Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the palimpsest: An exploratory agent-based model of surface archaeological deposit formation in a fluvial arid Australian landscape

Archaeologists make inferences about past human behaviour based on patterned material residues in various depositional contexts, including existing landsurfaces. These deposits are generated by processes that may obscure patterns at some observational scales while highlighting others, and interpretive differences can arise from a lack of explicit models of deposit formation. Here, an exploratory agent-based model based on the concept of the palimpsest is used to examine the effects of episodic sediment transport on the visibility and preservation of surface archaeological deposits in a fluvial context. Outcomes from the model indicate that the compound influences of preservation and visibility are capable of transforming a static radiocarbon record into one of increasing intensity towards the present, while simultaneously displaying periodic chronological gaps – features that have been used in our Australian study area to argue for demographic change driven by social or environmental factors. To differentiate between interpretations, expectations derived from the model are assessed against a second proxy from the same study area: Optically Stimulated Luminescence dates from hearth stones in surface contexts. Results indicate that patterns in the chronometric proxies from the study area are more consistent with episodic geomorphic change than explanations invoking changes in the local organization of human activity.
Improving Dhivehi Automatic Speech Recognition (ASR) with Sub-word Modelling, Language Model Decoding and Automatic Spelling Correction

Current state-of-the-art (SOTA) Automatic Speech Recognition (ASR) models are multilingual. While these models have greatly improved transcription accuracy for high-resourced languages, under-resourced languages still require further language specific optimizations and finetuning to achieve acceptable levels of accuracy. In this work we explore ways of improving ASR for Dhivehi, an under-resourced South Asian language, by finetuning pretrained multilingual ASR models, Sub-word Modelling, Language Model (LM) decoding and Automatic Spelling Correction. We finetune 5 Dhivehi ASR models and apply our accuracy boosting techniques, with one of our models achieving a new state-of-the-art Word Error Rate (WER) of 14.26% on the Dhivehi Common Voice ASR benchmark, which is a 31.93% relative WER improvement over the existing SOTA of 20.95%. We create a new Dhivehi text corpus, and train 2 new Dhivehi LMs to support our accuracy boosting techniques.
Extracting higher-order logic formulas from English sentences

We proposed a framework and its implementation as a Python library for converting English utterances into higher-order logic (HOL) formulas. HOL extends first-order logic and provides flexibility for representing natural language semantics. Our library uses a broad-coverage and robust HPSG grammar for English to produce minimal recursive semantics (MRS) structures. These open-source technologies from the DELPH-IN Consortium balance a rigorous linguistic grounding and composition-ality with practical aspects for natural language processing applications. Finally, we evaluated our approach over SICK, a popular dataset for text entailment.
Exploring the Categorical Nature of Colour Perception: Insights from Artificial Networks

This study delves into the categorical aspects of colour perception, employing the odd-one-out paradigm on artificial neural networks. We reveal a significant alignment between human data and unimodal vision networks (e.g., ImageNet object recognition). Vision-language models (e.g., CLIP text-image matching) account for the remaining unexplained data even in non-linguistic experiments. These results suggest that categorical colour perception is a language-independent representation, albeit partly shaped by linguistic colour terms during its development. Exploring the ubiquity of colour categories in Taskonomy unimodal vision networks highlights the task-dependent nature of colour categories, predominantly in semantic and 3D tasks, with a notable absence in low-level tasks. To explain this difference, we analysed kernels’ responses before the winnertaking-all, observing that networks with mismatching colour categories align in continuous representations. Our findings quantify the dual influence of visual signals and linguistic factors in categorical colour perception, thereby formalising a harmonious reconciliation of the universal and relative debates.
Spatial proteomics of ER tubules reveals CLMN, an ER-actin tether at focal adhesions that promotes cell migration

The endoplasmic reticulum (ER) is structurally and functionally diverse, yet how its functions are organized within morphological subdomains is incompletely understood. Utilizing TurboID-based proximity labeling and CRISPR knock-in technologies, here we map the proteomic landscape of the human ER and nuclear envelope. Spatial proteomics reveals enrichments of proteins into ER tubules, sheets, and nuclear envelope. We uncover an ER-enriched actin-binding protein, Calmin (CLMN), and define it as an ER-actin tether that localizes to focal adhesions adjacent to ER tubules. CLMN depletion perturbs focal adhesion disassembly, actin dynamics, and cell movement. Mechanistically, CLMN-depleted cells also exhibit defects in calcium signaling near ER-actin interfaces, suggesting CLMN promotes calcium signaling near adhesions to facilitate their disassembly. Collectively, we map the sub-organelle proteome landscape of the ER, identify CLMN as an ER-actin tether, and describe a non-canonical mechanism by which ER tubules engage actin to regulate cell migration.
A reduced vernalization requirement is a key component of the early-bolting trait in globe artichoke (Cynara cardunculus var. scolymus)

Early-bolting is a major breeding objective for globe artichoke (Cynara cardunculus var. scolymus L.). It has been suggested that globe artichoke bolting time is linked to a vernalization requirement, although environmental conditions under which vernalized plants and controls have been grown may not always allow for proper comparison. Here, we defined morphological markers to monitor the vegetative-to- reproductive phase transition at the shoot apex and linked these to expression changes of homologues of key Arabidopsis flowering regulators SOC1, FUL, and AP1. Importantly, we developed an experimental setup where control and vernalized plants grow under comparable conditions. These tools together allowed for comparison of the vegetative-to-reproductive phase transition between early- and late-bolting genotypes and how they respond to vernalization. Our results show that vernalization requirement is significantly lower in early-bolting genotypes, supporting the view that the early-bolting trait is partly underlain by alterations in the network controlling vernalization response.
Exploring the 6G Software Business Ecosystem: A Morphological Analysis Approach

6G technology, enabled by modern software technologies, is under development and expected to be commercially available within the coming decade. Existing 6G research mostly focuses on technology standardization and lacks business consideration. Aiming to bridge the gap between 6G technology and business strategy, this study envisions the 6G ecosystem through the lens of software business. Exploring and understanding the 6G software business ecosystem helps the stakeholders identify their competitive strengths in the earlier stage and strategically prepare for the 6G era. This paper adopts Morphological Analysis as a future-oriented research methodology to depict the 6G software business ecosystem by decomposing that ecosystem into manageable components. It identifies the main ecosystem building blocks including technology enablers, external opportunities and challenges, and stakeholder roles. The findings are pioneers in the 6G software business field and highlight the complexity of the open 6G software business ecosystem.
Optimal bin number for histogram binning method to calibrate binary probabilities

A receiver operating characteristic (ROC) curve analysis is an important instrument while selecting the best for the given dataset a classification algorithm by comparing the area under ROC curve. It is applied in areas: medicine, finance and e-commerce, information retrieval, quality control. However, the accuracy of area under ROC curve imposing on predicted probabilities a threshold >0.5, therefore when predicted probability is calculated with different logic then corresponding area under ROC curve is affected and ROC curve analysis's results are misleading. To guarantee the accuracy of area under ROC curve, predicted probabilities must be calibrated. The subject matter of the article is “fixed-width binning” method which is used to calibrate binary predicted probabilities of machine learning algorithms Naive Bayes Classifier, Random Forest Classifier. In this paper the focus is put on “fixed-width binning” method which algorithm is based on the constant number of bins. The goal of work is to increase the calibration scores by proposing a method to select bin number depending on simple statistics of binary predicted uncalibrated probabilities. To meet the goal in the research were evaluated the feasibility to use two different approaches for the identification of the optimal bins’ number: “rule-based” approach, “estimators-based” approach. The results of conducted experiments identified that often used 10 bins with “fixed-width binning” method is not optimal. Our proposal is to identify bin number dynamically according to “estimators-based” approach which algorithm is described in the paper.
Automated processing and analysis of medical texts

This study explores the development of methods and tools for automated processing and analysis of medical texts using the Java programming language. The analysis of medical texts holds significant promise for enhancing the quality of medical diagnosis, treatment planning, and scientific research. Leveraging Java as the primary programming language enables the creation of efficient and robust tools capable of handling substantial volumes of medical data. In this paper, we conduct a comprehensive review of the known sources pertaining to automated medical text processing. We delve into the methods and technologies employed for medical text analysis, emphasizing the crucial steps of data collection and preparation for subsequent analysis. A substantial portion of work centers on the practical implementation of a Java-based system for processing and analyzing medical texts. Utilization of various text-processing libraries, machine learning, deep learning tools, and the integration of databases for the storage of medical data has been explored. The efficacy of the developed system has been assessed and compared with other methods and tools commonly used in the analysis of medical texts. The obtained results shed light on the system's performance and highlight its potential advantages. As conclusion, insights into potential avenues for future research in this vital domain has been proposed.
Solving problems using the General Environment Description Language

The history of automated systems demonstrates their wide-ranging impact on industries. Artificial intelligence has potential for task automation without human oversight, but currently "narrow AI" focuses on specific tasks rather than general intelligence. Versatile systems that can learn autonomously and adapt to various environments are essential for technological progress. This can only be achieved with accurate environment description. The General Environment Description Language (GEDL) emerged as the most promising solution for conceptualizing agent and robot environments. The notation is inspired by human knowledge organization, with an emphasis on learning from errors and improving efficiency. GEDL's definition of conceptual systems, occurrences, and experiences makes it an effective problem-solving tool. The study's focus centers on developing an algorithm with the capability to solve a range of issues for general-purpose systems. A proposed solution takes the form of a universal approach to problem-solving with available resources. The outcome of implementing the algorithm to accomplish a basic task, such as navigating a board, is outlines. A procedure for improving the execution is also analyzed.
Baseline Design and Performance Analysis for Distributed Spaceborne Interferometric Synthetic Aperture Radar

Basing on linearized Kepler equation,the formula of formation flying orbit parameters is induced.The performance analysis formulas for spaceborne distributed InSAR system are explored,which give the measurement precision of height and velocity and the refinement of space resolution.According to the critical baseline analysis and the formation configuration design,a baseline design technique is proposed,which is applicable for the distributed spaceborne InSAR systems with general three dimensional formation flying orbit.Computer simulation results for the interferometric baseline analysis are demonstrated,with the formation flying orbital peremeters design for an L-band distributed spaceborne InSAR system,and the efficiency of the baseline design technique is validated by the performance analysis.
MUDANÇAS CLIMÁTICAS, INCERTEZAS HIDROLÓGICAS E VAZÃO FLUVIAL: O CASO DO ESTUÁRIO DO RIO ANIL

O gerenciamento de recursos hídricos tem se submetido a mudanças devido ao crescimento da população, melhoramento sanitário, desenvolvimento econômico, tecnológico, e tem se mudado circunstâncias legislativas e administrativas. Estas mudanças continuarão no futuro, com sua intensidade que é dependente dos processos demográficos e econômicos em várias partes do mundo. Esta introdução da mudança no clima adiciona uma dimensão nova à dinâmica de demanda de água. A proposta do presente trabalho é o de produzir alguns cenários climáticos, e discutir como a descarga fluvial se comporta em face aos cenários produzidos. Como resultado pode-se dizer que possivelmente o fator que mais influenciou na descarga fluvial (Qf), dentre os fatores estudados (temperatura e precipitação), a precipitação se mostrou mais eficiente em alterar a descarga fluvial. No entanto, um estudo mais profundo é recomendado.
Multi-modality fusion : registering photographs, videos, and LIDAR range scans

2D images and 3D LIDAR range scans provide very different but complementing information about a single subject and, when registered, can be used for a variety of exciting applications. Video sets can be fused with a 3D model and played in a single multi-dimensional environment. Imagery with temporal changes can be visualized simultaneously, unveiling changes in architecture, foliage, and human activity. Depth information for 2D photos and videos can be computed. Real-world measurements can be provided to users through simple interactions with traditional photographs. However, fusing multi-modality data is a very challenging task given the repetition and ambiguity that often occur in man-made scenes as well as the variety of properties different renderings of the same subject can possess. Image sets collected over a period of time during which the lighting conditions and scene content may have changed, different artistic renderings, varying sensor types, focal lengths, and exposure values can all contribute to visual variations in data sets. This dissertation addresses these obstacles using the common theme of incorporating contextual information to visualize regional properties that intuitively exist in each imagery source. We combine hard features that quantify the strong, stable edges that are often present in imagery along object boundaries and depth changes with soft features that capture distinctive texture information that can be unique to specific areas. We show that our detector and descriptor techniques can provide more accurate keypoint match sets between highly varying imagery than many traditional and state-of-the-art techniques, allowing us to fuse and align photographs, videos, and range scans containing both man-made and natural content.
Designing for Presence: Embodied Interaction in Computer-Mediated Realities

There is only embodied interaction in computer-mediated realities. We use our bodies to sense, interact with, process, and remember environments. Effective computer-mediated realities, i.e. mixed and virtual realities, depend on the user's feeling of being part of those environments. We are explaining our approach towards a theoretical model for this sense of presence. Based on techno-psychological, philosophical, and socio-psychological models we are presenting a theory for the understanding of and design for such mediated realities. We emphasize the role of the human body as a sensing and interacting instrument and argue that presence is achieved by self-controlled navigation, object interaction, and subject interaction. Presence therefore derives from body-related possibilities of interaction with the environment. We demonstrate this through the definition of a theoretical presence model for embodied interaction in computer-mediated realities. This article
Miocene Foraminifera from the Lajas Valley, southwest Puerto Rico

Fossils are recorded for the first time from a limestone in the Lajas Valley of SW. Puerto Rico. Twenty-three species of Foraminifera are listed, and the other elements of the fauna are also recorded. Two foraminiferal species are new, Pyrgo opima and Dendritina lajasensis. It is concluded that the fauna is Miocene in age.
Sclera-TransFuse: Fusing Swin Transformer and CNN for Accurate Sclera Segmentation

Sclera segmentation is a crucial step in sclera recognition, which has been greatly advanced by Convolutional Neural Networks (CNNs). However, when dealing with non-ideal eye images, many existing CNN-based approaches are still prone to failure. One major reason is that due to the limited range of receptive fields, CNNs are difficult to effectively model global semantic relevance and thus robustly resist noise interference. To solve this problem, this paper proposes a novel two-stream hybrid model, named Sclera-TransFuse, to integrate classical ResNet-34 and recently emerging Swin Transformer encoders. Specially, the self-attentive Swin Transformer has shown a strong ability in capturing long-range spatial dependencies and has a hierarchical structure similar to CNNs. The dual encoders firstly extract coarse- and fine-grained feature representations at hierarchical stages, separately. Then a novel Cross-Domain Fusion (CDF) module based on information interaction and self-attention mechanism is introduced to efficiently fuse the multi-scale features extracted from dual encoders. Finally, the fused features are progressively upsampled and aggregated to predict the sclera masks in the decoder meanwhile deep supervision strategies are employed to learn intermediate feature representations better and faster. Experimental results show that Sclera-TransFuse achieves state-of-the-art performance on various sclera segmentation benchmarks. Additionally, a UBIRIS.v2 subset of 683 eye images with manually labeled sclera masks, and our codes are publicly available to the community through https://github.com/Ihqqq/Sclera-TransFuse.
Text-based personality prediction using XLNet

Personality is a dynamic and organized set of characteristics that distinguish a person in thinking patterns, behaviors, emotions, and motivations based on biological and environmental factors. In particular, personality is a broad subject that is widely studied in various domains such as mental healthcare, web intelligence, and recommendation systems. Traditionally, researchers used psychology methods via psycholinguistic approaches (word counting in specific texts) to identify personality. Recently, social media data have been used for studies on personality. Psychologists and scientists are determining the personality of a person with the Myers – Briggs Type Indicator (MBTI) and Big Five model. Moreover, transformers-based models have shown better results in natural language processing tasks with context-dependent features. In this chapter, we propose a text-based personality prediction system using XLNet, which learns bidirectional context via factorization order and relative positional encoding. Experiments on two different gold-standard personality detection datasets show that the proposed model obtains up to 4% accuracy improvement.
Comparative Differences Between Traditional Chinese Medicine Comparative Differences Between Traditional Chinese Medicine and Western Medicine in Treating Type Two Diabetes Mellitus and Western Medicine in Treating Type Two Diabetes Mellitus

In the United States alone, there were 25.8 million people suffering from diabetes in 2010. The prevalence of diabetes is expected to markedly increase worldwide over the next 30 years, an estimated 2.8% in 2000 and 4.4% in 2030. For individuals diagnosed with type 2 diabetes mellitus (T2DM), treatment is essential to control adverse effects such as hypertension and diabetic neuropathy. The focus of this study is to examine various approaches to maintain and improve the lifestyle of individuals suffering from T2DM. A comparative approach has been used to evaluate the differences in the treatment of T2DM with the use of traditional Chinese medicine (TCM) and Western medicine. In Western society, pharmaceuticals are commonly used as a treatment method to manage hyperglycemia, along with life-style modifications. Furthermore, TCM views the human body and its functioning in a holistic way, stating that no single body part or symptom can be understood apart from its relation to a whole. Herbal medications and other treatments in TCM are targeted to treat underlying medical complaints that resulted in symptoms, instead of treating one specific manifestation. Data collection has been gathered through Qualitative over the phone interviews with patients suffering from T2DM, as well as TCM physicians. Interviews were conducted on patients that were diagnosed with T2DM (fasting plasma glucose levels of 126 or greater and HbAlc levels >8%), and had continued treatment longer than three months prior to interviews. Collection of chart notes containing glucose levels, levels of pain, lifestyle changes, and vital
EMPIRICAL STUDY The Efficacy of Gesture on Second Language Pronunciation: An Exploratory Study of Handclapping as a Classroom Instructional Tool

In this study, we examined the efﬁcacy of gestures for the acquisition of L2 segmental phonology. Despite teachers’ frequent use of gestures in the classroom to teach pronunciation, the ﬁeld lacks empirical support for this practice. We attempted to ﬁll this gap by investigating the effects of handclapping on the development of L2 Japanese segmentals (long vowels, geminates, and moraic nasals). We assigned L1 English university students in beginning Japanese courses to one of two groups where they practiced pronouncing the targets with or without handclapping in the classroom. They also completed picture elicitation (production) and dictation (perception) tasks as pretests, immediate posttests, and delayed posttests. The results show that, on the delayed perception posttest, only those who saw and performed handclapping maintained the instructional effect, indicating that the memory-enhancing effect of gestures, at least in the form of handclapping, might reach the level of segmental phonology in L2 acquisition.