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1. Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses

   https://doi.org/10.3389/fdgth.2023.1060828  

   Das_Swain, Vedant; Xie, Jiajia; Madan, Maanit; Sargolzaei, Sonia; Cai, James; De_Choudhury, Munmun; Abowd, Gregory D; Steimle, Lauren N; Prakash, B Aditya (May 2023, Frontiers in Digital Health)

   Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students’ learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility—a methodology we refer to asWiFi mobility models(WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC).WiMobcan construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally,WiMobenables us to designLCpolicies that close super-spreader locations on campus. By simulating disease spread with contact networks fromWiMob, we find thatLCmaintains the same reduction in cumulative infections asRIwhile showing greater reduction in peak infections and internal transmission. Moreover,LCreduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation.WiMobcan empower universities to conceive and assess a variety of closure policies to prevent future outbreaks. 

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2. Approximating Nash Social Welfare under Submodular Valuations through (Un)Matchings

   https://doi.org/10.1145/3613452  

   Garg, Jugal; Kulkarni, Pooja; Kulkarni, Rucha (October 2023, ACM Transactions on Algorithms)

   We study the problem of approximating maximum Nash social welfare (NSW) when allocatingmindivisible items amongnasymmetric agents with submodular valuations. TheNSWis a well-established notion of fairness and efficiency, defined as the weighted geometric mean of agents’ valuations. For special cases of the problem with symmetric agents and additive(-like) valuation functions, approximation algorithms have been designed using approaches customized for these specific settings, and they fail to extend to more general settings. Hence, no approximation algorithm with a factor independent ofmwas known either for asymmetric agents with additive valuations or for symmetric agents beyond additive(-like) valuations before this work. In this article, we extend our understanding of theNSWproblem to far more general settings. Our main contribution is two approximation algorithms for asymmetric agents with additive and submodular valuations. Both algorithms are simple to understand and involve non-trivial modifications of a greedy repeated matchings approach. Allocations of high-valued items are done separately by un-matching certain items and re-matching them by different processes in both algorithms. We show that these approaches achieve approximation factors ofO(n) andO(nlogn) for additive and submodular cases, independent of the number of items. For additive valuations, our algorithm outputs an allocation that also achieves the fairness property of envy-free up to one item (EF1). Furthermore, we show that theNSWproblem under submodular valuations is strictly harder than all currently known settings with an\(\frac{\mathrm{e}}{\mathrm{e}-1}\)factor of the hardness of approximation, even for constantly many agents. For this case, we provide a different approximation algorithm that achieves a factor of\(\frac{\mathrm{e}}{\mathrm{e}-1}\), hence resolving it completely. 

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3. STAR: A Cache-based Stream Warehouse System for Spatial Data

   https://doi.org/10.1145/3605944  

   Chen, Zhida; Cong, Gao; Aref, Walid G. (June 2023, ACM Transactions on Spatial Algorithms and Systems)

   Aref, Walid G. (Ed.)

   The proliferation of mobile phones and location-based services has given rise to an explosive growth in spatial data. In order to enable spatial data analytics, spatial data needs to be streamed into a data stream warehouse system that can provide real-time analytical results over the most recent and historical spatial data in the warehouse. Existing data stream warehouse systems are not tailored for spatial data. In this paper, we introduce theSTARsystem.STARis a distributed in-memory data stream warehouse system that provides low-latency and up-to-date analytical results over a fast-arriving spatial data stream.STARsupports both snapshot and continuous queries that are composed of aggregate functions and ad hoc query constraints over spatial, textual, and temporal data attributes.STARimplements a cache-based mechanism to facilitate the processing of snapshot queries that collectively utilizes the techniques of query-based caching (i.e., view materialization) and object-based caching. Moreover, to speed-up processing continuous queries,STARproposes a novel index structure that achieves high efficiency in both object checking and result updating. Extensive experiments over real data sets demonstrate the superior performance ofSTARover existing systems. 

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4. Multi-Group Regularized Gaussian Variational Estimation: Fast Detection of DIF

   https://doi.org/10.1017/psy.2024.15  

   Lyu, Weicong; Wang, Chun; Xu, Gongjun (March 2025, Psychometrika)

   Abstract Data harmonization is an emerging approach to strategically combining data from multiple independent studies, enabling addressing new research questions that are not answerable by a single contributing study. A fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across studies. In this study, we focus on a regularized explanatory multidimensional item response theory model (re-MIRT) for establishing measurement equivalence across instruments and studies, where regularization enables the detection of items that violate measurement invariance, also known as differential item functioning (DIF). Because the MIRT model is computationally demanding, we leverage the recently developed Gaussian Variational Expectation–Maximization (GVEM) algorithm to speed up the computation. In particular, the GVEM algorithm is extended to a more complicated and improved multi-group version with categorical covariates and Lasso penalty for re-MIRT, namely, the importance weighted GVEM with one additional maximization step (IW-GVEMM). This study aims to provide empirical evidence to support feasible uses of IW-GVEMM for re-MIRT DIF detection, providing a useful tool for integrative data analysis. Our results show that IW-GVEMM accurately estimates the model, detects DIF items, and finds a more reasonable number of DIF items in a real world dataset. The proposed method has been integrated intoRpackageVEMIRT(https://map-lab-uw.github.io/VEMIRT). 

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5. Supersaturation of even linear cycles in linear hypergraphs

   https://doi.org/10.1017/S0963548320000206  

   Jiang, Tao; Yepremyan, Liana (September 2020, Combinatorics, Probability and Computing)

   Abstract A classical result of Erdős and, independently, of Bondy and Simonovits [3] says that the maximum number of edges in ann-vertex graph not containingC_2k, the cycle of length 2k, isO(n^1+1/k). Simonovits established a corresponding supersaturation result forC_2k’s, showing that there exist positive constantsC,cdepending only onksuch that everyn-vertex graphGwithe(G)⩾Cn^1+1/kcontains at leastc(e(G)/v(G))^2kcopies ofC_2k, this number of copies tightly achieved by the random graph (up to a multiplicative constant). In this paper we extend Simonovits' result to a supersaturation result ofr-uniform linear cycles of even length inr-uniform linear hypergraphs. Our proof is self-contained and includes ther= 2 case. As an auxiliary tool, we develop a reduction lemma from general host graphs to almost-regular host graphs that can be used for other supersaturation problems, and may therefore be of independent interest. 

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