148 research outputs found
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
Transformer neural networks show promising capabilities, in particular for
uses in materials analysis, design and manufacturing, including their capacity
to work effectively with both human language, symbols, code, and numerical
data. Here we explore the use of large language models (LLMs) as a tool that
can support engineering analysis of materials, applied to retrieving key
information about subject areas, developing research hypotheses, discovery of
mechanistic relationships across disparate areas of knowledge, and writing and
executing simulation codes for active knowledge generation based on physical
ground truths. When used as sets of AI agents with specific features,
capabilities, and instructions, LLMs can provide powerful problem solution
strategies for applications in analysis and design problems. Our experiments
focus on using a fine-tuned model, MechGPT, developed based on training data in
the mechanics of materials domain. We first affirm how finetuning endows LLMs
with reasonable understanding of domain knowledge. However, when queried
outside the context of learned matter, LLMs can have difficulty to recall
correct information. We show how this can be addressed using
retrieval-augmented Ontological Knowledge Graph strategies that discern how the
model understands what concepts are important and how they are related.
Illustrated for a use case of relating distinct areas of knowledge - here,
music and proteins - such strategies can also provide an interpretable graph
structure with rich information at the node, edge and subgraph level. We
discuss nonlinear sampling strategies and agent-based modeling applied to
complex question answering, code generation and execution in the context of
automated force field development from actively learned Density Functional
Theory (DFT) modeling, and data analysis
Methods for data-related problems in person re-ID
In the last years, the ever-increasing need for public security has attracted wide attention in person re-ID. State-of-the-art techniques have achieved impressive results on academic datasets, which are nearly saturated. However, when it comes to deploying a re-ID system in a practical surveillance scenario, several challenges arise. 1) Full person views are often unavailable, and missing body parts make the comparison very challenging due to significant misalignment of the views. 2) Low diversity in training data introduces bias in re-ID systems. 3) The available data might come from different modalities, e.g., text and images. This thesis proposes Partial Matching Net (PMN) that detects body joints, aligns partial views, and hallucinates the missing parts based on the information present in the frame and a learned model of a person. The aligned and reconstructed views are then combined into a joint representation and used for matching images. The thesis also investigates different types of bias that typically occur in re-ID scenarios when the similarity between two persons is due to the same pose, body part, or camera view, rather than to the ID-related cues. It proposes a general approach to mitigate these effects named Bias-Control (BC) framework with two training streams leveraging adversarial and multitask learning to reduce bias-related features. Finally, the thesis investigates a novel mechanism for matching data across visual and text modalities. It proposes a framework Text (TAVD) with two complementary modules: Text attribute feature aggregation (TA) that aggregates multiple semantic attributes in a bimodal space for globally matching text descriptions with images and Visual feature decomposition (VD) which performs feature embedding for locally matching image regions with text attributes. The results and comparison to state of the art on different benchmarks show that the proposed solutions are effective strategies for person re-ID.Open Acces
Neutral Networks of Real-World Programs and their Application to Automated Software Evolution
The existing software development ecosystem is the product of evolutionary forces, and consequently real-world software is amenable to improvement through automated evolutionary techniques. This dissertation presents empirical evidence that software is inherently robust to small randomized program transformations, or \u27mutations. Simple and general mutation operations are demonstrated that can be applied to software source code, compiled assembler code, or directly to binary executables. These mutations often generate variants of working programs that differ significantly from the original, yet remain fully functional. Applying successive mutations to the same software program uncovers large \u27neutral networks\u27 of fully functional variants of real-world software projects. These properties of \u27mutational robustness\u27 and the corresponding \u27neutral networks\u27 have been studied extensively in biology and are believed to be related to the capacity for unsupervised evolution and adaptation. As in biological systems, mutational robustness and neutral networks in software systems enable automated evolution. The dissertation presents several applications that leverage software neutral networks to automate common software development and maintenance tasks. Neutral networks are explored to generate diverse implementations of software for improving runtime security and for proactively repairing latent bugs. Next, a technique is introduced for automatically repairing bugs in the assembler and executables compiled from off-the-shelf software. As demonstration, a proprietary executable is manipulated to patch security vulnerabilities without access to source code or any aid from the software vendor. Finally, software neutral networks are leveraged to optimize complex nonfunctional runtime properties. This optimization technique is used to reduce the energy consumption of the popular PARSEC benchmark applications by 20% as compared to the best available public domain compiler optimizations. The applications presented herein apply evolutionary computation techniques to existing software using common software engineering tools. By enabling evolutionary techniques within the existing software development toolchain, this work is more likely to be of practical benefit to the developers and maintainers of real-world software systems
Engineering cytokine therapeutics
Cytokines have pivotal roles in immunity, making them attractive as therapeutics for a variety of immune-related disorders. However, the widespread clinical use of cytokines has been limited by their short blood half-lives and severe side effects caused by low specificity and unfavourable biodistribution. Innovations in bioengineering have aided in advancing our knowledge of cytokine biology and yielded new technologies for cytokine engineering. In this Review, we discuss how the development of bioanalytical methods, such as sequencing and high-resolution imaging combined with genetic techniques, have facilitated a better understanding of cytokine biology. We then present an overview of therapeutics arising from cytokine re-engineering, targeting and delivery, mRNA therapeutics and cell therapy. We also highlight the application of these strategies to adjust the immunological imbalance in different immune-mediated disorders, including cancer, infection and autoimmune diseases. Finally, we look ahead to the hurdles that must be overcome before cytokine therapeutics can live up to their full potential
ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis
We use prompt engineering to guide ChatGPT in the automation of text mining
of metal-organic frameworks (MOFs) synthesis conditions from diverse formats
and styles of the scientific literature. This effectively mitigates ChatGPT's
tendency to hallucinate information -- an issue that previously made the use of
Large Language Models (LLMs) in scientific fields challenging. Our approach
involves the development of a workflow implementing three different processes
for text mining, programmed by ChatGPT itself. All of them enable parsing,
searching, filtering, classification, summarization, and data unification with
different tradeoffs between labor, speed, and accuracy. We deploy this system
to extract 26,257 distinct synthesis parameters pertaining to approximately 800
MOFs sourced from peer-reviewed research articles. This process incorporates
our ChemPrompt Engineering strategy to instruct ChatGPT in text mining,
resulting in impressive precision, recall, and F1 scores of 90-99%.
Furthermore, with the dataset built by text mining, we constructed a
machine-learning model with over 86% accuracy in predicting MOF experimental
crystallization outcomes and preliminarily identifying important factors in MOF
crystallization. We also developed a reliable data-grounded MOF chatbot to
answer questions on chemical reactions and synthesis procedures. Given that the
process of using ChatGPT reliably mines and tabulates diverse MOF synthesis
information in a unified format, while using only narrative language requiring
no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be
very useful across various other chemistry sub-disciplines.Comment: Published on Journal of the American Chemical Society (2023); 102
pages (18-page manuscript, 84 pages of supporting information
Utilizing ChatGPT to Enhance Clinical Trial Enrollment
Clinical trials are a critical component of evaluating the effectiveness of
new medical interventions and driving advancements in medical research.
Therefore, timely enrollment of patients is crucial to prevent delays or
premature termination of trials. In this context, Electronic Health Records
(EHRs) have emerged as a valuable tool for identifying and enrolling eligible
participants. In this study, we propose an automated approach that leverages
ChatGPT, a large language model, to extract patient-related information from
unstructured clinical notes and generate search queries for retrieving
potentially eligible clinical trials. Our empirical evaluation, conducted on
two benchmark retrieval collections, shows improved retrieval performance
compared to existing approaches when several general-purposed and task-specific
prompts are used. Notably, ChatGPT-generated queries also outperform
human-generated queries in terms of retrieval performance. These findings
highlight the potential use of ChatGPT to enhance clinical trial enrollment
while ensuring the quality of medical service and minimizing direct risks to
patients.Comment: Under Revie
Cadmium-free silica-encapsulated molecularly imprinted AuZnCeSeS quantum dots nanocomposite as an ultrasensitive fluorescence nanosensor for methamphetamine detection
One of the major challenges facing forensic drug analysis is the difficulty in detecting ultralow concentration of illicit drugs in biological matrices without the need for an extraction or a pre-treatment step. This work report on the development of a novel AuZnCeSeS quantum dots (QDs)-molecular imprinted polymer (MIP) nanocomposite fluorescent probe for methamphetamine (METH) recognition. Silica-coated AuZnCeSeS QDs were synthesized and characterized using spectrophotometric, spectroscopic and electron microscopy techniques. Via a free radical polymerization reaction, a thin layer of MIP shell with METH as the template was coated around the QDs surface leading to the formation of a QDs-MIP nanocomposite probe. The MIP coating passivated the QDs surface leading to radiative fluorescence enhancement of the bound QDs. Under optimum reaction conditions, METH was selectively and quantitatively detected via a fluorescence quenching reaction process. The unique selectivity of the nanoprobe for METH recognition showed clearly that METH was able to precisely re-bind to the MIP surface with size and shape reorganization. While the MIP shell functioned to provide the required selectivity, the AuZnCeSeS QDs functioned to fluorescently report the surface binding interaction. The use of a AuZnCeSeS QDs-non-imprinted polymer as probe to detect METH resulted in poor sensitivity and selectivity; hence, demonstrating the suitability of the AuZnCeSeS QDs-MIP nanoprobe to accurately detect METH. METH was detected within a wide concentration range from 0.05 to 50,000 nM with a detection limit of ∼0.02 nM (0.0036 ng/mL). The developed AuZnCeSeS QDs-MIP nanoprobe was efficiently used to detect METH in untreated urine sample with recovery efficiency from ∼100 to 110%
Image based surface reflectance remapping for consistent and tool independent material appearence
Physically-based rendering in Computer Graphics requires the knowledge of material properties other than 3D shapes, textures and colors, in order to solve the rendering equation. A number of material models have been developed, since no model is currently able to reproduce the full range of available materials. Although only few material models have been widely adopted in current rendering systems, the lack of standardisation causes several issues in the 3D modelling
workflow, leading to a heavy tool dependency of material appearance. In industry, final decisions about products are often based on a virtual prototype, a crucial step for the production pipeline, usually developed by a collaborations among several
departments, which exchange data. Unfortunately, exchanged data often tends to differ from the original, when imported into a different application. As a result, delivering consistent visual results requires time, labour and computational cost.
This thesis begins with an examination of the current state of the art in material appearance representation and capture, in order to identify a suitable strategy to tackle material appearance consistency. Automatic solutions to this problem are suggested in this work, accounting for the constraints of real-world scenarios, where the only available information is a reference rendering and the renderer used to obtain it, with no access to the implementation of the shaders. In particular, two image-based frameworks are proposed, working under these constraints.
The first one, validated by means of perceptual studies, is aimed to the remapping of BRDF parameters and useful when the parameters used for the reference rendering are available. The second one provides consistent material appearance across different renderers, even when the parameters used for the reference are unknown. It allows the selection of an arbitrary reference rendering tool, and manipulates the output of other renderers in order to be consistent with the reference
A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution
Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research
Generating Mathematical Derivations with Large Language Models
The derivation of mathematical results in specialised fields using Large
Language Models (LLMs) is an emerging research direction that can help identify
models' limitations, and potentially support mathematical discovery. In this
paper, we leverage a symbolic engine to generate derivations of equations at
scale, and investigate the capabilities of LLMs when deriving goal equations
from premises. Specifically, we employ in-context learning for GPT and
fine-tune a range of T5 models to compare the robustness and generalisation of
pre-training strategies to specialised models. Empirical results show that
fine-tuned FLAN-T5-large (MathT5) outperforms GPT models on all static and
out-of-distribution test sets in terms of absolute performance. However, an
in-depth analysis reveals that the fine-tuned models are more sensitive to
perturbations involving unseen symbols and (to a lesser extent) changes to
equation structure. In addition, we analyse 1.7K equations and over 200
derivations to highlight common reasoning errors such as the inclusion of
incorrect, irrelevant, and redundant equations, along with the tendency to skip
derivation steps. Finally, we explore the suitability of existing metrics for
evaluating mathematical derivations finding evidence that, while they capture
general properties such as sensitivity to perturbations, they fail to highlight
fine-grained reasoning errors and essential differences between models.
Overall, this work demonstrates that training models on synthetic data can
improve their mathematical capabilities beyond larger architectures.Comment: 13 page
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