Miracle-dz/UniFAR-FTUs huggingface dataset:
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling

User Modeling plays an essential role in industry. In this field, task-agnostic approaches, which generate general-purpose representation applicable to diverse downstream user cognition tasks, is a promising direction being more valuable and economical than task-specific representation learning. With the rapid development of Internet service platforms, user behaviors have been accumulated continuously. However, existing general-purpose user representation researches have little ability for full-life cycle modeling on extremely long behavior sequences since user registration. In this study, we propose a novel framework called full- Life cycle User Representation Model (LURM) to tackle this challenge. Specifically, LURM consists of two cascaded sub-models: (\romannumeral1) Bag-of-Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (\textite. \textitg. , 10^5 ); (\romannumeral2) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of BoI features to multiple low-dimensional user representations. Specially, SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user interests. Experiments on several benchmark datasets show that our approach outperforms state-of-the-art general-purpose representation methods.
Geometrically nonlinear quadratic solid/solid‐shell element based on consistent corotational approach for structural analysis under prescribed motion

This study proposed geometrically nonlinear quadratic solid/solid‐shell elements applicable for moving structures. Coordinates in the corotational (CR) formulation were established for a solid element. The proposed CR formulation was consistent with other hexahedral or tetrahedral solid type finite elements. The study involved an explicit description of relevant quantities induced during the derivation. Centrifugal and inertial terms were derived to analyze the behavior of moving structures. The formulation derived in the study was applicable for various solid type elements. Thus, an assumed‐strain 18‐node solid‐shell element was developed based on the Hellinger–Reissner principle to avoid locking in the local element. In addition, quadratic solid elements (i.e., a 10‐node tetrahedron and a 20‐node hexahedron) were developed in the CR solid formulation. Finally, the results were compared with those derived by previous researches involving typical static benchmark problems and commercial software. The findings indicated a good agreement between the comparisons and validated the proposed finite elements. Copyright © 2017 John Wiley & Sons, Ltd.
Perpetual Race to Stay Ahead

I find new results for the `Perpetual Race' analyzed in Horner 2004. The 'Perpetual Race' is an infinite horizon stochastic state space game for which Markov Perfect equilibria are characterized. My results characterize equilibrium strategy structure in a previously unexplored parameter region (spillovers) and offer a new impatience condition for the case examined by Horner (no spillovers). The technique, a generalization of an argument of Horner, uses the fact that a necessary condition for optimality is that an equilibrium strategy is deviation proof with respect to adjacent state pairs.
Investigating Unsupervised Neural Machine Translation for Low-resource Language Pair English-Mizo via Lexically Enhanced Pre-trained Language Models

The vast majority of languages in the world at present are considered to be low-resource languages. Since the availability of large parallel data is crucial for the success of most modern machine translation approaches, improving machine translation for low-resource languages is a key challenge. Most unsupervised techniques for translation benefit closely related languages with monolingual data of substantial quantity. To facilitate research in this direction for the extremely low resource language pair English (en) and Mizo (lus), we have developed a parallel and monolingual corpus for the Mizo language from various news websites. We explore Unsupervised Neural Machine Translation (UNMT) based on the developed monolingual data. We observe that cross-lingual embedding (CLWE) initializations on subword segmented data during pre-training, based on both masked language modelling and sequence-to-sequence generation tasks, improve translation performance. We experiment with cross-lingual alignment and combined alignment and joint training for learning the cross-lingual embedding representations. We also report baseline performances and the impact of CLWE initialization using semi-supervised and supervised neural machine translation. Empirical results show that both CLWE initializations work well for the distant pair English-Mizo compared to the baselines.
Multi-Objective Multi-Gene Genetic Programming for the Prediction of Leakage in Water Distribution Networks

Understanding leakage is an important challenge within the water sector to minimise waste, energy use and carbon emissions. Every Water Distribution Network (WDN) has leakage, usually approximated as Minimum Night Flow (MNF) for each District Metered Area (DMA). However, not all DMAs have instruments to monitor leakage directly, or the main dynamic factors that contribute to it. Therefore, this article will estimate the leakage of a DMA by using the recorded features of its pipes, making use of readily available asset data collected routinely by water companies. This article interprets this problem as a feature construction task and uses a multi-objective multi-gene strongly typed genetic programming approach to create a set of features. These features are used by a linear regression model to estimate the average long-term leakage in DMAs and Shapley values are used to understand the impact and importance of each tree. The methodology is applied to a dataset for a real-world WDN with over 700 DMAs and the results are compared to a previous work which used human-constructed features. The results show comparable performance with significantly fewer, and less complex features. In addition, novel features are found that were not part of the human-constructed features.
Multiobjective Optimization with a Quadratic Surrogate-assisted CMA-ES

We present a surrogate-assisted multiobjective optimization algorithm. The aggregation of the objectives relies on the Uncrowded Hypervolume Improvement (UHVI) which is partly replaced by a linear-quadratic surrogate that is integrated into the CMA-ES algorithm. Surrogating the UHVI poses two challenges. First, the UHVI is a dynamic function, changing with the empirical Pareto set. Second, it is a composite function, defined differently for dominated and nondominated points. The presented algorithm is thought to be used with expensive functions of moderate dimension (up to about 50) with a quadratic surrogate which is updated based on its ranking ability. We report numerical experiments which include tests on the COCO benchmark. The algorithm shows in particular linear convergence on the double sphere function with a convergence rate that is 6--20 times faster than without surrogate assistance.
Lattice spring methods for arbitrary meshes in two and three dimensions

The computation of elastic continua is widely used in today's engineering practice, and finite element models yield a reasonable approximation of the physically observed behaviour. In contrast, the failure of materials due to overloading can be seen as a sequence of discontinuous effects finally leading to a system failure. Until now, it has not been possible to sufficiently predict this process with numerical simulations. It has recently been shown that discrete models like lattice spring models are a promising alternative to finite element models for computing the breakdown of materials because of static overstress and fatigue. In this paper, we will address one of the downsides of current lattice spring models, the need for a periodic mesh leading to a mesh‐induced anisotropy of material failure in simulations. We will show how to derive irregular cells that still behave as part of a homogeneous continuum irrespectively of their shape and which should be able to eliminate mesh‐induced anisotropy. In addition, no restraints concerning the material stiffness tensor are introduced, which allows the simulation of non‐isotropic materials. Finally, we compare the elastic response of the presented model with present lattice spring models using mechanical systems with a known analytical stress field. Copyright © 2016 John Wiley & Sons, Ltd.
A smashed glass cannot be full: Generation of Commonsense Explanations through Prompt-based Few-shot Learning

We assume that providing explanations is a process to elicit implicit knowledge in human communication, and propose a general methodology to generate commonsense explanations from pairs of semantically related sentences. We take advantage of both prompting applied to large, encoder-decoder pre-trained language models, and few-shot learning techniques, such as pattern-exploiting training. Experiments run on the e-SNLI dataset show that the proposed method achieves state-of-the-art results on the explanation generation task, with a substantial reduction of labelled data. The obtained results open new perspective on a number of tasks involving the elicitation of implicit knowledge.
PlayGround Low Resource Machine Translation System for the 2023 AmericasNLP Shared Task

This paper presents PlayGround’s submission to the AmericasNLP 2023 shared task on machine translation (MT) into indigenous languages. We finetuned NLLB-600M, a multilingual MT model pre-trained on Flores-200, on 10 low-resource language directions and examined the effectiveness of weight averaging and back translation. Our experiments showed that weight averaging, on average, led to a 0.0169 improvement in the ChrF++ score. Additionally, we found that back translation resulted in a 0.008 improvement in the ChrF++ score.
Improving Zero-shot Relation Classification via Automatically-acquired Entailment Templates

While fully supervised relation classification (RC) models perform well on large-scale datasets, their performance drops drastically in low-resource settings. As generating annotated examples are expensive, recent zero-shot methods have been proposed that reformulate RC into other NLP tasks for which supervision exists such as textual entailment. However, these methods rely on templates that are manually created which is costly and requires domain expertise. In this paper, we present a novel strategy for template generation for relation classification, which is based on adapting Harris’ distributional similarity principle to templates encoded using contextualized representations. Further, we perform empirical evaluation of different strategies for combining the automatically acquired templates with manual templates. The experimental results on TACRED show that our approach not only performs better than the zero-shot RC methods that only use manual templates, but also that it achieves state-of-the-art performance for zero-shot TACRED at 64.3 F1 score.
Context-aware Medication Event Extraction from Unstructured Text

Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.
BCause: Reducing group bias and promoting cohesive discussion in online deliberation processes through a simple and engaging online deliberation tool

Facilitating healthy online deliberation in terms of sensemaking and collaboration of discussion participants proves extremely challenging due to a number of known negative effects of online communication on social media platforms. We start from concerns and aspirations about the use of existing online discussion systems as distilled in previous literature, we then combine them with lessons learned on design and engineering practices from our research team, to inform the design of an easy-to-use tool (BCause.app) that enables higher quality discussions than traditional social media. We describe the design of this tool, highlighting the main interaction features that distinguish it from common social media, namely: i. the low-cost argumentation structuring of the conversations with direct replies; ii. and the distinctive use of reflective feedback rather than appreciative-only feedback. We then present the results of a controlled A/B experiment in which we show that the presence of argumentative and cognitive reflective discussion elements produces better social interaction with less polarization and promotes a more cohesive discussion than common social media-like interactions.
Four Approaches to Low-Resource Multilingual NMT: The Helsinki Submission to the AmericasNLP 2023 Shared Task

The Helsinki-NLP team participated in the AmericasNLP 2023 Shared Task with 6 submissions for all 11 language pairs arising from 4 different multilingual systems. We provide a detailed look at the work that went into collecting and preprocessing the data that led to our submissions. We explore various setups for multilingual Neural Machine Translation (NMT), namely knowledge distillation and transfer learning, multilingual NMT including a high-resource language (English), language-specific fine-tuning, and multilingual NMT exclusively using low-resource data. Our multilingual Model B ranks first in 4 out of the 11 language pairs.
Uncovering the Potential for a Weakly Supervised End-to-End Model in Recognising Speech from Patient with Post-Stroke Aphasia

Post-stroke speech and language deficits (aphasia) significantly impact patients’ quality of life. Many with mild symptoms remain undiagnosed, and the majority do not receive the intensive doses of therapy recommended, due to healthcare costs and/or inadequate services. Automatic Speech Recognition (ASR) may help overcome these difficulties by improving diagnostic rates and providing feedback during tailored therapy. However, its performance is often unsatisfactory due to the high variability in speech errors and scarcity of training datasets. This study assessed the performance of Whisper, a recently released end-to-end model, in patients with post-stroke aphasia (PWA). We tuned its hyperparameters to achieve the lowest word error rate (WER) on aphasic speech. WER was significantly higher in PWA compared to age-matched controls (10.3% vs 38.5%, p<0.001). We demonstrated that worse WER was related to the more severe aphasia as measured by expressive (overt naming, and spontaneous speech production) and receptive (written and spoken comprehension) language assessments. Stroke lesion size did not affect the performance of Whisper. Linear mixed models accounting for demographic factors, therapy duration, and time since stroke, confirmed worse Whisper performance with left hemispheric frontal lesions.We discuss the implications of these findings for how future ASR can be improved in PWA.
Findings of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages

In this work, we present the results of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages of the Americas. This edition of the shared task featured eleven language pairs, one of which – Chatino-Spanish – uses a newly collected evaluation dataset, consisting of professionally translated text from the legal domain. Seven teams participated in the shared task, with a total of 181 submissions. Additionally, we conduct a human evaluation of the best system outputs, and compare them to the best submissions from the prior shared task. We find that this analysis agrees with the quantitative measures used to rank submissions, which shows further improvements of 9.64 ChrF on average across all languages, when compared to the prior winning system.
Do not Mask Randomly: Effective Domain-adaptive Pre-training by Masking In-domain Keywords

We propose a novel task-agnostic in-domain pre-training method that sits between generic pre-training and fine-tuning. Our approach selectively masks in-domain keywords, i.e., words that provide a compact representation of the target domain. We identify such keywords using KeyBERT (Grootendorst, 2020). We evaluate our approach using six different settings: three datasets combined with two distinct pre-trained language models (PLMs). Our results reveal that the fine-tuned PLMs adapted using our in-domain pre-training strategy outperform PLMs that used in-domain pre-training with random masking as well as those that followed the common pre-train-then-fine-tune paradigm. Further, the overhead of identifying in-domain keywords is reasonable, e.g., 7-15% of the pre-training time (for two epochs) for BERT Large (Devlin et al., 2019).
The Hidden Folk: Linguistic Properties Encoded in Multilingual Contextual Character Representations

To gain a better understanding of the linguistic information encoded in character-based language models, we probe the multilingual contextual CANINE model. We design a range of phonetic probing tasks in six Nordic languages, including Faroese as an additional zero-shot instance. We observe that some phonetic information is indeed encoded in the character representations, as consonants and vowels can be well distinguished using a linear classifier. Furthermore, results for the Danish and Norwegian language seem to be worse for the consonant/vowel distinction in comparison to other languages. The information encoded in these representations can also be learned in a zero-shot scenario, as Faroese shows a reasonably good performance in the same vowel/consonant distinction task.
Fabrication of advanced targets for laser driven nuclear fusion reactions through standard microelectronics technology approaches

Silicon targets enriched with hydrogen and doped with boron at high atomic concentration (1020–1022 cm−3) were designed and fabricated using ion implantation and thermal diffusion processes to be used for experiments in the field of laser driven nuclear fusion. Two main types of target were prepared: thin (2 μ m) foils and thick (500 μ m) slabs. Such targets were irradiated with a sub-nanosecond, kJ-class laser with a moderate intensity (∼ 1016 W/cm2) to trigger the p(11B,α)2α nuclear fusion reaction thanks to the acceleration of proton streams with energy of 0.1–1 MeV . The combination of the ad-hoc developed targets and the given laser pulse parameters allowed to generate a very high flux of alpha particles (107–109/sr per shot). The paper mainly focuses on microfabrication techniques and processes optimized for the fabrication of such advanced targets and on a comparison of the key results achieved with the different targets used in the experiment. Hydrodynamic simulations are also discussed.
Insights from DataFest point to new opportunities for undergraduate statistics courses: Team collaborations, designing research questions, and data ethics

As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re‐conceptualizing how we teach undergraduate statistics and data science courses for majors and non‐majors alike. In this paper, we focus on three crucial components for this re‐conceptualization: Developing research questions, professional ethics, and team collaborations. We share vignettes from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. These vignettes shed light on opportunities for re‐conceptualizing introductory courses to give more attention to issues of the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others. We provide some implications for teaching and learning as well as an example activity for educators to use in their courses.
Grief Process and Support Systems for Military Widows

ABSTRACT This qualitative study aimed to give voice to young military widows through a phenomenological approach using a feminist lens. The researchers interviewed four young military widows and explored how they experienced the sudden traumatic loss of their spouse, the supports they utilized to cope with their loss, and their perception of the supports they used. There were four major themes that emerged through participant interviews: 1) notification process; 2) response to the notification of the sudden traumatic loss of their husband; 3) finding own path from grief to healing; and 4) support systems utilized during the healing process. Some supports were perceived as effective, and others were perceived as ineffective. All the military widows who participated in this study went through a very individualized healing process after the loss of their spouse and utilized a variety of supports to heal. Findings from this study have clinical implications unique to military widows and families regarding life transitions following the loss of their partner and factors that can impact their mental health.
Steady - State and Transient Performance of HVDC Link Based 3-Level vsc Supplying a Passive Load = Performances en Régime Stationnaire et Transitoire de la Liaison CCHT Utilisant le VSC 3 Niveaux Alimentant une Charge Passive

This paper investigates the steady-state and transient performance of high-voltage DC (HVDC) transmission systems connected to passive network. The VSC HVDC tie employing PWM may well represent the ultimate FACTS device. Besides controlling the through power flow, it can supply reactive power and provide independent dynamic control at its two terminals. The control systems for rectifier and inverter are discussed in DC (HVDC) transmission systems based on three-level voltage source converters. The study involves analysis of active-reactive power capabilities (P-Q envelope) including active power flow and provision of voltage support to AC networks. The transient performance is explored by examining the VSC_HVDC response to external AC faults. Finally, the models and results are presented and tested by simulations using Matlab Simulink and its toolbox SimPowerSystems.