Miracle-dz/UniFAR-FTUs huggingface dataset:
Identity and Development: Tongan Culture, Agriculture, and the Perenniality of the Gift

Identity and Development presents a remarkable record of Tonga's increasing participation in the modern global economy, and provides anthropologists, economists, and historians with a detailed case study that bears heavily on major issues of the day, both practically and theoretically. The book focuses on issues of identity, entrepreneurship, and the intricacies of development and addresses the question: 'How (in the current state of the economy) can a Tongan become a successful grower? ' This question is set against the background of a boom in cash cropping, sparked by a burgeoning export trade with Japan.
Modelling and Simulations of Ferroelectric Materials and Ferroelectric-Based Nanoelectronic Devices : (Invited Paper)

This paper provides a brief introduction to the phenomenological aspects of the polarization in ferrroelectric materials, and then an analysis of a few selected topics related to the modelling of ferroelectrics. The description of ferroelectric-based devices is quite challenging, particularly because the ferroelectric is frequently stacked with other dielectrics or with a semiconductor, as opposed to being placed between metal electrodes. Predictive modelling of ferroelectric devices is admittedly diﬃcult, and thus the scrutiny and calibration of the models by comparison to sound experimental data is of paramount importance.
TempoGRAPHer: Aggregation Based Temporal Graph Exploration

Graphs offer a generic abstraction for modeling entities, and the interactions and relationships between them. Most real world graphs, such as social and cooperation networks evolve over time, and exploring their evolution may reveal important information. In this paper, we present TempoGRAPHer, a system for visualizing and analyzing the evolution of a temporal attributed graph. TempoGRAPHer supports both temporal and attribute aggregation. It also allows graph exploration by identifying periods of significant growth, shrinkage, or stability. Temporal exploration is supported by two complementary strategies, namely skyline and interaction-based exploration. Skyline-based exploration provides insights on the overall trends in the evolution, while interaction-based exploration offers a closer look at specific parts of the graph evolution history where significant changes appeared. We showcase the usefulness of TempoGRAPHer in understanding graph evolution by presenting a detailed scenario that explores the evolution of a contact network between primary school students.
Spatial and Temporal Structure of Precipitation in the Yellow River Basin Based on Kriging Method

Based on the series climate data of 97 meteorological stations in the Yellow River Basin from 1960 to 2000 compiled by the China Meteorological Administration,the spatial and temporal structure of precipitation in the Yellow River Basin was analyzed,by using Kriging method.The results showed that the distribution of long year average precipitation in the Yellow River Basin had an obvious regional difference,which was influenced not only by the weather system,but also by the topography and geographical environment.The main distribution trend was that there was more in the Southeastern and less in the Northwestern;In views of the changes of annual precipitation in the Yellow River Basin,it has an appreciably increasing trend and the tendency was 1.308mm/10a.Moreover,the precipitation was above normal in the 1960s and 1980s and below normal in the 1970s and 1990s.The precipitation increased obviously in the up stream,but decreased in the middle and lower reaches of the river;In respect to assigning within the year,the precipitation in the Yellow River Basin centralized greatly during June and September,but there were the largest in July and August.Furthermore,downriver precipitation gets more centralized than others'. The conclusions were important to research the evolvement of water resources the Yellow River Basin.
Dimensions of Online Conflict: Towards Modeling Agonism

Agonism plays a vital role in democratic dialogue by fostering diverse perspectives and robust discussions. Within the realm of online conflict there is another type: hateful antagonism, which undermines constructive dialogue. Detecting conflict online is central to platform moderation and monetization. It is also vital for democratic dialogue, but only when it takes the form of agonism. To model these two types of conflict, we collected Twitter conversations related to trending controversial topics. We introduce a comprehensive annotation schema for labelling different dimensions of conflict in the conversations, such as the source of conflict, the target, and the rhetorical strategies deployed. Using this schema, we annotated approximately 4,000 conversations with multiple labels. We then trained both logistic regression and transformer-based models on the dataset, incorporating context from the conversation, including the number of participants and the structure of the interactions. Results show that contextual labels are helpful in identifying conflict and make the models robust to variations in topic. Our research contributes a conceptualization of different dimensions of conflict, a richly annotated dataset, and promising results that can contribute to content moderation.
PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment

Fully decentralized, multiagent trajectory planners enable complex tasks like search and rescue or package delivery by ensuring safe navigation in unknown environments. However, deconflicting trajectories with other agents and ensuring collision-free paths in a fully decentralized setting is complicated by dynamic elements and localization uncertainty. To this end, this paper presents (1) an uncertainty-aware multiagent trajectory planner and (2) an image segmentation-based frame alignment pipeline. The uncertainty-aware planner propagates uncertainty associated with the future motion of detected obstacles, and by incorporating this propagated uncertainty into optimization constraints, the planner effectively navigates around obstacles. Unlike conventional methods that emphasize explicit obstacle tracking, our approach integrates implicit tracking. Sharing trajectories between agents can cause potential collisions due to frame misalignment. Addressing this, we introduce a novel frame alignment pipeline that rectifies inter-agent frame misalignment. This method leverages a zero-shot image segmentation model for detecting objects in the environment and a data association framework based on geometric consistency for map alignment. Our approach accurately aligns frames with only 0.18 m and 2.7 deg of mean frame alignment error in our most challenging simulation scenario. In addition, we conducted hardware experiments and successfully achieved 0.29 m and 2.59 deg of frame alignment error. Together with the alignment framework, our planner ensures safe navigation in unknown environments and collision avoidance in decentralized settings.
Purcell modified Doppler cooling of quantum emitters inside optical cavities

Standard cavity cooling of atoms or dielectric particles is based on the action of dispersive optical forces in high-finesse cavities. We investigate here a complementary regime characterized by large cavity losses, resembling the standard Doppler cooling technique. For a single two-level emitter a modification of the cooling rate is obtained from the Purcell enhancement of spontaneous emission in the large cooperativity limit. This mechanism is aimed at cooling of quantum emitters without closed transitions, which is the case for molecular systems, where the Purcell effect can mitigate the loss of population from the cooling cycle. We extend our analytical formulation to the many particle case governed by weak individual coupling but exhibiting collective strong Purcell enhancement to a cavity mode.
Enhanced physics-informed neural networks with domain scaling and residual correction methods for multi-frequency elliptic problems

In this paper, neural network approximation methods are developed for elliptic partial differential equations with multi-frequency solutions. Neural network work approximation methods have advantages over classical approaches in that they can be applied without much concerns on the form of the differential equations or the shape or dimension of the problem domain. When applied to problems with multi-frequency solutions, the performance and accuracy of neural network approximation methods are strongly affected by the contrast of the high- and low-frequency parts in the solutions. To address this issue, domain scaling and residual correction methods are proposed. The efficiency and accuracy of the proposed methods are demonstrated for multi-frequency model problems.
On the Performance of LoRa Empowered Communication for Wireless Body Area Networks

To remotely monitor the physiological status of the human body, long range (LoRa) communication has been considered as an eminently suitable candidate for wireless body area networks (WBANs). Typically, a Rayleigh-lognormal fading channel is encountered by the LoRa links of the WBAN. In this context, we characterize the performance of the LoRa system in WBAN scenarios with an emphasis on the physical (PHY) layer and medium access control (MAC) layer in the face of Rayleigh-lognormal fading channels and the same spreading factor interference. Specifically, closed-form approximate bit error probability (BEP) expressions are derived for the LoRa system. The results show that increasing the SF and reducing the interference efficiently mitigate the shadowing effects. Moreover, in the quest for the most suitable MAC protocol for LoRa based WBANs, three MAC protocols are critically appraised, namely the pure ALOHA, slotted ALOHA, and carrier-sense multiple access. The coverage probability, energy efficiency, throughput, and system delay of the three MAC protocols are analyzed in Rayleigh-lognormal fading channel. Furthermore, the performance of the equal-interval-based and equal-area-based schemes is analyzed to guide the choice of the SF. Our simulation results confirm the accuracy of the mathematical analysis and provide some useful insights for the future design of LoRa based WBANs.
Incorporating Long-term Climate Information into Stochastic Models of Annual Hydrological Data: A Bayesian Hierarchical Approach

The impact of climate variability on water supply drought security is currently the subject of considerable uncertainty. Water resource planners feel ill-equipped to provide reliable estimates of future drought security due to the shortcomings of the current suite of stochastic models. For example, there is considerable evidence that hydrological data are dynamically modulated by long-term climatic processes such as the Inter-decadal Pacific Oscillation (IPO) and El Nino Southern Oscillation (ENSO). The traditional stochastic models used to simulate annual hydrological data do not have a mechanism to emulate this long-term climate variability. A Bayesian hierarchical framework that can utilise long-term climate indices, such as the IPO, to inform a stochastic model of annual hydrological data is presented. The top level of the hierarchy uses the climatic indices to inform a stochastic model that represents the long-term variability. This is combined with the hydrological data at the lower level in the hierarchy to ensure the short-term variability is captured. The jointly calibrated hierarchical model displayed some evidence of multi-decadal persistence in the upper level. However, tests of the simulation of rainfall data revealed some components of the model require refinement. Future research endeavours aim to extend this simple model and include more data sources such as paleo data to further improve simulations.
Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation

Large Language Models (LLMs) are smart but forgetful. Recent studies, (e.g., (Bubeck et al., 2023)) on modern LLMs have shown that they are capable of performing amazing tasks typically necessitating human-level intelligence. However, unlike humans, frozen LLMs do not improve over time; they neither acquire new knowledge nor learn from their successes or failures. Some approaches to improving the intelligence of LLMs include fine-tuning models based on problem-solving performance (Zelikman et al., 2022), and building bigger and more sophisticated models (Bubeck et al., 2023). However, these methods have the drawback of requiring substantial data and computational resources to retrain existing models. In this paper, we explore the use of Retrieval Augmented Generation, also known as RAG (Lewis et al., 2021) to improve problem-solving performance. We propose ARM-RAG (Auxiliary Rationale Memory for Retrieval Augmented Generation), a system that learns from its successes without incurring high training costs. We demonstrate that the storage and subsequent retrieval of reasoning chains have a positive influence on performance in grade-school math problems.
Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion

In this paper, we highlight a problem of evaluation metrics adopted in the open-vocabulary segmentation. That is, the evaluation process still heavily relies on closed-set metrics on zero-shot or cross-dataset pipelines without considering the similarity between predicted and ground truth categories. To tackle this issue, we first survey eleven similarity measurements between two categorical words using WordNet linguistics statistics, text embedding, and language models by comprehensive quantitative analysis and user study. Built upon those explored measurements, we designed novel evaluation metrics, namely Open mIoU, Open AP, and Open PQ, tailored for three open-vocabulary segmentation tasks. We benchmarked the proposed evaluation metrics on 12 open-vocabulary methods of three segmentation tasks. Even though the relative subjectivity of similarity distance, we demonstrate that our metrics can still well evaluate the open ability of the existing open-vocabulary segmentation methods. We hope that our work can bring with the community new thinking about how to evaluate the open ability of models. The evaluation code is released in github.
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples

Aspect-based sentiment analysis (ABSA) have been extensively studied, but little light has been shed on the quadruple extraction consisting of four fundamental elements: aspects, categories, opinions and sentiments, especially with implicit aspects and opinions. In this paper, we propose a new method iACOS for extracting Implicit Aspects with Categories and Opinions with Sentiments. First, iACOS appends two implicit tokens at the end of a text to capture the context-aware representation of all tokens including implicit aspects and opinions. Second, iACOS develops a sequence labeling model over the context-aware token representation to co-extract explicit and implicit aspects and opinions. Third, iACOS devises a multi-label classifier with a specialized multi-head attention for discovering aspect-opinion pairs and predicting their categories and sentiments simultaneously. Fourth, iACOS leverages informative and adaptive negative examples to jointly train the multi-label classifier and the other two classifiers on categories and sentiments by multi-task learning. Finally, the experimental results show that iACOS significantly outperforms other quadruple extraction baselines according to the F1 score on two public benchmark datasets.
Multi-view Information Integration and Propagation for Occluded Person Re-identification

Occluded person re-identification (re-ID) presents a challenging task due to occlusion perturbations. Although great efforts have been made to prevent the model from being disturbed by occlusion noise, most current solutions only capture information from a single image, disregarding the rich complementary information available in multiple images depicting the same pedestrian. In this paper, we propose a novel framework called Multi-view Information Integration and Propagation (MVI$^{2}$P). Specifically, realizing the potential of multi-view images in effectively characterizing the occluded target pedestrian, we integrate feature maps of which to create a comprehensive representation. During this process, to avoid introducing occlusion noise, we develop a CAMs-aware Localization module that selectively integrates information contributing to the identification. Additionally, considering the divergence in the discriminative nature of different images, we design a probability-aware Quantification module to emphatically integrate highly reliable information. Moreover, as multiple images with the same identity are not accessible in the testing stage, we devise an Information Propagation (IP) mechanism to distill knowledge from the comprehensive representation to that of a single occluded image. Extensive experiments and analyses have unequivocally demonstrated the effectiveness and superiority of the proposed MVI$^{2}$P. The code will be released at \url{https://github.com/nengdong96/MVIIP}.
Video Instance Matting

Conventional video matting outputs one alpha matte for all instances appearing in a video frame so that individual instances are not distinguished. While video instance segmentation provides time-consistent instance masks, results are unsatisfactory for matting applications, especially due to applied binarization. To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence. To tackle this challenging problem, we present MSG-VIM, a Mask Sequence Guided Video Instance Matting neural network, as a novel baseline model for VIM. MSG-VIM leverages a mixture of mask augmentations to make predictions robust to inaccurate and inconsistent mask guidance. It incorporates temporal mask and temporal feature guidance to improve the temporal consistency of alpha matte predictions. Furthermore, we build a new benchmark for VIM, called VIM50, which comprises 50 video clips with multiple human instances as foreground objects. To evaluate performances on the VIM task, we introduce a suitable metric called Video Instance-aware Matting Quality~(VIMQ). Our proposed model MSG-VIM sets a strong baseline on the VIM50 benchmark and outperforms existing methods by a large margin. The project is open-sourced at https://github.com/SHI-Labs/VIM.
Hidden conformal symmetries for black holes in modified gravity

We determine hidden conformal symmetries behind the evolution equations of black hole perturbations in a vector-tensor theory of gravity. Such hidden symmetries are valid everywhere in the exterior region of a spherically symmetric, asymptotically flat black hole geometry. They allow us to factorize second order operators controlling the black hole perturbations into a product of two commuting first order operators. As a consequence, we are able to analytically determine the most general time-dependent solutions for the black hole perturbation equations. We focus on solutions belonging to a highest weight representation of a conformal symmetry, showing that they correspond to quasi-bound states with an ingoing behaviour into the black hole horizon, and exponential decay at spatial infinity. Their time-dependence is characterized by purely imaginary frequencies, with imaginary parts separated by integer numbers, as the overtones of quasi-normal modes in General Relativity.
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning

Unified Sequence Labeling that articulates different sequence labeling problems such as Named Entity Recognition, Relation Extraction, Semantic Role Labeling, etc. in a generalized sequence-to-sequence format opens up the opportunity to make the maximum utilization of large language model knowledge toward structured prediction. Unfortunately, this requires formatting them into specialized augmented format unknown to the base pretrained language model (PLMs) necessitating finetuning to the target format. This significantly bounds its usefulness in data-limited settings where finetuning large models cannot properly generalize to the target format. To address this challenge and leverage PLM knowledge effectively, we propose FISH-DIP, a sample-aware dynamic sparse finetuning strategy that selectively focuses on a fraction of parameters, informed by feedback from highly regressing examples, during the fine-tuning process. By leveraging the dynamism of sparsity, our approach mitigates the impact of well-learned samples and prioritizes underperforming instances for improvement in generalization. Across five tasks of sequence labeling, we demonstrate that FISH-DIP can smoothly optimize the model in low resource settings offering upto 40% performance improvements over full fine-tuning depending on target evaluation settings. Also, compared to in-context learning and other parameter-efficient fine-tuning approaches, FISH-DIP performs comparably or better, notably in extreme low-resource settings.
A Qualitative Analysis of the Impact of Popular Culture on Destination Image: A Case Study of Korean Wave from Japanese Fans

The purpose of the research was to investigate how a country’s popular culture impacts its destination image through a case study. For this purpose, the research indentified the characteristics of pop-star fans in this study. The study explored the phenomenon of the Korean wave through a case of a pop-star and his fans. Lastly, the study examined destination image perspectives and experiences of the pop-star fans who attended the pop-star’s summer camp. The research found that popular culture is a significant agent of Korea’s recent image. The study also revealed that the respondents’ high level of interest and empathy for the pop-star was a key reason for their visiting in the destination and changing their perception. The findings suggest that further research needs to consider emotional elements and unconscious motives as a means in understanding the fandom toward pop-stars and the destination.
A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle

One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and generation. Here, neural networks learn dependencies between functionalities and a geometry in a very effective way. For a neural network the functionalities are translated in conditions to a certain geometry. But the more conditions the design generation needs to reflect, the more difficult it is to learn clear dependencies. This leads to a multi criteria design problem due various conditions, which are not considered in the neural network structure so far. In this paper, we address this multi-criteria challenge for a 3D design use case related to an unmanned aerial vehicle (UAV) motor mount. We generate 10,000 abstract 3D designs and subject them all to simulations for three physical disciplines: mechanics, thermodynamics, and aerodynamics. Then, we train a Conditional Variational Autoencoder (CVAE) using the geometry and corresponding multicriteria functional constraints as input. We use our trained CVAE as well as the Marching cubes algorithm to generate meshes for simulation based evaluation. The results are then evaluated with the generated UAV designs. Subsequently, we demonstrate the ability to generate optimized designs under self-defined functionality conditions using the trained neural network.