Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization

In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation. We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of $(1-1/e-\epsilon)^3$ for any (polynomially small) $\epsilon > 0$. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function.Comment: An extended abstract appeared in the Proceedings of the 16th ACM Conference on Economics and Computation (EC), 201

Holographic Ricci dark energy: Interacting model and cosmological constraints

We extend the holographic Ricci dark energy model to include some direct, non-gravitational interaction between dark energy and dark matter. We consider three phenomenological forms for the interaction term $Q$ in the model, namely, $Q$ is taken proportional to the Hubble expansion rate and the energy densities of dark sectors (taken to be $\rho_{\rm de}$, $\rho_{\rm m}$, and $\rho_{\rm de}+\rho_{\rm m}$, respectively). We obtain a uniform analytical solution to the three interacting models. Furthermore, we constrain the models by using the latest observational data, including the 557 Union2 type Ia supernovae data, the cosmic microwave background anisotropy data from the 7-yr WMAP, and the baryon acoustic oscillation data from the SDSS. We show that in the interacting models of the holographic Ricci dark energy, a more reasonable value of $\Omega_{\rm m0}$ will be obtained, and the observations favor a rather strong coupling between dark energy and dark matter.Comment: 9 pages, 4 figures; to appear in EPJC; typos corrected, published versio

The Fundamental and Application of Surface Heat Flux Estimation by Inverse Method in Cryogen Spray Cooling

Surface heat flux is an important parameter in various industrial applications, which is often estimated based on measured temperature by solving inverse heat conduction problem (IHCP). In this chapter, the available IHCP methods including sequential function specification (SFS), transfer function (TF) and Duhamel’s theorem were compared, taking the example of surface heat flux estimation during spray cooling. The Duhamel’s theorem was improved to solve 1D multi-layer ICHP. Considering the significant nonuniformity of heat transfer, the 2D filter solution method was proposed to estimate surface heat flux for 2D multi-layer mediums. The maximum heat flux calculated by the 1D method was underestimated by 60% than that calculated by 2D filter solution, indicating that the lateral heat transfer cannot be ignored. The cooling performances based on 2D filter solution demonstrated that substituting the environment friendly R1234yf for R134a can remarkably reduce global warming potential to <1, but its cooling capacity is insufficient. The effective heat flux of R1234yf can be enhanced by 18.8% by reducing the nozzle diameter and decreasing the back pressure, providing the theoretical basis for the clinical potential substitution of R1234yf with low global warming potential (GWP) for commercial R134a with high GWP in laser dermatology

Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models

Transformers are remarkably good at in-context learning (ICL) -- learning from demonstrations without parameter updates -- but how they perform ICL remains a mystery. Recent work suggests that Transformers may learn in-context by internally running Gradient Descent, a first-order optimization method. In this paper, we instead demonstrate that Transformers learn to implement higher-order optimization methods to perform ICL. Focusing on in-context linear regression, we show that Transformers learn to implement an algorithm very similar to Iterative Newton's Method, a higher-order optimization method, rather than Gradient Descent. Empirically, we show that predictions from successive Transformer layers closely match different iterations of Newton's Method linearly, with each middle layer roughly computing 3 iterations. In contrast, exponentially more Gradient Descent steps are needed to match an additional Transformers layer; this suggests that Transformers have an comparable rate of convergence with high-order methods such as Iterative Newton, which are exponentially faster than Gradient Descent. We also show that Transformers can learn in-context on ill-conditioned data, a setting where Gradient Descent struggles but Iterative Newton succeeds. Finally, we show theoretical results which support our empirical findings and have a close correspondence with them: we prove that Transformers can implement $k$ iterations of Newton's method with $\mathcal{O}(k)$ layers

Involvement of microRNA-93, a new regulator of PTEN/Akt signaling pathway, in regulation of chemotherapeutic drug cisplatin chemosensitivity in ovarian cancer cells

AbstractThe mechanisms underlying ovarian cancer cell resistance to cisplatin (CDDP) are not fully understood. MicroRNAs (miRNAs) play important roles in tumorigenesis and drug resistance. In this paper, we utilized microRNA array and real-time PCR to show that miR-93 is significantly up-regulated in cisplatin-resistant ovarian cancer cells. In vitro assays show that over-expression and knock-down of miR-93 regulate apoptotic activity, and thereby cisplatin chemosensitivity, in ovarian cells. Furthermore, we found that miR-93 can directly target PTEN, and participates in the regulation of the AKT signaling pathway. MiR-93 inversely correlates with PTEN expression in CDDP-resistant and sensitive human ovarian cancer tissues. These results may have implications for therapeutic strategies aiming to overcome ovarian cancer cell resistance to cisplatin

Nonlinear Transport of Graphene in the Quantum Hall Regime

We have studied the breakdown of the integer quantum Hall (QH) effect with fully broken symmetry, in an ultra-high mobility graphene device sandwiched between two single crystal hexagonal boron nitride substrates. The evolution and stabilities of the QH states are studied quantitatively through the nonlinear transport with dc Hall voltage bias. The mechanism of the QH breakdown in graphene and the movement of the Fermi energy with the electrical Hall field are discussed. This is the first study in which the stabilities of fully symmetry broken QH states are probed all together. Our results raise the possibility that the v=6 states might be a better target for the quantum resistance standard.Comment: 15 pages,6 figure

Meat consumption and all-cause mortality in 5763 patients with inflammatory bowel disease: A retrospective cohort study

Background: Whether meat consumption is related to risk of mortality in patients with inflammatory bowel disease (IBD) remains poorly understood. Methods: In the UK Biobank, 5763 patients with IBD were recruited from 2007 to 2010 and finished a brief food frequency questionnaire at baseline. We followed them until March 13, 2021 to document all-cause death events. Cox proportional hazard models were used to estimate hazard ratios (HRs) for all-cause mortality associated with consumptions of fish, unprocessed poultry, unprocessed red meat, and processed meat among the patients. Findings: During 67,095 person-years (mean follow-up 11·7 years, mean age 57·3, 52·5% female), we documented 590 death events. Higher consumption of processed meat was associated with an increased risk of all-cause mortality in patients with IBD (HR comparing >4·0 with 0-0·9 time/week=1·52, 95% confidence interval (CI) 1·05-2·19), but the P-trend for each 25 g increment was 0·075. This association remained significant in patients with Crohn's disease (HR 1·77, 95% CI 1·01-3·10) but not in patients with ulcerative colitis (HR 1·34, 95% CI 0·82-2·20). Consumptions of fish (HR 1·27, 95% CI 0·84-1·91), unprocessed poultry (HR 0·59, 95% CI 0·28-1·21), or unprocessed red meat (HR 0·87, 95% CI 0·60-1·26) were not significantly associated with the mortality of patients with IBD. Interpretation: More frequent consumption of processed meat was associated with an increased risk of mortality in patients with IBD, while no associations were observed for consumption of other types of meat. Our exploratory and speculative findings should be cautiously interpreted and need further replication in other cohorts. Funding: The National Natural Science Foundation of China (81,970,494); Key Project of Research and Development Plan of Hunan Province (2019SK2041)

Pedestrian/Bicyclist Limb Motion Analysis from 110-Car TASI Video Data for Autonomous Emergency Braking Testing Surrogate Development

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions. We modeled the pedestrian/bicyclist limb motions in four layers: (1) the percentages of pedestrians and bicyclists who have limb motions when crossing the road; (2) the averaged action frequency and the corresponding distributions on when there are limb motions; (3) comparisons of the limb motion behavior between crossing and non-crossing cases; and (4) the effects of seasons on the limb motions when the pedestrians/bicyclists are crossing the road. The results of this study can provide empirical foundations supporting surrogate development, benefit analysis, and standardized testing of vehicular pedestrian/bicyclist detection and crash mitigation systems