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1. Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?

Author

Uchiyama, Fumiya, Kojima, Takeshi, Gambardella, Andrew, Cao, Qi, Iwasawa, Yusuke, and Matsuo, Yutaka

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Recent large language models (LLMs) have demonstrated remarkable generalization abilities in mathematics and logical reasoning tasks. Prior research indicates that LLMs pre-trained with programming language data exhibit high mathematical and reasoning abilities; however, this causal relationship has not been rigorously tested. Our research aims to verify which programming languages and features during pre-training affect logical inference performance. Specifically, we pre-trained decoder-based language models from scratch using datasets from ten programming languages (e.g., Python, C, Java) and three natural language datasets (Wikipedia, Fineweb, C4) under identical conditions. Thereafter, we evaluated the trained models in a few-shot in-context learning setting on logical reasoning tasks: FLD and bAbi, which do not require commonsense or world knowledge. The results demonstrate that nearly all models trained with programming languages consistently outperform those trained with natural languages, indicating that programming languages contain factors that elicit logic inference performance. In addition, we found that models trained with programming languages exhibit a better ability to follow instructions compared to those trained with natural languages. Further analysis reveals that the depth of Abstract Syntax Trees representing parsed results of programs also affects logical reasoning performance. These findings will offer insights into the essential elements of pre-training for acquiring the foundational abilities of LLMs.

Published
2024

2. Answer When Needed, Forget When Not: Language Models Pretend to Forget via In-Context Knowledge Unlearning

Author

Takashiro, Shota, Kojima, Takeshi, Gambardella, Andrew, Cao, Qi, Iwasawa, Yusuke, and Matsuo, Yutaka

Subjects
Computer Science - Computation and Language
Abstract

As large language models (LLMs) are applied across diverse domains, the ability to selectively unlearn specific information has become increasingly essential. For instance, LLMs are expected to provide confidential information to authorized internal users, such as employees or trusted partners, while withholding it from external users, including the general public and unauthorized entities. In response to this challenge, we propose a novel method termed ``in-context knowledge unlearning'', which enables the model to selectively forget information in test-time based on the context of the query. Our method fine-tunes pre-trained LLMs to enable prompt unlearning of target knowledge within the context, while preserving other knowledge. Experiments on the TOFU and AGE datasets using Llama2-7B/13B and Mistral-7B models show our method achieves up to 95% forgetting accuracy while retaining 80% of unrelated knowledge, significantly outperforming baselines in both in-domain and out-of-domain scenarios. Further investigation into the model's internal behavior revealed that while fine-tuned LLMs generate correct predictions in the middle layers and maintain them up to the final layer, they make the decision to forget at the last layer, i.e., ``LLMs pretend to forget''. Our findings offer valuable insights into enhancing the robustness of unlearning mechanisms in LLMs, setting a foundation for future research in the field.

Published
2024

3. On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons

Author

Kojima, Takeshi, Okimura, Itsuki, Iwasawa, Yusuke, Yanaka, Hitomi, and Matsuo, Yutaka

Subjects
Computer Science - Computation and Language
Abstract

Current decoder-based pre-trained language models (PLMs) successfully demonstrate multilingual capabilities. However, it is unclear how these models handle multilingualism. We analyze the neuron-level internal behavior of multilingual decoder-based PLMs, Specifically examining the existence of neurons that fire ``uniquely for each language'' within decoder-only multilingual PLMs. We analyze six languages: English, German, French, Spanish, Chinese, and Japanese, and show that language-specific neurons are unique, with a slight overlap (< 5%) between languages. These neurons are mainly distributed in the models' first and last few layers. This trend remains consistent across languages and models. Additionally, we tamper with less than 1% of the total neurons in each model during inference and demonstrate that tampering with a few language-specific neurons drastically changes the probability of target language occurrence in text generation., Comment: Accepted to NAACL2024. Our code is available at https://github.com/kojima-takeshi188/lang_neuron

Published
2024

4. Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment

Author

Kojima, Takeshi, Matsuo, Yutaka, and Iwasawa, Yusuke

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Vision Transformer (ViT) is becoming more popular in image processing. Specifically, we investigate the effectiveness of test-time adaptation (TTA) on ViT, a technique that has emerged to correct its prediction during test-time by itself. First, we benchmark various test-time adaptation approaches on ViT-B16 and ViT-L16. It is shown that the TTA is effective on ViT and the prior-convention (sensibly selecting modulation parameters) is not necessary when using proper loss function. Based on the observation, we propose a new test-time adaptation method called class-conditional feature alignment (CFA), which minimizes both the class-conditional distribution differences and the whole distribution differences of the hidden representation between the source and target in an online manner. Experiments of image classification tasks on common corruption (CIFAR-10-C, CIFAR-100-C, and ImageNet-C) and domain adaptation (digits datasets and ImageNet-Sketch) show that CFA stably outperforms the existing baselines on various datasets. We also verify that CFA is model agnostic by experimenting on ResNet, MLP-Mixer, and several ViT variants (ViT-AugReg, DeiT, and BeiT). Using BeiT backbone, CFA achieves 19.8% top-1 error rate on ImageNet-C, outperforming the existing test-time adaptation baseline 44.0%. This is a state-of-the-art result among TTA methods that do not need to alter training phase., Comment: Accepted to IJCAI-ECAI2022. Code is available at https://github.com/kojima-takeshi188/CFA

Published
2022

5. Large Language Models are Zero-Shot Reasoners

Author

Kojima, Takeshi, Gu, Shixiang Shane, Reid, Machel, Matsuo, Yutaka, and Iwasawa, Yusuke

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding "Let's think step by step" before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances on diverse benchmark reasoning tasks including arithmetics (MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled Objects), without any hand-crafted few-shot examples, e.g. increasing the accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with large InstructGPT model (text-davinci-002), as well as similar magnitudes of improvements with another off-the-shelf large model, 540B parameter PaLM. The versatility of this single prompt across very diverse reasoning tasks hints at untapped and understudied fundamental zero-shot capabilities of LLMs, suggesting high-level, multi-task broad cognitive capabilities may be extracted by simple prompting. We hope our work not only serves as the minimal strongest zero-shot baseline for the challenging reasoning benchmarks, but also highlights the importance of carefully exploring and analyzing the enormous zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or few-shot exemplars., Comment: Accepted to NeurIPS2022. Our code is available at https://github.com/kojima-takeshi188/zero_shot_cot

Published
2022

7. Variable Dose-Constraints Method for Enhancing Intensity-Modulated Radiation Therapy Treatment Planning.

Author

Obata, Norihisa, Abou Al-Ola, Omar M., Nakada, Ryosei, Kojima, Takeshi, and Yoshinaga, Tetsuya

Subjects
RADIOTHERAPY treatment planning, DYNAMICAL systems, INTENSITY modulated radiotherapy, DRUG dosage, PROBLEM solving
Abstract

The conventional approach to intensity-modulated radiation therapy treatment planning involves two distinct strategies: optimizing an evaluation function while accounting for dose constraints, and solving feasibility problems using feasibility-seeking projection methods that incorporate inequality constraints. This paper introduces a novel iterative scheme within the framework of continuous dynamical systems, wherein constraint conditions dynamically evolve to enhance the optimization process. The validity of dynamically varying dose constraints is theoretically established through the foundation of continuous-time dynamical systems theory. In particular, we formalize a system of differential equations, with both beam coefficients and dose constraints modeled as state variables. The asymptotic stability of the system's equilibrium is rigorously proven, ensuring convergence to a solution. In practical terms, we leverage a discretized iteration formula derived from the continuous-time system to achieve rapid computational speed. The mathematical structure of the proposed approach, which directly incorporates dose-volume constraints into the objective function, facilitates significant computational efficiency and solution refinement. The proposed method has an inherent dynamics that approaches more desirable solutions within the set of solutions when the solution to the optimization problem is not an isolated point. This property guarantees the identification of optimal solutions that respect the prescribed dose-volume constraints while enhancing accuracy when such constraints are feasible. By treating dose constraints as variables and concurrently solving the optimization problem with beam coefficients, we can achieve more accurate results when compared with using fixed values for prescribed dose conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence.

Author

Kojima, Takeshi, Yamaguchi, Yusaku, Abou Al-Ola, Omar M., and Yoshinaga, Tetsuya

Subjects
*COMPUTED tomography, *MACHINE learning, *DIAGNOSTIC imaging, *ALGORITHMS, *IMAGE reconstruction
Abstract

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Iterative Tomographic Image Reconstruction Algorithm Based on Extended Power Divergence by Dynamic Parameter Tuning.

Author

Yabuki, Ryuto, Yamaguchi, Yusaku, Abou Al-Ola, Omar M., Kojima, Takeshi, and Yoshinaga, Tetsuya

Subjects
IMAGE reconstruction algorithms, TOMOGRAPHY, COMPUTED tomography, SIGNAL-to-noise ratio, MATHEMATICAL optimization, IMAGE reconstruction
Abstract

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Iterative Image Reconstruction Algorithm with Parameter Estimation by Neural Network for Computed Tomography

Author

Kojima, Takeshi, Yoshinaga, Tetsuya, Kojima, Takeshi, and Yoshinaga, Tetsuya

Abstract

Recently, an extended family of power-divergence measures with two parameters was proposed together with an iterative reconstruction algorithm based on minimization of the divergence measure as an objective function of the reconstructed images for computed tomography. Numerical experiments on the reconstruction algorithm illustrated that it has advantages over conventional iterative methods from noisy measured projections by setting appropriate values of the parameters. In this paper, we present a novel neural network architecture for determining the most appropriate parameters depending on the noise level of the projections and the shape of the target image. Through experiments, we show that the algorithm of the architecture, which has an optimization sub-network with multiplicative connections rather than additive ones, works well.

Published
2023

12. Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text

Author

Cao, Qi, Kojima, Takeshi, Matsuo, Yutaka, Iwasawa, Yusuke, Cao, Qi, Kojima, Takeshi, Matsuo, Yutaka, and Iwasawa, Yusuke

Abstract

While Large Language Models (LLMs) have achieved remarkable performance in many tasks, much about their inner workings remains unclear. In this study, we present novel experimental insights into the resilience of LLMs, particularly GPT-4, when subjected to extensive character-level permutations. To investigate this, we first propose the Scrambled Bench, a suite designed to measure the capacity of LLMs to handle scrambled input, in terms of both recovering scrambled sentences and answering questions given scrambled context. The experimental results indicate that most powerful LLMs demonstrate the capability akin to typoglycemia, a phenomenon where humans can understand the meaning of words even when the letters within those words are scrambled, as long as the first and last letters remain in place. More surprisingly, we found that only GPT-4 nearly flawlessly processes inputs with unnatural errors, even under the extreme condition, a task that poses significant challenges for other LLMs and often even for humans. Specifically, GPT-4 can almost perfectly reconstruct the original sentences from scrambled ones, decreasing the edit distance by 95%, even when all letters within each word are entirely scrambled. It is counter-intuitive that LLMs can exhibit such resilience despite severe disruption to input tokenization caused by scrambled text., Comment: EMNLP 2023 (with an additional analysis section in appendix)

Published
2023

21. Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure

Author

Ishikawa, Kazuki, Yamaguchi, Yusaku, Abou Al-Ola, Omar M., Kojima, Takeshi, Yoshinaga, Tetsuya, Ishikawa, Kazuki, Yamaguchi, Yusaku, Abou Al-Ola, Omar M., Kojima, Takeshi, and Yoshinaga, Tetsuya

Abstract

Iterative reconstruction of density pixel images from measured projections in computed tomography has attracted considerable attention. The ordered-subsets algorithm is an acceleration scheme that uses subsets of projections in a previously decided order. Several methods have been proposed to improve the convergence rate by permuting the order of the projections. However, they do not incorporate object information, such as shape, into the selection process. We propose a block-iterative reconstruction from sparse projection views with the dynamic selection of subsets based on an estimating function constructed by an extended power-divergence measure for decreasing the objective function as much as possible. We give a unified proposition for the inequality related to the difference between objective functions caused by one iteration as the theoretical basis of the proposed optimization strategy. Through the theory and numerical experiments, we show that nonuniform and sparse use of projection views leads to a reconstruction of higher-quality images and that an ordered subset is not the most effective for block-iterative reconstruction. The two-parameter class of extended power-divergence measures is the key to estimating an effective decrease in the objective function and plays a significant role in constructing a robust algorithm against noise.

Published
2022

28. List of Contributors

Author

Abad, Juan-Carlos, primary, Abelson, Mark B, additional, Abramson, David H, additional, Acquadro, Martin A, additional, Adamis, Anthony P, additional, Adams, Wesley H, additional, Afshari, Natalie A, additional, Ai, Everett, additional, Aiello, Lloyd M, additional, Aiello, Lloyd P, additional, Akduman, Levent, additional, Albano, Marissa L, additional, Albert, Daniel M., additional, Alexandrou, Terry J, additional, Alfonso, Eduardo C, additional, Alié, Jorge L, additional, Alizadeh, Hassan, additional, Al Jadaan, Ibrahim A, additional, Al-Jastaneiah, Sabah, additional, Allen, Calliope E, additional, Allen, David, additional, Allen, Robert C, additional, Alm, Albert, additional, Al-Swailem, Samar, additional, Alt, Abigail K, additional, Altaweel, Michael M, additional, Anderson, Russell, additional, Andreoli, Christopher M, additional, Androudi, Sofia, additional, Ang, Leonard PK, additional, Anzaar, Fahd, additional, Apple, David J, additional, Arrigg, Claudia A, additional, Artal, Pablo, additional, Asbell, Penny, additional, Asdourian, George K, additional, Atebara, Neal, additional, Atmaca-Sonmez, Pelin, additional, Audo, Isabelle, additional, Auffarth, Gerd U, additional, Avery, Robin K, additional, Azar, Dimitri T, additional, Baker, Ann S, additional, Balles, Mark, additional, Barnes, Scott D, additional, Barnett, Donald M, additional, Barney, Neal P, additional, Barouch, Fina C, additional, Bartley, George B, additional, Barton, Jason JS, additional, Behlau, Irmgard, additional, Belda, Jose I, additional, Bennett, Jeffrey L, additional, Bennett, Timothy J, additional, Berdy, Gregg J, additional, Bernardino, Carlo Roberto, additional, Bernardino, Vitaliano, additional, Berson, Eliot L, additional, Bharadwaj, Amitabh, additional, Bhisitkul, Robert, additional, Bhui, Ravinder D, additional, Bilyk, Jurij, additional, Biousse, Valérie, additional, Bird, Alan C, additional, Blair, Norman Paul, additional, Blodi, Barbara A, additional, Blumenkranz, Mark S, additional, Boldt, H Culver, additional, Borchert, Mark S, additional, Borrillo, Luigi, additional, Borodic, Gary E, additional, Boruchoff, S Arthur, additional, Bose, Swaraj, additional, Boulton, Michael E, additional, Bowman, RW, additional, Bradley, Elizabeth A, additional, Brazitikos, Periklis D, additional, Breeze, Robert, additional, Bressler, Neil M, additional, Bressler, Susan B, additional, Brini, Alfred, additional, Budenz, Donald L, additional, Buffenn, Angela N, additional, Burk, Scott E, additional, Butrus, Salim, additional, Callanan, David, additional, Cameron, J Douglas, additional, Cantor, Louis B, additional, Cantore, William A, additional, Cantu-Dibildox, Jorge, additional, Casas, Victoria, additional, Casper, Miriam, additional, Casten, Robin J, additional, Catoira, Yara P, additional, Cavallerano, Jerry, additional, Chai, Samantha J, additional, Chalita, Maria R, additional, Chamberlain, Sherman M, additional, Chan, Audrey S, additional, Chan, Chi-Chao, additional, Chan, Paul, additional, Chapin, Matthew J, additional, Chapman, Karen L, additional, Chen, Eric, additional, Chen, Joe, additional, Chen, Julie A, additional, Chen, Teresa C, additional, Chen, Zhou, additional, Chévez-Barrios, Patricia, additional, Chew, Emily Y, additional, Chiang, Mark, additional, Chodosh, James, additional, Chong, Eva-Marie, additional, Chun, Denise, additional, Chylack, Leo T, additional, Ciardella, Antonio P, additional, Civan, Mortimer, additional, Clamen, Liane, additional, Clark, John I, additional, Cockerham, Glenn, additional, Cohen, Andre, additional, Cohen, Elisabeth J, additional, Colby, Kathryn A, additional, Coleman, Anne L, additional, Coleman, Hanna R, additional, Colin, Joseph, additional, Collier, J Michael, additional, Comer, Grant M, additional, Conlon, M Ronan, additional, Cooper, Kim E, additional, Corbett, James J, additional, Coma, Miguel C, additional, Cyrlin, Marshall N, additional, Dagi, Linda R, additional, Dahlgren, Matthew A, additional, Daley, Timothy J, additional, Da Mata, Andrea P, additional, Damato, Bertil, additional, D'Amico, Donald J, additional, Dana, Reza, additional, Danan-Husson, Aude, additional, Danesh-Meyer, Helen B, additional, Danis, Ronald P, additional, Darlington, Jason K, additional, Davidson, Stefanie L, additional, Davis, Janet L, additional, Davis, Elizabeth A, additional, de la Cruz, Jose J, additional, de la Garza, Adam G, additional, DeAngelis, Margaret M, additional, DeMartelaere, Sheri L, additional, Demer, Joseph L, additional, Dhaliwal, Avninder, additional, Dieckert, J Paul, additional, Do, Diana V, additional, Doane, Marshall G, additional, Dodds, Christopher, additional, Dohlman, Claes H, additional, Donati, Guy, additional, Donnenfeld, Eric D, additional, Drack, Arlene, additional, Dryja, Thaddeus P, additional, Dueker, David, additional, Duker, Jay S, additional, Dunbar, Jennifer A, additional, Dunn, James P, additional, Dupps, William J, additional, Durand, Marlene L, additional, Dutton, Jonathan J, additional, Eandi, Chiara M, additional, Edward, Deepak P, additional, Egan, Robert A, additional, Eichenbaum, David A, additional, Eklund, Susan E, additional, Engle, Elizabeth C, additional, Erickson, Kristine, additional, Esmaeli, Bita, additional, Fay, Aaron, additional, Feiner, Leonard, additional, Fekrat, Sharon, additional, Ferris, Frederick L, additional, Fine, Howard F, additional, Fletcher, Donald C, additional, Flikier, Paul, additional, Floyd, Richard P, additional, Flynn, Harry W, additional, Fong, Donald S, additional, Font, Ramon L, additional, Forbes, Brian JR, additional, Foroozan, Rod, additional, Foster, Bradley S, additional, Foster, C Stephen, additional, Foster, Jill A, additional, Foulks, Gary N, additional, Fountain, Tamara R, additional, Fox, Gregory M, additional, Freddo, Thomas F, additional, Freedman, Sharon F, additional, Freund, K Bailey, additional, Friberg, Thomas R, additional, Friedman, Alan H, additional, Friedman, David, additional, Friedman, Deborah I, additional, Friedman, Ephraim, additional, Fu, Arthur D, additional, Fulton, Anne B, additional, Galal, Ahmed, additional, Galetta, Steven, additional, Gallardo, Mark, additional, Gallie, Brenda, additional, Garner, Alec, additional, Garrity, James A, additional, Gatinel, Damien, additional, Gedde, Steven J, additional, Geist, Craig E, additional, Gerber, Steve, additional, Ghanem, Ramon C, additional, Gieser, Jon P, additional, Gilmore, Michael S, additional, Gimbel, Howard V, additional, Gipson, Ilene K, additional, Glover, Tyrone, additional, Goldberg, Robert A, additional, Goldenfeld, Mordechai, additional, Goldstein, Scott M, additional, Gomi, Cintia F, additional, Gong, Haiyan, additional, Gonzales, John A, additional, Goosey, John, additional, Gottlieb, Justin L, additional, Gould, Joshua, additional, Gragoudas, Evangelos S, additional, Granet, David B, additional, Greaney, Michael J, additional, Green, Daniel G, additional, Grehn, Franz, additional, Greiner, Jack V, additional, Greven, Craig M, additional, Griepentrog, Gregory J, additional, Groenewald, Carl, additional, Grosskreutz, Cynthia L, additional, Grover, Lori Latowski, additional, Gullapalli, Vamsi K, additional, Gulur, Padma, additional, Gunther, Jonathan, additional, Gupta, Manish, additional, Gupta, Mayank, additional, Guyer, David R, additional, Haivala, Darin R, additional, Haller, Julia A, additional, Halmagyi, GM, additional, Halperin, Lawrence S, additional, Hamdi, Islam M, additional, Hamilton, Steven R, additional, Hammersmith, Kristin M, additional, Han, Dennis P, additional, Hansen, Ronald M, additional, Harbour, J William, additional, Hariprasad, Seenu M, additional, Harissi-Dagher, Mona, additional, Hassan, Shirin E., additional, Hatton, Mark P, additional, Hawley, Pamela, additional, Hayashida, Yasutaka, additional, Heckenlively, John R, additional, Hedges, Thomas R, additional, Heggie, Alfred D, additional, Heher, Katrinka L, additional, Heier, Jeffrey S, additional, Hejtmancik, J Fielding, additional, Henderson, Bonnie A, additional, Hersh, Peter S, additional, Hidayat, Ahmed A, additional, Higginbotham, Eva Juliet, additional, Hirose, Tatsuo, additional, Ho, Allen C, additional, Ho, ThucAnh T, additional, Hogan, R Nick, additional, Holck, David E, additional, Holekamp, Nancy M, additional, Hovland, Peter G, additional, Hsu, Thomas C, additional, Hsu, William C, additional, Huang, Andrew JW, additional, Hughes, Mark S, additional, Hui, Jennifer, additional, Hunter, David G, additional, Huryn, Laryssa A, additional, Husain, Deeba, additional, Hyndiuk, Robert A, additional, Ip, Michael, additional, Jacobs, Brian J, additional, Jakobiec, Frederick A, additional, Jampol, Lee M, additional, Jensen, Harold G, additional, Ji, Fei, additional, Johnson, David L, additional, Johnson, Douglas H, additional, Johnson, Mark W, additional, Johnson, R Paul, additional, Johnson, Robert N, additional, Joos, Karen M, additional, Joyce, Nancy C, additional, Jumper, J Michael, additional, Jurkunas, Ula V., additional, Kahana, Alon, additional, Kahook, Malik Y, additional, Kanner, Elliott, additional, Kalwerisky, Kevin, additional, Kaplan, Henry J, additional, Karatza, Ekaterini C, additional, Kardon, Randy, additional, Katowitz, James A, additional, Katowitz, William R, additional, Kazlas, Melanie, additional, Keefe, Kelly S, additional, Kelley, Lara, additional, Kent, Charles J, additional, Kenyon, Kenneth R, additional, Khan, Bilal F, additional, Khan, Jemshed A, additional, Khan, Naheed W, additional, Khaw, Peng Tee, additional, Kherani, Femida, additional, Kim, Eva C, additional, Kim, Hee Joon, additional, Kim, Ivana K, additional, Kim, Jonathan W, additional, Kim, Rosa Y, additional, Kim, Stella K, additional, Kim, Tae-Im, additional, Klais, Christina M, additional, Klapper, Stephen R, additional, Klein, Barbara EK, additional, Kleinmann, Guy, additional, Klink, Thomas, additional, Klisovic, Dino D, additional, Klyce, Stephen D, additional, Kocaturk, Tolga, additional, Kohnen, Thomas, additional, Kojima, Takeshi, additional, Koller, Tobias, additional, Kostick, David A, additional, Kraut, Joel A, additional, Krishnan, Chandrasekharan, additional, Krueger, Ronald R, additional, Krug, Joseph H, additional, Krupsky, Sara, additional, Kuchtey, Rachel W, additional, Kurban, Ramsay S, additional, Kurz, Paul A, additional, Kuszak, JR, additional, Kwon, Young H, additional, Labbe, Thad A, additional, Lam, Deborah L, additional, Lamkin, Jeffrey C, additional, Lamping, Kathleen A, additional, Lane, Anne Marie, additional, Lane, Katherine A, additional, Lane, Keith J, additional, Lass, Jonathan H, additional, Lawrence, Mary G, additional, Lee, Andrew G, additional, Lee, Carol M, additional, Lee, Michael S, additional, Lee, Paul P, additional, Lee, William B, additional, Leibovitch, Igal, additional, Lemke, Bradley N, additional, Lemley, Craig A, additional, Leonardi, Andrea, additional, Lessell, Simmons, additional, Levin, Leonard A, additional, Levy-Clarke, Grace A, additional, Lew, Julie C, additional, Lewis, Craig, additional, Li, Wei, additional, Lim, Laurence S, additional, Lim, Lyndell L, additional, Lim, Wee-Kiak, additional, Liu, Grant T, additional, Loewenstein, John I, additional, Lott, McGregor N, additional, Lowry, Jonathan C, additional, Lyon, David B, additional, Lytle, Robert E, additional, MacCumber, Mathew, additional, Mackool, Bonnie T, additional, Madiwale, Nalini A, additional, Mah, Francis, additional, Mainster, Martin A, additional, Manning, Michael H, additional, Mansberger, Steven L, additional, Marc, Robert E, additional, Marchong, Mellone, additional, Marcus, Dennis M, additional, Mares, Julie A, additional, Marr, Brian P, additional, Martinez, Carlos E, additional, Massof, Robert W, additional, Matsumoto, Yukihiro, additional, Mattox, Cynthia, additional, Maus, Marlon, additional, McCabe, Cathleen M, additional, McCormick, Steven A, additional, McCrakken, Michael, additional, McCulley, James P, additional, McDermott, John A, additional, McDonald, H Richard, additional, McDonald, Marguerite B, additional, McDonnell, Peter J, additional, McGillivray, Robert, additional, McKeown, Craig A, additional, McLaughlin, James, additional, McMullen, W Wynn, additional, Melamed, Shlomo, additional, Meligonis, George, additional, Mendrinos, Efstratios, additional, Meyer, Dale R, additional, Meyerle, Catherine B, additional, Mieler, William F, additional, Migliori, Michael, additional, Mihm, Martin C, additional, Miller, Darlene, additional, Miller, David, additional, Miller, Joan W, additional, Miller, Neil R, additional, Mills, David M, additional, Mills, Monte D, additional, Milman, Tatyana, additional, Mogk, Lylas, additional, Mogk, Marja, additional, Monés, Jordi, additional, Montes-Micó, Robert, additional, Morse, Christie L, additional, Morton, Asa D, additional, Moskowitz, Anne, additional, Mukai, Shizuo, additional, Murphree, A Linn, additional, Murphy, Robert P, additional, Murray, Timothy G, additional, Murray, Philip I, additional, Nagao, Karina, additional, Neitz, Jay, additional, Neitz, Maureen, additional, Netland, Peter A, additional, Neufeld, Arthur H, additional, Newman, Nancy J, additional, Ng, Eugene WM, additional, Nguyen, Quan Dong, additional, Niederkorn, Jerry Y, additional, Noecker, Robert J, additional, Nussenblatt, Robert B, additional, O'Brien, Joan M, additional, O'Brien, Paul D, additional, O'Brien, Terrence P, additional, O'Day, Denis, additional, Olk, R Joseph, additional, Olsen, Karl R, additional, Onal, Sumru, additional, Ooi, Yen Hoong, additional, Opremcak, E Mitchel, additional, Ousler, George, additional, Ozment, Randall R, additional, Packer, Samuel, additional, Palmer, Millicent L, additional, Papaliodis, George N, additional, Park, DJ John, additional, Parke, David W, additional, Parsa, Cameron F, additional, Parsons, M Andrew, additional, Pasquale, Louis R, additional, Patel, Neha N, additional, Patel, Sayjal J, additional, Patrianakos, Thomas D, additional, Patrinely, James R, additional, Pavan-Langston, Deborah, additional, Peli, Eli, additional, Pepin, Susan M, additional, Perez, Victor L, additional, Pérez-Santonja, Juan J, additional, Perfect, John R, additional, Perry, Henry D, additional, Piatigorsky, Joram, additional, Pieramici, Dante, additional, Pierce, Eric A, additional, Pineda, Roberto, additional, Pless, Misha L, additional, Pomeranz, Howard D, additional, Pournaras, Constantin J, additional, Power, William, additional, Prakash, Manvi, additional, Prasad, Anita G, additional, Purvin, Valerie, additional, Quillen, David A, additional, Quinn, Graham E, additional, Rabena, Melvin D, additional, Rae, James L, additional, Raizman, Michael B, additional, Randazzo, Alessandro, additional, Rao, Narsing A, additional, Rapuano, Christopher J, additional, Reeves, Sherman W, additional, Regillo, Carl D, additional, Reichel, Elias, additional, Reinke, Martin H, additional, Rhee, Douglas, additional, Richter, Claudia U, additional, Rizzo, Joseph F, additional, Robb, Richard M, additional, Roden, Anja C, additional, Rodgers, I Rand, additional, Rodrigues, Merlyn M, additional, Ron, Yonina, additional, Rose, Geoffrey E, additional, Rosen, Emanuel S, additional, Rosenbaum, James T, additional, Rosenthal, Perry, additional, Rouse, Strutha C, additional, Rovner, Barry W, additional, Rozanowska, Malgorzata, additional, Rubin, Michael P, additional, Rubin, Peter AD, additional, Rumelt, Shimon, additional, Rustgi, Anil K, additional, Rutar, Tina, additional, Ruttum, Mark S, additional, Rutzen, Allan R, additional, Ryan, Edward T, additional, Sadun, Alfredo A, additional, Sahel, José-Alain, additional, Saligan, Leorey, additional, Salim, Sarwat, additional, Salmon, John F, additional, Salomão, Diva R, additional, Sami, David, additional, Sandberg, Michael A, additional, Sangwan, Virender S, additional, Saornil, Maria A, additional, Sassani, Joseph W, additional, Sayegh, Rony R, additional, Schachat, Andrew P, additional, Schell, Wiley A, additional, Schefler, Amy C, additional, Scheufele, Tina, additional, Schiedler, Vivian, additional, Schneider, Gretchen, additional, Schroeder, Alison, additional, Schuchard, Ronald A, additional, Schuman, Joel S, additional, Schwab, Ivan R, additional, Scott, Adrienne, additional, Scott, Ingrid U, additional, Sears, Marvin L, additional, Seddon, Johanna M, additional, Seiler, Theo, additional, Selkin, Robert P, additional, Semba, Richard D, additional, Serbanescu, Irina, additional, Sexton, Briar, additional, Shaarawy, Tarek M, additional, Shah, Peter, additional, Shapiro, Aron, additional, Sharma, Savitri, additional, Shein, Jean, additional, Shetlar, Debra J, additional, Shields, M Bruce, additional, Shields, Carol L, additional, Shields, Jerry A, additional, Shingleton, Bradford J, additional, Shore, John W, additional, Shuba, Lesya M, additional, Simon, Guy J Ben, additional, Simmons, Richard J, additional, Simpson, Michael, additional, Singh, Arun D, additional, Singh, Omah S, additional, Sisley, Karen, additional, Sit, Arthur J, additional, Smerdon, David, additional, Smiddy, William E, additional, Smith, Ronald E, additional, Smith, Terry J, additional, Snebold, Neal G, additional, Sobrin, Lucia, additional, Sorenson, John A, additional, Soukiasian, Sarkis H, additional, Spaeth, George L, additional, Spaide, Richard F, additional, Srivastava, Monika, additional, Srivastava, Sunil K, additional, Stangos, Alexandros N, additional, Starck, Tomy, additional, Stark, Walter J, additional, Stein, Joshua D, additional, Steinert, Roger F, additional, Strauss, Leon, additional, Streeten, Barbara W, additional, Streilein, J Wayne, additional, Strong, James D, additional, Sugino, Ilene K, additional, Suhler, Eric B, additional, Sullivan, Timothy J, additional, Sun, Jennifer K, additional, Sunness, Janet S, additional, Sutula, Francis C, additional, Syed, Nasreen A, additional, Ta, Christopher N, additional, Takei, Hidehiro, additional, Talamo, Jonathan H, additional, Tamesis, Richard R, additional, Tamhankar, Madhura, additional, Tarbet, Kristen J, additional, Tarver-Carr, Michelle, additional, Terry, Mark A, additional, Thomas, Joseph M, additional, Thompson, Vance, additional, Thorne, Jennifer E, additional, Thurtell, Matthew J, additional, Tingey, David P, additional, To, King W, additional, Tobaigy, Faisal M, additional, Tolentino, Michael J, additional, Tong, Melissa G, additional, Torkildsen, Gail, additional, Toth, Cynthia A, additional, Traboulsi, Elias I, additional, Trucksis, Michele, additional, Tsai, James C, additional, Tsai, Julie H, additional, Tse, David T, additional, Tseng, Scheffer CG, additional, Tu, Elmer Y, additional, Udell, Ira J, additional, Valenzuela, Alejandra A, additional, Van Gelder, Russell N, additional, Van Stavern, Gregory P, additional, Vander Veen, Deborah K, additional, Vavvas, Demetrios, additional, Verity, David H, additional, Vinciguerra, Paolo, additional, Vinger, Paul F, additional, Volpe, Nicholas J, additional, Wackernagel, Werner, additional, Wadhwa, Sonal Desai, additional, Wagoner, Michael D, additional, Waheed, Nadia K, additional, Walton, David S, additional, Wand, Martin, additional, Wang, Jie Jin, additional, Warden, Scott M, additional, Webb, Lennox, additional, Weber, David, additional, Wee, Daniel, additional, Westerfeld, Corey B, additional, Westfall, Christopher T, additional, Whitcup, Scott M, additional, White, Valerie A, additional, White, William L, additional, Wickens, Jason, additional, Wiggs, Janey L, additional, Wilensky, Jacob T, additional, Wilkinson, Charles P, additional, Williams, Patrick D, additional, Wilson, David J, additional, Wilson, M Roy, additional, Wilson, Steven E, additional, Winokur, Jules, additional, Wirostko, William J, additional, Wollstein, Gadi, additional, Wong, Albert Chak Ming, additional, Wong, Tien Y, additional, Woog, John J, additional, Wride, Michael, additional, Wu, Carolyn S, additional, WuDunn, Darrell, additional, Yang, Jean, additional, Yannuzzi, Lawrence A, additional, Yaremchuk, Michael J, additional, Yeatts, R Patrick, additional, Yee, Richard W, additional, Yeh, Steven, additional, Young, Lucy HY, additional, Yu, Jenny Y, additional, Yue, Beatrice YJT, additional, Zacks, Charles M, additional, Zagelbaum, Bruce M, additional, Zamani, Maryam, additional, Zarbin, Marco, additional, Zografos, Leonidas, additional, and Zoumalan, Christopher I, additional

Published
2008
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30. Natural history of upper and lower gastrointestinal bleeding in hemodialysis patients: A dual‐center long‐term cohort study

Author

Niikura, Ryota, primary, Aoki, Tomonori, additional, Kojima, Tetsu, additional, Kawahara, Takuya, additional, Yamada, Atsuo, additional, Nakamura, Hideyo, additional, Inoue, Kenji, additional, Morikoshi, Eita, additional, Migita, Raita, additional, Shimizu, Toshiko, additional, Kojima, Takeshi, additional, and Koike, Kazuhiko, additional

Published
2020
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40. Civil procedure reform in Japan.

Author

Kojima, Takeshi

Subjects
Law reform -- Analysis, Civil procedure -- Analysis, Japan -- Laws, regulations and rules
Published
1990

44. Natural history of upper and lower gastrointestinal bleeding in hemodialysis patients: A dual‐center long‐term cohort study.

Author

Niikura, Ryota, Aoki, Tomonori, Kojima, Tetsu, Kawahara, Takuya, Yamada, Atsuo, Nakamura, Hideyo, Inoue, Kenji, Morikoshi, Eita, Migita, Raita, Shimizu, Toshiko, Kojima, Takeshi, and Koike, Kazuhiko

Subjects
HEMODIALYSIS patients, CHRONIC kidney failure, NATURAL history, COHORT analysis, GASTROINTESTINAL hemorrhage
Abstract

Background and Aim: Knowledge on the risk of gastrointestinal (GI) bleeding in hemodialysis patients is limited. We evaluated the risk of GI bleeding in hemodialysis patients compared with non‐hemodialysis patients. Methods: We performed a retrospective cohort study from 1996 to 2017 at the Graduate School of Medicine, University of Tokyo, and Horinouchi Hospital. We analyzed patients on hemodialysis for chronic renal failure and controls not on hemodialysis. The primary endpoint was GI bleeding. A survival analysis was performed to estimate the cumulative incidence and hazard ratio of GI bleeding. Results: A total of 14 451 patients were analyzed (417 hemodialysis and 14 034 non‐hemodialysis patients). In total, 524 GI bleeding events occurred. Upper and lower GI bleeding occurred in 432 and 92 patients in the hemodialysis and non‐hemodialysis groups, respectively. The most frequent source of upper and lower GI bleeding was gastric ulcer and colonic diverticular bleeding, respectively. The cumulative incidence of GI bleeding was 4.44% at 1 year, 7.15% at 3 years, and 10.40% at 5 years in hemodialysis patients; the respective rates were 2.35%, 2.98%, and 3.79% in non‐hemodialysis patients during a mean follow‐up period of 3.5 years. Hemodialysis was significantly associated with an increased risk of GI bleeding after adjustment (hazard ratio 1.67, P = 0.01, 95% confidence interval 1.13–2.50). Conclusions: Hemodialysis patients had a GI bleeding rate of 10% over 5 years, and hemodialysis was a risk factor for GI bleeding. [ABSTRACT FROM AUTHOR]

Published
2021
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46. Tomographic Image Reconstruction Based on Minimization of Symmetrized Kullback-Leibler Divergence

Author

Kasai, Ryosuke, Yamaguchi, Yusaku, Kojima, Takeshi, Yoshinaga, Tetsuya, Kasai, Ryosuke, Yamaguchi, Yusaku, Kojima, Takeshi, and Yoshinaga, Tetsuya

Abstract

Iterative reconstruction (IR) algorithms based on the principle of optimization are known for producing better reconstructed images in computed tomography. In this paper, we present an IR algorithm based on minimizing a symmetrized Kullback-Leibler divergence (SKLD) that is called Jeffreys’ J-divergence. The SKLD with iterative steps is guaranteed to decrease in convergence monotonically using a continuous dynamical method for consistent inverse problems. Specifically, we construct an autonomous differential equation for which the proposed iterative formula gives a first-order numerical discretization and demonstrate the stability of a desired solution using Lyapunov’s theorem. We describe a hybrid Euler method combined with additive and multiplicative calculus for constructing an effective and robust discretization method, thereby enabling us to obtain an approximate solution to the differential equation.We performed experiments and found that the IR algorithm derived from the hybrid discretization achieved high performance.

Published
2018

47. Multivalued Discrete Tomography Using Dynamical System That Describes Competition

Author

Kojima, Takeshi, Ueta, Tetsushi, Yoshinaga, Tetsuya, Kojima, Takeshi, Ueta, Tetsushi, and Yoshinaga, Tetsuya

Abstract

Multivalued discrete tomography involves reconstructing images composed of three or more gray levels from projections. We propose a method based on the continuous-time optimization approach with a nonlinear dynamical system that effectively utilizes competition dynamics to solve the problem of multivalued discrete tomography. We perform theoretical analysis to understand how the system obtains the desired multivalued reconstructed image. Numerical experiments illustrate that the proposed method also works well when the number of pixels is comparatively high even if the exact labels are unknown.

Published
2017

50. <ARTICLES>An Investigation of the Public Understanding of Science

Author

KOJIMA, Takeshi

Abstract

The purpose of this paper is to consider how the public who are not engaged in special scientific work understand and interact with science. Recently we are surrounded by various risks caused by science, so the public needs to know and participate in increasing more scientific related affairs. To deal with this issue of "Public Understanding of Science", first, we sum up the scientific enlightenment action and policy during the twentieth century in the United Kingdom. We refer in particular to the Royal Society's report The Public Understanding of Science. In this report an evidence is found of aims to increase the publics understanding of science for national prosperity, and a "deficit model" is given which regard the public as scientifically vacant, ignorant people. On the contrary, there are studies that aim to make it clear that the public understanding of science has its own actuality and positive significance. We take Misunderstanding Science? as the representative study of this kind. Two examples in this book are introduced in this paper, one explains how the patients of Familial Hypercholesterolaemia get on with scientific and medical knowledge, and the other explains how sheep farmers around the Sellafield Nuclear Plant get on with the scientists sent from the United Kingdom. From these examples, we can find that there are rationalities in the lay public's scientific judgment. However, these examples are local and specific, so in order to make it clear that the lay public's scientific judgment has the great power to make scientific policies more democratic, we introduce global consumers' action against Royal Dutch Shell concerning the Brent Spar, an old oil rig that was dumped in the Atlantic ocean. This affair happened as global consumers ignored the authorized scientists' opinions, displaying the power of the public's own judgment. The main theme of this paper, that the public understanding of science has the critical power for scientific governance, is arranged in the last section.

Published
2001

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