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1. KCRL: A Prior Knowledge Based Causal Discovery Framework With Reinforcement Learning

   Hasan, Uzma; Gani, Md Osman (April 2022, Proceedings of Machine Learning Research)

   Causal discovery is an important problem in many sciences that enables us to estimate causal relationships from observational data. Particularly, in the healthcare domain, it can guide practitioners in making informed clinical decisions. Several causal discovery approaches have been developed over the last few decades. The success of these approaches mostly relies on a large number of data samples. In practice, however, an infinite amount of data is never available. Fortunately, often we have some prior knowledge available from the problem domain. Particularly, in healthcare settings, we often have some prior knowledge such as expert opinions, prior RCTs, literature evidence, and systematic reviews about the clinical problem. This prior information can be utilized in a systematic way to address the data scarcity problem. However, most of the existing causal discovery approaches lack a systematic way to incorporate prior knowledge during the search process. Recent advances in reinforcement learning techniques can be explored to use prior knowledge as constraints by penalizing the agent for their violations. Therefore, in this work, we propose a framework KCRL that utilizes the existing knowledge as a constraint to penalize the search process during causal discovery. This utilization of existing information during causal discovery reduces the graph search space and enables a faster convergence to the optimal causal mechanism. We evaluated our framework on benchmark synthetic and real datasets as well as on a real-life healthcare application. We also compared its performance with several baseline causal discovery methods. The experimental findings show that penalizing the search process for constraint violation yields better performance compared to existing approaches that do not include prior knowledge. 

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2. ALT-GEN: Benchmarking Table Union Search using Large Language Models

   Pal, Koyena; Khatiwada, Ammod; Shraga, Roee; Miller, Renée J (August 2024, PVLDB Workshop on Tabular Data Analysis (TaDA))

   We consider the table union search problem which has emerged as an important data discovery problem in data lakes. Semantic problems like table union search cannot be benchmarked using only synthetic data. Our current methods for creating benchmarks for this problem involve the manual curation and human label- ing of real data. These methods are not robust or scalable and perhaps more importantly, it is not clear how comprehensive the created benchmarks are. We propose to use generative AI models to create structured data benchmarks for table union search. We present a novel method for using generative models to create ta- bles with specied properties. Using this method, we create a new benchmark containing pairs of tables that are both unionable and non-unionable, but related. We use this benchmark to provide new insights into the strengths and weaknesses of existing methods. We evaluate state-of-the-art table union search methods over both existing benchmarks and our new benchmarks. We also present and evaluate a new table search method based on large language models over all benchmarks. We show that the new benchmarks are more challenging for all methods than hand-curated benchmarks. We examine why this is the case and show that our new methodology for creating benchmarks permits more detailed analysis and com- parison of methods. We discuss how our generation method (and benchmarks created using it) sheds more light into the successes and failures of table union search methods sparking new insights that can help advance the eld. We also discuss how our benchmark generation methodology can be applied to other semantic problems including entity matching and related table search. 

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3. SpecSafe: detecting cache side channels in a speculative world

   https://doi.org/10.1145/3485506  

   Brotzman, Robert; Zhang, Danfeng; Kandemir, Mahmut Taylan; Tan, Gang (October 2021, Proceedings of the ACM on Programming Languages)

   The high-profile Spectre attack and its variants have revealed that speculative execution may leave secret-dependent footprints in the cache, allowing an attacker to learn confidential data. However, existing static side-channel detectors either ignore speculative execution, leading to false negatives, or lack a precise cache model, leading to false positives. In this paper, somewhat surprisingly, we show that it is challenging to develop a speculation-aware static analysis with precise cache models: a combination of existing works does not necessarily catch all cache side channels. Motivated by this observation, we present a new semantic definition of security against cache-based side-channel attacks, called Speculative-Aware noninterference (SANI), which is applicable to a variety of attacks and cache models. We also develop SpecSafe to detect the violations of SANI. Unlike other speculation-aware symbolic executors, SpecSafe employs a novel program transformation so that SANI can be soundly checked by speculation-unaware side-channel detectors. SpecSafe is shown to be both scalable and accurate on a set of moderately sized benchmarks, including commonly used cryptography libraries. 

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4. A Phenomenological Study of Expert Problem Solving

   https://doi.org/10.18260/1-2--45703  

   De_Rosa, Alexander; Reed, Teri (April 2024, ASEE Conferences)

   I initially became interested in knowledge transfer after observing my students’ general inability to use mathematical knowledge and skills in an applied (engineering) context. My personal belief was that the students should have an understanding of basic basic mathematical concepts, like integration, and be able to use them correctly to solve problems. Clearly, something was missing in my students’ understanding or perhaps memory that was causing them problems in this regard. In my initial work on knowledge transfer, I found that many students did not even recognize the need to transfer knowledge and for example, to integrate to solve a problem framed in an engineering context unless they were prompted to do so. Concerned by this troubling observation, coupled with my belief that engineers should be able to both understand and apply mathematical concepts in their coursework and careers, I determined to investigate the cause of the problem and, if possible, evidence a potential solution to help students transfer mathematical knowledge into an applied (engineering) context. In this study, I examine an expert (faculty) approach to problem solving using a semi-structured, think-aloud interview protocol coupled with a thorough thematic analysis for phenomenological themes. This analysis, grounded in an existing framework of knowledge transfer, provides a rich, thick description of the knowledge transfer, and problem solving process employed by the faculty expert and serves as a useful comparative case against which student approaches to problem solving and knowledge transfer can be judged. Important findings of this study relate to the extensive use of reflective and evaluative practices employed by the faculty member at all stages of the problem solving process. These internal checks and balances are rarely observed among novice problem solvers and perhaps represent behaviors that we, as educators, should seek to impart in our students if they are to become more adaptable engineers who are better equipped to transfer their knowledge and skills across a range of contexts. 

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5. Harmonizing Speculative and Non-Speculative Execution in Architectures for Ordered Parallelism

   https://doi.org/10.1109/MICRO.2018.00026  

   Jeffrey, Mark C.; Ying, Victor A.; Subramanian, Suvinay; Lee, Hyun Ryong; Emer, Joel; Sanchez, Daniel (October 2018, Proceedings of the 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-51))

   Multicore systems should support both speculative and non-speculative parallelism. Speculative parallelism is easy to use and is crucial to scale many challenging applications, while non-speculative parallelism is more efficient and allows parallel irrevocable actions (e.g., parallel I/O). Unfortunately, prior techniques are far from this goal. Hardware transactional memory (HTM) systems support speculative (transactional) and non-speculative (non-transactional) work, but lack coordination mechanisms between the two, and are limited to unordered parallelism. Prior work has extended HTMs to avoid the limitations of speculative execution, e.g., through escape actions and open-nested transactions. But these mechanisms are incompatible with systems that exploit ordered parallelism, which parallelize a broader range of applications and are easier to use. We contribute two techniques that enable seamlessly composing and coordinating speculative and non-speculative work in the context of ordered parallelism: (i) a task-based execution model that efficiently coordinates concurrent speculative and non-speculative ordered tasks, allowing them to create tasks of either kind and to operate on shared data; and (ii) a safe way for speculative tasks to invoke software-managed speculative actions that avoid hardware version management and conflict detection. These contributions improve efficiency and enable new capabilities. Across several benchmarks, they allow the system to dynamically choose whether to execute tasks speculatively or non-speculatively, avoid needless conflicts among speculative tasks, and allow speculative tasks to safely invoke irrevocable actions. 

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