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Conversational recommender systems (CRS) dynamically obtain the users' preferences via multi-turn questions and answers. The existing CRS solutions are widely dominated by deep reinforcement learning algorithms. However, deep reinforcement learning methods are often criticized for lacking interpretability and requiring a large amount of training data to perform.In this paper, we explore a simpler alternative and propose a decision tree based solution to CRS. The underlying challenge in CRS is that the same item can be described differently by different users. We show that decision trees are sufficient to characterize the interactions between users and items, and solve the key challenges in multi-turn CRS: namely which questions to ask, how to rank the candidate items, when to recommend, and how to handle user's negative feedback on the recommendations. Firstly, the training of decision trees enables us to find questions which effectively narrow down the search space. Secondly, by learning embeddings for each item and tree nodes, the candidate items can be ranked based on their similarity to the conversation context encoded by the tree nodes. Thirdly, the diversity of items associated with each tree node allows us to develop an early stopping strategy to decide when to make recommendations. Fourthly, when the user rejects a recommendation, we adaptively choose the next decision tree to improve subsequent questions and recommendations. Extensive experiments on three publicly available benchmark CRS datasets show that our approach provides significant improvement to the state of the art CRS methods. 

Octopus-inspired switchable adhesives are integrated with sensing, processing, and control for robust underwater manipulation. 

Vaccination is the primary intervention for controlling the spread of infectious diseases. A certain level of vaccination rate (referred to as “herd immunity”) is needed for this intervention to be effective. However, there are concerns that herd immunity might not be achieved due to an increasing level of hesitancy and opposition to vaccines. One of the primary reasons for this is the cost of non-conformance with one’s peers. We use the framework of network coordination games to study the persistence of anti-vaccine sentiment in a population. We extend it to incorporate the opposing forces of the pressure of conforming to peers, herd-immunity and vaccination benefits. We study the structure of the equilibria in such games, and the characteristics of unvaccinated nodes. We also study Stackelberg strategies to reduce the number of nodes with anti-vaccine sentiment. Finally, we evaluate our results on different kinds of real world social networks. 

The devastating economic and societal impacts of COVID-19 can be substantially compounded by other secondary events that increase individuals’ exposure through mass gatherings such as protests or sheltering due to a natural disaster. Based on the Crichton’s Risk Triangle model, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation framework to estimate the impact of mass gatherings on COVID-19 infections by adjusting levels of exposure and vulnerability. To this end, a case study of New York City is considered, at which the impact of mass gathering at public shelters due to a hypothetical hurricane will be studied. The simulation results will be discussed in the context of determining effective policies for reducing the impact of multi-hazard generalizability of our approach to other secondary events that can cause mass gatherings during a pandemic will also be discussed. 

We report a pulsed laser annealing method to convert carbon fibers and nanotubes into diamond fibers at ambient temperature and pressure in air. The conversion of carbon nanofibers and nanotubes into diamond nanofibers involves melting in a super undercooled state using nanosecond laser pulses, and quenching rapidly to convert into phase-pure diamond. The conversion process occurs at ambient temperature and pressure, and can be carried out in air. The structure of diamond fibers has been confirmed by selected-area electron diffraction in transmission electron microscopy, electron-back-scatter-diffraction in high-resolution scanning electron microscopy, all showing characteristic diffraction lines for the diamond structure. The bonding characteristics were determined by Raman spectroscopy with a strong peak near 1332 cm −1 , and high-resolution electron-energy-loss spectroscopy in transmission electron microscopy with a characteristic peak at 292 eV for σ* for sp 3 bonding and the absence of π* for sp 2 bonding. The Raman peak at 1332 cm −1 downshifts to 1321 cm −1 for diamond nanofibers due to the phonon confinement in nanodiamonds. These laser-treated carbon fibers with diamond seeds are used to grow larger diamond crystallites further by using standard hot-filament chemical vapor deposition (HFCVD). We compare these results with those obtained without laser treating the carbon fibers. The details of diamond conversion and HFCVD growth are presented in this paper.