12,246 research outputs found
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy
Answering database queries while preserving privacy is an important problem
that has attracted considerable research attention in recent years. A canonical
approach to this problem is to use synthetic data. That is, we replace the
input database R with a synthetic database R* that preserves the
characteristics of R, and use R* to answer queries. Existing solutions for
relational data synthesis, however, either fail to provide strong privacy
protection, or assume that R contains a single relation. In addition, it is
challenging to extend the existing single-relation solutions to the case of
multiple relations, because they are unable to model the complex correlations
induced by the foreign keys. Therefore, multi-relational data synthesis with
strong privacy guarantees is an open problem. In this paper, we address the
above open problem by proposing PrivLava, the first solution for synthesizing
relational data with foreign keys under differential privacy, a rigorous
privacy framework widely adopted in both academia and industry. The key idea of
PrivLava is to model the data distribution in R using graphical models, with
latent variables included to capture the inter-relational correlations caused
by foreign keys. We show that PrivLava supports arbitrary foreign key
references that form a directed acyclic graph, and is able to tackle the common
case when R contains a mixture of public and private relations. Extensive
experiments on census data sets and the TPC-H benchmark demonstrate that
PrivLava significantly outperforms its competitors in terms of the accuracy of
aggregate queries processed on the synthetic data.Comment: This is an extended version of a SIGMOD 2023 pape
Technical Dimensions of Programming Systems
Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art.
In computer science, much has been said and done to allow comparison of programming languages, yet no similar theory exists for programming systems; we believe that programming systems deserve a theory too.
We present a framework of technical dimensions which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them.
We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions.
Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems.
Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering
In this work, we present an end-to-end Knowledge Graph Question Answering
(KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text
pre-trained language model. The model takes a question in natural language as
input and produces a simpler form of the intended SPARQL query. In the simpler
form, the model does not directly produce entity and relation IDs. Instead, it
produces corresponding entity and relation labels. The labels are grounded to
KG entity and relation IDs in a subsequent step. To further improve the
results, we instruct the model to produce a truncated version of the KG
embedding for each entity. The truncated KG embedding enables a finer search
for disambiguation purposes. We find that T5 is able to learn the truncated KG
embeddings without any change of loss function, improving KGQA performance. As
a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata
datasets on end-to-end KGQA over Wikidata.Comment: 16 pages single column format accepted at ESWC 2023 research trac
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
The political stance prediction for news articles has been widely studied to
mitigate the echo chamber effect -- people fall into their thoughts and
reinforce their pre-existing beliefs. The previous works for the political
stance problem focus on (1) identifying political factors that could reflect
the political stance of a news article and (2) capturing those factors
effectively. Despite their empirical successes, they are not sufficiently
justified in terms of how effective their identified factors are in the
political stance prediction. Motivated by this, in this work, we conduct a user
study to investigate important factors in political stance prediction, and
observe that the context and tone of a news article (implicit) and external
knowledge for real-world entities appearing in the article (explicit) are
important in determining its political stance. Based on this observation, we
propose a novel knowledge-aware approach to political stance prediction (KHAN),
employing (1) hierarchical attention networks (HAN) to learn the relationships
among words and sentences in three different levels and (2) knowledge encoding
(KE) to incorporate external knowledge for real-world entities into the process
of political stance prediction. Also, to take into account the subtle and
important difference between opposite political stances, we build two
independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by
ourselves and learn to fuse the different political knowledge. Through
extensive evaluations on three real-world datasets, we demonstrate the
superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3)
effectiveness.Comment: 12 pages, 5 figures, 10 tables, the Web Conference 2023 (WWW
Procedure-Aware Pretraining for Instructional Video Understanding
Our goal is to learn a video representation that is useful for downstream
procedure understanding tasks in instructional videos. Due to the small amount
of available annotations, a key challenge in procedure understanding is to be
able to extract from unlabeled videos the procedural knowledge such as the
identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or
the potential next steps given partial progress in its execution. Our main
insight is that instructional videos depict sequences of steps that repeat
between instances of the same or different tasks, and that this structure can
be well represented by a Procedural Knowledge Graph (PKG), where nodes are
discrete steps and edges connect steps that occur sequentially in the
instructional activities. This graph can then be used to generate pseudo labels
to train a video representation that encodes the procedural knowledge in a more
accessible form to generalize to multiple procedure understanding tasks. We
build a PKG by combining information from a text-based procedural knowledge
database and an unlabeled instructional video corpus and then use it to
generate training pseudo labels with four novel pre-training objectives. We
call this PKG-based pre-training procedure and the resulting model Paprika,
Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We
evaluate Paprika on COIN and CrossTask for procedure understanding tasks such
as task recognition, step recognition, and step forecasting. Paprika yields a
video representation that improves over the state of the art: up to 11.23%
gains in accuracy in 12 evaluation settings. Implementation is available at
https://github.com/salesforce/paprika.Comment: CVPR 202
Exploring Potential Domains of Agroecological Transformation in the United States
There is now substantial evidence that agroecology constitutes a necessary pathway towards socially just and ecologically resilient agrifood systems. In the United States, however, agroecology remains relegated to the margins of research and policy spaces. This dissertation explores three potential domains of agroecological transformation in the US. Domains of transformation are sites of contestation in which agroecology interfaces with the industrial agrifood system; these material and conceptual spaces may point to important pathways for scaling agroecology. To explore this concept, I examine formal agroecology education (Chapter 1), extension services and statewide discourses around soil health (Chapter 2), and models of farmland access not based on private property (Chapter 3). While these constitute three distinct topics, I seek to demonstrate that they are linked by similar forces that enable and constrain the extent to which these domains can be sites of agroecological transformation.
First, I use case study methodology to explore the evolution of an advanced undergraduate agroecology course at the University of Vermont. I examine how course content and pedagogy align with a transformative framing of agroecology as inherently transdisciplinary, participatory, action-oriented, and political. I find that student-centered pedagogies and experiential education on farms successfully promote transformative learning whereby students shift their understanding of agrifood systems and their role(s) within them. In my second chapter, I zoom out to consider soil health discourses amongst farmers and extension professionals in Vermont. Using co-created mental models and participatory analysis, I find that a singular notion of soil health based on biological, chemical, and physical properties fails to capture the diverse ways in which farmers and extension professionals understand soil health. I advocate for a principles-based approach to soil health that includes social factors and may provide a valuable heuristic for mobilizing knowledge towards agroecology transition pathways. My third chapter, conducted in collaboration with the national non-profit organization Agrarian Trust, considers equitable farmland access. Through semi-structured interviews with 13 farmers and growers across the US, I explore both farmer motivations for engaging with alternative land access models (ALAMs) and the potential role(s) these models may play within broader transformation processes. I argue that ALAMs constitute material and conceptual ‘third spaces’ within which the private property regime is challenged and new identities and language around land ownership can emerge; as such, ALAMs may facilitate a (re)imagining of land-based social-ecological relationships.
I conclude the dissertation by identifying conceptual and practical linkages across the domains explored in Chapters 1-3. I pay particular attention to processes that challenge neoliberal logics, enact plural ways of knowing, and prefigure just futures. In considering these concepts, I apply an expansive notion of pedagogy to explore how processes of teaching and (un)learning can contribute to cultivating foundational capacities for transition processes
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
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