263 research outputs found
The effect of heterogeneous dynamics of online users on information filtering
The rapid expansion of the Internet requires effective information filtering techniques to extract the most essential and relevant information for online users. Many recommendation algorithms have been proposed to predict the future items that a given user might be interested in. However, there is an important issue that has always been ignored so far in related works, namely the heterogeneous dynamics of online users. The interest of active users changes more often than that of less active users, which asks for different update frequency of their recommendation lists. In this paper, we develop a framework to study the effect of heterogeneous dynamics of users on the recommendation performance. We find that the personalized application of recommendation algorithms results in remarkable improvement in the recommendation accuracy and diversity. Our findings may help online retailers make better use of the existing recommendation methods
Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach
Deep learning based decoding networks have shown significant improvement in
decoding LDPC codes, but the neural decoders are limited by rate-matching
operations such as puncturing or extending, thus needing to train multiple
decoders with different code rates for a variety of channel conditions. In this
correspondence, we propose a Multi-Task Learning based rate-compatible LDPC
ecoding network, which utilizes the structure of raptor-like LDPC codes and can
deal with multiple code rates. In the proposed network, different portions of
parameters are activated to deal with distinct code rates, which leads to
parameter sharing among tasks. Numerical experiments demonstrate the
effectiveness of the proposed method. Training the specially designed network
under multiple code rates makes the decoder compatible with multiple code rates
without sacrificing frame error rate performance
Mott-Hubbard Transition of Bosons in Optical Lattices with Three-body Interactions
In this paper, the quantum phase transition between superfluid state and
Mott-insulator state is studied based on an extended Bose-Hubbard model with
two- and three-body on-site interactions. By employing the mean-field
approximation we find the extension of the insulating 'lobes' and the existence
of a fixed point in three dimensional phase space. We investigate the link
between experimental parameters and theoretical variables. The possibility to
obverse our results through some experimental effects in optically trapped
Bose-Einstein Condensates(BEC) is also discussed.Comment: 7 pages, 4 figures; to be appear in Phys. Rev.
Rank the spreading influence of nodes using dynamic Markov process
Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node’s spreading ability is to evaluate the fraction of susceptible nodes being infected by the target node during the outbreak, i.e. the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbors. We have employed the susceptible-infected-recovered and susceptible-infected-susceptible models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes’ outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process
Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors
A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering
Solving Solar Neutrino Puzzle via LMA MSW Conversion
We analyze the existing solar neutrino experiment data and show the allowed
regions. The result from SNO's salt phase itself restricts quite a lot the
allowed region's area. Reactor neutrinos play an important role in determining
oscillation parameters. KamLAND gives decisive conclusion on the solution to
the solar neutrino puzzle, in particular, the spectral distortion in the 766.3
Ty KamLAND data gives another new improvement in the constraint of solar
MSW-LMA solutions. We confirm that at 99.73% C.L. the high-LMA solution is
excluded.Comment: 6 eps figure
Mast Cells Modulate Acute Toxoplasmosis in Murine Models
The role of mast cells (MCs) in Toxoplasma gondii infection is poorly known. Kunming outbred mice were infected intraperitoneally with RH strain T. gondii, either treated with compound 48/80 (C48/80, MC activator) or disodium cromoglycate (DSCG, MC inhibitor). Compared with infected controls, infected mice treated with C48/80 exhibited significantly increased inflammation in the liver (P \u3c 0.01), spleen (P \u3c 0.05), and mesentery (P \u3c 0.05) tissues, higher parasite burden in the peritoneal lavage fluids (P \u3c 0.01), and increased levels of mRNA transcripts of T. gondii tachyzoite surface antigen 1 (SAG1) gene in the spleen and liver tissues (P \u3c 0.01), accompanied with significantly increased Th1 cytokine (IFN-γ, IL-12p40, and TNF-α) (P \u3c 0.01) and decreased IL-10 (P \u3c 0.01) mRNA expressions in the liver, and increased IFN-γ (P \u3c 0.01) and IL-12p40 (P \u3c 0.01) but decreased TNF-α (P \u3c 0.01) and IL-4 (P \u3c 0.01) in the spleens of infected mice treated with C48/80 at day 9-10 p.i. Whereas mice treated with DSCG had significantly decreased tissue lesions (P \u3c 0.01), lower parasite burden in the peritoneal lavage fluids (P \u3c 0.01) and decreased SAG1 expressions in the spleen and liver tissues (P \u3c 0.01), accompanied with significantly increased IFN-γ (P \u3c 0.01) and IL-12p40 (P \u3c 0.05) in the liver, and decreased IFN-γ (P \u3c 0.05) and TNF-α (P \u3c 0.01) in the spleens; IL-4 and IL-10 expressions in both the spleen and liver were significantly increased (P \u3c 0.01) in the infected mice treated with DSCG. These findings suggest that mediators associated with the MC activation may play an important role in modulating acute inflammatory pathogenesis and parasite clearance during T. gondii infection in this strain of mice. Thus, MC activation/inhibition mechanisms are potential novel targets for the prevention and control of T. gondii infection
Information filtering in evolving online networks
Recommender systems use the records of users' activities and profiles of both users and products to predict users' preferences in the future. Considerable works towards recommendation algorithms have been published to solve the problems such as accuracy, diversity, congestion, cold-start, novelty, coverage and so on. However, most of these research did not consider the temporal effects of the information included in the users' historical data. For example, the segmentation of the training set and test set was completely random, which was entirely different from the real scenario in recommender systems. More seriously, all the objects are treated as the same, regardless of the new, the popular or obsoleted products, so do the users. These data processing methods always lose useful information and mislead the understanding of the system's state. In this paper, we detailed analyzed the difference of the network structure between the traditional random division method and the temporal division method on two benchmark data sets, Netflix and MovieLens. Then three classical recommendation algorithms, Global Ranking method, Collaborative Filtering and Mass Diffusion method, were employed. The results show that all these algorithms became worse in all four key indicators, ranking score, precision, popularity and diversity, in the temporal scenario. Finally, we design a new recommendation algorithm based on both users' and objects' first appearance time in the system. Experimental results showed that the new algorithm can greatly improve the accuracy and other metrics
Industry upgrading: recommendations of new products based on world trade network
GDP is a classic indicator of the extent of national economic development. Research based on the World Trade Network has found that a country’s GDP depends largely on the products it exports. In order to increase the competitiveness of a country and further increase its GDP, a crucial issue is finding the right direction to upgrade the industry so that the country can enhance its competitiveness. The proximity indicator measures the similarity between products and can be used to predict the probability that a country will develop a new industry. On the other hand, the Fitness–Complexity algorithm can help to find the important products and developing countries. In this paper, we find that the maximum of the proximity between a certain product and a country’s existing products is highly correlated with the probability that the country exports this new product in the next year. In addition, we find that the more products that are related to a certain product, the higher probability of the emergence of the new product. Finally, we combine the proximity indicator and the Fitness–Complexity algorithm and then attempt to provide a recommendation list of new products that can help developing countries to upgrade their industry. A few examples are given in the end
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