisely localized with a spatial resolution of ~200 nm in the range of a radial distance of a few micrometers to the beam core. Our results provide a more flexible way to localize atoms in a two-dimensional system, possibly paving one-step closer to the nanometer-scale atom lithography and ultraprecise microscopy.

quant-ph↗

arXiv2020

Ultraprecise Rydberg atomic localization using optical vortices

We propose a robust localization of the highly-excited Rydberg atoms, interacting with doughnut-shaped optical vortices. Compared with the earlier standing-wave (SW)-based localization methods, a vortex beam can provide an ultrahigh-precision two-dimensional localization solely in the zero-intensity center, within a confined excitation region down to the nanometer scale. We show that the presence of the Rydberg-Rydberg interaction permits counter-intuitively much stronger confinement towards a high spatial resolution when it is partially compensated by a suitable detuning. In addition, applying an auxiliary SW modulation to the two-photon detuning allows a three-dimensional confinement of Rydberg atoms. In this case, the vortex field provides a transverse confinement while the SW modulation of the two-photon detuning localizes the Rydberg atoms longitudinally. To develop a new subwavelength localization technique, our results pave one-step closer to reduce excitation volumes to the level of a few nanometers, representing a feasible implementation for the future experimental applications.

quant-ph↗

arXiv2020

Camera Bias in a Fine Grained Classification Task

We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a model that "cheats" at an image classification task by recognizing which camera took the image and inferring the class label from the camera. We show that models trained on a dataset with camera / label correlations do not generalize well to images in which those correlations are absent, nor to images from unencountered cameras. Furthermore, we investigate which visual features they are exploiting for camera recognition. Our experiments present evidence against the importance of global color statistics, lens deformation and chromatic aberration, and in favor of high frequency features, which may be introduced by image processing algorithms built into the cameras.

cs.CV↗

arXiv2020

Strongly confined atomic localization by Rydberg coherent population trapping

In this letter we investigate the possibility to attain strongly confined atomic localization using interacting Rydberg atoms in a Coherent Population Trapping (CPT) ladder configuration, where a standing-wave (SW) is used as a coupling field in the second leg of the ladder. Depending on the degree of compensation of the Rydberg level energy shift induced by the van der Waals (vdW) interaction, by the coupling field detuning, we distinguish between two antiblockade regimes, i.e. a partial antiblockade (PA) and a full antiblockade (FA). While a periodic pattern of tightly localized regions can be achieved for both regimes, the PA allows much faster converge of spatial confinement yielding a high resolution Rydberg state-selective superlocalization regime for higher-lying Rydberg levels. In comparison, for lower-lying Rydberg levels the PA leads to an anomalous change of spectra linewidth, confirming the importance of using a stable uppermost state to achieve a superlocalization regime.

physics.atom-ph↗

arXiv2020

A study of the effect of the illumination model on the generation of synthetic training datasets

The use of computer generated images to train Deep Neural Networks is a viable alternative to real images when the latter are scarce or expensive. In this paper, we study how the illumination model used by the rendering software affects the quality of the generated images. We created eight training sets, each one with a different illumination model, and tested them on three different network architectures, ResNet, U-Net and a combined architecture developed by us. The test set consisted of photos of 3D printed objects produced from the same CAD models used to generate the training set. The effect of the other parameters of the rendering process, such as textures and camera position, was randomized. Our results show that the effect of the illumination model is important, comparable in significance to the network architecture. We also show that both light probes capturing natural environmental light, and modelled lighting environments, can give good results. In the case of light probes, we identified as two significant factors affecting performance the similarity between the light probe and the test environment, as well as the light probe's resolution. Regarding modelled lighting environment, similarity with the test environment was again identified as a significant factor.

cs.CV↗

arXiv2020

A quasisymmetric function for matroids

A new isomorphism invariant of matroids is introduced, in the form of a quasisymmetric function. This invariant (1) defines a Hopf morphism from the Hopf algebra of matroids to the quasisymmetric functions, which is surjective if one uses rational coefficients, (2) is a multivariate generating function for integer weight vectors that give minimum total weight to a unique base of the matroid, (3) is equivalent, via the Hopf antipode, to a generating function for integer weight vectors which keeps track of how many bases minimize the total weight, (4) behaves simply under matroid duality, (5) has a simple expansion in terms of P-partition enumerators, and (6) is a valuation on decompositions of matroid base polytopes. This last property leads to an interesting application: it can sometimes be used to prove that a matroid base polytope has no decompositions into smaller matroid base polytopes. Existence of such decompositions is a subtle issue arising in work of Lafforgue, where lack of such a decomposition implies the matroid has only a finite number of realizations up to projective equivalence.

math.CO↗

arXiv2019

Watermark retrieval from 3D printed objects via synthetic data training

We present a deep neural network based method for the retrieval of watermarks from images of 3D printed objects. To deal with the variability of all possible 3D printing and image acquisition settings we train the network with synthetic data. The main simulator parameters such as texture, illumination and camera position are dynamically randomized in non-realistic ways, forcing the neural network to learn the intrinsic features of the 3D printed watermarks. At the end of the pipeline, the watermark, in the form of a two-dimensional bit array, is retrieved through a series of simple image processing and statistical operations applied on the confidence map generated by the neural network. The results demonstrate that the inclusion of synthetic DR data in the training set increases the generalization power of the network, which performs better on images from previously unseen 3D printed objects. We conclude that in our application domain of information retrieval from 3D printed objects, where access to the exact CAD files of the printed objects can be assumed, one can use inexpensive synthetic data to enhance neural network training, reducing the need for the labour intensive process of creating large amounts of hand labelled real data or the need to generate photorealistic synthetic data.

cs.CV↗

arXiv2019

A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks

Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare each fairly. Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques are often ambiguous. To address these issues, we make a thorough investigation of the mainstream deep convolutional neural network architectures for multi-label image classification and present a strong baseline. With the use of proper data augmentation techniques and model ensembles, the basic deep architectures can achieve better performance than many existing more complex ones on three benchmark datasets, providing great insight for the future studies on multi-label image classification.

cs.CV↗

arXiv2015

Integrated information storage and transfer with a coherent magnetic device

Quantum systems are inherently dissipation-less, making them excellent candidates even for classical information processing. We propose to use an array of large-spin quantum magnets for realizing a device which has two modes of operation: memory and data-bus. While the weakly interacting low-energy levels are used as memory to store classical information (bits), the high-energy levels strongly interact with neighboring magnets and mediate the spatial movement of information through quantum dynamics. Despite the fact that memory and data-bus require different features, which are usually prerogative of different physical systems -- well isolation for the memory cells, and strong interactions for the transmission -- our proposal avoids the notorious complexity of hybrid structures. The proposed mechanism can be realized with different setups. We specifically show that molecular magnets, as the most promising technology, can implement hundreds of operations within their coherence time, while adatoms on surfaces probed by a scanning tunneling microscope is a future possibility.

quant-ph↗

arXiv2015

Cellular automaton model simulating spatiotemporal patterns, phase transitions and evolution concavity in traffic flow

This paper firstly show that a recent model (Tian et al., Transpn. Res. B 71, 138-157, 2015) is not able to well replicate the evolution concavity in traffic flow, i.e. the standard deviation of vehicles increases in a concave/linear way along the platoon. Then we propose an improved model by introducing the safe speed, the logistic function of the randomization probability, and small randomization deceleration for low-speed vehicles into the model. Simulations show that the improved model can well reproduce the metastable states, the spatiotemporal patterns, the phase transition behaviors of traffic flow, and the evolution concavity of traffic oscillations. Validating results show that the empirical time series of traffic speed obtained from Floating Car Data can be well simulated as well.

nlin.CG↗

arXiv2013

Route guidance strategies revisited: Comparison and evaluation in an asymmetric two-route traffic system

To alleviate traffic congestion, a variety of route guidance strategies has been proposed for intelligent transportation systems. A number of the strategies are proposed and investigated on a symmetric two-route traffic system over the past decade. To evaluate the strategies in a more general scenario, this paper conducts eight prevalent strategies on a asymmetric two-route traffic network with different slowdown behaviors on alternative routes. The results show that only mean velocity feedback strategy is able to equalize travel time, i.e., approximate user optimality; while the others fail due to incapability of establishing relations between the feedback parameters and travel time. The paper helps better understand these strategies, and suggests mean velocity feedback strategy if the authority intends to achieve user optimality.

physics.soc-ph↗

arXiv2011

Stability, Adiabaticity and Transfer efficiency in a nonlinear Λ-system

We investigate the relationship between stability, adiabaticity and transfer efficiency in a Λ-type atom-molecule coupling system via a nonlinear stimulated Raman adiabatic passage. We find that only when the pump and control lasers overlap in time domain, the coherent population trapping (CPT) state could become unstable. If the overlapping time of the two lasers is short so that unstable growth of the deviation from the CPT state is negligible, then good adiabaticity of the CPT state could be maintained even in the unstable region. In this case, a high atom-molecule transfer efficiency could be obtained by chirping applied laser pulses to elegantly compensate the frequency shift induced by intra-atomic collision. Our results could be useful for efficiently photoassociating ground-state molecules from a cold atomic gas with strong atom-atom collisional interaction.

quant-ph↗

arXiv2008

Duality of antidiagonals and pipe dreams

Weighted enumeration of reduced pipe dreams (or rc-graphs) results in a combinatorial expression for Schubert polynomials. The duality between the set of reduced pipe dreams and certain antidiagonals has important geometric implications [A. Knutson and E. Miller, Gröbner geometry of Schubert polynomials, Ann. Math. 161, 1245-1318]. The original proof of the duality was roundabout, relying on the algebra of certain monomial ideals and a recursive characterization of reduced pipe dreams. This paper provides a direct combinatorial proof.

math.CO↗