Political Dynasties in Canada
Alex B. Rivard
Jean-François Godbout
Marc André Bodet
Using a unique dataset of legislators' electoral and biographical data in the Canadian provinces of Ontario, Quebec, New Brunswick, Nova Sco… (see more)tia and the federal parliament, this article analyses the extent to which family dynasties affected the career development of legislators since the mid-18th century. We find that the prevalence of dynasties was higher in provincial legislatures than it was in the federal parliament, that the number of dynasties in the Senate increased until the mid-20th century, and that the proportion of dynastic legislators at the subnational level was similar to the numbers seen in the United Kingdom during the early 19th century. Our results confirm the existence of a clear career benefit in terms of cabinet and senate appointments. In contrast to the American case and in line with the United Kingdom experience, we find no causal relationship between a legislator's tenure length and the presence of a dynasty.
2024-06-03
Government and Opposition (published)
doi.org
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages
Jiayi Wang
David Ifeoluwa Adelani
Sweta Agrawal
Marek Masiak
Ricardo Rei
Eleftheria Briakou
Marine Carpuat
Xuanli He
Sofia Bourhim
Andiswa Bukula
Muhidin A. Mohamed
Temitayo Olatoye
Tosin Adewumi
Hamam Mokayed
Christine Mwase
Wangui Kimotho
Foutse Yuehgoh
Aremu Anuoluwapo
Jessica Ojo
Shamsuddeen Hassan Muhammad … (see 41 more)
Salomey Osei
Abdul-Hakeem Omotayo
Chiamaka Ijeoma Chukwuneke
Perez Ogayo
Oumaima Hourrane
Salma El Anigri
Lolwethu Ndolela
Thabiso Mangwana
Shafie Abdi Mohamed
Hassan Ayinde
Ayinde Hassan
Oluwabusayo Olufunke Awoyomi
Lama Alkhaled
sana Sabah al-azzawi
Naome Etori
Millicent Ochieng
Clemencia Siro
Samuel Njoroge
Njoroge Kiragu
Eric Muchiri
Wangari Kimotho
Lyse Naomi Wamba
Daud Abolade
Simbiat Ajao
Iyanuoluwa Shode
Ricky Macharm
Ruqayya Nasir Iro
Saheed Salahudeen Abdullahi
Stephen Moore
Bernard Opoku
Zainab Akinjobi
Abeeb Afolabi
Nnaemeka Casmir Obiefuna
Onyekachi Ogbu
Sam Brian
Sam Ochieng’
Verrah Akinyi Otiende
CHINEDU EMMANUEL MBONU
Toadoum Sari Sakayo
Yao Lu
Pontus Stenetorp
Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measur… (see more)ing this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).
2024-06-01
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (published)
doi.org
arxiv.org
Attention as a Hypernetwork
Simon Schug
Seijin Kobayashi
Yassir Akram
João Sacramento
Razvan Pascanu
Transformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during t… (see more)raining, but whose compositions have not. What mechanisms underlie this ability for compositional generalization? By reformulating multi-head attention as a hypernetwork, we reveal that a composable, low-dimensional latent code specifies key-query specific operations. We find empirically that this latent code is predictive of the subtasks the network performs on unseen task compositions, revealing that latent codes acquired during training are reused to solve unseen problem instances. To further examine the hypothesis that the intrinsic hypernetwork of multi-head attention supports compositional generalization, we ablate whether making the hypernetwork-generated linear value network nonlinear strengthens compositionality. We find that this modification improves compositional generalization on abstract reasoning tasks. In particular, we introduce a symbolic version of the Raven's Progressive Matrices human intelligence test, which gives us precise control over the problem compositions encountered during training and evaluation. We demonstrate on this task how scaling model size and data enables compositional generalization in transformers and gives rise to a functionally structured latent space.
2024-06-01
arXiv (published)
doi.org
arxiv.org
Better entity matching with transformers through ensembles
Jwen Fai Low
Benjamin Fung
Pulei Xiong
2024-06-01
Knowledge-Based Systems (published)
doi.org
Caffeine induces age-dependent increases in brain complexity and criticality during sleep
Philipp Thölke
Maxine Arcand-Lavigne
Tarek Lajnef
Sonia Frenette
Julie Carrier
Karim Jerbi
Caffeine is the most widely consumed psychoactive stimulant worldwide. Yet important gaps persist in understanding its effects on the brain,… (see more) especially during sleep. We analyzed sleep EEG in 40 subjects, contrasting 200mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. Being most prominent during non-REM sleep, these results suggest that caffeine shifts the brain towards a critical regime and more diverse neural dynamics. Interestingly, this was more pronounced in younger adults (20-27 years) compared to middle-aged participants (41-58 years) whose sleep brain dynamics were less affected by caffeine. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance provides novel insights into the effects caffeine has on the sleeping brain.
2024-06-01
bioRxiv (preprint)
doi.org
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Haorui Wang
Marta Skreta
Cher Tian Ser
Wenhao Gao
Lingkai Kong
Felix Streith-Kalthoff
Chenru Duan
Yuchen Zhuang
Yue Yu
Yanqiao Zhu 0001
Yuanqi Du
Alan Aspuru-Guzik
Kirill Neklyudov
Chao Zhang
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectiv… (see more)es can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by performing random mutations and crossovers, leading to a large number of expensive objective evaluations. In this work, we ameliorate this shortcoming by incorporating chemistry-aware Large Language Models (LLMs) into EAs. Namely, we redesign crossover and mutation operations in EAs using LLMs trained on large corpora of chemical information. We perform extensive empirical studies on both commercial and open-source models on multiple tasks involving property optimization, molecular rediscovery, and structure-based drug design, demonstrating that the joint usage of LLMs with EAs yields superior performance over all baseline models across single- and multi-objective settings. We demonstrate that our algorithm improves both the quality of the final solution and convergence speed, thereby reducing the number of required objective evaluations. Our code is available at http://github.com/zoom-wang112358/MOLLEO
2024-06-01
arXiv (published)
doi.org
arxiv.org
Evaluating In-Context Learning of Libraries for Code Generation
Arkil Patel
Siva Reddy
Dzmitry Bahdanau
Pradeep Dasigi
2024-06-01
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (published)
doi.org
arxiv.org
Immunotherapeutic targeting of surfaceome heterogeneity in AML.
Marie-Eve Bordeleau
Éric Audemard
Arnaud Metois
Louis Theret
Véronique Lisi
Azer Farah
Jean-Francois Spinella
Jalila Chagraoui
Ossama Moujaber
Léo Aubert
Banafsheh Khakipoor
Laure Mallinger
Isabel Boivin
Nadine Mayotte
Azadeh Hajmirza
Eric Bonneil
Francois Béliveau
Sybille Pfammatter
Albert Feghaly
Geneviève Boucher … (see 9 more)
Patrick Gendron
Pierre Thibault
Frederic Barabe
Sébastien Lemieux
Guillaume Richard-Carpentier
Josée Hébert
Vincent-Philippe Lavallee
Philippe Roux
Guy Sauvageau
2024-06-01
Cell Reports (published)
doi.org
Implementation of a Global Pediatric Trauma Course in an Upper Middle–Income Country: A Pilot Study
Abbie Naus
Madeleine Carroll
Ayla Gerk
David P. Mooney
Natalie L. Yanchar
Julia Ferreira
Dan Poenaru
Karen E. Gripp
Caroline Ouellet
Fabio Botelho
2024-06-01
Journal of Surgical Research (published)
doi.org
A machine learning pipeline for automated insect monitoring
Aditya Jain
Fagner Cunha
M. J. Bunsen
L. Pasi
Anna Viklund
Maxim Larrivée
David Rolnick
Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosyste… (see more)m services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.
2024-06-01
arXiv (published)
doi.org
arxiv.org
Methods, Applications, and Directions of Learning-to-Rank in NLP Research
Justin Lee
Gabriel Bernier-Colborne
Tegan Maharaj
Sowmya Vajjala
Learning-to-rank (LTR) algorithms aim to order a set of items according to some criteria. They are at the core of applications such as web s… (see more)earch and social media recommendations, and are an area of rapidly increasing interest, with the rise of large language models (LLMs) and the widespread impact of these technologies on society. In this paper, we survey the diverse use cases of LTR methods in natural language processing (NLP) research, looking at previously under-studied aspects such as multilingualism in LTR applications and statistical significance testing for LTR problems. We also consider how large language models are changing the LTR landscape. This survey is aimed at NLP researchers and practitioners interested in understanding the formalisms and best practices regarding the application of LTR approaches in their research.
2024-06-01
Findings of the Association for Computational Linguistics: NAACL 2024 (published)
doi.org
"One-Size-Fits-All"? Examining Expectations around What Constitute"Fair"or"Good"NLG System Behaviors
Li Lucy
Su Lin Blodgett
Milad Shokouhi
Hanna Wallach
Alexandra Olteanu
Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to beh… (see more)ave identically for social groups, to adaptation, where behaviors should instead vary across them. To illuminate tensions around invariance and adaptation, we conduct five case studies, in which we perturb different types of identity-related language features (names, roles, locations, dialect, and style) in NLG system inputs. Through these cases studies, we examine people's expectations of system behaviors, and surface potential caveats of these contrasting yet commonly held assumptions. We find that motivations for adaptation include social norms, cultural differences, feature-specific information, and accommodation; in contrast, motivations for invariance include perspectives that favor prescriptivism, view adaptation as unnecessary or too difficult for NLG systems to do appropriately, and are wary of false assumptions. Our findings highlight open challenges around what constitute"fair"or"good"NLG system behaviors.
2024-06-01
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (published)
doi.org
arxiv.org