AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement

Growing attention has been paid to Reinforcement Learning (RL) algorithms when optimizing long-term user engagement in sequential recommendation tasks. One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies. When formulated as a Markov Decision Process (MDP), the dynamics and reward functions of the recommendation system are continuously affected by these changes. Existing RL algorithms for recommendation systems will suffer from distribution shift and struggle to adapt in such an MDP. In this paper, we introduce a novel paradigm called Adaptive Sequential Recommendation (AdaRec) to address this issue. AdaRec proposes a new distance-based representation loss to extract latent information from users' interaction trajectories. Such information reflects how RL policy fits to current user behavior patterns, and helps the policy to identify subtle changes in the recommendation system. To make rapid adaptation to these changes, AdaRec encourages exploration with the idea of optimism under uncertainty. The exploration is further guarded by zero-order action optimization to ensure stable recommendation quality in complicated environments. We conduct extensive empirical analyses in both simulator-based and live sequential recommendation tasks, where AdaRec exhibits superior long-term performance compared to all baseline algorithms.Comment: Preprint. Under Revie

Gas-phase synthesis of silaformaldehyde (h2sio) and hydroxysilylene (hsioh) in outflows of oxygen-rich asymptotic giant branch stars

Silicon- and oxygen-containing species such as silicon monoxide (SiO) and silicon dioxide (SiO2) represent basic molecular building blocks connected to the growth of silicate grains in outflows of oxygen-rich asymptotic giant branch (AGB) stars like R Doradus. Yet the fundamental mechanisms of the formation of silicate grains and the early processes that initiate the coupling of the silicon with the oxygen chemistries in circumstellar envelopes have remained obscure. Here, in a crossed molecular beams experiment combined with ab initio electronic structure calculations, we reveal that at least the d2-silaformaldehyde (D2SiO) and d2-hydroxysilylene (DSiOD) molecules -proxies for the astronomically elusive silaformaldehyde (H2SiO) and hydroxysilylene (HSiOH) molecules-can be synthesized via the reaction of the D1-silylidyne radical (SiD; X2π) with D2-water (D2O) under single-collision conditions. This system represents a benchmark of a previously overlooked class of reactions, in which the silicon- oxygen bond coupling can be initiated by a reaction between the simplest silicon-bearing radical (silylidyne) and one of the most abundant species in the circumstellar envelopes of evolved oxygen-rich AGB stars (water). As supported by novel astrochemical modeling, considering that silicon- and oxygen-containing species like H2SiO and HSiOH might be photolyzed easily, they ultimately connect to simple molecular precursors such as SiO that drive a chain of reactions conceivably forming higher molecular weight silicon oxides and, ultimately, a population of silicates at high temperatures

Low-temperature gas-phase formation of indene in the interstellar medium

Polycyclic aromatic hydrocarbons (PAHs) are fundamental molecular building blocks of fullerenes and carbonaceous nanostructures in the interstellar medium and in combustion systems. However, an understanding of the formation of aromatic molecules carrying five-membered rings—the essential building block of nonplanar PAHs—is still in its infancy. Exploiting crossed molecular beam experiments augmented by electronic structure calculations and astrochemical modeling, we reveal an unusual pathway leading to the formation of indene (C9H8)—the prototype aromatic molecule with a five-membered ring—via a barrierless bimolecular reaction involving the simplest organic radical—methylidyne (CH)—and styrene (C6H5C2H3) through the hitherto elusive methylidyne addition–cyclization–aromatization (MACA) mechanism. Through extensive structural reorganization of the carbon backbone, the incorporation of a five-membered ring may eventually lead to three-dimensional PAHs such as corannulene (C20H10) along with fullerenes (C60, C70), thus offering a new concept on the low-temperature chemistry of carbon in our galaxy

Disrupted Asymmetry of Inter- and Intra-Hemispheric Functional Connectivity at Rest in Medication-Free Obsessive-Compulsive Disorder

Disrupted functional asymmetry of cerebral hemispheres may be altered in patients with obsessive-compulsive disorder (OCD). However, little is known about whether anomalous brain asymmetries originate from inter- and/or intra-hemispheric functional connectivity (FC) at rest in OCD. In this study, resting-state functional magnetic resonance imaging was applied to 40 medication-free patients with OCD and 38 gender-, age-, and education-matched healthy controls (HCs). Data were analyzed using the parameter of asymmetry (PAS) and support vector machine methods. Patients with OCD showed significantly increased PAS in the left posterior cingulate cortex, left precentral gyrus/postcentral gyrus, and right inferior occipital gyrus and decreased PAS in the left dorsolateral prefrontal cortex (DLPFC), bilateral middle cingulate cortex (MCC), left inferior parietal lobule, and left cerebellum Crus I. A negative correlation was found between decreased PAS in the left DLPFC and Yale–Brown Obsessive-compulsive Scale compulsive behavior scores in the patients. Furthermore, decreased PAS in the bilateral MCC could be used to distinguish OCD from HCs with a sensitivity of 87.50%, an accuracy of 88.46%, and a specificity of 89.47%. These results highlighted the contribution of disrupted asymmetry of intra-hemispheric FC within and outside the cortico-striato-thalamocortical circuits at rest in the pathophysiology of OCD, and reduced intra-hemispheric FC in the bilateral MCC may serve as a potential biomarker to classify individuals with OCD from HCs

Online Bayesian Data Fusion in Environment Monitoring Sensor Networks

Assuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon and the fusion node, and a discrete K -ary input and K -ary output channel is presented to model the data transfer channel, where K is the number of quantification levels at the sensor. Then, discrete time series models are used to estimate the mean value of the physical phenomenon, and the estimation error is modeled as a Gaussian process. Finally, based on the transition probability of the proposed data transfer channel and the probability of the estimated value transited to specific quantification levels, the level with the maximum posterior probability is decided to be the current value of the physical phenomenon. Evaluations based on real sensor data show that significant gain can be achieved by the proposed algorithms in environment monitoring sensor networks compared with channel-unaware algorithms

Vehicle Transport Security System Based on the Self-Security Intelligence of Radioactive Material

This paper proposes to design security measures based on the radioactive material package as the basic unit. The principle of four-layer defense in depth is put forward. Based on the concept of self-security intelligence, combined with out-of-vehicle monitoring, in-vehicle monitoring, and Beidou positioning technology, a security system for transport of radioactive materials was designed. It realized the perception, early warning, delay, and alarm functions and greatly improved the security