arXiv subjects

Bin Cao

Publications and source records attributed to Bin Cao.

80 records · Page 5Linked to original sources

arXiv2017

A Double Auction Mechanism for Mobile Crowd Sensing with Data Reuse

Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a flexible and scalable sensing coverage with a low deployment cost, by employing mobile users/devices to perform sensing tasks. In this work, we propose a novel MCS framework with data reuse, where multiple tasks with common data requirement can share (reuse) the common data with each other through an MCS platform. We study the optimal assignment of mobile users and tasks (with data reuse) systematically, under both information symmetry and asymmetry, depending on whether the user cost and the task valuation are public information. In the former case, we formulate the assignment problem as a generalized Knapsack problem and solve the problem by using classic algorithms. In the latter case, we propose a truthful and optimal double auction mechanism, built upon the above Knapsack assignment problem, to elicit the private information of both users and tasks and meanwhile achieve the same optimal assignment as under information symmetry. Simulation results show by allowing data reuse among tasks, the social welfare can be increased up to 100~380%, comparing with those without data reuse. We further show that the proposed double auction is not budget balance for the auctioneer, mainly due to the data reuse among tasks. To this end, we further introduce a reserve price into the double auction (for each data item) to achieve a desired tradeoff between the budget balance and the social efficiency.

cs.GT↗

arXiv2017

MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension

Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the words, which are very crucial to the quality of encoding. Moreover, existing attention methods represent each query word as a vector or use a single vector to represent the whole query sentence, neither of them can handle the proper weight of the key words in query sentence. In this paper, we introduce a novel neural network architecture called Multi-layer Embedding with Memory Network(MEMEN) for machine reading task. In the encoding layer, we employ classic skip-gram model to the syntactic and semantic information of the words to train a new kind of embedding layer. We also propose a memory network of full-orientation matching of the query and passage to catch more pivotal information. Experiments show that our model has competitive results both from the perspectives of precision and efficiency in Stanford Question Answering Dataset(SQuAD) among all published results and achieves the state-of-the-art results on TriviaQA dataset.

cs.AI↗

arXiv2016

Two coupled nonlinear cavities in a driven-dissipative environment

We investigate two coupled nonlinear cavities that are coherently driven in a dissipative environment. We perform semiclassical, numerical and analytical quantum studies of this dimer model when both cavities are symmetrically driven. In the semiclassical analysis, we find steady-state solutions with different photon occupations in two cavities. Such states can be considered analogs of the closed system double well symmetry breaking states. We analyze the occurrence and properties of these localized states in the system parameter space and examine how the symmetry breaking states, in form of a bistable pair, are associated to the single cavity bistable behavior. In a full quantum calculation of the master equation dynamics that includes quantum fluctuations, the symmetry breaking states and bistability disappear due to the quantum fluctuations. In quantum trajectory picture, we observe enhanced quantum jumps and switching which indicate the presence of the underlying semiclassical symmetry breaking states. Finally, we present a set of analytical solutions for the steady state correlation functions using the complex P-representation and discuss its regime of validity.

quant-ph↗

arXiv2016

Observing spin Hall effect of pseudo-thermal light through weak measurement

It is well known that the spin Hall effect of light (SHEL) can be easily observed via weak measurement by the dark strip resulted from the splitting of different polarization components. We find that the SHEL of partially coherent beam (PCB) also has similar phenomenon. However, the existence of the dark strip in the SHEL of PCB can not be properly justified by considering the beam as a statistical assemble of coherent speckles. Also, the dark strip of PCB is not purely dark. By analyzing the autocorrelation of the central dark strip part of the SHEL of PCB, we show that SHEL of PCB is essentially the direct result of overlapping coherent speckles' SHEL. Then we further prove our conclusion by adjusting the converging level and the incident angle of the beam. Finally, we develop a qualitative theory by taking the intensity distribution of PCB into account, to fully clarify the SHEL of PCB. The feasibility of our theory to Goos-Hänchen (GH) scenario is also demonstrated.

physics.optics↗

arXiv2014

Noise-induced quantum coherence in photosynthetic complexes with multiple energy transfer pathways

We theoretically investigate exciton relaxation dynamics in molecular aggregates based on model photosynthetic complexes under various conditions of incoherent excitation. We show that noise-induced quantum coherence is generated between spatially-separated exciton states which belong to the same or different energy transfer pathways, coupled via real and virtual transfer processes. Such quantum coherence effects may be used to improve light-harvesting efficiency and to reveal quantum phenomena in biology.

physics.bio-ph↗

arXiv2013

Facile residue analysis of recent and prehistoric cook-stones using handheld Raman spectrometry

We performed food residue analysis of cook-stones from experimental and prehistoric earth ovens using a handheld Raman spectrometry. Progress in modern optical technology provides a facile means of rapid non-destructive identification of residue artifacts from archaeological sites. For this study spectral signatures were obtained on sotol (Dasylirion spp.) experimentally baked in an earth oven as well as sotol residue on an experimentally used processing tool. Inulin was the major residue component. The portable handheld Raman spectrometer also detected traces of inulin on boiling stones used to boil commercially obtained inulin. The Raman spectra of inulin and sotol may be useful as signatures of wild plant residues in archaeology. Spectroscopic analysis of millennia-old cook-stones from prehistoric archaeological sites in Fort Hood, TX revealed the presence of residues whose further identification requires improvement of current optical methods.

physics.optics↗

arXiv2013

Dual-tip-enhanced ultrafast CARS nanoscopy

Coherent anti-Stokes Raman scattering (CARS) and, in particular, femtosecond adaptive spectroscopic techniques (FAST CARS) have been successfully used for molecular spectroscopy and microscopic imaging. Recent progress in ultrafast nanooptics provides flexibility in generation and control of optical near fields, and holds promise to extend CARS techniques to the nanoscale. In this theoretical study, we demonstrate ultrafast subwavelentgh control of coherent Raman spectra of molecules in the vicinity of a plasmonic nanostructure excited by ultrashort laser pulses. The simulated nanostructure design provides localized excitation sources for CARS by focusing incident laser pulses into subwavelength hot spots via two self-similar nanolens antennas connected by a waveguide. Hot-spot-selective dual-tip-enhanced CARS (2TECARS) nanospectra of DNA nucleobases are obtained by simulating optimized pump, Stokes and probe near fields using tips, laser polarization- and pulse-shaping. This technique may be used to explore ultrafast energy and electron transfer dynamics in real space with nanometre resolution and to develop novel approaches to DNA sequencing.

physics.optics↗

arXiv2012

Multi-Domain Collaborative Filtering

Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods.

cs.IR↗