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1. Target and Non-target Speaker Discrimination by Humans and Machines

   https://doi.org/10.1109/ICASSP.2019.8683362  

   Park, Soo Jin; Afshan, Amber; Kreiman, Jody; Yeung, Gary; Alwan, Abeer (May 2019, IEEE ICASSP 2019)

   The manner in which acoustic features contribute to perceiving speaker identity remains unclear. In an attempt to better understand speaker perception, we investigated human and machine speaker discrimination with utterances shorter than 2 seconds. Sixty-five listeners performed a same vs. different task. Machine performance was estimated with i-vector/PLDA-based automatic speaker verification systems, one using mel-frequency cepstral coefficients (MFCCs) and the other using voice quality features (VQual2) inspired by a psychoacoustic model of voice quality. Machine performance was measured in terms of the detection and log-likelihood-ratio cost functions. Humans showed higher confidence for correct target decisions compared to correct non-target decisions, suggesting that they rely on different features and/or decision making strategies when identifying a single speaker compared to when distinguishing between speakers. For non-target trials, responses were highly correlated between humans and the VQual2-based system, especially when speakers were perceptually marked. Fusing human responses with an MFCC-based system improved performance over human-only or MFCC-only results, while fusing with the VQual2-based system did not. The study is a step towards understanding human speaker discrimination strategies and suggests that automatic systems might be able to supplement human decisions especially when speakers are marked. 

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2. Structural Target Controllability of Undirected Networks

   https://doi.org/10.1109/CDC.2018.8619399  

   Li, Jingqi; Chen, Ximing; Pequito, Sergio; Pappas, George J.; Preciado, Victor M. (December 2018, IEEE)
3. Target-Aware Implementation of Real Expressions

   https://doi.org/10.1145/3669940.3707277  

   Saiki, Brett; Brough, Jackson; Regehr, Jonas; Ponce, Jesus; Pradeep, Varun; Akhileshwaran, Aditya; Tatlock, Zachary; Panchekha, Pavel (March 2025, ACM)
4. Identification of target genes downstream of semaphorin6A/PlexinA2 signaling in zebrafish: Target Genes Downstream of Sema6A/PlxnA2 Signaling

   https://doi.org/10.1002/dvdy.24512  

   Emerson, Sarah E.; St. Clair, Riley M.; Waldron, Ashley L.; Bruno, Sierra R.; Duong, Anna; Driscoll, Heather E.; Ballif, Bryan A.; McFarlane, Sarah; Ebert, Alicia M. (July 2017, Developmental Dynamics)
5. Distributed Simultaneous Action and Target Assignment for Multi-Robot Multi-Target Tracking

   https://doi.org/10.1109/ICRA.2018.8460974  

   Sung, Yoonchang; Budhiraja, Ashish Kumar; Williams, Ryan K.; Tokekar, Pratap (May 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA))

   We study two multi-robot assignment problems for multi-target tracking. We consider distributed approaches in order to deal with limited sensing and communication ranges. We seek to simultaneously assign trajectories and targets to the robots. Our focus is on \emph{local} algorithms that achieve performance close to the optimal algorithms with limited communication. We show how to use a local algorithm that guarantees a bounded approximate solution within $$\mathcal{O}(h\log{1/\epsilon})$$ communication rounds. We compare with a greedy approach that achieves a $$2$$--approximation in as many rounds as the number of robots. Simulation results show that the local algorithm is an effective solution to the assignment problem. 

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