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1. Is This the Same Code? A Comprehensive Study of Decompilation Techniques for WebAssembly Binaries

Author

Wu, Wei-Cheng, Yan, Yutian, Egilsson, Hallgrimur David, Park, David, Chan, Steven, Hauser, Christophe, and Wang, Weihang

Subjects
Computer Science - Software Engineering
Abstract

WebAssembly is a low-level bytecode language designed for client-side execution in web browsers. The need for decompilation techniques that recover high-level source code from WASM binaries has grown as WASM continues to gain widespread adoption and its security concerns. However little research has been done to assess the quality of decompiled code from WASM. This paper aims to fill this gap by conducting a comprehensive comparative analysis between decompiled C code from WASM binaries and state-of-the-art native binary decompilers. We presented a novel framework for empirically evaluating C-based decompilers from various aspects including correctness/ readability/ and structural similarity. The proposed metrics are validated practicality in decompiler assessment and provided insightful observations regarding the characteristics and constraints of existing decompiled code. This in turn contributes to bolstering the security and reliability of software systems that rely on WASM and native binaries., Comment: SecureComm'24: Proceedings of the 20th EAI International Conference on Security and Privacy in Communication Networks

Published
2024

2. AI Surrogate Model for Distributed Computing Workloads

Author

Park, David K., Ren, Yihui, Kilic, Ozgur O., Korchuganova, Tatiana, Vatsavai, Sairam Sri, Boudreau, Joseph, Chowdhury, Tasnuva, Feng, Shengyu, Khan, Raees, Kim, Jaehyung, Klasky, Scott, Maeno, Tadashi, Nilsson, Paul, Outschoorn, Verena Ingrid Martinez, Podhorszki, Norbert, Suter, Frederic, Yang, Wei, Yang, Yiming, Yoo, Shinjae, Klimentov, Alexei, and Hoisie, Adolfy

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Large-scale international scientific collaborations, such as ATLAS, Belle II, CMS, and DUNE, generate vast volumes of data. These experiments necessitate substantial computational power for varied tasks, including structured data processing, Monte Carlo simulations, and end-user analysis. Centralized workflow and data management systems are employed to handle these demands, but current decision-making processes for data placement and payload allocation are often heuristic and disjointed. This optimization challenge potentially could be addressed using contemporary machine learning methods, such as reinforcement learning, which, in turn, require access to extensive data and an interactive environment. Instead, we propose a generative surrogate modeling approach to address the lack of training data and concerns about privacy preservation. We have collected and processed real-world job submission records, totaling more than two million jobs through 150 days, and applied four generative models for tabular data -- TVAE, CTAGGAN+, SMOTE, and TabDDPM -- to these datasets, thoroughly evaluating their performance. Along with measuring the discrepancy among feature-wise distributions separately, we also evaluate pair-wise feature correlations, distance to closest record, and responses to pre-trained models. Our experiments indicate that SMOTE and TabDDPM can generate similar tabular data, almost indistinguishable from the ground truth. Yet, as a non-learning method, SMOTE ranks the lowest in privacy preservation. As a result, we conclude that the probabilistic-diffusion-model-based TabDDPM is the most suitable generative model for managing job record data., Comment: 8 pages, 5 figures, to be presented in SC24 AI4S Workshop

Published
2024

5. AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions

Author

Real, Esteban, Chen, Yao, Rossini, Mirko, de Souza, Connal, Garg, Manav, Verghese, Akhil, Firsching, Moritz, Le, Quoc V., Cubuk, Ekin Dogus, and Park, David H.

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning, Mathematics - Numerical Analysis, I.2.2, I.2.6, G.1.2
Abstract

Computers calculate transcendental functions by approximating them through the composition of a few limited-precision instructions. For example, an exponential can be calculated with a Taylor series. These approximation methods were developed over the centuries by mathematicians, who emphasized the attainability of arbitrary precision. Computers, however, operate on few limited precision types, such as the popular float32. In this study, we show that when aiming for limited precision, existing approximation methods can be outperformed by programs automatically discovered from scratch by a simple evolutionary algorithm. In particular, over real numbers, our method can approximate the exponential function reaching orders of magnitude more precision for a given number of operations when compared to previous approaches. More practically, over float32 numbers and constrained to less than 1 ULP of error, the same method attains a speedup over baselines by generating code that triggers better XLA/LLVM compilation paths. In other words, in both cases, evolution searched a vast space of possible programs, without knowledge of mathematics, to discover previously unknown optimized approximations to high precision, for the first time. We also give evidence that these results extend beyond the exponential. The ubiquity of transcendental functions suggests that our method has the potential to reduce the cost of scientific computing applications.

Published
2023

6. The Real Effects of Supply Chain Transparency Regulation: Evidence from Section 1502 of the Dodd–Frank Act

Author

BAIK, BOK, EVEN‐TOV, OMRI, HAN, RUSSELL, and PARK, DAVID

Subjects
Transportation, Logistics and Supply Chains, Commerce, Management, Tourism and Services, Peace, Justice and Strong Institutions, real effects, Dodd-Frank Act, conflict minerals disclosures, nonfinancial disclosure, corporate social responsibility, responsible sourcing, ESG, due diligence, supply chain, resource curse, Accounting, Auditing and Accountability, Banking, Finance and Investment, Accounting, Accounting, auditing and accountability, Banking, finance and investment
Abstract

ABSTRACT: Section 1502 of the Dodd–Frank Act requires SEC‐registered issuers to conduct supply chain due diligence and submit conflict minerals disclosures (CMDs) that indicate whether their products contain tantalum, tin, tungsten, or gold (3TG) sourced from the Democratic Republic of the Congo (DRC) or its neighboring countries (“covered countries”). Consistent with the reputational cost hypothesis, we find that heightened public attention to CMDs increases responsible sourcing. After Section 1502 takes effect, we find higher demand for 3TG products processed in certified smelters, decreased conflicts in covered countries’ mining regions relative to other regions, and reduced sensitivity of conflict risk to conflict minerals’ price spikes. Finally, we find that conflicts decrease in Eastern DRC territories with prevalent 3T (tantalum, tin, and tungsten) mines but increase in territories with prevalent gold mines. Overall, our findings highlight the real effects of enhanced supply chain transparency regulation.

Published
2024

7. Samplable Anonymous Aggregation for Private Federated Data Analysis

Author

Talwar, Kunal, Wang, Shan, McMillan, Audra, Jina, Vojta, Feldman, Vitaly, Bansal, Pansy, Basile, Bailey, Cahill, Aine, Chan, Yi Sheng, Chatzidakis, Mike, Chen, Junye, Chick, Oliver, Chitnis, Mona, Ganta, Suman, Goren, Yusuf, Granqvist, Filip, Guo, Kristine, Jacobs, Frederic, Javidbakht, Omid, Liu, Albert, Low, Richard, Mascenik, Dan, Myers, Steve, Park, David, Park, Wonhee, Parsa, Gianni, Pauly, Tommy, Priebe, Christian, Rishi, Rehan, Rothblum, Guy, Scaria, Michael, Song, Linmao, Song, Congzheng, Tarbe, Karl, Vogt, Sebastian, Winstrom, Luke, and Zhou, Shundong

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private algorithms require little trust but are (provably) limited in their utility. Centrally differentially private algorithms can allow significantly better utility but require a trusted curator. This gap has led to significant interest in the design and implementation of simple cryptographic primitives, that can allow central-like utility guarantees without having to trust a central server. Our first contribution is to propose a new primitive that allows for efficient implementation of several commonly used algorithms, and allows for privacy accounting that is close to that in the central setting without requiring the strong trust assumptions it entails. {\em Shuffling} and {\em aggregation} primitives that have been proposed in earlier works enable this for some algorithms, but have significant limitations as primitives. We propose a {\em Samplable Anonymous Aggregation} primitive, which computes an aggregate over a random subset of the inputs and show that it leads to better privacy-utility trade-offs for various fundamental tasks. Secondly, we propose a system architecture that implements this primitive and perform a security analysis of the proposed system. Our design combines additive secret-sharing with anonymization and authentication infrastructures., Comment: 34 pages

Published
2023

11. CyberKnife Robotic Stereotactic Radiosurgery

Author

Rahimy, Elham, Szalkowski, Gregory, Wang, Lei, Gibbs, Iris, Park, David J., Soltys, Scott G., Pollom, Erqi, Trifiletti, Daniel M., editor, Chao, Samuel T., editor, Sahgal, Arjun, editor, and Sheehan, Jason P., editor

Published
2024
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13. Stereotactic Radiosurgery for Hemangioblastomas

Author

Park, David J., Yoo, Kelly H., Windisch, Paul Y., Pollom, Erqi L., Meola, Antonio, Soltys, Scott G., Chang, Eric L., editor, Brown, Paul D., editor, Lo, Simon S., editor, Sahgal, Arjun, editor, and Suh, John H., editor

Published
2024
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14. Private Federated Statistics in an Interactive Setting

Author

McMillan, Audra, Javidbakht, Omid, Talwar, Kunal, Briggs, Elliot, Chatzidakis, Mike, Chen, Junye, Duchi, John, Feldman, Vitaly, Goren, Yusuf, Hesse, Michael, Jina, Vojta, Katti, Anil, Liu, Albert, Lyford, Cheney, Meyer, Joey, Palmer, Alex, Park, David, Park, Wonhee, Parsa, Gianni, Pelzl, Paul, Rishi, Rehan, Song, Congzheng, Wang, Shan, and Zhou, Shundong

Subjects
Computer Science - Cryptography and Security
Abstract

Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interactivity. We argue that an aggregation protocol can enable an interactive private federated statistics system where user's devices maintain control of the privacy assurance. We describe the architecture of such a system, and analyze its security properties.

Published
2022

34. The Impact of Digital Credit in Developing Economies: A Review of Recent Evidence

Author

Robinson, Jonathan, Park, David S, and Blumenstock, Joshua E

Subjects
digital credit, developing economies, development economies, credit, low income countries, middle income countries, microcredit, welfare
Abstract

In recent years, a new generation of “digital credit” products have transformed the consumer lending landscape in many low- and middle-income countries. Offering short- term, high-interest loans via mobile phones or other digital platforms, these products have become wildly popular. This article reviews the small but emerging evidence on the welfare impacts of digital credit. These studies document very high rates of takeup – well in excess of traditional microcredit – despite the fact that customers often do not understand the terms of their loans. Overall, there is little evidence that access to credit has consistent positive impacts on borrower welfare, though two impact evaluations document positive effects on resilience and subjective well-being, respectively. No study finds statistically significant negative impacts of digital credit.

Published
2022

36. SAPPHIRE: phase III study of sitravatinib plus nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer

Author

Abdel-Karim, Isam, Abdelsalam, Mahmoud, Addeo, Alfredo, Aguado, Carlos, Alexander, Patrick, Alt, Jürgen, Azzi, Georges, Balaraman, Rama, Biesma, Bonne, Blackhall, Fiona, Bohnet, Sabine, Boleti, Ekaterini, Borghaei, Hossein, Bradbury, Penelope, Brighenti, Matteo, Campbell, Nicholas, Campbell, Toby, Canon, Jean-Luc, Cappuzzo, Federico, Costa, Enric Carcereny, Cavanna, Luigi, Cetnar, Jeremy, Chella, Antonio, Chouaid, Christos, Christoph, Daniel, Castán, Javier Cortés, Dakhil, Shaker, de Castro Carpeño, Francisco Javier, de Marinis, Filippo, Delmonte, Angelo, Demedts, Ingel, Demey, Wim, Dits, Joyce, del Pilar Diz Taín, Maria, Gómez, Manuel Dómine, Dorius, Timothy, Dumoulin, Daphne, Duruisseaux, Michaël, Eaton, Keith, González, Emilio Esteban, Evans, Devon, Faehling, Martin, Farrell, Nicholas, Feinstein, Trevor, Font, Enriqueta Felip, Garcia Campelo, Maria Rosario, Garon, Edward, Garrido López, María Pilar, Germonpré, Paul, Gersten, Todd, Cao, Maria Gonzalez, Gopaluni, Srivalli, Greillier, Laurent, Grossi, Francesco, Guisier, Florian, Gurubhagavatula, Sarada, Calderón, Vanesa Gutiérrez, Hakimian, David, Hall, Richard, Jr., Hao, Desirée, Harris, Ronald, Hashemi, Sayed, He, Kai, Hendriks, Lizza, Huang, Chao, Ibrahim, Emad, Jain, Sharad, Johnson, Melissa, Jones, Benjamin, Jones, Monte, Juan Vidal, Óscar José, Juergens, Rosalyn, Kaderbhai, Courèche, Kastelijn, Elisabeth A (Lisanne), Keresztes, Roger, Kio, Ebenezer, Kokowski, Konrad, Konduri, Kartik, Kulkarni, Swati, Kuon, Jonas, Kurkjian, Carla, Labbé, Catherine, Lerner, Rachel, Lim, Farah, Madroszyk-Flandin, Anne, Marathe, Omkar, Martincic, Danko, McClay, Edward, McIntyre, Kristi, Mekhail, Tarek, Misino, Andrea, Molinier, Olivier, Morabito, Alessandro, Morócz, Éva, Müller, Veronika, Nagy, Tünde, Nguyen, Anthony V., Nidhiry, Emmanuel, Okazaki, Ian, Ortega-Granados, Ana Laura, Ostoros, Gyula, Oubre, David, Owen, Scott, Pachipala, Krishna, Park, David, Patel, Pareshkumar, Percent, Ivor, Pérol, Maurice, Peters, Solange, Piet, Berber, Planchard, David, Polychronis, Andreas, Aix, Santiago Ponce, Pons-Tostivint, Elvire, Popat, Sanjaykumar, Pulla, Mariano Provencio, Quantin, Xavier, Quéré, Gilles, Rafique, Noman, Ramaekers, Ryan, Reck, Martin, Reiman, Anthony, Reinmuth, Niels, Reynolds, Craig, Rodríguez-Abreu, Delvys, Romano, Gianpiero, Roque, Tammy, Salzberg, Matthew, Sanborn, Rachel, Sandiego, Sergio, Schaefer, Eric, Schreeder, Marshall, Seetharamu, Nagashree, Seneviratne, Lasika, Shah, Purvi, Shunyakov, Leonid, Slater, Dennis, Parra, Hector Soto, Stigt, Johannes, Stilwill, Joseph, Su, Jingdong, Surmont, Veerle, Swink, Alicia, Szalai, Zsuzsanna, Talbot, Toby, Garcia, Alvaro Taus, Theelen, Willemijn, Thompson, Jonathan, Tiseo, Marcello, Uprety, Dipesh, Uyeki, James, van der Leest, Kornelius Cor, Van Ho, Anthony, van Putten, John, Estévez, Sergio Vázquez, Veatch, Andrea, Vergnenègre, Alain, Ward, Patrick, Weise, Amy, Weiss, Matthias, Whitehurst, Matthew, Zai, Silvia, Zalcman, Gérard, Zuniga, Richard, Borghaei, H., de Marinis, F., Dumoulin, D., Reynolds, C., Theelen, W.S.M.E., Percent, I., Gutierrez Calderon, V., Johnson, M.L., Madroszyk-Flandin, A., Garon, E.B., He, K., Planchard, D., Reck, M., Popat, S., Herbst, R.S., Leal, T.A., Shazer, R.L., Yan, X., Harrigan, R., and Peters, S.

Published
2024
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38. F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax

Author

Choi, Byung-Ju, Hong, Jimin, Park, David Keetae, and Lee, Sang Wan

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

Despite recent advances in neural text generation, encoding the rich diversity in human language remains elusive. We argue that the sub-optimal text generation is mainly attributable to the imbalanced token distribution, which particularly misdirects the learning model when trained with the maximum-likelihood objective. As a simple yet effective remedy, we propose two novel methods, F^2-Softmax and MefMax, for a balanced training even with the skewed frequency distribution. MefMax assigns tokens uniquely to frequency classes, trying to group tokens with similar frequencies and equalize frequency mass between the classes. F^2-Softmax then decomposes a probability distribution of the target token into a product of two conditional probabilities of (i) frequency class, and (ii) token from the target frequency class. Models learn more uniform probability distributions because they are confined to subsets of vocabularies. Significant performance gains on seven relevant metrics suggest the supremacy of our approach in improving not only the diversity but also the quality of generated texts., Comment: EMNLP 2020

Published
2020

44. What and Where to Translate: Local Mask-based Image-to-Image Translation

Author

Cho, Wonwoong, Choi, Seunghwan, Park, Junwoo, Park, David Keetae, Qin, Tao, and Choo, Jaegul

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, image-to-image translation has obtained significant attention. Among many, those approaches based on an exemplar image that contains the target style information has been actively studied, due to its capability to handle multimodality as well as its applicability in practical use. However, two intrinsic problems exist in the existing methods: what and where to transfer. First, those methods extract style from an entire exemplar which includes noisy information, which impedes a translation model from properly extracting the intended style of the exemplar. That is, we need to carefully determine what to transfer from the exemplar. Second, the extracted style is applied to the entire input image, which causes unnecessary distortion in irrelevant image regions. In response, we need to decide where to transfer the extracted style. In this paper, we propose a novel approach that extracts out a local mask from the exemplar that determines what style to transfer, and another local mask from the input image that determines where to transfer the extracted style. The main novelty of this paper lies in (1) the highway adaptive instance normalization technique and (2) an end-to-end translation framework which achieves an outstanding performance in reflecting a style of an exemplar. We demonstrate the quantitative and qualitative evaluation results to confirm the advantages of our proposed approach.

Published
2019

45. Did COVID-19 Market Disruptions Disrupt Food Security? Evidence from Households in Rural Liberia and Malawi

Author

Aggarwal, Shilpa, Jeong, Dahyeon, Kumar, Naresh, Park, David S., Robinson, Jonathan, and Spearot, Alan

Subjects
COVID-19, market disruption, food security, cash transfers, Liberia, Malawi
Abstract

We quantify the effect of market disruptions due to COVID-19 on the lives of householdsin rural areas of Liberia and Malawi, utilizing panel data from phone surveys that were implemented as part of a randomized cash transfer experiment. The surveys began collection several months before the pandemic and have continued throughout it. The household survey included a consistent set of internationally accepted and validated questions on food security (the householddietary diversity score, the household hunger scale, and the food consumption score). In both countries, market activity was severely disrupted and we observe large declines in income among market vendors, but we find no evidence of declines in food security for households in the short run. Even though we observe no adverse effects of the lockdowns on food securityamong the control group, cash transfers improved dietary quality and quantity over the low levels observed at baseline.

Published
2020

46. Early Serial Echocardiographic and Ultrasonographic Findings in Critically Ill Patients With COVID-19

Author

Calhoun, Nicole, Herrick, Judy, Hoffman, Eric, McKillop, Amanda, Murthy, Kempapura, Smith, Michael, Zayed, Martha, De Souza, Lesley, Kindle, Ryan, Kozikowski, Lori-Ann, Ouellette, Scott, Thornton-Thompson, Sherell, Bolstad, Michael, Ciottone, Robert, Coviello, Brianna, Devilla, Arnaldo, Grafals, Ana, Higgins, Conor, Ottanelli, Carlo, Redman, Kimberly, Scaffidi, Douglas, Weingart, Alexander, Lewis, Nathaniel, Olson, Samantha, Ashok, Kiran, Brennan, Connery, Mehkri, Omar, Mitchell, Megan, Poynter, Bryan, Stanley, Nicholas, Lohuis, Caitlin ten, Caspers, Sean, Erikson, Heidi, Hendrickson, Audrey, Kaus, Olivia, Maruggi, Ellen, Scharber, Tyler, Tordsen, Walker, Aston, Valerie, Bowers, Robert, Jorgensen, Jeffrey, King, Jennifer, Ali, Harith, Rothman, Richard E., Nair, Rahul, Chen, Jen-Ting, Karow, Sarah, Robart, Emily, Maldonado, Paulo Nunes, Khan, Maryiam, So, Preston, Schwartz, Elizabeth, So, Madison, Weigand, Michael, Luong, Andrea, Martinez, Jesus, Huynh, Bao, Ibrahim, Habiba, Villanueva-Vargas, Cynthia, Jung, Haeun, Villanueva-Vargas, Juliana, Quadri, Suha, Gordon, Alexandra Jun, Levitt, Joe, Perez, Cynthia, Visweswaran, Anita, Roque, Jonasel, Rivera, Adreanne, Frankel, Trevor, Goff, Jennifer, Huynh, David, Jensen, Kelly, Driver, Conner, Chambers, Ian, Nassar, Paul, Stout, Lori, Sibenaller, Zita, Walter, Alicia, Mares, Jasmine, Olson, Logan, Clinansmith, Bradley, Gershengorn, Hayley, Rivas, Carolina, McSpadden, E.J., Truscon, Rachel, Kaniclides, Anne, Thomas, Lara, Bielak, Ramsay, Valvano, Weronika Damek, Fong, Rebecca, Fitzsimmons, William J., Blair, Christopher, Valesano, Andrew, Baker, Leigh, Gilbert, Julie, Crider, Christine D., Steinbock, Kyle A., Paulson, Thomas C., Anderson, Layla A., Kampe, Christy, Johnson, Jakea, Short, Laura L., Ezzell, Lauren J., Whitsett, Margaret E., McHenry, Rendie E., Hargrave, Samarian J., Blair, Marica, Luther, Jennifer L., Pulido, Claudia Guevara, Peterson, Bryan P.M., LaRose, Mary, Landreth, Leigha, Hicks, Madeline, Parks, Lisa, Bongu, Jahnavi, McDonald, David, Cass, Candice, Seiler, Sondra, Park, David, Hink, Tiffany, Wallace, Meghan, Burnham, Carey-Ann, Arter, Olivia G., Lanspa, Michael J., Dugar, Siddharth P., Prigmore, Heather L., Boyd, Jeremy S., Rupp, Jordan D., Lindsell, Chris J., Rice, Todd W., Qadir, Nida, Lim, George W., Shiloh, Ariel L., Dieiev, Vladyslav, Gong, Michelle N., Fox, Steven W., Hirshberg, Eliotte L., Khan, Akram, Kornfield, James, Schoeneck, Jacob H., Macklin, Nicholas, Files, D.Clark, Gibbs, Kevin W., Prekker, Matthew E., Parsons-Moss, Daniel, Bown, Mikaele, Olsen, Troy D., Knox, Daniel B., Cirulis, Meghan M., Duggal, Abhijit, Tenforde, Mark W., Patel, Manish M., Self, Wesley H., and Brown, Samuel M.

Published
2023
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49. Abstract 15659: Bupropion Overdose Requiring Veno-Arterial Extracorporeal Membrane Oxygenation

Author

Golob, Stephanie, Ting, Peter, Pires, Kyle, Soetanto, Vanessa, Pagano, Benjamin, Wan, Kah-Loon, Hall, Sylvie, Bianco, Lauren, Hawkins, Joseph, Schimmer, Hannah, Yunaev, Victoria, Howe, William, Landau, Jessica, Greco, Allison, Postelnicu, Radu, Harari, Rafael, Smith, Silas, Horowitz, James, Park, David, Keller, Norma, Bangalore, Sripal, Katz, Stuart, and Alviar, Carlos

Published
2023
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