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251. RIFM fragrance ingredient safety assessment, geranyl isovalerate, CAS Registry Number 109-20-6

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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252. Update to RIFM fragrance ingredient safety assessment, 2-methylbutanol, CAS Registry Number 137-32-6

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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253. RIFM fragrance ingredient safety assessment, geranyl formate, CAS Registry Number 105-86-2

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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254. RIFM fragrance ingredient safety assessment, phenylacetaldehyde 2,4-dihydroxy-2-methylpentane acetal, CAS Registry Number 67633-94-7

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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255. RIFM fragrance ingredient safety assessment, patchouli alcohol, CAS Registry Number 5986-55-0

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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256. RIFM fragrance ingredient safety assessment, 5,6,7-trimethylocta-2,5-dien-4-one, CAS Registry Number 358331-95-0

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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257. RIFM fragrance ingredient safety assessment, 2-methylpropyl 3-methylbutyrate, CAS Registry Number 589-59-3

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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258. RIFM fragrance ingredient safety assessment, propyl butyrate, CAS Registry Number 105-66-8

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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259. RIFM fragrance ingredient safety assessment, geranyl isobutyrate, CAS Registry Number 2345-26-8

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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260. RIFM fragrance ingredient safety assessment, cyclohexanone,5-methyl-2-(1-methylethyl)-,oxime, CAS registry number 19096-86-7

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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261. RIFM fragrance ingredient safety assessment, phytol, CAS Registry Number 150-86-7

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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262. RIFM fragrance ingredient safety assessment, geranyl butyrate, CAS Registry Number 106-29-6

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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263. RIFM fragrance ingredient safety assessment, ω-6-hexadecenlactone, CAS Registry Number 7779-50-2

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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264. RIFM fragrance ingredient safety assessment, α-methylbenzyl isobutyrate, CAS Registry Number 7775-39-5

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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265. RIFM fragrance ingredient safety assessment, 4-hydroxy-3-pentenoic acid lactone, CAS Registry number 591-12-8

Author

Api, A.M., Belsito, D., Botelho, D., Bruze, M., Burton, G.A., Jr., Cancellieri, M.A., Chon, H., Dagli, M.L., Date, M., Dekant, W., Deodhar, C., Fryer, A.D., Jones, L., Joshi, K., Kumar, M., Lapczynski, A., Lavelle, M., Lee, I., Liebler, D.C., Moustakas, H., Na, M., Penning, T.M., Ritacco, G., Romine, J., Sadekar, N., Schultz, T.W., Selechnik, D., Siddiqi, F., Sipes, I.G., Sullivan, G., Thakkar, Y., and Tokura, Y.

Published
2024
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272. Benefit of Adjuvant Chemotherapy for Patients Older Than 75 Years With Completely Resected p-Stage II-IIIA Non–Small-Cell Lung Cancer: A Retrospective Cohort Study Using Japanese Nationwide Real-World Data

Author

Adachi, Hiroyuki, Morohoshi, Takao, Shintani, Yasushi, Okami, Jiro, Ito, Hiroyuki, Ohtsuka, Takashi, Mori, Takeshi, Watanabe, Shun-ichi, Chida, Masayuki, Endo, Shunsuke, Nakanishi, Ryoichi, Kadokura, Mitsutaka, Suzuki, Hidemi, Miyaoka, Etsuo, Yoshino, Ichiro, and Date, Hiroshi

Published
2024
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276. The Deep Space Radiation Probe: Development of a first lunar science payload for space environment studies and capacity building

Author

Chang, Loren C., Lin, Wei-Yi, Chou, Yi-Hsuan, Lin, Jen-Siang, Lung, Chieh, Chen, I., Hou, Kai-Jie, Franco Gacal, Glenn, Chiu, Yi-Chung, Wang, Yushun, Chou, Hui-Hui, Chao, Chi-Kuang, Liu, Jann-Yenq, Hsiao, Tung-Yuan, Cho, I-Chun, Date, Takumi, Urata, Masayuki, Taeda, Masahiro, Tanaka, Kenichiro, Vasovic, Nikola, and Keegan, Niall

Published
2024
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278. Prospective Observational Study Evaluating the Prognostic Value of the G8 Screening Tool for Extensive-Stage Small Cell Lung Cancer Patients Who Received Programmed Death-Ligand 1 Inhibitor plus Platinum–Etoposide Chemotherapy

Author

Morimoto, Kenji, Yamada, Tadaaki, Takeda, Takayuki, Shiotsu, Shinsuke, Date, Koji, Harada, Taishi, Tamiya, Nobuyo, Chihara, Yusuke, Takemura, Yoshizumi, Yamada, Takahiro, Kanda, Hibiki, Ishida, Masaki, Yoshimura, Akihiro, Iwasaku, Masahiro, Tokuda, Shinsaku, Kim, Young Hak, and Takayama, Koichi

Published
2023
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288. Subtyping of Group 3/4 medulloblastoma as a potential prognostic biomarker among patients treated with reduced dose of craniospinal irradiation: a Japanese Pediatric Molecular Neuro-Oncology Group study

Author

Kohei Fukuoka, Jun Kurihara, Tomoko Shofuda, Naoki Kagawa, Kai Yamasaki, Ryo Ando, Joji Ishida, Masayuki Kanamori, Atsufumi Kawamura, Young-Soo Park, Chikako Kiyotani, Takuya Akai, Dai Keino, Yosuke Miyairi, Atsushi Sasaki, Junko Hirato, Takeshi Inoue, Atsuko Nakazawa, Katsuyoshi Koh, Ryo Nishikawa, Isao Date, Motoo Nagane, Koichi Ichimura, and Yonehiro Kanemura

Subjects
Medulloblastoma, Methylation analysis, Prognostic stratification, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background One of the most significant challenges in patients with medulloblastoma is reducing the dose of craniospinal irradiation (CSI) to minimize neurological sequelae in survivors. Molecular characterization of patients receiving lower than standard dose of CSI therapy is important to facilitate further reduction of treatment burden. Methods We conducted DNA methylation analysis using an Illumina Methylation EPIC array to investigate molecular prognostic markers in 38 patients with medulloblastoma who were registered in the Japan Pediatric Molecular Neuro-Oncology Group and treated with reduced-dose CSI. Results Among the patients, 23 were classified as having a standard-risk and 15 as high-risk according to the classic classification based on tumor resection rate and presence of metastasis, respectively. The median follow-up period was 71.5 months (12.0–231.0). The median CSI dose was 18 Gy (15.0–24.0) in both groups, and 5 patients in the high-risk group received a CSI dose of 18.0 Gy. Molecular subgrouping revealed that the standard-risk cohort included 5 WNT, 2 SHH, and 16 Group 3/4 cases; all 15 patients in the high-risk cohort had Group 3/4 medulloblastoma. Among the patients with Group 3/4 medulloblastoma, 9 of the 31 Group 3/4 cases were subclassified as subclass II, III, and V, which were known to an association with poor prognosis according to the novel subtyping among the subgroups. Patients with poor prognostic subtype showed worse prognosis than that of others (5-year progression survival rate 90.4% vs. 22.2%; p

Published
2023
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289. Encoding integers and rationals on neuromorphic computers using virtual neuron

Author

Prasanna Date, Shruti Kulkarni, Aaron Young, Catherine Schuman, Thomas Potok, and Jeffrey Vetter

Subjects
Medicine, Science
Abstract

Abstract Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network–based machine learning applications. However, they are known to be Turing-complete, and in theory can perform all general-purpose computation. One of the biggest bottlenecks in realizing general-purpose computations on neuromorphic computers today is the inability to efficiently encode data on the neuromorphic computers. To fully realize the potential of neuromorphic computers for energy-efficient general-purpose computing, efficient mechanisms must be devised for encoding numbers. Current encoding mechanisms (e.g., binning, rate-based encoding, and time-based encoding) have limited applicability and are not suited for general-purpose computation. In this paper, we present the virtual neuron abstraction as a mechanism for encoding and adding integers and rational numbers by using spiking neural network primitives. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware. We estimate that the virtual neuron could perform an addition operation using just 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor. We also demonstrate the utility of the virtual neuron by using it in some of the μ-recursive functions, which are the building blocks of general-purpose computation.

Published
2023
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290. Prognostic impact of clinical factors for immune checkpoint inhibitor with or without chemotherapy in older patients with non-small cell lung cancer and PD-L1 TPS ≥ 50%

Author

Shota Takei, Hayato Kawachi, Tadaaki Yamada, Motohiro Tamiya, Yoshiki Negi, Yasuhiro Goto, Akira Nakao, Shinsuke Shiotsu, Keiko Tanimura, Takayuki Takeda, Asuka Okada, Taishi Harada, Koji Date, Yusuke Chihara, Isao Hasegawa, Nobuyo Tamiya, Yuki Katayama, Naoya Nishioka, Kenji Morimoto, Masahiro Iwasaku, Shinsaku Tokuda, Takashi Kijima, and Koichi Takayama

Subjects
chemoimmunotherapy, immunochemotherapy, immune checkpoint inhibitor, non-small-cell lung cancer, PD-L1, Immunologic diseases. Allergy, RC581-607
Abstract

IntroductionThe proportion of older patients diagnosed with advanced-stage non-small cell lung cancer (NSCLC) has been increasing. Immune checkpoint inhibitor (ICI) monotherapy (MONO) and combination therapy of ICI and chemotherapy (COMBO) are standard treatments for patients with NSCLC and programmed cell death ligand-1 (PD-L1) tumor proportion scores (TPS) ≥ 50%. However, evidence from the clinical trials specifically for older patients is limited. Thus, it is unclear which older patients benefit more from COMBO than MONO.MethodsWe retrospectively analyzed 199 older NSCLC patients of Eastern Cooperative Oncology Group performance status (ECOG PS) 0-1 and PD-L1 TPS ≥ 50% who were treated with MONO or COMBO. We analyzed the association between treatment outcomes and baseline patient characteristics in each group, using propensity score matching.ResultsOf the 199 patients, 131 received MONO, and 68 received COMBO. The median overall survival (OS; MONO: 25.2 vs. COMBO: 42.2 months, P = 0.116) and median progression-free survival (PFS; 10.9 vs. 11.8 months, P = 0.231) did not significantly differ between MONO and COMBO group. In the MONO group, OS was significantly shorter in patients without smoking history compared to those with smoking history [HR for smoking history against non-smoking history: 0.36 (95% CI: 0.16-0.78), P = 0.010]. In the COMBO group, OS was significantly shorter in patients with PS 1 than those with PS 0 [HR for PS 0 against PS 1: 3.84 (95% CI: 1.44-10.20), P = 0.007] and for patients with squamous cell carcinoma (SQ) compared to non-squamous cell carcinoma (non-SQ) [HR for SQ against non-SQ: 0.17 (95% CI: 0.06-0.44), P < 0.001]. For patients with ECOG PS 0 (OS: 26.1 months vs. not reached, P = 0.0031, PFS: 6.5 vs. 21.7 months, P = 0.0436) or non-SQ (OS: 23.8 months vs. not reached, P = 0.0038, PFS: 10.9 vs. 17.3 months, P = 0.0383), PFS and OS were significantly longer in the COMBO group.ConclusionsECOG PS and histological type should be considered when choosing MONO or COMBO treatment in older patients with NSCLC and PD-L1 TPS ≥ 50%.

Published
2024
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292. The Effect of a Loading Dose Regimen in the Switch to Brolucizumab for Patients with Aflibercept-Resistant nAMD

Author

Hiroyuki Kamao, Erika Mitsui, Yuto Date, Katsutoshi Goto, Kenichi Mizukawa, and Atsushi Miki

Subjects
Ophthalmology, RE1-994
Abstract

Purpose. To evaluate the one-year outcomes of switching to brolucizumab with and without a loading dose regimen (three monthly injections) in eyes with aflibercept-resistant neovascular age-related macular degeneration (nAMD). Methods. We retrospectively studied nAMD patients who had retinal exudate under bimonthly injections of aflibercept and were switched to brolucizumab from aflibercept. Patients were grouped into intravitreal brolucizumab injection (IVBr) with a loading dose regimen (loading group) and without a loading dose regimen (nonloading group). We assessed the best-corrected visual acuity (BCVA), central retinal thickness (CRT) at the fovea, subfoveal choroidal thickness (SFCT), IVBr status (number of injections and last injection interval), and retinal exudate status on optical coherence tomography. Results. Overall, 52 eyes received ≥1 IVBr; 26 eyes received ≥3 IVBr with 12-month follow-up. A total of 13 eyes in the loading group and 13 eyes in the nonloading group were reviewed. One year after switching, BCVA changed from 0.28 ± 0.25 to 0.19 ± 0.28 in the loading group (P=0.28) and from 0.25 ± 0.20 to 0.23 ± 0.25 in the nonloading group (P=0.92). The mean CRT decreased from 263.6 ± 40.7 µm to 221.7 ± 54.6 µm in the loading group (P=0.03), while it only changed from 244.9 ± 77.2 µm to 221.0 ± 78.7 µm in the nonloading group (P=0.26). Both the loading and nonloading groups achieved 69% dry macula. The number of injections received was significantly higher in the loading group (7.6 ± 0.6 vs. 6.8 ± 0.4, P

Published
2024
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293. Statistical modeling of pneumothorax deformation by mapping CT and cone-beam CT images

Author

Nakao, Megumi, Maekawa, Hinako, Mineura, Katsutaka, Chen-Yoshikawa, Toyofumi F., Date, Hiroshi, and Matsuda, Tetsuya

Subjects
Computer Science - Computational Geometry, Mathematics - Numerical Analysis
Abstract

In this study, we introduce statistical modeling methods for pneumothorax deformation using paired cone-beam computed tomography (CT) images. We designed a deformable mesh registration framework for shape changes involving non-linear deformation and rotation of the lungs. The registered meshes with local correspondences are available for both surgical guidance in thoracoscopic surgery and building statistical deformation models with inter-patient variations. In addition, a kernel-based deformation learning framework is proposed to reconstruct intraoperative deflated states of the lung from the preoperative CT models. This paper reports the findings of pneumothorax deformation and evaluation results of the kernel-based deformation framework.

Published
2020

294. On viscid-inviscid interactions of a pair of bubbles rising near the wall

Author

Maeda, Kazuki, Date, Masanobu, Sugiyama, Kazuyasu, Takagi, Shu, and Matsumoto, Yoichiro

Subjects
Physics - Fluid Dynamics
Abstract

Series of experiments on turbulent bubbly channel flows observed bubble clusters near the wall which can change large-scale flow structures. To gain insights into clustering mechanisms, we study the interaction of a pair of spherical bubbles rising in a vertical channel through combined experiments and modeling. Experimental imaging identifies that pairwise bubbles of 1.0 mm diameter take two preferred configurations depending on their mutual distance: side-by-side positions for a short distance ($S<5$) and nearly inline, oblique positions for a long distance ($S>5$), where $S$ is the mutual distance normalized by the bubble radius. In the model, we formulate the motions of pairwise bubbles rising at $Re=O(100)$. Analytical drag and lift, and semi-empirical, spatio-temporal stochastic forcing are employed to represent the mean acceleration and the fluctuation due to turbulent agitation, respectively. The model is validated against the experiment through comparing Lagrangian statistics of the bubbles. Simulations using this model identify two distinct timescales of interaction dynamics which elucidate the preferred configurations. For pairs initially in-line, the trailing bubble rapidly escapes from the viscous wake of the leading bubble to take the oblique position. Outside of the wake, the trailing bubble travels on a curve-line path with a slower velocity driven by potential interaction and horizontally approaches the leading bubble to become side-by-side. Moreover, statistical analysis identifies that the combination of the wake and the agitation can significantly accelerate the side-by-side clustering of in-line pairs. These results indicate positive contributions of liquid viscosity and turbulence to the formation of bubble clusters., Comment: 29 pages, 18 figures

Published
2020
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295. Analysis of Heterogeneity of Pneumothorax-associated Deformation using Model-based Registration

Author

Nakao, Megumi, Kobayashi, Kotaro, Tokuno, Junko, Chen-Yoshikawa, Toyofumi, Date, Hiroshi, and Matsuda, Tetsuya

Subjects
Mathematics - Numerical Analysis, Quantitative Biology - Tissues and Organs
Abstract

Recent advances in imaging techniques have enabled us to visualize lung tumors or nodules in early-stage cancer. However, the positions of nodules can change because of intraoperative lung deflation, and the modeling of pneumothorax-associated deformation remains a challenging issue for intraoperative tumor localization. In this study, we introduce spatial and geometric analysis methods for inflated/deflated lungs and discuss heterogeneity in pneumothorax-associated deformation. Contrast-enhanced CT images simulating intraoperative conditions were acquired from live Beagle dogs. Deformable mesh registration techniques were designed to map the surface and subsurface tissues of lung lobes. The developed framework addressed local mismatches of bronchial tree structures and achieved stable registration with a Hausdorff distance of less than 1 mm and a target registration error of less than 5 mm. Our results show that the strain of lung parenchyma was 35% higher than that of bronchi, and that subsurface deformation in the deflated lung is heterogeneous.

Published
2020

296. Quantum Discriminator for Binary Classification

Author

Date, Prasanna and Smith, Wyatt

Subjects
Quantum Physics, Computer Science - Machine Learning, Statistics - Machine Learning, 68T05, 81P68, 68Q09, I.2.6, I.5.1
Abstract

Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces -- this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in O(N logN) time, and inferencing is performed on a universal quantum computer in linear time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit initialized to the zero state and outputs the predicted label. We analyze its performance on the Iris data set and show that the quantum discriminator can attain 99% accuracy in simulation.

Published
2020

297. Training Deep Neural Networks with Constrained Learning Parameters

Author

Date, Prasanna, Carothers, Christopher D., Mitchell, John E., Hendler, James A., and Magdon-Ismail, Malik

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning, 68T07, 90C27, I.2.6
Abstract

Today's deep learning models are primarily trained on CPUs and GPUs. Although these models tend to have low error, they consume high power and utilize large amount of memory owing to double precision floating point learning parameters. Beyond the Moore's law, a significant portion of deep learning tasks would run on edge computing systems, which will form an indispensable part of the entire computation fabric. Subsequently, training deep learning models for such systems will have to be tailored and adopted to generate models that have the following desirable characteristics: low error, low memory, and low power. We believe that deep neural networks (DNNs), where learning parameters are constrained to have a set of finite discrete values, running on neuromorphic computing systems would be instrumental for intelligent edge computing systems having these desirable characteristics. To this extent, we propose the Combinatorial Neural Network Training Algorithm (CoNNTrA), that leverages a coordinate gradient descent-based approach for training deep learning models with finite discrete learning parameters. Next, we elaborate on the theoretical underpinnings and evaluate the computational complexity of CoNNTrA. As a proof of concept, we use CoNNTrA to train deep learning models with ternary learning parameters on the MNIST, Iris and ImageNet data sets and compare their performance to the same models trained using Backpropagation. We use following performance metrics for the comparison: (i) Training error; (ii) Validation error; (iii) Memory usage; and (iv) Training time. Our results indicate that CoNNTrA models use 32x less memory and have errors at par with the Backpropagation models.

Published
2020
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298. Adiabatic Quantum Optimization Fails to Solve the Knapsack Problem

Author

Pusey-Nazzaro, Lauren and Date, Prasanna

Subjects
Quantum Physics, Computer Science - Artificial Intelligence
Abstract

In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in computer science, with applications in economics, business, finance, etc. We attempt to solve a number of small knapsack problems whose optimal solutions are known; we find that adiabatic quantum optimization fails to produce solutions corresponding to optimal filling of the knapsack in all problem instances. We compare results obtained on the quantum hardware to the classical simulated annealing algorithm and two solvers employing a hybrid branch-and-bound algorithm. The simulated annealing algorithm also fails to produce the optimal filling of the knapsack, though solutions obtained by simulated and quantum annealing are no more similar to each other than to the correct solution. We discuss potential causes for this observed failure of adiabatic quantum optimization.

Published
2020

299. Balanced k-Means Clustering on an Adiabatic Quantum Computer

Author

Arthur, Davis and Date, Prasanna

Subjects
Computer Science - Machine Learning, Physics - Data Analysis, Statistics and Probability, Quantum Physics, 62H30, 68T05, I.2.6
Abstract

Adiabatic quantum computers are a promising platform for approximately solving challenging optimization problems. We present a quantum approach to solving the balanced $k$-means clustering training problem on the D-Wave 2000Q adiabatic quantum computer. Existing classical approaches scale poorly for large datasets and only guarantee a locally optimal solution. We show that our quantum approach better targets the global solution of the training problem, while achieving better theoretic scalability on large datasets. We test our quantum approach on a number of small problems, and observe clustering performance similar to the best classical algorithms.

Published
2020

300. QUBO Formulations for Training Machine Learning Models

Author

Date, Prasanna, Arthur, Davis, and Pusey-Nazzaro, Lauren

Subjects
Computer Science - Machine Learning, Physics - Data Analysis, Statistics and Probability, Statistics - Machine Learning, 62J05, 68T05, 68Q12, I.2.6
Abstract

Training machine learning models on classical computers is usually a time and compute intensive process. With Moore's law coming to an end and ever increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers like the D-Wave 2000Q can approximately solve NP-hard optimization problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore's law era. In order to solve a problem on adiabatic quantum computers, it must be formulated as a QUBO problem, which is a challenging task in itself. In this paper, we formulate the training problems of three machine learning models---linear regression, support vector machine (SVM) and equal-sized k-means clustering---as QUBO problems so that they can be trained on adiabatic quantum computers efficiently. We also analyze the time and space complexities of our formulations and compare them to the state-of-the-art classical algorithms for training these machine learning models. We show that the time and space complexities of our formulations are better (in the case of SVM and equal-sized k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

Published
2020
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