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1. Convolutional beamspace for linear arrays

   https://doi.org/DOI: 10.1109/TSP.2020.3021670  

   Chen, P.C ; Vaidyanathan, P.P ( September 2020 , IEEE transactions on signal processing)

   null (Ed.)

   Anew beamspace method for array processing, called convolutional beamspace (CBS), is proposed. It enjoys the advantages of classical beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. But unlike classical beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for ULAs without additional preparation since the Vandermonde structure and the shift-invariance are preserved under the CBS transformation. The method produces more accurate DOA estimates than classical beamspace, and for correlated sources, better estimates than element-space. The method also generalizes to sparse arrays by effective use of the difference coarray. For this, the autocorrelation evaluated on theULAportion of the coarray is filtered appropriately to produce the coarray CBS. It is also shown how CBS can be used in the context of sparse signal representation with dictionaries, where the dictionaries have columns that resemble steering vectors at a dense grid of frequencies. Again CBS processing with dictionaries offers better resolution, accuracy, and lower computational complexity. As only the filter responses at discrete frequencies on the dictionary grid are relevant, the problem of designing discrete-frequency FIR filters is also addressed. 

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2. Backbone flexibility on conjugated polymer's crystallization behavior and thin film mechanical stability

   https://doi.org/10.1002/pol.20210462  

   Qian, Zhiyuan ; Galuska, Luke A. ; Ma, Guorong ; McNutt, William W. ; Zhang, Song ; Mei, Jianguo ; Gu, Xiaodan ( September 2021 , Journal of Polymer Science)

   Extensive efforts have been made to develop flexible electronics with conjugated polymers that are intrinsically stretchable and soft. We recently systematically investigated the influence of conjugation break spacers (CBS) on the thermomechanical properties of a series n‐type naphthalene diimide‐based conjugated polymer and found that CBS can significantly reduce chain rigidity, melting point, as well as glass transition temperature. In the current work, we further examined the influence of CBS on the crystallization behaviors of PNDI‐C3 to C6, including isothermal crystallization kinetics, crystal polymorphism and subsequently time‐dependent modulus, in a holistic approach using differential scanning calorimetry, X‐ray scattering, polarized optical microscopy, atomic force microscopy, and pseudo‐free‐standing tensile test. Results demonstrate that increasing the length of CBS increases the crystallization half‐time by 1 order of magnitude from PNDI‐C3 to PNDI‐C6 from approximately 10³to 10⁴ s. The crystallization rate shows a bimodal dependence on the temperature due to the presence of different polymorphs. In addition, crystallization significantly affects the mechanical response, a stiffening in the modulus of nearly three times is observed for PNDI‐C5 when annealed at room temperature for 12 h. Crystallization kinetic is also influenced by molecular weight (MW). Higher MW PNDI‐C3 crystallizes slower. In addition, an odd–even effect was observed below 50°C, odd‐number PNDI‐Cxs (C3 and C5) crystallize slower than the adjacent even‐numbered PNDI‐Cxs (C4 and C6). Our work provides an insight to design flexible electronics by systematically tuning the mechanical properties through control of polymer crystallization by tuning backbone rigidity.

    

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3. Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

   Zhang, Jianyi ; Li, Ang ; Tang, Minxue ; Sun, Jingwei ; Chen, Xiang ; Zhang, Fan ; Chen, Changyou ; Chen, Yiran ; Li, Hai ( July 2023 , International Conference on Machine Learning)

   Due to the often limited communication bandwidth of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training at each communication round. Compared with engaging all the available clients, such a random-selection mechanism could lead to significant performance degradation on non-IID (independent and identically distributed) data. In this paper, we present our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from randomly selected clients. Based on this observation, we design an efficient heterogeneity-aware client sampling mechanism, namely, Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the grouped dataset from the intentionally selected clients. We first propose a measure of class-imbalance which can be derived in a privacy-preserving way. Based on this measure, we design a computationefficient client sampling strategy such that the actively selected clients will generate a more classbalanced grouped dataset with theoretical guarantees. Experimental results show that Fed-CBS outperforms the status quo approaches in terms of test accuracy and the rate of convergence while achieving comparable or even better performance than the ideal setting where all the available clients participate in the FL training. 

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4. Multi-Agent Path Finding with Mutex Propagation

   Zhang, H. ; Li, J. ; Surynek, P ; Koenig, S. ; Kumar, S. ( June 2020 , Proceedings of the International Conference on Automated Planning and Scheduling)

   Mutex propagation is a form of efficient constraint propagation popularly used in AI planning to tightly approximate the reachable states from a given state. We utilize this idea in the context of Multi-Agent Path Finding (MAPF). When adapted to MAPF, mutex propagation provides stronger constraints for conflict resolution in Conflict-Based Search (CBS), a popular optimal MAPF algorithm, and provides it with the ability to identify and reason with symmetries in MAPF. While existing work identifies a limited form of symmetries using rectangle reasoning and requires the manual design of symmetry-breaking constraints, mutex propagation is more general and allows for the automated design of symmetry-breaking constraints. Our experimental results show that CBS with mutex propagation is capable of outperforming CBSH with rectangle reasoning, a state-of-the-art variant of CBS, with respect to runtime and success rate. 

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5. Understanding Convergence Bids During Blackouts: Analytical Results and Real-World Implications

   https://doi.org/10.1109/TPWRS.2022.3208227  

   Samani, Ehsan ; Mohsenian-Rad, Hamed ( July 2023 , IEEE Transactions on Power Systems)

   This paper, for the first time, investigates the operation and impact of convergence bids (CBs) during blackouts . First, the amount of load shedding in the real-time market (RTM) is modeled as a function of the amount of the cleared CBs in the day-ahead market (DAM). The sign of the slope of this function is proposed as a metric to determine if a CB exacerbates or heals the power outages. Next, a series of mathematical theorems are developed to characterize this new metric under different network conditions. It is proved that, when there is no congestion in the DAM, the metric is always greater than or equal to zero. When there is congestion in the DAM, the metric can be positive or negative. Using numerical case studies, we show that, this metric in fact most often is positive. Therefore, supply CBs almost always hurt the system during blackouts while demand CBs almost always help the system. Furthermore, the impact of load shedding on the profit of CBs is also analyzed. It is shown that, load shedding usually creates advantage for supply CBs and disadvantage for demand CBs in their profit. The implications of these results are discussed. We also analyze the real-world market data from the California Independent System Operator (ISO) during the blackouts in August 2020. It is shown that, the decision by the California ISO to suspend CBs during this event matches the mathematical and numerical results in this paper. 

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