Modeling mutual feedback between users and recommender systems

Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy

Accumulative time-based ranking method to reputation evaluation in information networks

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users' rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications

Light-Driven Spatiotemporal Pickering Emulsion Droplet Manipulation Enabled by Plasmonic Hybrid Microgels

The past decades have witnessed the development of various stimuli-responsive materials with tailored functionalities, enabling droplet manipulation through external force fields. Among different strategies, light exhibits excellent flexibility for contactless control of droplets, particularly in three-dimensional space. Here, we present a facile synthesis of plasmonic hybrid microgels based on the electrostatic heterocoagulation between cationic microgels and anionic Au nanoparticles. The hybrid microgels are effective stabilizers of oil-in-water Pickering emulsions. In addition, the laser irradiation on Au nanoparticles creats a “cascade effect” to thermally responsive microgels, which triggers a change in microgel wettability, resulting in microgel desorption and emulsion destabilization. More importantly, the localized heating generated by a focused laser induces the generation of a vapor bubble inside oil droplets, leading to the formation of a novel air-in-oil-in-water (A/O/W) emulsion. These A/O/W droplets are able to mimic natural microswimmers in an aqueous environment by tracking the motion of a laser spot, thus achieving on-demand droplet merging and chemical communication between isolated droplets. Such proposed systems are expected to extend the applications of microgel-stabilized Pickering emulsions for substance transport, programmed release and controlled catalytic reactions

Association between Zolpidem Use and Glaucoma Risk: A Taiwan Population-Based Case-control Study

[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]US

The flow and turbulence structure at a rectangular bridge pier with a low angle of attack

River hydrodynamicsInteraction with structure

Magnon-induced non-Markovian friction of a domain wall in a ferromagnet

Motivated by the recent study on the quasiparticle-induced friction of solitons in superfluids, we theoretically study magnon-induced intrinsic friction of a domain wall in a one-dimensional ferromagnet. To this end, we start by obtaining the hitherto overlooked dissipative interaction of a domain wall and its quantum magnon bath to linear order in the domain-wall velocity and to quadratic order in magnon fields. An exact expression for the pertinent scattering matrix is obtained with the aid of supersymmetric quantum mechanics. We then derive the magnon-induced frictional force on a domain wall in two different frameworks: time-dependent perturbation theory in quantum mechanics and the Keldysh formalism, which yield identical results. The latter, in particular, allows us to verify the fluctuation-dissipation theorem explicitly by providing both the frictional force and the correlator of the associated stochastic Langevin force. The potential for magnons induced by a domain wall is reflectionless, and thus the resultant frictional force is non-Markovian similarly to the case of solitons in superfluids. They share an intriguing connection to the Abraham-Lorentz force that is well-known for its causality paradox. The dynamical responses of a domain wall are studied under a few simple circumstances, where the non-Markovian nature of the frictional force can be probed experimentally. Our work, in conjunction with the previous study on solitons in superfluids, shows that the macroscopic frictional force on solitons can serve as an effective probe of the microscopic degrees of freedom of the system.Comment: 13 pages, 2 figure

Neuroprotective Effect of Paeonol Mediates Anti-Inflammation via Suppressing Toll-Like Receptor 2 and Toll-Like Receptor 4 Signaling Pathways in Cerebral Ischemia-Reperfusion Injured Rats

Paeonol is a phenolic compound derived from Paeonia suffruticosa Andrews (MC) and P. lactiflora Pall (PL). Paeonol can reduce cerebral infarction volume and improve neurological deficits through antioxidative and anti-inflammatory effects. However, the anti-inflammatory pathway of paeonol remains unclear. This study investigated the relationship between anti-inflammatory responses of paeonol and signaling pathways of TLR2 and TLR4 in cerebral infarct. We established the cerebral ischemia-reperfusion model in Sprague Dawley rats by occluding right middle cerebral artery for 60 min, followed by reperfusion for 24 h. The neurological deficit score was examined, and the brains of the rats were removed for cerebral infarction volume and immunohistochemistry (IHC) analysis. The infarction volume and neurological deficits were lower in the paeonol group (pretreatment with paeonol; 20 mg/kg i.p.) than in the control group (without paeonol treatment). The IHC analysis revealed that the number of TLR2-, TLR4-, Iba1-, NF-κB- (P50-), and IL-1β-immunoreactive cells and TUNEL-positive cells was significantly lower in the paeonol group; however, the number of TNF-α-immunoreactive cells did not differ between the paeonol and control groups. The paeonol reveals some neuroprotective effects in the model of ischemia, which could be due to the reduction of many proinflammatory receptors/mediators, although the mechanisms are not clear