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1. Computation of Head-Related Transfer Functions Using Graphics Processing Units and a Pereptual Validation of the Computed HRTFs against Measured HRTFs

   Fan, Z; Arce, T; Lu, C; Zhang, K; Wu, T. W.; McMullen, K (August 2019, 2019 Audio Engineering Society Conference on Headphone Technology)

   Fast generation of personalized head-related transfer functions is essential for rendering spatial audio. In this paper we propose to generate head-related transfer functions using a single graphics processing unit (GPU). We optimize the implementation of the conventional boundary element solver on a GPU. The simulation of a single frequency can be completed in seconds. A psychoacoustic experiment is conducted to study the perceptual performance of the computed HRTFs. In general, perceptual accuracy in the back is better than that in the front. 

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2. Memory-based predictions prime perceptual judgments across head turns in immersive, real-world scenes

   https://doi.org/10.1016/j.cub.2024.11.024  

   Mynick, Anna; Steel, Adam; Jayaraman, Adithi; Botch, Thomas L; Burrows, Allie; Robertson, Caroline E (January 2025, Current Biology)

   Each view of our environment captures only a subset of our immersive surroundings. Yet, our visual experience feels seamless. A puzzle for human neuroscience is to determine what cognitive mechanisms enable us to overcome our limited field of view and efficiently anticipate new views as we sample our visual surroundings. Here, we tested whether memory-based predictions of upcoming scene views facilitate efficient perceptual judgments across head turns. We tested this hypothesis using immersive, head-mounted virtual reality (VR). After learning a set of immersive real-world environments, participants (n = 101 across 4 experiments) were briefly primed with a single view from a studied environment and then turned left or right to make a perceptual judgment about an adjacent scene view. We found that participants’ perceptual judgments were faster when they were primed with images from the same (vs. neutral or different) environments. Importantly, priming required memory: it only occurred in learned (vs. novel) environments, where the link between adjacent scene views was known. Further, consistent with a role in supporting active vision, priming only occurred in the direction of planned head turns and only benefited judgments for scene views presented in their learned spatiotopic positions. Taken together, we propose that memory-based predictions facilitate rapid perception across large-scale visual actions, such as head and body movements, and may be critical for efficient behavior in complex immersive environments. 

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3. SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs

   https://doi.org/10.1145/3534678.3539405  

   Ren, Hongyu; Dai, Hanjun; Dai, Bo; Chen, Xinyun; Zhou, Denny; Leskovec, Jure; Schuurmans, Dale (August 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)

   Knowledge graphs (KGs) capture knowledge in the form of head– relation–tail triples and are a crucial component in many AI systems. There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query. Embedding-based methods solve both tasks by first computing an embedding for each entity and relation, then using them to form predictions. However, existing scalable KG embedding frameworks only support single-hop knowledge graph completion and cannot be applied to the more challenging multi-hop reasoning task. Here we present Scalable Multi-hOp REasoning (SMORE), the first general framework for both single-hop and multi-hop reasoning in KGs. Using a single machine SMORE can perform multi-hop reasoning in Freebase KG (86M entities, 338M edges), which is 1,500× larger than previously considered KGs. The key to SMORE’s runtime performance is a novel bidirectional rejection sampling that achieves a square root reduction of the complexity of online training data generation. Furthermore, SMORE exploits asynchronous scheduling, overlapping CPU-based data sampling, GPU-based embedding computation, and frequent CPU–GPU IO. SMORE increases throughput (i.e., training speed) over prior multi-hop KG frameworks by 2.2× with minimal GPU memory requirements (2GB for training 400-dim embeddings on 86M-node Freebase) and achieves near linear speed-up with the number of GPUs. Moreover, on the simpler single-hop knowledge graph completion task SMORE achieves comparable or even better runtime performance to state-of-the-art frameworks on both single GPU and multi-GPU settings. 

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4. Variation in head and pinna morphology of preserved Peromyscus spp. specimens and implications for auditory function

   https://doi.org/10.1111/joa.70011  

   Sergott, Casey_E; Rodman, Katelynn; Greene, Nathaniel_T; Li, Ben‐Zheng; Joseph, Luberson; Machado, Fabio_A; Alarcon, Genesis_A; McCullagh, Elizabeth_A (July 2025, Journal of Anatomy)

   Abstract The physical characteristics of an animal's head and pinna mark the beginning of auditory communication. Auditory communication is broadly achieved by receiving sounds from the environment and plays a vital role in an animal's ability to perceive and localize sounds. Natural history museums and collections, along with their vast repositories of specimens, provide a unique resource for examining how the variability in both the size and shape of the head and pinna causes variability in the detection of acoustic signals across species. Using this approach, we measured morphological features of the head and pinna on over 1200 preserved specimens ofPeromyscus boylii,P. californicus,P. gossypinus,P. leucopus,P. maniculatus, andP. truei, followed by a series of head‐related transfer functions (HRTFs) on several individuals to study the relationship between morphology and available auditory information. Our morphological results show significant variation in pinna length and width, as well as in the distance between the two ears across the six species. Interaural time differences and interaural level differences were calculated and demonstrated consistent results across species, suggesting the differences in head and pinna size do not significantly modify these cues. Not only does this study contribute to existing research on external morphology and auditory function, but it also provides valuable insight into the use of preserved zoological specimens in auditory research, an area that is currently understudied. 

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5. Multi-GPU Graph Analytics

   https://doi.org/10.1109/IPDPS.2017.117  

   Pan, Yuechao; Wang, Yangzihao; Wu, Yuduo; Yang, Carl; Owens, John D. (May 2017, Proceedings of the 31st IEEE International Parallel and Distributed Processing Symposium)

   We present a single-node, multi-GPU programmable graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graphs with billions of edges. Directly using the single-GPU implementations, our design only requires programmers to specify a few algorithm-dependent concerns, hiding most multi-GPU related implementation details. We analyze the theoretical and practical limits to scalability in the context of varying graph primitives and datasets. We describe several optimizations, such as direction optimizing traversal, and a just-enough memory allocation scheme, for better performance and smaller memory consumption. Compared to previous work, we achieve best-of-class performance across operations and datasets, including excellent strong and weak scalability on most primitives as we increase the number of GPUs in the system. 

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