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1. Submodular Clustering in Low Dimensions

   https://doi.org/10.4230/LIPIcs.SWAT.2020.8  

   Backurs, Arturs; Har-Peled, Sariel (January 2020, 17th Scandinavian Symposium and Workshops on Algorithm Theory)

   We study a clustering problem where the goal is to maximize the coverage of the input points by k chosen centers. Specifically, given a set of n points P ⊆ ℝ^d, the goal is to pick k centers C ⊆ ℝ^d that maximize the service ∑_{p∈P}φ(𝖽(p,C)) to the points P, where 𝖽(p,C) is the distance of p to its nearest center in C, and φ is a non-increasing service function φ: ℝ+ → ℝ+. This includes problems of placing k base stations as to maximize the total bandwidth to the clients - indeed, the closer the client is to its nearest base station, the more data it can send/receive, and the target is to place k base stations so that the total bandwidth is maximized. We provide an n^{ε^-O(d)} time algorithm for this problem that achieves a (1-ε)-approximation. Notably, the runtime does not depend on the parameter k and it works for an arbitrary non-increasing service function φ: ℝ+ → ℝ+. 

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2. Low-Latency In-Band Integration of Multiple Low-Power Wide-Area Networks

   https://doi.org/10.1109/RTAS52030.2021.00034  

   Modekurthy, Venkata P.; Ismail, Dali; Rahman, Mahbubur; Saifullah, Abusayeed (May 2021, 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS))

   null (Ed.)
3. Multilevel Coding for Achieving Low Latency and Low Outage in mmWave Networks

   https://doi.org/10.1109/TWC.2025.3547924  

   Dogan, Mine Gokce; Shah, Jaimin; Cardone, Martina; Fragouli, Christina; Mao, Wei; Nikopour, Hosein; Vannithamby, Rath (January 2025, IEEE Transactions on Wireless Communications)
4. Unraveling climate change-induced compound low-solar-low-wind extremes in China

   https://doi.org/10.1093/nsr/nwae424  

   Wang, Licheng; Liu, Yawen; Zhao, Lei; Lu, Xi; Huang, Liangdian; Jin, Yana; Davis, Steven J; Aghakouchak, Amir; Huang, Xin; Zhu, Tong; et al (December 2024, National Science Review)

   ABSTRACT China's pursuit of carbon neutrality targets hinges on a profound shift towards low-carbon energy, primarily reliant on intermittent and variable, yet crucial, solar and wind power sources. In particular, low-solar-low-wind (LSLW) compound extremes present a critical yet largely ignored threat to the reliability of renewable electricity generation. While existing studies have largely evaluated the impacts of average climate-induced changes in renewable energy resources, comprehensive analyses of the compound extremes and, particularly, the underpinning dynamic mechanisms remain scarce. Here we show the dynamic evolution of compound LSLW extremes and their underlying mechanisms across China via coupling multi-model simulations with diagnostic analysis. Our results unveil a strong topographic dependence in the frequency of compound LSLW extremes, with a national average frequency of 16.4 (10th–90th percentile interval ranges from 5.3 to 32.6) days/yr, when renewable energy resources in eastern China are particularly compromised (∼80% lower than that under an average climate). We reveal a striking increase in the frequency of LSLW extremes, ranging from 12.4% under SSP126 to 60.2% under SSP370, primarily driven by both renewable energy resource declines and increasingly heavily-tailed distributions, resulting from weakened meridional temperature (pressure) gradient, increased frequency of extremely dense cloud cover and additional distinctive influence of increased aerosols under SSP370. Our study underscores the urgency of preparing for significantly heightened occurrences of LSLW events in a warmer future, emphasizing that such climate-induced compound LSLW extreme changes are not simply by chance, but rather projectable, thereby underscoring the need for proactive adaptation strategies. Such insights are crucial for countries navigating a similar transition towards renewable energy. 

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5. Low-Complexity, Low-Regret Link Rate Selection in Rapidly Time-Varying Wireless Channels

   H. Gupta, A. Eryilmaz (April 2018, INFOCOMP ... the ... International Conference on Advanced Communications and Computation)

   We consider the problem of transmitting at the optimal rate over a rapidly-varying wireless channel with unknown statistics when the feedback about channel quality is very limited. One motivation for this problem is that, in emerging wireless networks, the use of mmWave bands means that the channel quality can fluctuate rapidly and thus, one cannot rely on full channel-state feedback to make transmission rate decisions. Inspired by related problems in the context of multi-armed bandits, we consider a well-known algorithm called Thompson sampling to address this problem. However, unlike the traditional multi-armed bandit problem, a direct application of Thompson sampling results in a computational and storage complexity that grows exponentially with time. Therefore, we propose an algorithm called Modified Thompson sampling (MTS), whose computational and storage complexity is simply linear in the number of channel states and which achieves at most logarithmic regret as a function of time when compared to an optimal algorithm which knows the probability distribution of the channel states. 

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