Long Text Generation via Adversarial Training with Leaked Information

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation. However, the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process. As such, it limits its success when the length of the generated text samples is long (more than 20 words). In this paper, we propose a new framework, called LeakGAN, to address the problem for long text generation. We allow the discriminative net to leak its own high-level extracted features to the generative net to further help the guidance. The generator incorporates such informative signals into all generation steps through an additional Manager module, which takes the extracted features of current generated words and outputs a latent vector to guide the Worker module for next-word generation. Our extensive experiments on synthetic data and various real-world tasks with Turing test demonstrate that LeakGAN is highly effective in long text generation and also improves the performance in short text generation scenarios. More importantly, without any supervision, LeakGAN would be able to implicitly learn sentence structures only through the interaction between Manager and Worker.Comment: 14 pages, AAAI 201

Free-riding Analysis Via Dynamic Game with Incomplete Information

AbstractP2P networks are distributed, acentric and self-organized systems. Due to the incomplete information of network environment, the uncertainty of trust relationship among peers and the selfishness of the peers in P2P networks, which give rise to many free-riders that seriously impact the stability and scalability of P2P networks. In this paper, by analyzing the incomplete information of network environment, the uncertainty of trust relationship among nodes, the phenomenon of the free-riding is studied based on game theory. The IIDGTrust (Incomplete Information Dynamic Game Trust)mechanism is presented through the case “Supplying the Public Resources”. Updating the trust relationship among the nodes according to the Bayesian law, which make nodes choose better strategies in time. The experimental results demonstrate that the IIDGTrust mechanism can effectively reduce the proportion of the free-riders in the P2P networks and maintain the stability of networks better

Real-Time Bidding by Reinforcement Learning in Display Advertising

The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks.Comment: WSDM 201

High visibility on-chip quantum interference of single surface plasmons

Quantum photonic integrated circuits (QPICs) based on dielectric waveguides have been widely used in linear optical quantum computation. Recently, surface plasmons have been introduced to this application because they can confine and manipulate light beyond the diffraction limit. In this study, the on-chip quantum interference of two single surface plasmons was achieved using dielectric-loaded surface-plasmon-polariton waveguides. The high visibility (greater than 90%) proves the bosonic nature of single plasmons and emphasizes the feasibility of achieving basic quantum logic gates for linear optical quantum computation. The effect of intrinsic losses in plasmonic waveguides with regard to quantum information processing is also discussed. Although the influence of this effect was negligible in the current experiment, our studies reveal that such losses can dramatically reduce quantum interference visibility in certain cases; thus, quantum coherence must be carefully considered when designing QPIC devices.Comment: 6 pages, 4 figure

An intrinsic link between long-term UV/optical variations and X-ray loudness in quasars

Observations have shown that UV/optical variation amplitude of quasars depend on several physi- cal parameters including luminosity, Eddington ratio, and likely also black hole mass. Identifying new factors which correlate with the variation is essential to probe the underlying physical processes. Combining ~ten years long quasar light curves from SDSS stripe 82 and X-ray data from Stripe 82X, we build a sample of X-ray detected quasars to investigate the relation between UV/optical variation amplitude ($\sigma_{rms}$) and X-ray loudness. We find that quasars with more intense X-ray radiation (com- pared to bolometric luminosity) are more variable in UV/optical. Such correlation remains highly significant after excluding the effect of other parameters including luminosity, black hole mass, Ed- dington ratio, redshift, rest-frame wavelength (i.e., through partial correlation analyses). We further find the intrinsic link between X-ray loudness and UV/optical variation is gradually more prominent on longer timescales (up to 10 years in the observed frame), but tends to disappear at timescales < 100 days. This suggests a slow and long-term underlying physical process. The X-ray reprocessing paradigm, in which UV/optical variation is produced by a variable central X-ray emission illuminating the accretion disk, is thus disfavored. The discovery points to an interesting scheme that both the X-ray corona heating and UV/optical variation is quasars are closely associated with magnetic disc turbulence, and the innermost disc turbulence (where corona heating occurs) correlates with the slow turbulence at larger radii (where UV/optical emission is produced).Comment: 9 pages, 4 figures, 1 table, accepted by Ap

No-go guide for late-time solutions to the Hubble tension: Matter perturbations

The Hubble tension seems to be a crisis with $\sim5\sigma$ discrepancy between the most recent local distance ladder measurement from type Ia supernovae calibrated by Cepheids and the global fitting constraint from the cosmic microwave background data. To narrow down the possible late-time solutions to the Hubble tension, we have used in a recent study [Phys. Rev. D 105, L021301 (2022)] an improved inverse distance ladder method calibrated by the absolute measurements of the Hubble expansion rate at high redshifts from the cosmic chronometer data, and found no appealing evidence for new physics at the late time beyond the $\Lambda$CDM model characterized by a parametrization based on the cosmic age. In this paper, we further investigate the perspective of this improved inverse distance ladder method by including the late-time matter perturbation growth data. Independent of the dataset choices, model parametrizations, and diagnostic quantities ($S_8$ and $S_{12}$), the new physics at the late time beyond the $\Lambda$CDM model is strongly disfavored so that the previous late-time no-go guide for the Hubble tension is further strengthened.Comment: v1, 15 pages, 4 figures, 6 tables; v2, 16 pages, 5 figures, 6 tables, covariance matrix added for the cosmic chronometer data, accepted for publication in Physical Review D; v3, to match the published versio