Miracle-dz/UniFAR-FTUs huggingface dataset:
The Epistemology of Fine-Grained News Classification

The process of news digitalization over the past decades has released massive amounts of news content, revolutionizing consumer access to news and disrupting traditional business models. These radical changes have also introduced new opportunities for media content analysis, potentially opening up new scenarios for ambitious large-scale media analytics initiatives, which can go well beyond the relatively small-scale studies currently carried out by media scholars and practitioners. However, take-up of computational methods to support media content analysis activities has been rather modest, reflecting a degree of disconnect between the needs of scholars and practitioners for task-specific and usable software solutions and the state of the art in computational techniques for news media analysis. In this paper we perform an initial step towards bridging this gap, by looking in detail at the task of fine-grained news classification . In particular, we propose a typology of news topics, which is formally specified and realised into a family of reusable ontologies. The proposed model has been validated empirically, through an analysis of a multilingual news corpus, as well as formally, in terms of the functional and logical properties of the ontologies. Our analysis brings together the media and computer science literature, connecting the formal definitions provided in this paper to the concepts used by media scholars.
Mouvements et utilisation de l'habitat en hiver chez un prédateur nomade: le harfang des neiges

V Liste des tableaux . XI Liste des figures . XIII Remerciements . XVII Avant-propos . XX CHAPITRE 1 : Introduction générale . 1 1.1. La migration aviaire . 2 1.1.1. Les types de migration . 3 1.2. Utilisation de l’habitat et de l’espace . 6 1.3. Fidélité et dispersion . 7 1.4. Effets reportés . 8 1.5. Écologie hivernale chez les migrateurs aviaires : la mal connue. 9 1.6. Techniques d’étude des espèces migratrices . 9 1.7. Le harfang des neiges Bubo scandiacus . 11 1.7. 1. Écologie de la reproduction. 11 1.7. 2. Écologie hivernale . 12 1.7. 3. Mouvements migratoires hivernaux . 13 1.8. Contexte et objectifs de la thèse . 14 CHAPITRE 2: Wintering space use and site fidelity in a nomadic species, the snowy owl . 18 2.1. Résumé . 19 2.2. Summary . 20 2.3. Introduction . 21 2.4. Material and methods . 23 2.4.1. Study area and snowy owl captures . 23 2.4.2. Locations of radio-marked individuals . 24 2.4.3. Environmental covariates . 24 2.4. 4. Data analysis . 25 2.4. 4.1. Determination of breeding and wintering periods . 25 2.4. 4.2. Determination of habitat categories . 25 2.4. 4.3. Determination of space use parameters . 26
Journal of International Women's Studies Journal of International Women's Studies

A substantial amount of literature dealing with conceptualisations of the nation has neglected the importance that gender and the politics of reproduction play in the construction of national identities. Analysing images of political campaigns and activists as well as public discourses on motherhood, abortion and childcare, I will illustrate the importance that gender and sexuality assumed in German nation-building projects before and after its unification in 1990. After 1949, East and West German ideas of nationhood were premised on opposing ideas of gender roles, in that politicians within these two German nations mobilised distinct gender identities to assert their respective political system as superior and progressive. While in East Germany, the progressiveness of the socialist project was measured along the lines of women’s integration into the labour force; in West Germany, the idea that a woman’s identity was primarily rooted in motherhood played an influential role in nationalist discourses. Once East and West Germany reunified in 1990, these opposing ideas of gender roles clashed. This became particularly visible in the context of political debates around abortion and childcare. An analysis of these debates suggests that the “new” unified German nation was premised upon a story in which the West German idea of the housewife-breadwinner model prevailed. This was diametrically opposed to what was framed as the East German “woman-worker” who had free access to abortion, and was abjected as immoral and backward. Analysing how such a national story was constructed is highly valuable, as it elucidates the ways in which gender has become a constitutive and structural element in the nation-building process of unified Germany to the present day.
Impact of Leadership style on Employee Commitment in Public Sector

Workers tend to experience higher levels of contentment and dedication to their organizations when their leaders exhibit the courage to communicate a clear vision and articulate the mission within a friendly and pleasant working environment. It is advisable for leaders to embrace an open-door policy, fostering an atmosphere where employees feel encouraged to engage in discussions about work-related matters and other issues hindering their optimal performance. This research seeks to explore the correlation between leadership styles and organizational commitment, while accounting for the job satisfaction of frontline staff at Southern Railway's Chennai Division. The study focuses on a sample size of 775 participants, utilizing Karl Pearson's Coefficient of Correlation to examine the relationship between organizational commitment and leadership style while controlling for frontline staff's job satisfaction. Employees consistently desire leaders who excel at problem-solving, provide guidance, serve as role models through inspirational motivation and team spirit — essential elements for task accomplishment. This approach not only enhances extrinsic job satisfaction but also fosters active organizational commitment, positively impacting employee turnover. The study's findings indicate a positive partial correlation between organizational commitment and leadership style when factoring in the job satisfaction of frontline staff at Southern Railway.
Examining the role of Big Data & Predictive Analytics on Collaborative Performance in context of Sustainable Consumption and Production Behaviour

The organizations engaged in sustainable development programmes are increasingly paying serious attention towards synergetic relationships between focal firms and their partners to achieve the goal of sustainable consumption and production (SCP) via big data and predictive analytics (BDPA). The study examines the role of BDPA in collaborative performance (CP) among the partners engaged in sustainable development programme to achieve the goal of SCP. The study further investigates the contingent effect of organization fit on the impact of BDPA on CP. We used variance based structural equation modelling (PLS SEM) to test research hypotheses using a sample of 190 respondents working in auto-components manufacturing organizations in India drawn from the ACMA and Dun & Bradstreet databases. The results indicate that BDPA has a significant positive impact on the CP among partners and the organizational compatibility and resource complementarity have positive moderating effects on the path joining BDPA and CP. The study contributes to the understanding of BDPA and collaboration literature in the context of sustainable development. These findings extend the dynamic capability view (DCV) to create a better understanding of contemporary applications of big data and predictive analytics capability, while also providing theoretically grounded directions to managers who seek to use information processing technologies to continuously improve the collaboration in supply chain networks. We have also noted some of the limitations of our study and identified numerous further research directions
On the Dependability of Bidirectional Encoder Representations from Transformers (BERT) to Soft Errors

—Transformers are widely used in natural language processing and computer vision, and Bidirectional Encoder Representations from Transformers (BERT) is one of the most popular pre-trained transformer models for many applications. This paper studies the dependability and impact of soft errors on BERT implemented with different floating-point formats using two case studies: sentence emotion classification and question & answering. Simulation by error injection is conducted to assess the impact of errors on different parts of the BERT model and different bits of the parameters. The analysis of the results leads to the following findings: 1) in both single and half precision, there is a Critical Bit (CB) on which errors significantly affect the performance of the model; 2) in single precision, errors on the CB may cause overflow in many cases, which leads to a fixed result regardless of the input; 3) in half precision, the errors do not cause overflow but they may still introduce a large accuracy loss. In general, the impact of errors is significantly larger in single-precision than half-precision parameters. Error propagation analysis is also considered to further study the effects of errors on different types of parameters and reveal the mitigation effects of the activation function and the intrinsic redundancy of BERT.
Enhancing Plausibility Evaluation for Generated Designs with Denoising Autoencoder

A great interest has arisen in using Deep Generative Models (DGM) for generative design. When assessing the quality of the generated designs, human designers focus more on structural plausibility, e.g., no missing component, rather than visual artifacts, e.g., noises in the images. Meanwhile, commonly used metrics such as Fr\'echet Inception Distance (FID) may not evaluate accurately as they tend to penalize visual artifacts instead of structural implausibility. As such, FID might not be suitable to assess the performance of DGMs for a generative design task. In this work, we propose to encode the input designs with a simple Denoising Autoencoder (DAE) and measure the distribution distance in the latent space thereof. We experimentally test our DAE-based metrics with FID and other state-of-the-art metrics on three data sets: compared to FID and some more recent works, e.g., FD$_\text{DINO-V2}$ and topology distance, DAE-based metrics can effectively detect implausible structures and are more consistent with structural inspection by human experts.
Shape of 12C

We have examined the hypothesis by Bijker and Iachello who asserted that 12C has an internal structure with three alpha particles arranged in a triangular shape, leading to the formation of the ground rotational band consisting of 0+, 2+, 3-, 4$^\pm$ and 5- states. Following this idea, we reconstructed the intrinsic shape of 12C using experimental electron scattering data with minimal theoretical assumption. Our sole assumption was that the observed 0+, 2+, 3-, and 4+ states share a common internal structure, forming a rotational spectrum. The reconstructed intrinsic density showed a beautiful triangular shape with three peaks implying alpha cluster formation in the ground band.
Assessment of Awareness Level of Nutritional and Medicinal Benefits of Moringa Oleifera Plant among Farmers in South West Nigeria

Moringa oleifera belongs to the monogeneric family Moringaceae (Verdcourt 1985; Olson 2002). The family Moringaceae includes species exhibiting a wide range of forms, from bottle trees to slender trees, sarcorhizal trees, or tuberous shrubs (Olson and Carlquist 2001). All these species are native to the Indian subcontinent, the Red Sea area, and parts of Africa, including Madagascar. Although Moringa is native to India and Pakistan (Morton 1991; Duke 2001), it is widely cultivated, especially in dry tropical areas of the Middle East and Africa (Fahey 2005; Palada et.al. 2007; Nouman et.al. 2013) and more recently in many countries located within the tropics, such as Nicaragua, because its pods, seeds, leaves, and roots are useful as fodder, vegetable, and plant growth enhancers (Sanchez et.al. 2006; Nouman et.al. 2013). Besides being consumed by humans, (Bennett et.al. 2003; Gidamis et.al. 2003), it is also used as animal fodder (Sanchez et.al. 2006; Nouman et.al. 2013), a natural coagulant of turbid water (Suarez et.al. 2003), and a source of phytomedical compounds (Anwar et.al. 2006). Moringa is a drought tolerant plant that can be grown in diverse soils, except those that are waterlogged. Slightly alkaline clay and sandy loam soils are considered the best media for this species due to their good drainage (Ramchandran et.al. 1980; Abdul 2007). Thus, Moringa can be grown in versatile conditions including hot, humid, dry tropical and subtropical regions, except for waterlogged conditions. It can perform better under marginal conditions with ample nutritional quality. Almost every part of the tree is of value for food. The leaf of moringa is a power house of nutritional value (Moringa News, 2008). The seed ABSTRACT: The study assessed the awareness level of nutritional and medicinal benefits of moringa plant among farmers in south west Nigeria. Osun, Ondo and Ekiti states were purposively selected among the six states in South West Nigeria based on the predominance of tree crop farming. Multi-stage sampling technique was used to select three hundred and twelve (312) respondents while interview schedule was used to collect data from the respondents. The results of thestudyrevealed that respondents recorded high level of awareness (72.8%) of the various nutritional value of the plant and medicinal values of Moringa oleifera leaf (75.6%), seed (60.3%), and flower (61.5%). Also, they were highly aware of the use of moringa plant as growth enhancer (92.9%), forage plant for livestock (82.1%) and in agroforestry (89.7%) The study therefore recommends the need for agricultural extension service to develop an impact point on the use of Moringa oleifera to enhance dissemination of information, most especially on the technology of moringa oil extraction, disease prevention and curation.
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Diffusion models have demonstrated remarkable performance in the domain of text-to-image generation. However, most widely used models still employ CLIP as their text encoder, which constrains their ability to comprehend dense prompts, encompassing multiple objects, detailed attributes, complex relationships, long-text alignment, etc. In this paper, we introduce an Efficient Large Language Model Adapter, termed ELLA, which equips text-to-image diffusion models with powerful Large Language Models (LLM) to enhance text alignment without training of either U-Net or LLM. To seamlessly bridge two pre-trained models, we investigate a range of semantic alignment connector designs and propose a novel module, the Timestep-Aware Semantic Connector (TSC), which dynamically extracts timestep-dependent conditions from LLM. Our approach adapts semantic features at different stages of the denoising process, assisting diffusion models in interpreting lengthy and intricate prompts over sampling timesteps. Additionally, ELLA can be readily incorporated with community models and tools to improve their prompt-following capabilities. To assess text-to-image models in dense prompt following, we introduce Dense Prompt Graph Benchmark (DPG-Bench), a challenging benchmark consisting of 1K dense prompts. Extensive experiments demonstrate the superiority of ELLA in dense prompt following compared to state-of-the-art methods, particularly in multiple object compositions involving diverse attributes and relationships.
DiffClass: Diffusion-Based Class Incremental Learning

Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden access to previous task data. Recent exemplar-free CIL methods attempt to mitigate catastrophic forgetting by synthesizing previous task data. However, they fail to overcome the catastrophic forgetting due to the inability to deal with the significant domain gap between real and synthetic data. To overcome these issues, we propose a novel exemplar-free CIL method. Our method adopts multi-distribution matching (MDM) diffusion models to unify quality and bridge domain gaps among all domains of training data. Moreover, our approach integrates selective synthetic image augmentation (SSIA) to expand the distribution of the training data, thereby improving the model's plasticity and reinforcing the performance of our method's ultimate component, multi-domain adaptation (MDA). With the proposed integrations, our method then reformulates exemplar-free CIL into a multi-domain adaptation problem to implicitly address the domain gap problem to enhance model stability during incremental training. Extensive experiments on benchmark class incremental datasets and settings demonstrate that our method excels previous exemplar-free CIL methods and achieves state-of-the-art performance.
Scaling of pre-equilibrium dilepton production in QCD Kinetic Theory

We use QCD kinetic theory to compute dilepton production coming from the pre-equilibrium phase of the Quark-Gluon Plasma created in high-energy heavy-ion collisions. We demonstrate that the dilepton spectrum exhibits a simple scaling in terms of the specific shear viscosity $\eta/s$ and entropy density $dS/d\zeta \sim {\scriptstyle \left(T\tau^{1/3}\right)_\infty^{3/2}}$, which can be derived from dimensional analysis in the presence of a pre-equilibrium attractor. Based on this scaling we perform event-by-event calculations of in-medium dilepton production and determine the invariant mass range where the pre-equilibrium yield is the leading contribution.
A Survey on Human-AI Teaming with Large Pre-Trained Models

In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and decision-making processes. The advent of Large Pre-trained Models (LPtM) has significantly transformed this landscape, offering unprecedented capabilities by leveraging vast amounts of data to understand and predict complex patterns. This paper surveys the pivotal integration of LPtMs with HAI, emphasizing how these models enhance collaborative intelligence beyond traditional approaches. It examines the synergistic potential of LPtMs in augmenting human capabilities, discussing this collaboration for AI model improvements, effective teaming, ethical considerations, and their broad applied implications in various sectors. Through this exploration, the study sheds light on the transformative impact of LPtM-enhanced HAI Teaming, providing insights for future research, policy development, and strategic implementations aimed at harnessing the full potential of this collaboration for research and societal benefit.
Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities

Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotation, and computational constraints remain. We introduce EdgeVL, a novel framework that bridges this gap by seamlessly integrating dual-modality knowledge distillation and quantization-aware contrastive learning. This approach enables the adaptation of large VL models, like CLIP, for efficient use with both RGB and non-RGB images on resource-limited devices without the need for manual annotations. EdgeVL not only transfers visual language alignment capabilities to compact models but also maintains feature quality post-quantization, significantly enhancing open-vocabulary classification performance across various visual modalities. Our work represents the first systematic effort to adapt large VL models for edge deployment, showcasing up to 15.4% accuracy improvements on multiple datasets and up to 93-fold reduction in model size.
Efficient Algorithms for Personalized PageRank Computation: A Survey

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes $s$ and $t$, the PPR value $\pi_s(t)$ equals the probability that an $\alpha$-discounted random walk from $s$ terminates at $t$ and reflects the importance between $s$ and $t$ in a bidirectional way. As a generalization of Google's celebrated PageRank centrality, PPR has been extensively studied and has found multifaceted applications in many fields, such as network analysis, graph mining, and graph machine learning. Despite numerous studies devoted to PPR over the decades, efficient computation of PPR remains a challenging problem, and there is a dearth of systematic summaries and comparisons of existing algorithms. In this paper, we recap several frequently used techniques for PPR computation and conduct a comprehensive survey of various recent PPR algorithms from an algorithmic perspective. We classify these approaches based on the types of queries they address and review their methodologies and contributions. We also discuss some representative algorithms for computing PPR on dynamic graphs and in parallel or distributed environments.
Denoising Autoregressive Representation Learning

In this paper, we explore a new generative approach for learning visual representations. Our method, DARL, employs a decoder-only Transformer to predict image patches autoregressively. We find that training with Mean Squared Error (MSE) alone leads to strong representations. To enhance the image generation ability, we replace the MSE loss with the diffusion objective by using a denoising patch decoder. We show that the learned representation can be improved by using tailored noise schedules and longer training in larger models. Notably, the optimal schedule differs significantly from the typical ones used in standard image diffusion models. Overall, despite its simple architecture, DARL delivers performance remarkably close to state-of-the-art masked prediction models under the fine-tuning protocol. This marks an important step towards a unified model capable of both visual perception and generation, effectively combining the strengths of autoregressive and denoising diffusion models.
Deep Sea Currents Driven by Breaking Internal Tides on the Continental Slope

Mooring data collected on the continental slope of the South China Sea show that along‐slope deep sea bottom currents are generated when large spring internal tides (internal waves with tidal frequency) are observed, with the maximum velocity amplitude exceeding 0.15 m/s. The observations are consistent with predictions that near‐bottom breaking of internal waves can result in generation of along‐slope flows when these waves obliquely approach the slope. A linear internal tide model in one horizontal dimension with realistic topography and stratification is used to show that the breaking of internal tides is likely due to near‐critical reflection on the slope. Combining the mooring observations and the model simulation, an along‐slope near‐bottom transport of ~0.5 Sv is estimated. Along‐slope bottom flows caused by breaking internal waves potentially provide a significant way to deform continental slopes and affect deep water exchange between the marginal sea and open ocean.