Miracle-dz/UniFAR-FTUs huggingface dataset:
Prompting ChatGPT to Draw Morphological Connections for New Word Comprehension

Though more powerful, Large Language Models need to be periodically retrained for updated information, consuming resources and energy. In this respect, prompt engineering can prove a possible solution to re-training. To explore this line of research, this paper uses a case study, namely, finding the best prompting strategy for asking ChatGPT to define new words based on morphological connections. To determine the best prompting strategy, each definition provided by the prompt was ranked in terms of plausibility and humanlikeness criteria. The findings of this paper show that adding contextual information, operationalised as the keywords ‘new’ and ‘morpheme’, significantly improve the performance of the model for any prompt. While no single prompt significantly outperformed all others, there were differences between performances on the two criteria for most prompts. ChatGPT also provided the most correct definitions with a persona-type prompt.
Automatic Assessment Of Spoken English Proficiency Based on Multimodal and Multitask Transformers

This paper describes technology developed to automatically grade students on their English spontaneous spoken language proficiency with common european framework of reference for languages (CEFR) level. Our automated assessment system contains two tasks: elicited imitation and spontaneous speech assessment. Spontaneous speech assessment is a challenging task that requires evaluating various aspects of speech quality, content, and coherence. In this paper, we propose a multimodal and multitask transformer model that leverages both audio and text features to perform three tasks: scoring, coherence modeling, and prompt relevancy scoring. Our model uses a fusion of multiple features and multiple modality attention to capture the interactions between audio and text modalities and learn from different sources of information.
Bridging the Gap between Subword and Character Segmentation in Pretrained Language Models

Pretrained language models require the use of consistent segmentation (e.g., subword- or character-level segmentation) in pretraining and finetuning. In NLP, many tasks are modeled by subword-level segmentation better than by character-level segmentation. However, because of their format, several tasks require the use of character-level segmentation. Thus, in order to tackle both types of NLP tasks, language models must be independently pretrained for both subword and character-level segmentation. However, this is an inefficient and costly procedure. Instead, this paper proposes a method for training a language model with unified segmentation. This means that the trained model can be finetuned on both subword- and character-level segmentation. The principle of the method is to apply the subword regularization technique to generate a mixture of subword- and character-level segmentation. Through experiment on BERT models, we demonstrate that our method can halve the computational cost of pretraining.
Using Wikidata for Enhancing Compositionality in Pretrained Language Models

One of the many advantages of pre-trained language models (PLMs) such as BERT and RoBERTa is their flexibility and contextual nature. These features give PLMs strong capabilities for representing lexical semantics. However, PLMs seem incapable of capturing high-level semantics in terms of compositionally. We show that when augmented with the relevant semantic knowledge, PMLs learn to capture a higher degree of lexical compositionality. We annotate a large dataset from Wikidata highlighting a type of semantic inference that is easy for humans to understand but difficult for PLMs, like the correlation between age and date of birth. We use this resource for finetuning DistilBERT, BERT large and RoBERTa. Our results show that the performance of PLMs against the test data continuously improves when augmented with such a rich resource. Our results are corroborated by a consistent improvement over most GLUE benchmark natural language understanding tasks.
An Evaluation of Source Factors in Concatenation-Based Context-Aware Neural Machine Translation

We explore the use of source factors in context-aware neural machine translation, specifically concatenation-based models, to improve the translation quality of inter-sentential phenomena. Context sentences are typically concatenated to the sentence to be translated, with string-based markers to separate the latter from the former. Although previous studies have measured the impact of prefixes to identify and mark context information, the use of learnable factors has only been marginally explored. In this study, we evaluate the impact of single and multiple source context factors in English-German and Basque-Spanish contextual translation. We show that this type of factors can significantly enhance translation accuracy for phenomena such as gender and register coherence in Basque-Spanish, while also improving BLEU results in some scenarios. These results demonstrate the potential of factor-based context identification to improve context-aware machine translation in future research.
HAQA and QUQA: Constructing Two Arabic Question-Answering Corpora for the Quran and Hadith

It is neither possible nor fair to compare the performance of question-answering systems for the Holy Quran and Hadith Sharif in Arabic due to both the absence of a golden test dataset on the Hadith Sharif and the small size and easy questions of the newly created golden test dataset on the Holy Quran. This article presents two question–answer datasets: Hadith Question–Answer pairs (HAQA) and Quran Question–Answer pairs (QUQA). HAQA is the first Arabic Hadith question–answer dataset available to the research community, while the QUQA dataset is regarded as the more challenging and the most extensive collection of Arabic question–answer pairs on the Quran. HAQA was designed and its data collected from several expert sources, while QUQA went through several steps in the construction phase; that is, it was designed and then integrated with existing datasets in different formats, after which the datasets were enlarged with the addition of new data from books by experts. The HAQA corpus consists of 1598 question–answer pairs, and that of QUQA contains 3382. They may be useful as gold–standard datasets for the evaluation process, as training datasets for language models with question-answering tasks and for other uses in artificial intelligence.
Party Extraction from Legal Contract Using Contextualized Span Representations of Parties

Extracting legal entities from legal documents, particularly legal parties in contract documents, poses a significant challenge for legal assistive software. Many existing party extraction systems tend to generate numerous false positives due to the complex structure of the legal text. In this study, we present a novel and accurate method for extracting parties from legal contract documents by leveraging contextual span representations. To facilitate our approach, we have curated a large-scale dataset comprising 1000 contract documents with party annotations. Our method incorporates several enhancements to the SQuAD 2.0 question-answering system, specifically tailored to handle the intricate nature of the legal text. These enhancements include modifications to the activation function, an increased number of encoder layers, and the addition of normalization and dropout layers stacked on top of the output encoder layer. Baseline experiments reveal that our model, fine-tuned on our dataset, outperforms the current state-of-the-art model. Furthermore, we explore various combinations of the aforementioned techniques to further enhance the accuracy of our method. By employing a hybrid approach that combines 24 encoder layers with normalization and dropout layers, we achieve the best results, exhibiting an exact match score of 0.942 (+6.2% improvement).
A method for evaluating hunger and thirst in monkeys by measuring blood ghrelin and osmolality levels

Hunger and thirst drive animals’ consumption behavior and regulate their decision-making regarding rewards. We previously assessed the thirst states of monkeys by measuring blood osmolality under controlled water access and related the thirst states to risky decisions for fluid rewards. However, hunger assessments in monkeys have been poorly performed. Moreover, the lack of precise measures for hunger states leads to another issue regarding how hunger and thirst states interact with each other in each individual. Thus, when controlling food access to motivate subject performances, how these two physiological needs are satisfied in captive monkeys remains unclear. Here, we measured blood ghrelin levels and osmolality for hunger and thirst, respectively, in four captive macaques. Using enzyme-linked immunosorbent assay, we found that the levels of blood ghrelin, a widely measured hunger-related peptide hormone in humans, were high after 20 h of no food access (with ad libitum water), which is a typical controlled food access condition. One hour after consuming the regular dry meal, the value decreased in three out of four monkeys to within the range of individual blood ghrelin levels. Additionally, blood osmolality measured from the same blood sample, the standard hematological index of hydration status, increased after consuming regular dry meals with no water access. Thus, ghrelin and osmolality may provide a precise reflection of the physiological states of individual monkeys for hunger and thirst, suggesting that these indices can be used as a tool for monitoring hunger and thirst levels that mediate the animal’s decision-making for consuming rewards.
Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of anti-malarial drug resistance

The establishment and spread of anti-malarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in Sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.
Zinc stimulation of coastal productivity in low carbon dioxide environments

Zinc (Zn) is a key micronutrient used by phytoplankton for carbon (C) acquisition, yet there have been few observations of its influence on natural oceanic phytoplankton populations. In this study, we observed Zn limitation of growth in the natural phytoplankton community of Terra Nova Bay, Antarctica, due to low (∼220 μatm) pCO2 conditions, in addition to primary iron (Fe) limitation. Shipboard incubation experiments amended with Zn and Fe resulted in significantly higher chlorophyll a content and dissolved inorganic carbon drawdown compared to Fe addition alone. Zn and Fe response proteins detected in incubation and environmental biomass provided independent verification of algal co-stress for these micronutrients. These observations of Zn limitation under low pCO2 conditions demonstrate Zn can influence coastal primary productivity. Yet, as surface ocean pCO2 rises with continued anthropogenic emissions, the occurrence of Zn/C co-limitation will become rarer, impacting the biogeochemical cycling of Zn and other trace metal micronutrients. Significance Statement Zinc has tremendous nutritional importance in life, and due to its central role in carbon acquisition in major phytoplankton species, is directly connected to ocean carbon cycling. As a result, it has been hypothesized that co-limitation by zinc and CO2 could influence primary productivity, but a connection between Zn and CO2 had not yet been observed in field experiments. The present study demonstrates that Zn can be a limiting nutrient in natural waters, particularly in coastal environments where CO2 is diminished by bloom events, and explores the implications for Zn-C co-limitation in the context of climate change: the oceans may have decreased susceptibility to Zn-C co-limitations in the future due to the rapid input of anthropogenic CO2 to the atmosphere.
Progressively shifting patterns of co-modulation among premotor cortex neurons carry dynamically similar signals during action execution and observation

Many neurons in the premotor cortex show firing rate modulation whether the subject performs an action or observes another individual performing the same action. Although such “mirror neurons” have been thought to have highly congruent discharge during execution and observation, many if not most show non-congruent activity. Studies of such neuronal populations have shown that the most prevalent patterns of co-modulation—captured as neural trajectories—pass through subspaces which are shared in part, but in part are visited exclusively during either execution or observation. These studies focused on reaching movements for which the neural trajectories show comparatively simple dynamical motifs. But the neural dynamics of hand movements are more complex. We developed a novel approach to examine prevalent patterns of co-modulation during execution and observation of a task that involved reaching, grasping and manipulation. Rather than following neural trajectories in subspaces that contain their entire time course, we identified time series of instantaneous subspaces, sampled trajectory segments at the times of selected behavioral events, and projected each segment into the series of instantaneous subspaces. We found that instantaneous neural subspaces were partially shared between execution and observation in only one of three monkeys and were otherwise exclusive to one context or the other. Nevertheless, the patterns during execution and observation could be aligned with canonical correlation, indicating that though distinct, neural representations during execution and observation show dynamical similarity that may enable the nervous system to recognize particular actions whether performed by the subject or by another individual.
Streambank erosion along two rivers in Iowa

Rates and processes of bank erosion were evaluated for reaches of two alluvial, equiwidth to wide bend, point bar streams in Iowa: East Nishnabotna River and Des Moines River. The evaluation was made using historical records (airphotos, maps, and streamflow records), field measurements, and soil analyses. The erosion rates were correlated with channel characteristics such as width, depth, curvature, arc angle of channel centerline, channel slope, friction factor, and degree of vegetation on the banks. The correlation was studied using a meander flow model developed by the author. The meander flow model simulated the velocity distribution in the bends and the location of the erosion occurrences. Estimates were made of total sediment influx to the rivers from cutbank erosion.
Knowledge by omission: the significance of omissions in the 5-choice serial reaction time task

The 5-choice serial reaction time task (5-CSRTT) is commonly used to assess attention in rodents. Manipulation of this task by decreasing the light stimulus duration is often used to probe attentional capacity and causes a decrease in accuracy and an increase in omissions. However, although a decrease in response accuracy is commonly interpreted as a decrease in attention, it is more difficult to interpret an increase in omissions in terms of attentional performance. Here we present a series of experiments in rats that seeks to investigate the origins of these key behavioral measures of attention in the 5-CSRTT. After an initial training in the 5-CSRTT, rats were tested in a variable stimulus duration procedure to increase task difficulty and probe visual attentional capacity under several specific controlled conditions. We found that response accuracy reflects visuospatial sustained attentional processing, as commonly interpreted, while response omission reflects rats’ ignorance about the stimulus location, presumably due to failure to pay attention to the curved wall during its presentation. Moreover, when rats lack of relevant information, they choose not to respond instead of responding randomly. Overall, our results indicate that response accuracy and response omission thus correspond to two distinct attentional states.
Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output

Biosensors play key roles in medical research and diagnostics, but there currently is a lack of sensing platforms that combine easy adaptation to new targets, strategies to tune the response window to relevant analyte concentration ranges and allow for the incorporation of multiple sensing elements to benefit from multivalency. Utilizing a DNA origami nanostructure as a scaffold for arranging the different sensor components, we here propose an approach for the development of modular and tunable single-molecule sensors capable of detecting a variety of biomolecular targets such as nucleic acids, antibodies and restriction enzymes while offering mechanisms to tune the dynamic window, the specificity, and the cooperativity of the sensor.
Heritability within groups is uninformative about differences among groups: Cases from behavioral, evolutionary, and statistical genetics

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give—directly or indirectly—some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the “between-group heritability” sometimes invoked in behavior genetics, the statistic PST used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.
Foraging task specialization in honey bees (Apis mellifera): the contribution of floral rewards on the learning performance of pollen and nectar foragers

Social insects live in communities where cooperative actions heavily rely on the individual cognitive abilities of their members. In the honey bee (Apis mellifera), the specialization in nectar or pollen collection is associated with variations in gustatory sensitivity, affecting both associative and non-associative learning. Gustatory sensitivity fluctuates as a function of changes in motivation for the specific floral resource throughout the foraging cycle, yet differences in learning abilities between nectar and pollen foragers at the onset of recollection remains unexplored. Here, we examined nectar and pollen foragers captured upon arrival at food sources. We subjected them to an olfactory PER conditioning using a 10% sucrose solution paired (S10%+P) or unpaired (S10%) with pollen as a co-reinforcement. For non-associative learning, we habituated foragers with a 10% sucrose solution paired (S10%+P) or unpaired (S10%) with pollen, followed by dishabituation tests with either S50% or S10%+P. Our results indicate that pollen foragers show lower performance than nectar foragers when conditioned with S10%. Interestingly, performance improves to levels similar to those of nectar foragers when pollen is included as rewarding stimulus (S10%+P). In non-associative learning, pollen foragers tested with S10%+P displayed a lower degree of habituation than nectar foragers and a higher degree of dishabituation when pollen was used as the dishabituating stimulus (S10%+P). Altogether, our results support the idea that pollen and nectar honey bee foragers differ in their perception of rewards, leading to inter-individual differences in learning that contribute to foraging specialization.
Effect of Calcium Nutrition on Reducing the Effects of Salinity on Tomato Plant

Since, the indiscriminate and irregular use of chemical fertilizers and improper management of cultivation and irrigation caused to increase the development process of saline areas and decrease of the agricultural crops growth is one of the important effects of salinity that in these conditions sodium and chloride ions in shoots and roots have been increased while we can see that the potassium and calcium ions have been decreased. By maintaining the proper amount of calcium ions in the soil, toxicity of sodium ions is controlled. In this study the effects of three different nutrient solutions, including the sodium chloride concentration with 0, 30, 60 and 90 mM , Calcium chloride concentrations with 0, 10, 20, 30 mM and sodium chloride + calcium chloride respectively to the same concentration were reviewed and investigated on two cultivated tomato varieties in a hydroponic form and pots containing Coco Peat. In the vegetative growth stage, relative water content of tissue and cytoplasmic membrane stability and leaf chlorophyll concentration after removal of the root and shoot dry weight were measured. Results showed that increase of salinity caused a significant reduction in relative water content of tissues, cytoplasmic membrane stability and chlorophyll concentration in leaves. Dry weight yield of roots and shoots also decreased with increasing the salinity so that all the characters were lowest in 90 mM of sodium chloride concentration. The results showed that the growth terms of calcium chloride and chloride + calcium chloride solutions were better, as the 10 mM concentration of calcium chloride has a significant impact on improving the damage caused by the salinity.