Miracle-dz/UniFAR-FTUs huggingface dataset:
Character alignment methods for dialect-to-standard normalization

This paper evaluates various character alignment methods on the task of sentence-level standardization of dialect transcriptions. We compare alignment methods from different scientific traditions (dialectometry, speech processing, machine translation) and apply them to Finnish, Norwegian and Swiss German dialect datasets. In the absence of gold alignments, we evaluate the methods on a set of characteristics that are deemed undesirable for the task. We find that trained alignment methods only show marginal benefits to simple Levenshtein distance. On this particular task, eflomal outperforms related methods such as GIZA++ or fast_align by a large margin.
Fine-tuning Sentence-RoBERTa to Construct Word Embeddings for Low-resource Languages from Bilingual Dictionaries

Conventional approaches to learning word embeddings (Mikolov et al., 2013; Pennington et al., 2014) are limited to relatively few languages with sufficiently large training corpora. To address this limitation, we propose an alternative approach to deriving word embeddings for Wolastoqey and Mi’kmaq that leverages definitions from a bilingual dictionary. More specifically, following Bear and Cook (2022), we experiment with encoding English definitions of Wolastoqey and Mi’kmaq words into vector representations using English sequence representation models. For this, we consider using and finetuning sentence-RoBERTa models (Reimers and Gurevych, 2019). We evaluate our word embeddings using a similar methodology to that of Bear and Cook using evaluations based on word classification, clustering and reverse dictionary search. We additionally construct word embeddings for higher-resource languages English, German and Spanishusing our methods and evaluate our embeddings on existing word-similarity datasets. Our findings indicate that our word embedding methods can be used to produce meaningful vector representations for low-resource languages such as Wolastoqey and Mi’kmaq and for higher-resource languages.
Enriching WayunaikiSpanish Neural Machine Translation with Linguistic Information

We present the first neural machine translation system for the low-resource language pair WayunaikiSpanish and explore strategies to inject linguistic knowledge into the model to improve translation quality. We explore a wide range of methods and combine complementary approaches. Results indicate that incorporating linguistic information through linguistically motivated subword segmentation, factored models, and pretrained embeddings helps the system to generate improved translations, with the segmentation contributing the most.In order to evaluate translation quality in a general domain and go beyond the available religious domain data, we gather and make publicly available a new test set and supplementary material.Although translation quality as measured with automatic metrics is low, we hope these resources will facilitate and support further research on Wayunaiki.
Back-Transliteration of English Loanwords in Japanese

We propose methods for transliterating English loanwords in Japanese from their Japanese written form (katakana/romaji) to their original English written form. Our data is a Japanese-English loanwords dictionary that we have created ourselves. We employ two approaches: the direct transliteration, which directly converts words from katakana to English, and the indirect transliteration, which utilizes the English pronunciation as an intermediate step. Additionally, we compare the effectiveness of using katakana versus romaji as input characters. We develop 6 models of 2 types for our experiments: one with an English lexicon-filter, and the other without. For each type, we built 3 models, including a pair n-gram based on WFSTs and two sequence-to-sequence models leveraging LSTM and transformer. Our best performing model was the pair n-gram model with a lexicon-filter, directly transliterating from katakana to English.
Measuring Lexico-Semantic Alignment in Debates with Contextualized Word Representations

Dialog participants sometimes align their linguistic styles, e.g., they use the same words and syntactic constructions as their interlocutors. We propose to investigate the notion of lexico-semantic alignment: to what extent do speakers convey the same meaning when they use the same words? We design measures of lexico-semantic alignment relying on contextualized word representations. We show that they reflect interesting semantic differences between the two sides of a debate and that they can assist in the task of debate’s winner prediction.
Rare Codes Count: Mining Inter-code Relations for Long-tail Clinical Text Classification

Multi-label clinical text classification, such as automatic ICD coding, has always been a challenging subject in Natural Language Processing, due to its long, domain-specific documents and long-tail distribution over a large label set. Existing methods adopt different model architectures to encode the clinical notes. Whereas without digging out the useful connections between labels, the model presents a huge gap in predicting performances between rare and frequent codes. In this work, we propose a novel method for further mining the helpful relations between different codes via a relation-enhanced code encoder to improve the rare code performance. Starting from the simple code descriptions, the model reaches comparable, even better performances than models with heavy external knowledge. Our proposed method is evaluated on MIMIC-III, a common dataset in the medical domain. It outperforms the previous state-of-art models on both overall metrics and rare code performances. Moreover, the interpretation results further prove the effectiveness of our methods. Our code is publicly available at https://github.com/jiaminchen-1031/Rare-ICD.
Pronunciation Ambiguities in Japanese Kanji

Japanese writing is a complex system, and a large part of the complexity resides in the use of kanji. A single kanji character in modern Japanese may have multiple pronunciations, either as native vocabulary or as words borrowed from Chinese. This causes a problem for text-to-speech synthesis (TTS) because the system has to predict which pronunciation of each kanji character is appropriate in the context. The problem is called homograph disambiguation. To solve the problem, this research provides a new annotated Japanese single kanji character pronunciation data set and describes an experiment using the logistic regression (LR) classifier. A baseline is computed to compare with the LR classifier accuracy. This experiment provides the first experimental research in Japanese single kanji homograph disambiguation. The annotated Japanese data is freely released to the public to support further work.
Extending laser plasma accelerators into the mid-IR spectral domain with a next-generation ultra-fast CO2 laser

Expanding the scope of relativistic plasma research to wavelengths longer than the λ/≈ 0.8–1.1 μm range covered by conventional mode-locked solid-state lasers would offer attractive opportunities due to the quadratic scaling of the ponderomotive electron energy and critical plasma density with λ. Answering this quest, a next-generation mid-IR laser project is being advanced at the BNL ATF as a part of the user facility upgrade. We discuss the technical approach to this conceptually new 100 TW, 100 fs, λ = 9–11 μm CO2 laser BESTIA (Brookhaven Experimental Supra-Terawatt Infrared at ATF) that encompasses several innovations applied for the first time to molecular gas lasers. BESTIA will enable new regimes of laser plasma accelerators. One example is shock-wave ion acceleration (SWA) from gas jets. We review ongoing efforts to achieve stable, monoenergetic proton acceleration by dynamically shaping the plasma density profile from a hydrogen gas target with laser-produced blast waves. At its full power, 100 TW BESTIA promises to achieve proton beams at an energy exceeding 200 MeV. In addition to ion acceleration in over-critical plasma, the ultra-intense mid-IR BESTIA will open up new opportunities in driving wakefields in tenuous plasmas, expanding the landscape of laser wakefield accelerator (LWFA) studies into the unexplored long-wavelength spectral domain. Simple wavelength scaling suggests that a 100 TW CO2 laser beam will be capable of efficiently generating plasma ‘bubbles’ a thousand times greater in volume compared with a near-IR solid state laser of an equivalent power. Combined with a femtosecond electron linac available at the ATF, this wavelength scaling will facilitate the study of external seeding and staging of LWFAs.
In Pieces: An Approach to Critical Reflexivity with Science Teachers through Storytelling and in Community

ABSTRACT This study examines how storytelling can guide the critical reflexivity of secondary science teachers engaged in a professional learning community. Traditionally, storytelling has been used in Black communities to “teach the people to know themselves. ” The author engages in racial storytelling to remember, envision and consider what could have been, to imagine new possibilities. Through narrative case study analysis, the author examines two science educators' past, present, and futures through their narratives. The author asserts that critical consciousness is necessary for teachers as they aspire to support the needs of diverse learners.
Riparian health conditions of headwater streams in Southwestern Nigeria

ABSTRACT River health assessments and threat analysis on the riparian corridors in Sub-Saharan Africa have received little or no attention, therefore this study assessed the healthiness of riparian corridors using the Rapid Appraisal of Riparian Condition (RARC) indices within a river catchment. The biogeography of the riparian corridor, factors responsible for the modification of riparian vegetation, and causes of riparian disturbance were examined. Twelve study sites were randomly selected from where reaches were identified. The riparian corridor extents were delimited from the bank of the river to the uppermost point within reaches. Species diversity indices were computed while bank height, width, and average depth were measured. Also, cross-sectional area and river discharge were derived along each riparian zone using standard measurement techniques. The ecological values and threat severity along each riparian zone were assessed following the RARC model. The ecological values and disturbance level revealed r as −0.7(p < 0.05). The variation in species composition along the river orders suggests that the first-order streams have the highest number of taxa. The level of ecological destruction is fairly low in the first-order streams and increases as order increases. This study concludes that higher-order rivers have low diversity, low ecological values, and consequently, relatively poor health, especially when under severe anthropogenic pressure.
“This Type of Teaching Moves Beyond Academic Achievement on Tests as the Only Measure of a Good Science Education and Student Achievement”: Exploring Pre-Service Science Teachers’ Conceptualizations of Justice-Centered Science Pedagogy Through a Class Social Justice Project

ABSTRACT Science content, teaching, and teacher preparation are all political acts. However, accepting the political nature of science as well as that of science teaching has proven to be a hard shift for many in the science community. In this paper, we suggest a shift away from an apolitical teaching of science toward a system of practices that transforms the purposes of science education through a justice-centered science pedagogy approach. For science teachers to engage their students in this type of transformative teaching and learning, we argue that the science teachers themselves need to be immersed in conditions under which their learning experiences foregrounds the transformative power of justice centered science pedagogy. In this study, we explore a residency program, and in particular, a secondary science methods course in which pre-service teachers engaged in a social justice project exploring social justice science issues to illustrate preservice teachers’ conceptualizations of justice-centered science teaching and learning.
“People Need to Speak Up”: Preservice Secondary Science Teachers’ Movement Toward a Justice-Centered Science Education

ABSTRACT Teacher education programs must prepare their preservice science teachers to center social justice and to meet the academic needs of culturally, racially, and linguistically diverse students, as justice-centered discourses are traditionally absent from science classrooms yet integral to the teaching and learning of rich and relevant phenomena. In this study, we investigated a cohort of preservice secondary science teachers enrolled in a yearlong, post-baccalaureate teacher education program that attended to social justice. We conducted four interviews with each participant across their program and qualitatively analyzed their discussions of social justice ideas and teaching practices using three tenets of a justice-centered science pedagogy framework: enacting an antiracist and equitable science education, grounding instruction in social and environmental justice phenomena, and framing students as transformative intellectuals. We found preservice teacher participants discussed enacting antiracist and equitable science teaching by using a critical lens to identify inequities in classrooms and schools, and by attending to high academic expectations. Preservice teachers described focusing on socioscientific phenomena and local contexts as starting points for teaching about social justice science issues. Participants also shared their work toward framing students as transformative intellectuals by developing teacher-student relationships, building from students’ ideas, and discussing emerging ideas and efforts for student advocacy. Findings from this study underscore the need for more focused work on ways to prioritize the justice component of social justice science issues and the student advocacy component of students as transformative intellectuals so as to prepare preservice teachers to fully enact a socially just science education.
Adaptive eigenspace for inverse problems in the frequency domain

Inverse scattering problems are used in a vast number of applications, such as geophysical exploration and medical imaging. The goal is to recover unknown media using wave prop- agation. The inverse problem is designed to minimize simulated data with observation data, using partial differential equations (PDE) as constrains. The resulting minimiza- tion problem is often severely ill-posed and contains a large number of local minima. To tackle ill-posedness, several optimization and regularization techniques have been explored. However, the applications are still asking for improvement and stability. In this thesis, a nonlinear optimization method is proposed for the solution of inverse scattering problems in the frequency domain, when the scattered field is governed by the Helmholtz equation. The time-harmonic inverse medium problem is formulated as a PDE-constrained optimization problem and solved by an inexact truncated Newton- type method. Instead of a grid-based discrete representation, the unknown wave speed is projected to a particular finite-dimensional basis, which is iteratively adapted during the optimization. Truncating the adaptive eigenspace (AE) basis at a (small and slowly increasing) finite number of eigenfunctions effectively introduces regularization into the inversion and thus avoids the need for standard Tikhonov-type regularization. We actually show how to build an AE from the gradients of Tikhonov-regularization functionals. Both analytical and numerical evidence underpin the accuracy of the AE representation. Numerical experiments demonstrate the efficiency and robustness to missing or noisy data of the resulting adaptive eigenspace inversion (AEI) method. We also consider missing frequency data and apply the AEI to the multi-parameter inverse scattering problem.
α-Carboxysome Size Is Controlled by the Disordered Scaffold Protein CsoS2

Carboxysomes are protein microcompartments that function in the bacterial CO2 concentrating mechanism (CCM) to facilitate CO2 assimilation. To do so, carboxysomes assemble from thousands of constituent proteins into an icosahedral shell which encapsulates the enzymes rubisco and carbonic anhydrase to form structures typically >100 nm and >300 megadaltons. Although many of the protein interactions driving the assembly process have been determined, it remains unknown how size and composition are precisely controlled. Here we show that the size of α-carboxysomes is controlled by the disordered scaffolding protein CsoS2. CsoS2 contains two classes of related peptide repeats which bind to the shell in a distinct fashion, and our data indicate that size is controlled by the relative number of these interactions. We propose an energetic and structural model wherein the two repeat classes bind at the junction of shell hexamers but differ in their preferences for the shell contact angles, and thus the local curvature. In total, this model suggests that a set of specific and repeated interactions between CsoS2 and shell proteins collectively achieve the large size and monodispersity of α-carboxysomes.
High-Z Ultrathin Foil Fabrication for Intense Laser Experiments

Abstract High-Z metallic foils including rare-earth (RE) elements are required for some experiments implemented on the Laser Megajoule. A specific process based on physical vapor deposition and laser machining was developed to produce high-Z material foils meeting strict specifications. This process allows pure metallic ultrathin foil fabrication from a few hundred nanometers to several microns of thickness of any high-Z materials. In the case of RE metals sensitive to oxidation, thin foils are buried under aluminum protective layers of about a few hundred nanometers. These metallic thin foils are flat, show thickness uniformity over 95%/cm2, and have roughness of about 10 nm. The foils are opaque to light, have a density similar to bulk material, present an oxygen content of about 1 at. %, and are stable over months under atmospheric conditions.
Research on the Turning of Graphic Design Information Communication from Static Presentation to Interactive

Aiming at the problem that the static presentation of graphic design information cannot meet the needs of current designers, a curve fitting algorithm based on inflection point detection is proposed. Combine it with the electronic whiteboard system to build an interactive whiteboard system for railway graphic design, and realize the transformation of graphic design information from static to dynamic in this way. The performance comparison experiment of the curve fitting algorithm proposed in the research shows that the accuracy rate of the algorithm is 88.2%, which is higher than that of similar comparison algorithms. The results show that the proposed curve fitting algorithm has better fitting performance. In the empirical analysis of the interactive electronic whiteboard system of railway plane information, it is found that the happy and excited emotional scores of users using the system are increased by 1.8 and 1.3 points respectively. In addition, the Timeliness and Convenience index scores of the transformed graphic design information transmission mode have increased by 1.7 and 1.3 points respectively. The result shows that the transformation of graphic design information transmission mode into dynamic interactive mode through the curve fitting algorithm can effectively improve its user experience, thus improving the designer's graphic design efficiency.
Simultaneous test procedures for one-way ANOVA and MANOVA, based on rank scores

A MANOVA statistic based on rank scores is proposed and its distribution derived along the lines of Puri and Sen (1966). Using the simultaneous inference approach of Gabriel (1967) this statistic is shown to provide a procedure which simultaneously tests all sub-groups of samples on all subsets of variables, and to set simultaneous confidence bounds on all location differences. The formulae for actual ranks, i.e., Wilcoxon type scores, are worked out explicitly.
Easy peasy: A new handy method for multiple COTS IoT devices

—Context-based IoT device paring is a popular so-lution for devices that lack an interface. However, it takes a proximate distance or a long time for IoT devices to sense highly correlated context with enough entropy. In this work, we present a novel approach for fast and secure multiple commercial off-the-shelf (COTS) devices pairing in IoT scenarios. Our approach is based on the key idea that devices co-located within a physically-secure boundary can perceive qualified context under the help of human-in-the-loop (HITL). Specifically, we leverage received-signal-strength (RSS) trajectory data with manually-generated interferences in a certain period as the shared secret to achieve fast and secure device pairing. Moreover, the real-time RSS trajectory data can be utilized to generate random numbers in lieu of pre-shared key (PSK), which makes our scheme more resistant to background attacks. We theoretically prove the security of our pairing scheme and implement it in some real-world environments. Our experimental results demonstrate that our scheme can effectively defend against malicious devices by imposing a threshold on the similarity of RSS trajectory data. The experimental results also show that, compared with the traditional context-based pairing that takes up to 24 hours, the legitimate device in our scheme takes only 10 seconds to pass the similarity check on average, which is efficient and feasible.
Neural network-based image segmentation for image interpolation

A novel image interpolation scheme is presented, in which a neural network is used to segment the image according to the presence of oriented edges; a set of different directional adaptive filters then interpolate the image, the filter outputs being weighted according to the neural network output. The filters are designed in order to accurately reproduce both smooth areas and sharp edges. In the paper, the structure is presented and the neural network and the directional filters are described. Simulation results show that both objective and subjective image quality obtained by the proposed method are higher than using linear interpolators.