165 research outputs found
Improving routine operation management
The focus of the research project is to investigate the daily operation management in an organisation and give suggestions to improve their business. This research project is to help the organisation improve their daily operations which will tend to improve the overall business. A SWOT analysis was conducted to identify errors and poor performance areas for operations. The 3c’s theory was utilised to enhance the results and research. That theory includes competitors, customers and climate, because if someone wants to improve their business then they must compete with their competitors and gather knowledge of their customers. To complete this research, I will go through the personal observation and some informal discussion with the co-workers and managers. The qualitative research method has been selected. To conclude, the business can improve by cultivating work efficiency and maximum utilisation of equipment
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An Engineered Calmodulin-Based Allosteric Switch for Peptide Biosensing
This work describes the development of a new platform for allosteric protein engineering that takes advantage of the ability of calmodulin to change conformation upon binding to peptide and protein ligands. The switch we have developed consists of a fusion protein in which calmodulin is genetically inserted into the sequence of TEM1 β-lactamase. In this approach, calmodulin acts as the input domain, whose ligand-dependent conformational changes control the activity of the β-lactamase output domain. The new allosteric enzyme exhibits up to 120 times higher catalytic activity in the activated (peptide bound) state compared to the inactive (no peptide bound) state in vitro. Activation of the enzyme is ligand-dependent-peptides with higher affinities for wild-type calmodulin exhibit increased switch activity. Calmodulin's ability to "turn on" the activity of β-lactamase makes this a potentially valuable scaffold for the directed evolution of highly specific biosensors for detecting toxins and other clinically relevant biomarkers.Chemistry and Chemical Biolog
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In
these media, dynamic and still elements are juxtaposed to create an artistic
and narrative experience. Creating a high-quality, aesthetically pleasing
cinemagraph requires isolating objects in a semantically meaningful way and
then selecting good start times and looping periods for those objects to
minimize visual artifacts (such a tearing). To achieve this, we present a new
technique that uses object recognition and semantic segmentation as part of an
optimization method to automatically create cinemagraphs from videos that are
both visually appealing and semantically meaningful. Given a scene with
multiple objects, there are many cinemagraphs one could create. Our method
evaluates these multiple candidates and presents the best one, as determined by
a model trained to predict human preferences in a collaborative way. We
demonstrate the effectiveness of our approach with multiple results and a user
study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary
materia
Controllable Text-to-Image Generation with GPT-4
Current text-to-image generation models often struggle to follow textual
instructions, especially the ones requiring spatial reasoning. On the other
hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable
precision in generating code snippets for sketching out text inputs
graphically, e.g., via TikZ. In this work, we introduce Control-GPT to guide
the diffusion-based text-to-image pipelines with programmatic sketches
generated by GPT-4, enhancing their abilities for instruction following.
Control-GPT works by querying GPT-4 to write TikZ code, and the generated
sketches are used as references alongside the text instructions for diffusion
models (e.g., ControlNet) to generate photo-realistic images. One major
challenge to training our pipeline is the lack of a dataset containing aligned
text, images, and sketches. We address the issue by converting instance masks
in existing datasets into polygons to mimic the sketches used at test time. As
a result, Control-GPT greatly boosts the controllability of image generation.
It establishes a new state-of-art on the spatial arrangement and object
positioning generation and enhances users' control of object positions, sizes,
etc., nearly doubling the accuracy of prior models. Our work, as a first
attempt, shows the potential for employing LLMs to enhance the performance in
computer vision tasks
Scaling Novel Object Detection with Weakly Supervised Detection Transformers
Weakly supervised object detection (WSOD) enables object detectors to be
trained using image-level class labels. However, the practical application of
current WSOD models is limited, as they operate at small scales and require
extensive training and refinement. We propose the Weakly Supervised Detection
Transformer, which enables efficient knowledge transfer from a large-scale
pretraining dataset to WSOD finetuning on hundreds of novel objects. We
leverage pretrained knowledge to improve the multiple instance learning
framework used in WSOD, and experiments show our approach outperforms the
state-of-the-art on datasets with twice the novel classes than previously
shown.Comment: CVPR 2022 Workshop on Attention and Transformers in Visio
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Exploring Defocus Matting: Nonparametric Acceleration, Super-Resolution, and Off-Center Matting
Defocus matting is a fully automatic and passive method for pulling mattes from video captured with coaxial cameras that have different depths of field and planes of focus. Nonparametric sampling can accelerate the video-matting process from minutes to seconds per frame. In addition, a super-resolution technique efficiently bridges the gap between mattes from high-resolution video cameras and those from low-resolution cameras. Off-center matting pulls mattes for an external high-resolution camera that doesn't share the same center of projection as the low-resolution cameras used to capture the defocus matting data.Engineering and Applied Science
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