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1. Low- pT e+e− Pair Production in Au+Au Collisions at sNN=200  GeV and U+U Collisions at sNN=193  GeV at STAR

   https://doi.org/10.1103/PhysRevLett.121.132301  

   Adam, J. ; Adamczyk, L. ; Adams, J. R. ; Adkins, J. K. ; Agakishiev, G. ; Aggarwal, M. M. ; Ahammed, Z. ; Ajitanand, N. N. ; Alekseev, I. ; Anderson, D. M. ; et al ( September 2018 , Physical Review Letters)
2. PHASE: An Open-Source Program for the Analysis of Drosophila Ph ase, A ctivity, and S leep Under E ntrainment

   https://doi.org/10.1177/07487304221093114  

   Persons, J. L. ; Abhilash, L. ; Lopatkin, A. J. ; Roelofs, A. ; Bell, E. V. ; Fernandez, Maria P. ; Shafer, Orie T. ( June 2022 , Journal of Biological Rhythms)

   The problem of entrainment is central to circadian biology. In this regard, Drosophila has been an important model system. Owing to the simplicity of its nervous system and the availability of powerful genetic tools, the system has shed significant light on the molecular and neural underpinnings of entrainment. However, much remains to be learned regarding the molecular and physiological mechanisms underlying this important phenomenon. Under cyclic light/dark conditions, Drosophila melanogaster displays crepuscular patterns of locomotor activity with one peak anticipating dawn and the other anticipating dusk. These peaks are characterized through an estimation of their phase relative to the environmental light cycle and the extent of their anticipation of light transitions. In Drosophila chronobiology, estimations of phases are often subjective, and anticipation indices vary significantly between studies. Though there is increasing interest in building flexible analysis tools in the field, none incorporates objective measures of Drosophila activity peaks in combination with the analysis of fly activity/sleep in the same program. To this end, we have developed PHASE, a MATLAB-based program that is simple and easy to use and (i) supports the visualization and analysis of activity and sleep under entrainment, (ii) allows analysis of both activity and sleep parameters within user-defined windows within a diurnal cycle, (iii) uses a smoothing filter for the objective identification of peaks of activity (and therefore can be used to quantitatively characterize them), and (iv) offers a series of analyses for the assessment of behavioral anticipation of environmental transitions.

    

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3. Constraining the baryon abundance with the kinematic Sunyaev-Zel’dovich effect: Projected-field detection using Planck , WMAP , and unWISE

   https://doi.org/10.1103/PhysRevD.104.043518  

   Kusiak, Aleksandra ; Bolliet, Boris ; Ferraro, Simone ; Hill, J. Colin ; Krolewski, Alex ( August 2021 , Physical Review D)
4. Constraining the galaxy-halo connection of infrared-selected unWISE galaxies with galaxy clustering and galaxy-CMB lensing power spectra

   https://doi.org/10.1103/PhysRevD.106.123517  

   Kusiak, Aleksandra ; Bolliet, Boris ; Krolewski, Alex ; Hill, J. Colin ( December 2022 , Physical Review D)
5. S u G e R: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation

   https://doi.org/10.1145/3511808.3557707  

   Zhang, Zhenning ; Du, Boxin ; Tong, Hanghang ( October 2022 , CIKM)

   Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networks (GNNs) have been applied to this problem and achieved superior performance, existing methods underexplore the graph-level GNN methods, which exhibit great potential in traditional recommender system. Furthermore, they usually lack the transferability from one domain with sufficient supervision to another domain which might suffer from the label scarcity issue. In this work, we propose a subgraph-based Graph Neural Network model, SuGeR, for bundle recommendation to handle these limitations. SuGeR generates heterogeneous subgraphs around the user-bundle pairs and then maps those subgraphs to the users' preference predictions via neural relational graph propagation. Experimental results show that SUGER significantly outperforms the state-of-the-art baselines in the basic and the transfer bundle recommendation tasks by up to 77.17% by NDCG@40. The source code is available at: https://github.com/Zhang-Zhenning/SUGER. 

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