159 research outputs found
PARAGEN : A Parallel Generation Toolkit
PARAGEN is a PyTorch-based NLP toolkit for further development on parallel
generation. PARAGEN provides thirteen types of customizable plugins, helping
users to experiment quickly with novel ideas across model architectures,
optimization, and learning strategies. We implement various features, such as
unlimited data loading and automatic model selection, to enhance its industrial
usage. ParaGen is now deployed to support various research and industry
applications at ByteDance. PARAGEN is available at
https://github.com/bytedance/ParaGen.Comment: 9 pages, 1 figure, 6 table
Language Models Hallucinate, but May Excel at Fact Verification
Recent progress in natural language processing (NLP) owes much to remarkable
advances in large language models (LLMs). Nevertheless, LLMs frequently
"hallucinate," resulting in non-factual outputs. Our carefully designed human
evaluation substantiates the serious hallucination issue, revealing that even
GPT-3.5 produces factual outputs less than 25% of the time. This underscores
the importance of fact verifiers in order to measure and incentivize progress.
Our systematic investigation affirms that LLMs can be repurposed as effective
fact verifiers with strong correlations with human judgments, at least in the
Wikipedia domain. Surprisingly, FLAN-T5-11B, the least factual generator in our
study, performs the best as a fact verifier, even outperforming more capable
LLMs like GPT3.5 and ChatGPT. Delving deeper, we analyze the reliance of these
LLMs on high-quality evidence, as well as their deficiencies in robustness and
generalization ability. Our study presents insights for developing trustworthy
generation models.Comment: 9 page
Producing optimized structures with inflatables
In this paper we describe the research of the best combination of construction, material and typology for structures with bending moments constructed with fabric formwork. The results are inflated three dimensional (open cell) structures rigidized and structurally optimized. The structure is 40% lighter as conventional beams of the same material and was realized and tested in prototypes and full scale models
Performance Assessment Strategies
Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process.
Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts.
Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships.
The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria.
Both the use of PAS and its recommended future developments are addressed in the thesis
Performance Assessment Strategies:
Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process.
Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts.
Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships.
The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria.
Both the use of PAS and its recommended future developments are addressed in the thesis
Performance Assessment Strategies:
Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process.
Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts.
Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships.
The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria.
Both the use of PAS and its recommended future developments are addressed in the thesis
Genetic Improvement for Code Obfuscation
Genetic improvement (GI) is a relatively new area of software engineering and thus the extent of its applicability is yet to be explored. Although a growing interest in GI in recent years started with the work on automatic bug fixing, the area flourished when results on optimisation of non-functional software properties, such as efficiency and energy consumption, were published. Further success of GI in transplanting functionality from one program to another leads to a question: what other software engineering areas can benefit from the use of genetic improvement techniques? We propose to utilise GI for code obfuscation
Generative Reciprocity: A Computational Approach for Performance-Based and Fabrication-Aware Design of Reciprocal Systems
Using the capabilities of computation and digital fabrication this thesis provides a basis for a novel process of design to fabrication for reciprocal systems.
In the traditional sense, reciprocal structures combine the advantages of timber as a renewable source of construction material and low-energy production with the modular fabrication, fabrication efficiency, structural capacities, and elegance of reciprocal interconnection of members. The unique benefits of reciprocal systems come from their discrete geometry, which simplifies the connection detailing and provides freedom for local and global variations in the system. However, this reduction in construction complexity and flexibility of local variation is replaced with geometrical complexity due to numerous compatibility constraints coupled with the structural behavior of the system. This research therefore identifies the key design parameters and design constraints of reciprocal systems. The results demonstrate the complex coupling of geometry, structural performance and fabrication in these systems, hence an essential need for application of an integrative design process. Through the application of computation, simulation, and digital fabrication this research proposes an integrative computational design process which can effectively address the coupling of design, analysis and fabrication of reciprocal systems and accommodate design exploration and optimization.
First, a novel computational method for geometric modelling and form-finding is presented to resolve the compatibility constraints and generate the essential geometric and topological data for analysis and fabrication. Second, a flexible and scalable analysis method is implemented to study the interplay of the design parameters and the structural behavior of reciprocal systems. A comprehensive parametric study reveals a complex relationship between the geometric parameters and the structural performance and demonstrates the essential need for a real-time performance feedback for optimal design of free-form reciprocal systems. Third, a generalizable and efficient fabrication process is proposed for reciprocal systems with 3-D module geometry using 5-axis CNC machinery. Towards this goal, four different connection types are proposed, and different fabrication parameters are studied through digital and physical prototyping, destructive structural testing, detailed finite element simulation, and fabrication of a scaled structure. The results are summarized as a guideline for selection of the main fabrication parameters including joint detailing and fabrication tolerances. The computational design process is then developed by rethinking and replacing the conventional direct incremental development by a modular integrative computational process using multi-directional dataflow between different design phases. Finally, the proposed framework is used for a full-scale design to fabrication case study to validate the applicability of the proposed design process.PHDArchitectureUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155121/1/oliyan_1.pd
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