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Cerebral aneurysm clips are biomedical implants applied by neurosurgeons to re-approximate arterial vessel walls and prevent catastrophic aneurysmal hemorrhages in patients. Current methods of aneurysm clip production are labor intensive and time-consuming, leading to high costs per implant and limited variability in clip morphology. Metal additive manufacturing is investigated as an alternative to traditional manufacturing methods that may enable production of patient-specific aneurysm clips to account for variations in individual vascular anatomy and possibly reduce surgical complication risks. Relevant challenges to metal additive manufacturing are investigated for biomedical implants, including material choice, design limitations, postprocessing, printed material properties, and combined production methods. Initial experiments with additive manufacturing of 316 L stainless steel aneurysm clips are carried out on a selective laser melting (SLM) system. The dimensions of the printed clips were found to be within 0.5% of the dimensions of the designed clips. Hardness and density of the printed clips (213 ± 7 HV1 and 7.9 g/cc, respectively) were very close to reported values for 316 L stainless steel, as expected. No ferrite and minimal porosity is observed in a cross section of a printed clip, with some anisotropy in the grain orientation. A clamping force of approximately 1 N is measured with a clip separation of 1.5 mm. Metal additive manufacturing shows promise for use in the creation of custom aneurysm clips, but some of the challenges discussed will need to be addressed before clinical use is possible.
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Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio
The famous “laurel/yanny” phenomenon references an audio clip that elicits dramatically different responses from different listeners. For the original clip, roughly half the population hears the word “laurel,” while the other half hears “yanny.” How common are such ``polyperceivable'' audio clips? In this paper we apply ML techniques to study the prevalence of polyperceivability in spoken language. We devise a metric that correlates with polyperceivability of audio clips, use it to efficiently find new “laurel/yanny”-type examples, and validate these results with human experiments. Our results suggest that polyperceivable examples are surprisingly prevalent, existing for >2% of English words.
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- Award ID(s):
- 1813049
- PAR ID:
- 10275107
- Date Published:
- Journal Name:
- ACL-IJCNLP
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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