Making Quantum Computing Open: Lessons from Open-Source Projects

Quantum computing (QC) is an emerging computing paradigm with potential to revolutionize the field of computing. QC is a field that is quickly developing globally and has high barriers of entry. In this paper we explore both successful contributors to the field as well as wider QC community with the goal of understanding the backgrounds and training that helped them succeed. We gather data on 148 contributors to open-source quantum computing projects hosted on GitHub and survey 46 members of QC community. Our findings show that QC practitioners and enthusiasts have diverse backgrounds, with most of them having a PhD and trained in physics or computer science. We observe a lack of educational resources on quantum computing. Our goal for these findings is to start a conversation about how best to prepare the next generation of QC researchers and practitioners

Solution uniqueness and noise impact in a static spectropolarimeter based on birefringent prisms for full Stokes parameter retrieval

peer reviewedAn innovative model of a static spectropolarimeter able to cover the entire Stokes vector is discussed. The optical layout is based on a birefringent modulator formed by two antiparallel prisms stuck together with the help of an intermediary part of the same material. This optical model has the advantage of being extremely compact. It avoids any movable parts or rotating components. By its architecture, the device induces a complete modulation on the vertical direction of any incoming polarized light, facilitating the determination of the entire Stokes vector through a single measurement. Because the modulation is also wavelength-dependent, spectral dependencies of the polarization states can be derived. The behavior of the model was first investigated in noise-free conditions. The existence of a unique solution was proven in the absence of noise and for any Stokes vector configuration. Under noisy conditions, the uncertainty on the Stokes parameters and the efficiency of the modulation scheme were evaluated as a function of the analyzer’s angle and for two different configurations of the modulator. The simulations show that an almost ideal efficiency is reachable, qualifying the concept for the high-precision measurement of the polarization

Incorporating External Knowledge through Pre-training for Natural Language to Code Generation

Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa. The code and resources are available at https://github.com/neulab/external-knowledge-codegen.Comment: Accepted by ACL 202

Going farther together:the impact of social capital on sustained participation in open source

Sustained participation by contributors in open-source software is critical to the survival of open-source projects and can provide career advancement benefits to individual contributors. However, not all contributors reap the benefits of open-source participation fully, with prior work showing that women are particularly underrepresented and at higher risk of disengagement. While many barriers to participation in open-source have been documented in the literature, relatively little is known about how the social networks that open-source contributors form impact their chances of long-term engagement. In this paper we report on a mixed-methods empirical study of the role of social capital (i.e., the resources people can gain from their social connections) for sustained participation by women and men in open-source GitHub projects. After combining survival analysis on a large, longitudinal data set with insights derived from a user survey, we confirm that while social capital is beneficial for prolonged engagement for both genders, women are at disadvantage in teams lacking diversity in expertise.\u3cbr/\u3

Studies on Romanian prehistoric bronze artifacts at ATOMKI Debrecen

Compositional studies on ancient bronze artefacts and on the possible provenance of their copper and tin contents are performed all around the world. For South-Eastern Europe, Balkan Bronze Age artifacts have been studied in relation to the compositional pattern of ancient mines from Serbia and Bulgaria. We decided to start a similar investigation on Bronze Age bronze artifacts found on Romanian territory and to compare the results with already published data on old regional mines – Austria, ..

EnTagRec(++): An enhanced tag recommendation system for software information sites

Software engineers share experiences with modern technologies using software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. Tags help to improve the organization of questions and simplify the browsing of questions for users. However, tags assigned to objects tend to be noisy and some objects are not well tagged. For instance, 14.7% of the questions that were posted in 2015 on Stack Overflow needed tag re-editing after the initial assignment. To improve the quality of tags in software information sites, we propose EnTagRec++, which is an advanced version of our prior work EnTagRec. Different from EnTagRec, EnTagRec++ does not only integrate the historical tag assignments to software objects, but also leverages the information of users, and an initial set of tags that a user may provide for tag recommendation. We evaluate its performance on five software information sites, Stack Overflow, Ask Ubuntu, Ask Different, Super User, and Freecode. We observe that even without considering an initial set of tags that a user provides, it achieves Recall@5 scores of 0.821, 0.822, 0.891, 0.818 and 0.651, and Recall@10 scores of 0.873, 0.886, 0.956, 0.887 and 0.761, on Stack Overflow, Ask Ubuntu, Ask Different, Super User, and Freecode, respectively. In terms of Recall@5 and Recall@10, averaging across the 5 datasets, it improves upon TagCombine, which is the prior state-of-the-art approach, by 29.3% and 14.5% respectively. Moreover, the performance of our approach is further boosted if users provide some initial tags that our approach can leverage to infer additional tags: when an initial set of tags is given, Recall@5 is improved by 10%