An Error-Correcting Line Code for a HEP Rad-Hard Multi-GigaBit Optical Link

This paper presents a line encoding scheme designed for the GBT ASIC, a transceiver under development for a multigigabit optical link upgrade of the TTC system. A general overview of issues related to optical links placed in radiation environments is given, and the required properties of the line code discussed. A scheme that preserves the DC-balance of the line and allows forward error correction is proposed. It is implemented through the concatenation of scrambling, a Reed-Solomon error-correction scheme and the addition of an error-tolerant DC-balanced header. The properties of the code are verified for two different interleaving options, which achieve different error correction capability at different implementation costs. One of the two options was implemented in a fully digital ASIC fabricated in a 0.13 μm CMOS technology, and ASIC implementation details and test results are reported

Editorial: Special Issue on Deep Learning for Data Quality

This editorial summarizes the content of the Special Issue on Deep Learning for Data Quality of the Journal of Data and Information Quality (JDIQ)

QATCH: Benchmarking SQL-centric tasks with Table Representation Learning Models on Your Data

Table Representation Learning (TRL) models are commonly pre-trained on large open-domain datasets comprising millions of tables and then used to address downstream tasks. Choosing the right TRL model to use on proprietary data can be challenging, as the best results depend on the content domain, schema, and data quality. Our purpose is to support end-users in testing TRL models on proprietary data in two established SQL-centric tasks, i.e., Question Answering (QA) and Semantic Parsing (SP). We present QATCH (Query-Aided TRL Checklist), a toolbox to highlight TRL models' strengths and weaknesses on relational tables unseen at training time. For an input table, QATCH automatically generates a testing checklist tailored to QA and SP. Checklist generation is driven by a SQL query engine that crafts tests of different complexity. This design facilitates inherent portability, allowing the checks to be used by alternative models. We also introduce a set of cross-task performance metrics evaluating the TRL model's performance over its output. Finally, we show how QATCH automatically generates tests for proprietary datasets to evaluate various state-of-the-art models including TAPAS, TAPEX, and CHATGPT

Neuroendocrine bronchial and thymic tumors: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up

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Cleaning data with Llunatic

Data cleaning (or data repairing) is considered a crucial problem in many database-related tasks. It consists in making a database consistent with respect to a given set of constraints. In recent years, repairing methods have been proposed for several classes of constraints. These methods, however, tend to hard-code the strategy to repair conflicting values and are specialized toward specific classes of constraints. In this paper, we develop a general chase-based repairing framework, referred to as Llunatic, in which repairs can be obtained for a large class of constraints and by using different strategies to select preferred values. The framework is based on an elegant formalization in terms of labeled instances and partially ordered preference labels. In this context, we revisit concepts such as upgrades, repairs and the chase. In Llunatic, various repairing strategies can be slotted in, without the need for changing the underlying implementation. Furthermore, Llunatic is the first data repairing system which is DBMS-based. We report experimental results that confirm its good scalability and show that various instantiations of the framework result in repairs of good quality

Synchronous Phase Shift at LHC

The electron cloud in vacuum pipes of accelerators of positively charged particle beams causes a beam energy loss which could be estimated from the synchronous phase. Measurements done with beams of 75 ns, 50 ns, and 25 ns bunch spacing in the LHC for some fills in 2010 and 2011 show that the average energy loss depends on the total beam intensity in the ring. Later measurements during the scrubbing run with 50 ns beams show the reduction of the electron cloud due to scrubbing. Finally, measurements of the individual bunch phase give us information about the electron cloud build-up inside the batch and from batch to batch.Comment: Presented at ECLOUD'12: Joint INFN-CERN-EuCARD-AccNet Workshop on Electron-Cloud Effects, La Biodola, Isola d'Elba, Italy, 5-9 June 201

Similarity Measures For Incomplete Database Instances

The problem of comparing database instances with incompleteness is prevalent in applications such as analyzing how a dataset has evolved over time (e.g., data versioning), evaluating data cleaning solutions (e.g., compare an instance produced by a data repair algorithm against a gold standard), or comparing solutions generated by data exchange systems (e.g., universal vs core solutions). In this work, we propose a framework for computing similarity of instances with labeled nulls, even of those without primary keys. As a side-effect, the similarity score computation returns a mapping between the instances’ tuples, which explains the score. We demonstrate that computing the similarity of two incomplete instances is NP-hard in the instance size in general. To be able to compare instances of realistic size, we present an approximate PTIME algorithm for instance comparison. Experimental results demonstrate that the approximate algorithm is up to three orders of magnitude faster than an exact algorithm for the computation of the similarity score, while the difference between approximate and exact scores is always smaller than 1%

Characteristics of the patients referred to a Hypertension Unit between 1989 and 2003

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