Miracle-dz/UniFAR-FTUs huggingface dataset:
Epstein-Barr Virus Epitope–Major Histocompatibility Complex Interaction Combined with Convergent Recombination Drives Selection of Diverse T Cell Receptor (cid:2) and (cid:3) Repertoires

Recognition modes of individual T cell receptors (TCRs) are well studied, but factors driving the selection of TCR repertoires from primary through persistent human virus infections are less well understood. Using deep sequencing, we demonstrate a high degree of diversity of Epstein-Barr virus (EBV)-speciﬁc clono-types in acute infectious mononucleosis (AIM). Only 9% of unique clonotypes detected in AIM persisted into convalescence; the majority (91%) of unique clono-types detected in AIM were not detected in convalescence and were seeming replaced by equally diverse “ de novo ” clonotypes. The persistent clonotypes had a greater probability of being generated than nonpersistent clonotypes due to convergence recombination of multiple nucleotide sequences to encode the same amino acid sequence, as well as the use of shorter complementarity-determining regions 3 (CDR3s) with fewer nucleotide additions (i.e., sequences closer to germ line). More-over, the two most immunodominant HLA-A2-restricted EBV epitopes, BRLF1 109 and BMLF1 280 , show highly distinct antigen-speciﬁc public (i.e., shared between individuals) features. In fact,
Visual Quality with a Focus on 3D Blur Discrimination and Texture Granularity

Blur is an important attribute in the study and modeling of the human visual system. In this work, 3D blur discrimination experiments are conducted to measure the just noticeable additional blur required to differentiate a target blur from the reference blur level. The past studies on blur discrimination have measured the sensitivity of the human visual system to blur using 2D test patterns. In this dissertation, subjective tests are performed to measure blur discrimination thresholds using stereoscopic 3D test patterns. The results of this study indicate that, in the symmetric stereo viewing case, binocular disparity does not affect the blur discrimination thresholds for the selected 3D test patterns. In the asymmetric viewing case, the blur discrimination thresholds decreased and the decrease in threshold values is found to be dominated by the eye observing the higher blur. The second part of the dissertation focuses on texture granularity in the context of 2D images. A texture granularity database referred to as GranTEX, consisting of textures with varying granularity levels is constructed. A subjective study is conducted to measure the perceived granularity level of textures present in the GranTEX database. An objective index that automatically measures the perceived granularity level of textures is also presented. It is shown that the proposed granularity metric correlates well with the subjective granularity scores and outperforms the other methods presented in the literature. A subjective study is conducted to assess the effect of compression on textures with varying degrees of granularity. A logarithmic function model is proposed as a fit to the subjective test data. It is demonstrated that the proposed model can be used for rate-distortion control by allowing the automatic selection of the needed compression ratio for a target visual quality. The proposed model can also be used
Comparative analysis of classification algorithms for crop yield prediction

A machine learning model is an essential tool for deciding which crops to produce and what to do during those crops' growing seasons. The employment of various machine learning algorithms in research to forecast higher crop output has considerably benefited the agriculture sector. In this study, an appropriate crop recommendation solution is constructed utilizing a Kaggle dataset that incorporates several factors such as (N-Nitrogen, K-Potassium, P-Phosphorus, Humidity, pH value of the soil, rainfall and temperature). The major goal of this model is to estimate which crop would grow best on a given farm based on the parameters that were used to create the model. The evaluated models revealed random forest to have the highest prediction accuracy with a score of 99 percent, K-Nearest Neighbor was next with a score of 97 and logistic regression recorded 96 percent. Hence random forest produced the highest accuracy score of 0.99 in recommending appropriate crops to farmers especially in times of drought and low soil fertility.
NATURE AND VARIATION OF SOIL PROPERTIES UNDER DIFFERENT LAND MANAGEMENT PRACTICES IN SOUTHERN PART OF KADUNA, KADUNA STATE, NIGERIA

Understanding the nature and variation of soil properties under different land management practices (LMPs) is imperative in determining the types of soil management practices that could be implemented by farmers for improving the soil quality as well as soil productivity. It is also applicable in addressing the issues of agricultural sustainability. This study examined the nature and variability of soil properties under different LMPs. Soil samples were systematically taken at 0-20cm soil depth from three locations on each demarcated hectare cell of the different LMPs in Katugal, Madakiya, Kurmin Sara, Mailafiya and Zankam. This produced a total of 25 composite soil samples. Soil laboratory analysis and Global Positioning System were the instruments employed for data collection. Descriptive statistics and analysis of variance were adopted in the analyses of data. The result of the study showed that about 56% of the soils studied were sandy-loam and 44% loamy in nature. Soil particle sizes, organic matter, total nitrogen, Cation Exchangeable Capacity (CEC), phosphorus, micronutrients and micro biomass carbon were heterogeneous (CV>33%), while bulk density, available water holding capacity, soil pH, electrical conductivity, micro biomass nitrogen and basal respiration were homogenous (CV=15 to 32%). There were significance variations within the level of pH and soil basal respiration at 0.05% among the land management practices. The study recommended the use of animal dung, compost, inorganic fertilizers and crop residue as soil conditioners in addition to the adoption of eco-friendly LMPs to ameliorate soil properties in the low to moderate variability farmlands.
THE ROLE OF JOB STANDARDIZATION IN BUILDING INTERNAL SERVICE QUALITY AND JOB SATISFACTION IN THE INDONESIAN PUBLIC SECTOR

This study seeks to analyse the pivotal role of Job Standardization in fostering Internal Service Quality and Job Satisfaction with the ultimate aim of cultivating Customer Orientation within the Indonesian Public Sector, specifically within the Provincial Civil Service Agency (BKD). Employing a quantitative research design to fulfil the research objective, the study utilizes cluster sampling, selecting respondents from provinces with varying employee populations. The sample comprises 404 respondents from BKD offices in South Kalimantan, North Sulawesi, South Sumatera, Lampung, South Sulawesi, Western Sumatera, Western Papua, North Maluku, and Papua Provinces. Utilizing Structural Equation Modelling (SEM) through the LISREL 8.80 application
The Use of Macro and Micro Structures in Pakistani Prime Minister’s Speech at UNGA: A Critical Discourse Analysis Approach Naeem Afzal

Political speeches delivered by state heads or politicians may stir controversies sometimes, especially, when speakers give arguments for or against some issues. To investigate so-called political discourses, critical discourse studies are conducted in different contexts. Critical Discourse Analysis (CDA), for instance, explores connection between different uses of discourse structures and socio-political contexts in which these structures occur. It also highlights issues such as power, dominance, ideology, and manipulation. The current study investigated Pakistani Prime Minister Imran Khan’s maiden speech to the 74 th Session of the United Nations General Assembly (UNGA) on September 27, 2019 – watched by over four million viewers on YouTube. This study used van Dijk’s (1980) framework to analyze macro and micro structures in the speech and the underlying agenda behind employing such discourse structures. The qualitative data consisted of speech transcript, delivered in English, which contained about 2577 words. Findings reveal that macro propositions in the speech summarized global, regional, and national issues such as climate change, money laundering, corruption, Islamophobia, Pakistan’s war on terror, the Kashmir conflict, and Pakistan’s relations with India. The use of microstructural elements (pronouns, rhetorical question, references, number game, presupposition, modality, lexis and conjunctions) enabled the speaker to voice his ideological and political beliefs on aforementioned issues.
Publications and Preprints

Mario Maican 18. On the geometry of the moduli space of sheaves supported on curves of genus four contained in a quadric surface. arXiv:1704.07011 17. Moduli of stable sheaves on a quadric surface supported on curves of genus three. arXiv:1704.00810 16. Moduli of sheaves supported on curves of genus two in a quadric surface. Geometriae Dedicata, in press. 15. Moduli of space sheaves with Hilbert polynomial 4m + 1. Canadian Mathematical Bulletin 61 (2018), 328–345. 14. (with Marcos Jardim and Alexander Tikhomirov) Moduli spaces of rank 2 instanton sheaves on the projective space. Pacific Journal of Mathematics 291 (2017), 399–424. 13. On two moduli spaces of sheaves supported on quadric surfaces. Osaka Journal of Mathematics 54 (2017), 323–333. 12. Flag Hilbert schemes and moduli spaces of torsion plane sheaves. Communications in Algebra 45 (2017), 332–342. 11. (with Jinwon Choi and Kiryong Chung) Moduli of sheaves supported on quartic space curves. Michigan Mathematical Journal 65 (2016), 637–671. 10. On the homology of the moduli space of plane sheaves with Hilbert polynomial 5m+ 3. Bulletin des Sciences Mathématiques 139 (2015), 1–32. 9. (with Jinwon Choi) Torus action on the moduli spaces of torsion plane sheaves of multiplicity four. Journal of Geometry and Physics 83 (2014), 18–35. 8. The homology groups of certain moduli spaces of plane sheaves. International Journal of Mathematics 24 (2013), Article ID 1350098, 42 p. 7. The classification of semistable plane sheaves supported on sextic curves. Kyoto Journal of Mathematics 53 (2013), 739–786. 6. On some quotients modulo non-reductive groups. Bulletin Mathématique de la Société des Sciences Mathématiques de Roumanie 55 (103) (2012), 375–412. 5. On the moduli spaces of semi-stable plane sheaves of dimension one and multiplicity five. Illinois Journal of Mathematics 55 (2011), 1467–1532. 4. (with Jean-Marc Drézet) On the geometry of the moduli spaces of semi-stable sheaves supported on plane quartics. Geometriae Dedicata 152 (2011), 17–49. 3. A duality result for moduli spaces of semistable sheaves supported on projective curves. Rendiconti del Seminario Matematico della Università di Padova 123 (2010), 55–68. 2. On two notions of semistability. Pacific Journal of Mathematics 234 (2008), 69–135. 1. On vector bundles of finite order. Manuscripta Mathematica 124 (2007), 97–137.
Factors Involved in Decision-Making Dilemmas Faced by Parents of Children with Severe Asthma in PICU During the Development of Discharge Care Plans: A Phenomenological Study

Purpose This study aims to explore the complicated decision-making dilemma and challenges confronted by parents of children suffering from severe asthma within the Pediatric Intensive Care Unit (PICU) when participating in the development of their children’s discharge care plans. Patients and Methods Employing a phenomenological methodology, a purposive sampling was performed to engage with 17 parents who participated in in-depth and semi-structured interviews between October 2022 and February 2023. The transcripts of these interviews were transcribed into textual data, which was then subjected to Colaizzi’s seven-step analysis for meticulous coding and comprehensive thematic elucidation. Results The comprehensive analysis of the factors involved in the intricate decision-making dilemmas faced by parents of children with severe asthma during the process of crafting discharge care plans in the PICU revealed five themes and eight sub-themes: 1) Complexity of asthma-related information; 2) Insufficient provision of comprehensive decision-making support; 3) Encountering negative emotions and wavering confidence; 4) Navigating realistic constraints impacting both parents and HCPs; 5) Balancing the advantages and disadvantages of various plans. Conclusion Parents of children with severe asthma in the PICU encounter intricate and multifaceted decision-making dilemmas while engaging in the formulation of discharge care plans. These complexities significantly dampen their decision-making enthusiasm and introduce potential risks to the children’s prognosis and recovery. In the future, it is imperative to leverage the guidance provided by healthcare professionals (HCPs) in the decision-making process, develop tailored decision support tools specifically designed for the formulation of discharge care plans for children with severe asthma in the PICU.
Intraday Trading Algorithm for Predicting Cryptocurrency Price Movements Using Twitter Big Data Analysis

Cryptocurrencies have emerged as a novel financial asset garnering significant attention in recent years. A defining characteristic of these digital currencies is their pronounced short-term market volatility, primarily influenced by widespread sentiment polarization, particularly on social media platforms such as Twitter. Recent research has underscored the correlation between sentiment expressed in various networks and the price dynamics of cryptocurrencies. This study delves into the 15-minute impact of informative tweets disseminated through foundation channels on trader behavior, with a focus on potential outcomes related to sentiment polarization. The primary objective is to identify factors that can predict positive price movements and potentially be leveraged through a trading algorithm. To accomplish this objective, we conduct a conditional examination of return and excess return rates within the 15 minutes following tweet publication. The empirical findings reveal statistically significant increases in return rates, particularly within the initial three minutes following tweet publication. Notably, adverse effects resulting from the messages were not observed. Surprisingly, sentiments were found to have no discerni-ble impact on cryptocurrency price movements. Our analysis further identifies that inves-tors are primarily influenced by the quality of tweet content, as reflected in the choice of words and tweet volume. While the basic trading algorithm presented in this study does yield some benefits within the 15-minute timeframe, these benefits are not statistically significant. Nevertheless, it serves as a foundational framework for potential enhance-ments and further investigations.
Large Language Models aren't all that you need

This paper describes the architecture and systems built towards solving the SemEval 2023 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) [1]. We evaluate two approaches (a) a traditional Conditional Random Fields model and (b) a Large Language Model (LLM) fine-tuned with a customized head and compare the two approaches. The novel ideas explored are: 1) Decaying auxiliary loss (with residual) - where we train the model on an auxiliary task of Coarse-Grained NER and include this task as a part of the loss function 2) Triplet token blending - where we explore ways of blending the embeddings of neighboring tokens in the final NER layer prior to prediction 3) Task-optimal heads - where we explore a variety of custom heads and learning rates for the final layer of the LLM. We also explore multiple LLMs including GPT-3 and experiment with a variety of dropout and other hyperparameter settings before arriving at our final model which achieves micro&macro f1 of 0.85/0.84 (on dev) and 0.67/0.61 on the test data . We show that while pre-trained LLMs, by themselves, bring about a large improvement in scores as compared to traditional models, we also demonstrate that tangible improvements to the Macro-F1 score can be made by augmenting the LLM with additional feature/loss/model engineering techniques described above.
Realizing Open and Decentralized Marketplace for Exchanging Data of Expected IoT Behaviors

With rising concerns about the security of IoT devices, network operators need better ways to handle potential risks. Luckily, IoT devices show consistent patterns in how they communicate. But despite previous efforts, it remains unclear how knowledge of these patterns can be made available. As data marketplaces become popular in different domains, this paper1 proposes creating a special marketplace focused on IoT cybersecurity. The goal is to openly share knowledge about IoT devices' behavior, using structured data formats like Manufacturer Usage Description (MUD) files. To make this work, we employ technologies like blockchain and smart contracts to build a practical and secure foundation for sharing and accessing important information about how IoT devices should behave on the network. Our contributions are two-fold. (1) We identify the essential features of an effective marketplace for sharing data related to the expected behaviors of IoT devices. We develop a smart contract on the Ethereum blockchain with five concrete functions; and, (2) We implement a prototype of our marketplace in a private chain environment-our codes are publicly released. We demonstrate how effectively our marketplace functions through experiments involving MUD files from consumer IoT devices. Our marketplace enables suppliers and consumers to share MUD data on the Ethereum blockchain for under a hundred dollars, promoting accessibility and participation.
Detecting Imprinting and Maternal Effects Using Monte Carlo Expectation Maximization Algorithm

Numerous statistical methods have been developed to explore genomic imprinting and maternal effects, which are causes of parent-of-origin patterns in complex human diseases. However, most of them either only model one of these two confounded epigenetic effects, or make strong yet unrealistic assumptions about the population to avoid over-parameterization. A recent partial likelihood method (LIME) can identify both epigenetic effects based on case-control family data without those assumptions. Theoretical and empirical studies have shown its validity and robustness. However, because LIME obtains parameter estimation by maximizing partial likelihood, it is interesting to compare its efficiency with full likelihood maximizer. To overcome the difficulty in over-parameterization when using full likelihood, in this study we propose a Monte Carlo Expectation Maximization (MCEM) method to detect imprinting and maternal effects jointly. Those unknown mating type probabilities, the nuisance parameters, can be considered as latent variables in EM algorithm. Monte Carlo samples are used to numerically approximate the expectation function that cannot be solved algebraically. Our simulation results show that though this MCEM algorithm takes longer computational time, and can give higher bias in some simulations compared to LIME, it can generally detect both epigenetic effects with higher power and smaller standard error which demonstrates that it can be a good complement of LIME method.
Moduli spaces of arrangements of 12 projective lines with a sextic point

*This paper is from 2018* In this paper, we try to classify moduli spaces of arrangements of $12$ lines with sextic points. We show that moduli spaces of arrangements of $12$ lines with sextic points can consist of more than two connected components. We also present defining equations of the arrangements whose moduli spaces are not irreducible taking quotients by the complex conjugation by supply some potential Zariski pairs. Through complex conjugation we take quotients and supply some potential Zariski pairs.
Searching, fast and slow, through product catalogs

String matching algorithms in the presence of abbreviations, such as in Stock Keeping Unit (SKU) product catalogs, remains a relatively unexplored topic. In this paper, we present a unified architecture for SKU search that provides both a real-time suggestion system (based on a Trie data structure) as well as a lower latency search system (making use of character level TF-IDF in combination with language model vector embeddings) where users initiate the search process explicitly. We carry out ablation studies that justify designing a complex search system composed of multiple components to address the delicate trade-off between speed and accuracy. Using SKU search in the Dynamics CRM as an example, we show how our system vastly outperforms, in all aspects, the results provided by the default search engine. Finally, we show how SKU descriptions may be enhanced via generative text models (using gpt-3.5-turbo) so that the consumers of the search results may get more context and a generally better experience when presented with the results of their SKU search.
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation

Building embodied agents on integrating Large Language Models (LLMs) and Reinforcement Learning (RL) have revolutionized human-AI interaction: researchers can now leverage language instructions to plan decision-making for open-ended tasks. However, existing research faces challenges in meeting the requirement of open-endedness. They typically either train LLM/RL models to adapt to a fixed counterpart, limiting exploration of novel skills and hindering the efficacy of human-AI interaction. To this end, we present OpenPAL, a co-training framework comprising two stages: (1) fine-tuning a pre-trained LLM to translate human instructions into goals for planning, and goal-conditioned training a policy for decision-making; (2) co-training to align the LLM and policy, achieving instruction open-endedness. We conducted experiments using Contra, an open-ended FPS game, demonstrating that an agent trained with OpenPAL not only comprehends arbitrary instructions but also exhibits efficient execution. These results suggest that OpenPAL holds the potential to construct open-ended embodied agents in practical scenarios.
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://xcodemind.github.io). At last, we also point out several challenging and promising directions for future research.