Boolean Statements Research Articles

SAT-Based Exact Modulo Scheduling Mapping for Resource-Constrained CGRAs

Coarse-Grain Reconfigurable Arrays (CGRAs) represent emerging low-power architectures designed to accelerate Compute-Intensive Loops (CILs). The effectiveness of CGRAs in providing acceleration relies on the quality of mapping: how efficiently the CIL is compiled onto the platform. State-of-the-Art (SoA) compilation techniques utilize modulo scheduling to minimize the Iteration Interval (II) and use graph algorithms like Max-Clique Enumeration to address mapping challenges. Our work approaches the mapping problem through a satisfiability (SAT) formulation. We introduce the Kernel Mobility Schedule (KMS), an ad hoc schedule used with the Data Flow Graph and CGRA architectural information to generate Boolean statements that, when satisfied, yield a valid mapping. Experimental results demonstrate SAT-MapIt outperforming SoA alternatives in almost 50% of explored benchmarks. Additionally, we evaluated the mapping results in a synthesizable CGRA design and emphasized the runtime metrics trends, i.e., energy efficiency and latency, across different CILs and CGRA sizes. We show that a hardware-agnostic analysis performed on compiler-level metrics can optimally prune the architectural design space, while still retaining Pareto-optimal configurations. Moreover, by exploring how implementation details impact cost and performance on real hardware, we highlight the importance of holistic software-to-hardware mapping flows, as the one presented herein.

Developing a Word Lexicon from Electronic Health Records for Natural Language Processing Analysis of Free-Text Reports for Patients with Venous Thromboembolism.

Patients with gliomas, particularly glioblastomas (GBM) have a higher risk of developing venous thromboembolism (VTE), including both pulmonary embolism (PE) or deep vein thrombosis (DVT), both correlating with overall survival (OS) and potential biological signaling relevance for analysis of the tumor state in conjunction with evolving biomarkers. Artificial intelligence (AI) approaches thar employ VTE as a clinical feature in brain tumor patients is understudied due to the difficulty in analyzing unstructured clinical data in electronic health records (EHR). Data expansion by creating a word lexicon for natural language processing (NLP) of free-text clinical reports will allow exposure of VTE for classification of large-scale data sets, NLP and AI. Patients with a pathologic diagnosis of GBM (2005-2021) were extracted from EHR and screened for the development of VTE based on radiology free-text reports - ultrasound (US) of extremities and Computed Topography-pulmonary angiogram (CT). Language data were collected manually and verified computationally using radiology report text filtration. Common phrases and words employed were collected to generate a lexicon for future computational analysis. Kaplan-Meier survival analyses were generated for VTE in relation OS and progression-free survival (PFS). A total of 163 patients (mean age = 56.1 ± 12.1, 65% male) were included, 48 (29.4%) were screened for VTE following clinical suspicion on history or physical exam, and 15 (9.2%) were found to have a VTE. Screening methods were US 83.3% (40) or CT 13.9% (6), or both 4.6% (2). 28.6% (12) of US and 37.5% (3) of CT resulted in a positive VTE diagnosis. Terms that were only detected in patients with VTE were for US: "complete" 44%, "thrombosis detected" (8.3%), "occlusive" (16%), "partial" (16%), "critical" (25%), "residual" (8.3%) and for CT: "critical" (67%) and "pulmonary emboli" (33%). Words used in free-text reports showed a high degree of inter-reporter variability. The words "partial", "residual", "complete", "critical" or "clotted" when used as an "or" Boolean statement applied to US and CT radiology reports identified ∼93% of the patients with VTE. Patients with VTE had worse OS (median 14 vs. 19 months, p = .0189) and PFS (median 6 vs. 9 months, p = .0239) compared to patients without VTE indicating underlying pathology associated with both prevalence of VTE and tumor burden and the need to incorporate VTE as a clinical feature in large scale data analyses. US and CT yield a similar percentage of positive VTE findings while employing different terms to characterize VTE. We confirm that patients with VTE have poorer outcomes and present a word combination that identifies patients with VTE in large-scale radiology report data. Further research will focus on validation in other datasets, NLP approaches to optimize and data aggregation with laboratory data and large-scale omics panels to expose thrombosis as a clinical feature for biomarker analysis.

CLRM-04 DEVELOPING A WORD LEXICON FOR VENOUS THROMBOEMBOLISM DATA ANNOTATION USING RADIOLOGY REPORTS IN GLIOBLASTOMA PATIENTS - LINKING LARGE-SCALE DATA SETS TO EXPAND CLINICAL FEATURES FOR AI

Abstract Patients with gliomas, particularly glioblastomas (GBM), have a higher risk of developing venous thromboembolism (VTE), correlating with overall survival (OS). Artificial intelligence (AI) approaches that employ VTE as a clinical feature in brain tumor patients is understudied due to the difficulty in analyzing electronic health records (EHR). Data expansion by creating a word lexicon for natural language processing (NLP) of free-text clinical reports will allow exposure of VTE for the classification of large-scale data sets, NLP, and AI. Patients with a pathologic diagnosis of GBM (2005-2021) were screened for the development of VTE based on radiology free-text reports (ultrasound (US) of extremities and Computed Topography-pulmonary angiogram (CT)). Kaplan-Meier survival analyses about overall survival (OS) and progression-free survival (PFS) were generated for VTE. 163 patients (mean age = 56.1 ± 12.1, 65% male) were included, 48 (29.4%) were screened for VTE following clinical suspicion on history or physical exam, and 15 (9.2%) were found to have a VTE. Screening methods were ultrasound 83.3% (40) or CT 13.9% (6), or both 4.6% (2). 28.6% (12) of US and 37.5% (3) of CT resulted in a positive VTE diagnosis. The words “partial”, “residual”, “complete”, “critical” or “clotted” when used as an “or” boolean statement applied to US and CT radiology reports identified ~93% of the patients with VTE. Patients with VTE had worse OS (median 14 vs. 19 months, p = .0189) and PFS (median 6 vs. 9 months, p = .0239) than patients without VTE, indicating underlying pathology associated with both prevalence of VTE and tumor burden. US and CT yield a similar percentage of positive VTE findings while employing different terms to characterize VTE. We confirm that patients with VTE have poorer outcomes and present a word combination that identifies patients with VTE in large-scale radiology report data.

Retrieval of very large numbers of items in the <i>Web of Science</i>: an exercise to develop accurate search strategies

The Web of Science interface counts at most 100,000 retrieved items from a single query. If the query results in a dataset containing more than 100,000 items the number of retrieved items is indicated as >100,000. The problem studied here is how to find the exact number of items in a query that leads to more than 100,000 items. One way to achieve this objective is presented. The retrieval of the entire scientific production from the United States in a specific year (2007) is counted and an advanced search strategy is designed. Different sections of items can be retrieved using the Source field of the database. A Boolean statement was created with the aim of eliminating overlapped sections and improving the accuracy of this search strategy.

The algebra of Bonferroni bounds: discrete tubes and extensions

Bonferroni, or inclusion-exclusion, bounds and identities have a rich history. They concern the indicator function, and hence the probability content, of the union of sets. In previous work, the authors defined a discrete tube which yields upper and lower bounds which are at least as tight as the standard bounds obtained by truncating inclusion-exclusion identities at particular depths. Here, some connections to other fields are made, based particularly on the algebra of indicator functions. These leads to the consideration of the complexity of more general Boolean statements.

Concurrent Error Detection for Group Look-ahead Binary Adders

This paper presents an evaluation of the relative merits of two schemes for performing concurrent error detection in group look-ahead adders. One of the schemes is a residue mod 3 check and the other is a parity prediction check. The Boolean statements that define the operation of group look-ahead adders, concurrent error detection and the Boolean difference serve as background for interpreting the results of the study. The Boolean difference is a tool for calculating the of elements in a logical network by error-checking schemes. Some weaknesses in prior studies of coverage calculation are brought to light. Tables showingt he number of circuit elements in the various portions of adder and error-checking circuits are given. It is shown that the residue mod 3 check adder is not economical unless the addition operands are already provided with the mod 3 check bits. Thus, a worthwhile comparison of the checking schemes should not proceed without considering the overall data flow checking strategy. In machine organizations with three or more data transfer checks, the parity-checked adder seems to offer a cost advantage.

Mobidac, new data system

This chapter explains analog and digital signals and their interconversion. Bridging the gap between the analog and the digital at some point is important if one wants to market the latest gadget. The chapter discusses how to make the analog-to-digital leap. Analog means a continuously variable signal. It means that the item being measured can be chopped up into infinite little pieces over time. The simplest form of digital is two states: It's either there or not. Digital signals could be used to represent Boolean statements, one level indicating true and the other indicating false. Boolean logic is based on a digital representation of the world. Digital processing speeds eventually increased enough to take over almost all applications. An analog signal is converted to digital by chopping it up into chunks at predetermined time intervals. (This chopping is called the sample rate. The faster the sample rate, the higher the frequency that can be digitized.) Then the signal is measured at that point in time and assigned a digital value, which is called sampling the signal. Digital signals (typically represented as 1 or 0) can be crammed together to indicate different levels of analog. A single digit can indicate two levels. To convert a digital signal back to analog, the circuit has to simulate the analog signal it represents. This always requires some kind of filtering. There are many ways to convert digital to analog. One is by pulse width modulation (PWM). The digital PWM is fed into a low-pass filter that removes the switching frequency of the signal, essentially leaving an analog signal.