Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06

Federated Learning with Intermediate Representation Regularization

In contrast to centralized model training that involves data collection, federated learning (FL) enables remote clients to collaboratively train a model without exposing their private data. However, model performance usually degrades in FL due to the heterogeneous data generated by clients of diverse characteristics. One promising strategy to maintain good performance is by limiting the local training from drifting far away from the global model. Previous studies accomplish this by regularizing the distance between the representations learned by the local and global models. However, they only consider representations from the early layers of a model or the layer preceding the output layer. In this study, we introduce FedIntR, which provides a more fine-grained regularization by integrating the representations of intermediate layers into the local training process. Specifically, FedIntR computes a regularization term that encourages the closeness between the intermediate layer representations of the local and global models. Additionally, FedIntR automatically determines the contribution of each layer's representation to the regularization term based on the similarity between local and global representations. We conduct extensive experiments on various datasets to show that FedIntR can achieve equivalent or higher performance compared to the state-of-the-art approaches. Our code is available at https://github.com/YLTun/FedIntR.Comment: IEEE BigComp 202

Trajectory Optimization and Phase-Shift Design in IRS Assisted UAV Network for High Speed Trains

The recent trend towards the high-speed transportation system has spurred the development of high-speed trains (HSTs). However, enabling HST users with seamless wireless connectivity using the roadside units (RSUs) is extremely challenging, mostly due to the lack of line of sight link. To address this issue, we propose a novel framework that uses intelligent reflecting surfaces (IRS)-enabled unmanned aerial vehicles (UAVs) to provide line of sight communication to HST users. First, we formulate the optimization problem where the objective is to maximize the minimum achievable rate of HSTs by jointly optimizing the trajectory of UAV and the phase-shift of IRS. Due to the non-convex nature of the formulated problem, it is decomposed into two subproblems: IRS phase-shift problem and UAV trajectory optimization problem. Next, a Binary Integer Linear Programming (BILP) and a Soft Actor-Critic (SAC) are constructed in order to solve our decomposed problems. Finally, comprehensive numerical results are provided in order to show the effectiveness of our proposed framework.Comment: This paper has been submitted to IEEE Wireless Communications Letter

Cross-genotype protection of live-attenuated vaccine candidate for severe fever with thrombocytopenia syndrome virus in a ferret model

Severe fever with thrombocytopenia syndrome (SFTS) virus (SFTSV) is an emerging tick-borne virus classified within the Banyangvirus genus. SFTS disease has been reported throughout East Asia since 2009 and is characterized by high fever, thrombocytopenia, and leukopenia and has a 12 to 30% case fatality rate. Due to the recent emergence of SFTSV, there has been little time to conduct research into preventative measures aimed at combatting the virus. SFTSV is listed as one of the World Health Organization’s Prioritized Pathogens for research into antiviral therapeutics and vaccine development. Here, we report 2 attenuated recombinant SFTS viruses that induce a humoral immune response in immunized ferrets and confer complete cross-genotype protection to lethal challenge. Animals infected with rHB29NSsP102A or rHB2912aaNSs (both genotype D) had a reduced viral load in both serum and tissues and presented without high fever, thrombocytopenia, or mortality associated with infection. rHB29NSsP102A- or rHB2912aaNSs-immunized animals developed a robust anti-SFTSV immune response against cross-genotype isolates of SFTSV. This immune response was capable of neutralizing live virus in a focus-reduction neutralization test (FRNT) and was 100% protective against a cross-genotype lethal challenge with the CB1/2014 strain of SFTSV (genotype B). Thus, using our midsized, aged ferret infection model, we demonstrate 2 live attenuated vaccine candidates against the emerging pathogen SFTSV

The Safety and Efficacy of Transconjunctival Sutureless 23-gauge Vitrectomy

PURPOSE: To evaluate the efficacy and safety of vitreoretinal surgery using a 23-gauge transconjunctival sutureless vitrectomy (TSV) system for various vitreoretinal diseases. METHODS: A retrospective, consecutive, interventional case series was performed for 40 eyes of 40 patients. The patients underwent vitreoretinal procedures using the 23-gauge TSV system, including idiopathic epiretinal membrane (n=7), vitreous hemorrhage (n=11), diabetic macular edema (n=10), macular hole (n=5), vitreomacular traction syndrome (n=5), diabetic tractional retinal detachment (n=1), and rhegmatogenous retinal detachment (n=1). Best corrected visual acuity (BCVA), intraocular pressure (IOP), and intra- and post-operative complications were evaluated. RESULTS: Intraoperative suture placement was necessary in 3 eyes (7.5%). The median BCVA improved from 20/400 (LogMAR, 1.21+/-0.63) to 20/140 (LogMAR, 0.83+/-0.48) at 1 week (p=0.003), 20/100 (LogMAR, 0.85+/-0.65) at 1 month (p=0.002), 20/100 (LogMAR, 0.73+/-0.6) at 3 months (p=0.001). In 1 eye, IOP was 5 mmHg at 2 hours and 4 mmHg at 5 hours, but none of the eyes showed hypotony after 1 postoperative day. No serous postoperative complications were observed during a mean follow-up of 8.4+/-3.4 months (range 3-13 months) CONCLUSIONS: The 23-gauge TSV system shows promise as an effective and safe technique for a variety of vitreoretinal procedures. It appears to be a less traumatic, more convenient alternative to 20-gauge vitrectomy in some indications

Analyzing the advantages of subcutaneous over transcutaneous electrical stimulation for activating brainwaves

Transcranial electrical stimulation (TES) is a widely accepted neuromodulation modality for treating brain disorders. However, its clinical efficacy is fundamentally limited due to the current shunting effect of the scalp and safety issues. A newer electrical stimulation technique called subcutaneous electrical stimulation (SES) promises to overcome the limitations of TES by applying currents directly at the site of the disorder through the skull. While SES seems promising, the electrophysiological effect of SES compared to TES is still unknown, thus limiting its broader application. Here we comprehensively analyze the SES and TES to demonstrate the effectiveness and advantages of SES. Beagles were bilaterally implanted with subdural strips for intracranial electroencephalography and electric field recording. For the intracerebral electric field prediction, we designed a 3D electromagnetic simulation framework and simulated TES and SES. In the beagle model, SES induces three to four-fold larger cerebral electric fields compared to TES, and significant changes in power ratio of brainwaves were observed only in SES. Our prediction framework suggests that the field penetration of SES would be several-fold larger than TES in human brains. These results demonstrate that the SES would significantly enhance the neuromodulatory effects compared to conventional TES and overcome the TES limitations.11Ysciescopu

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms