6,053 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Automatic Caption Generation for Aerial Images: A Survey
Aerial images have attracted attention from researcher community since long time. Generating a caption for an aerial image describing its content in comprehensive way is less studied but important task as it has applications in agriculture, defence, disaster management and many more areas. Though different approaches were followed for natural image caption generation, generating a caption for aerial image remains a challenging task due to its special nature. Use of emerging techniques from Artificial Intelligence (AI) and Natural Language Processing (NLP) domains have resulted in generation of accepted quality captions for aerial images. However lot needs to be done to fully utilize potential of aerial image caption generation task. This paper presents detail survey of the various approaches followed by researchers for aerial image caption generation task. The datasets available for experimentation, criteria used for performance evaluation and future directions are also discussed
MolFM: A Multimodal Molecular Foundation Model
Molecular knowledge resides within three different modalities of information
sources: molecular structures, biomedical documents, and knowledge bases.
Effective incorporation of molecular knowledge from these modalities holds
paramount significance in facilitating biomedical research. However, existing
multimodal molecular foundation models exhibit limitations in capturing
intricate connections between molecular structures and texts, and more
importantly, none of them attempt to leverage a wealth of molecular expertise
derived from knowledge graphs. In this study, we introduce MolFM, a multimodal
molecular foundation model designed to facilitate joint representation learning
from molecular structures, biomedical texts, and knowledge graphs. We propose
cross-modal attention between atoms of molecular structures, neighbors of
molecule entities and semantically related texts to facilitate cross-modal
comprehension. We provide theoretical analysis that our cross-modal
pre-training captures local and global molecular knowledge by minimizing the
distance in the feature space between different modalities of the same
molecule, as well as molecules sharing similar structures or functions. MolFM
achieves state-of-the-art performance on various downstream tasks. On
cross-modal retrieval, MolFM outperforms existing models with 12.13% and 5.04%
absolute gains under the zero-shot and fine-tuning settings, respectively.
Furthermore, qualitative analysis showcases MolFM's implicit ability to provide
grounding from molecular substructures and knowledge graphs. Code and models
are available on https://github.com/BioFM/OpenBioMed.Comment: 31 pages, 15 figures, and 15 table
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach
to probabilistic forecasting. Uncertainty about all unknowns that characterize
any forecasting problem -- model, parameters, latent states -- is able to be
quantified explicitly, and factored into the forecast distribution via the
process of integration or averaging. Allied with the elegance of the method,
Bayesian forecasting is now underpinned by the burgeoning field of Bayesian
computation, which enables Bayesian forecasts to be produced for virtually any
problem, no matter how large, or complex. The current state of play in Bayesian
forecasting in economics and finance is the subject of this review. The aim is
to provide the reader with an overview of modern approaches to the field, set
in some historical context; and with sufficient computational detail given to
assist the reader with implementation.Comment: The paper is now published online at:
https://doi.org/10.1016/j.ijforecast.2023.05.00
Motion-Based Sign Language Video Summarization using Curvature and Torsion
An interesting problem in many video-based applications is the generation of
short synopses by selecting the most informative frames, a procedure which is
known as video summarization. For sign language videos the benefits of using
the -parameterized counterpart of the curvature of the 2-D signer's wrist
trajectory to identify keyframes, have been recently reported in the
literature. In this paper we extend these ideas by modeling the 3-D hand motion
that is extracted from each frame of the video. To this end we propose a new
informative function based on the -parameterized curvature and torsion of
the 3-D trajectory. The method to characterize video frames as keyframes
depends on whether the motion occurs in 2-D or 3-D space. Specifically, in the
case of 3-D motion we look for the maxima of the harmonic mean of the curvature
and torsion of the target's trajectory; in the planar motion case we seek for
the maxima of the trajectory's curvature. The proposed 3-D feature is
experimentally evaluated in applications of sign language videos on (1)
objective measures using ground-truth keyframe annotations, (2) human-based
evaluation of understanding, and (3) gloss classification and the results
obtained are promising.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Serving to secure "Global Korea": Gender, mobility, and flight attendant labor migrants
This dissertation is an ethnography of mobility and modernity in contemporary South Korea (the Republic of Korea) following neoliberal restructuring precipitated by the Asian Financial Crisis (1997). It focuses on how comparative “service,” “security,” and “safety” fashioned “Global Korea”: an ongoing state-sponsored project aimed at promoting the economic, political, and cultural maturation of South Korea from a once notoriously inhospitable, “backward” country (hujin’guk) to a now welcoming, “advanced country” (sĹŹnjin’guk). Through physical embodiments of the culturally-specific idiom of “superior” service (sĹŹbisĹ), I argue that aspiring, current, and former Korean flight attendants have driven the production and maintenance of this national project.
More broadly, as a driver of this national project, this occupation has emerged out of the country’s own aspirational flights from an earlier history of authoritarian rule, labor violence, and xenophobia. Against the backdrop of the Korean state’s aggressive neoliberal restructuring, globalization efforts, and current “Hell Chosun” (Helchosŏn) economy, a group of largely academically and/or class disadvantaged young women have been able secure individualized modes of pleasure, self-fulfillment, and class advancement via what I deem “service mobilities.” Service mobilities refers to the participation of mostly women in a traditionally devalued but growing sector of the global labor market, the “pink collar” economy centered around “feminine” care labor. Korean female flight attendants share labor skills resembling those of other foreign labor migrants (chiefly from the “Global South”), who perform care work deemed less desirable. Yet, Korean female flight attendants elude the stigmatizing, classed, and racialized category of “labor migrant.” Moreover, within the context of South Korea’s unique history of rapid modernization, the flight attendant occupation also commands considerable social prestige.
Based on ethnographic and archival research on aspiring, current, and former Korean flight attendants, this dissertation asks how these unique care laborers negotiate a metaphorical and literal series of sustained border crossings and inspections between Korean flight attendants’ contingent status as lowly care-laboring migrants, on the one hand, and ostensibly glamorous, globetrotting elites, on the other. This study contends the following: first, the flight attendant occupation in South Korea represents new politics of pleasure and pain in contemporary East Asia. Second, Korean female flight attendants’ enactments of soft, sanitized, and glamorous (hwaryŏhada) service help to purify South Korea’s less savory past. In so doing, Korean flight attendants reconstitute the historical role of female laborers as burden bearers and caretakers of the Korean state.U of I OnlyAuthor submitted a 2-year U of I restriction extension request
Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review
Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)
Embedding Based Link Prediction for Knowledge Graph Completion
Knowledge Graphs (KGs) are the most widely used representation of structured information about a particular domain consisting of billions of facts in the form of entities (nodes) and relations (edges) between them. Besides, the KGs also encapsulate the semantic type information of the entities. The last two decades have witnessed a constant growth of KGs in various domains such as government, scholarly data, biomedical domains, etc. KGs have been used in Machine Learning based applications such as entity linking, question answering, recommender systems, etc. Open KGs are mostly heuristically created, automatically generated from heterogeneous resources such as text, images, etc., or are human-curated. However, these KGs are often incomplete, i.e., there are missing links between the entities and missing links between the entities and their corresponding entity types. This thesis focuses on addressing these two challenges of link prediction for Knowledge Graph Completion (KGC):
\textbf{(i)} General Link Prediction in KGs that include head and tail prediction, triple classification, and
\textbf{(ii)} Entity Type Prediction.
Most of the graph mining algorithms are proven to be of high complexity, deterring their usage in KG-based applications. In recent years, KG embeddings have been trained to represent the entities and relations in the KG in a low-dimensional vector space preserving the graph structure. In most published works such as the translational models, convolutional models, semantic matching, etc., the triple information is used to generate the latent representation of the entities and relations.
In this dissertation, it is argued that contextual information about the entities obtained from the random walks, and textual entity descriptions, are the keys to improving the latent representation of the entities for KGC. The experimental results show that the knowledge obtained from the context of the entities supports the hypothesis. Several methods have been proposed for KGC and their effectiveness is shown empirically in this thesis. Firstly, a novel multi-hop attentive KG embedding model MADLINK is proposed for Link Prediction. It considers the contextual information of the entities by using random walks as well as textual entity descriptions of the entities. Secondly, a novel architecture exploiting the information contained in a pre-trained contextual Neural Language Model (NLM) is proposed for Triple Classification. Thirdly, the limitations of the current state-of-the-art (SoTA) entity type prediction models have been analysed and a novel entity typing model CAT2Type is proposed that exploits the Wikipedia Categories which is one of the most under-treated features of the KGs. This model can also be used to predict missing types of unseen entities i.e., the newly added entities in the KG.
Finally, another novel architecture GRAND is proposed to predict the missing entity types in KGs using multi-label, multi-class, and hierarchical classification by leveraging different strategic graph walks in the KGs. The extensive experiments and ablation studies show that all the proposed models outperform the current SoTA models and set new baselines for KGC.
The proposed models establish that the NLMs and the contextual information of the entities in the KGs together with the different neural network architectures benefit KGC. The promising results and observations open up interesting scopes for future research involving exploiting the proposed models in domain-specific KGs such as scholarly data, biomedical data, etc. Furthermore, the link prediction model can be exploited as a base model for the entity alignment task as it considers the neighbourhood information of the entities
Financial reporting in Europe: Accounting for regulatory and technical challenges
This thesis explores the challenges facing financial reporting in Europe both regulatory and technical in nature. This has involved research into the background of European legislation and conducting face to face semi-structured interviews with senior elite actors from institutions governing the regulatory and technical arrangements of general-purpose financial reporting practice in Europe. European companies are required to disclose information about their financial affairs. The European legislation governing company financial reporting was delegated to the International Accounting Standards Board (IASB) by the EU institutions via Regulation 1606/2002. This thesis argues that European agencies (represented by EFRAG) are caught in a devolved regulatory relationship where the International Accounting Standards Board (IASB) has been able to assume a relatively strong self-regulatory position. This weakens the agency that European legislative institutions have over their own legislation with regards to financial reporting practice. This thesis argues this loss of agency by European institutions over their legislation governing accounting practice is not a fait accompli but is challenged and contested as European institutions seek and need a more co-regulated arrangement. A key argument developed in this thesis is that regulatory arrangements governing accounting practice are evolving in terms of the distribution of responsibilities and control over European financial reporting practice. To understand how the regulatory landscape governing European accounting practice is changing we employ an investigative lens that is grounded in accounting. This investigative lens employs three elements that are regarded in the literature review as significant technical challenges facing accounting practice in Europe. The first of these is retaining or not prudent accounting practice, the second is concerned with the development of non-financial reporting and the third, concerns with installing the public interest not just investor interests in financial disclosures. It is through this investigative lens that this thesis assesses the extent to which regulatory arrangements and agency governing accounting practice in Europe are shifting sands
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