pwc-archive/papers-with-abstracts huggingface dataset:
A new adaptive pricing framework for perpetual protocols using liquidity curves and on-chain oracles
Authors: Chester Bella, Danny Boahen, Sudeep Biswas
Date: 2023-08-30 00:00:00

This whitepaper introduces an innovative mechanism for pricing perpetual contracts and quoting fees to traders based on current market conditions. The approach employs liquidity curves and on-chain oracles to establish a new adaptive pricing framework that considers various factors, ensuring pricing stability and predictability. The framework utilizes parabolic and sigmoid functions to quote prices and fees, accounting for liquidity, active long and short positions, and utilization. This whitepaper provides a detailed explanation of how the adaptive pricing framework, in conjunction with liquidity curves, operates through mathematical modeling and compares it to existing solutions. Furthermore, we explore additional features that enhance the overall efficiency of the decentralized perpetual protocol.
DFlow: Learning to Synthesize Better Optical Flow Datasets via a Differentiable Pipeline
Authors: Kwon Byung-Ki, Nam Hyeon-Woo, Ji-Yun Kim, Tae-Hyun Oh
Date: 2023-02-02 00:00:00
Proceeding: ICLR 2023 2
Tasks: Optical Flow Estimation

Comprehensive studies of synthetic optical flow datasets have attempted to reveal what properties lead to accuracy improvement in learning-based optical flow estimation. However, manually identifying and verifying the properties that contribute to accurate optical flow estimation require large-scale trial-and-error experiments with iteratively generating whole synthetic datasets and training on them, \ie, impractical. To address this challenge, we propose a differentiable optical flow data generation pipeline and a loss function to drive the pipeline, called DFlow. DFlow efficiently synthesizes a dataset effective for a target domain without the need for cumbersome try-and-errors.  This favorable property is achieved by proposing an efficient dataset comparison method that uses neural networks to approximately encode each dataset and compares the proxy networks instead of explicitly comparing datasets in a pairwise way. Our experiments show the competitive performance of our DFlow against the prior arts in pre-training. Furthermore, compared to competing datasets, DFlow achieves the best fine-tuning performance on the Sintel public benchmark with RAFT.
Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation
Authors: MUHAMMAD JUNAID, MUBEEN GHAFOOR, Ali Hassan, SHEHZAD KHALID, SYED ALI TARIQ, GHUFRAN AHMED, Tehseen Zia
Date: 2020-03-02 00:00:00
Proceeding: IEEE 2020 3
Tasks: Autonomous Vehicles, Road Segmentation, Segmentation, Semantic Segmentation

This study presents a shallow and robust road segmentation model. The computer-aided
real-time applications, like driver assistance, require real-time and accurate processing. Current studies
use Deep Convolutional Neural Networks (DCNN) for road segmentation. However, DCNN requires high
computational power and lots of labeled data to learn abstract features for deeper layers. The deeper the layer
is, the more abstract information it tends to learn. Moreover, the prediction time of the DCNN network is an
important aspect of autonomous vehicles. To overcome these issues, a Multi-feature View-based Shallow
Convolutional Neural Network (MVS-CNN) is proposed that utilizes the abstract features extracted from
the explicitly derived representations of the input image. Gradient information of the input image is used
as additional channels to enhance the learning process of the proposed deep learning architecture. The
multi-feature views are fed to a fully-connected neural network to accurately segment the road regions.
The testing accuracy demonstrates that the proposed MVS-CNN achieved an improvement of 2.7% as
compared to baseline CNN consisting of only RGB inputs. Furthermore, the comparison of the proposed
method with the popular semantic segmentation network (SegNet) has shown that the proposed scheme
performs better while being more efficient during training and evaluation. Unlike traditional segmentation
techniques, which are based on the encoder-decoder architecture, the proposed MVS-CNN consists of only
the encoder network. The proposed MVS-CNN has been trained and validated with two well-known datasets:
the KITTI Vision Benchmark and the Cityscapes dataset. The results have been compared with the state-ofthe-art deep learning architectures. The proposed MVS-CNN outperforms and shows supremacy in terms of
model accuracy, processing time, and segmentation accuracy. Based on the experimental results, the proposed
architecture can be considered as an efficient road segmentation architecture for autonomous vehicle systems.
New MEMS based micro gripper using SMA for micro level object manipulation and assembling
Authors: K.C. Munasinghe, B.G.C.T. Bowatta, H.Y.R. Abayarathne, N. Kumararathna, L.K.A.H. Maduwantha, N.M.P. Arachchige, Y.W.R. Amarasinghe
Date: 2016-05-30 00:00:00
Proceeding: IEEE 2016 5

Micro electro mechanical systems (MEMS) based micro gripper for micro-assembling processes is designed. Gripper arms are actuated using a combination of Ni-Ti shape memory alloys and Si springs allowing a gripping range of 1-120μm. Heat which is needed to actuate shape memory alloy (SMA) is obtained by electro thermal properties of Ni-Ti by passing a current which is controlled by specially designed microprocessor controlled circuit design. Stability of the gripper design is validated using a commercially available finite element analysis (FEA) tool COMSOL Multiphysics simulations for stresses in gripper arms and for Eigen frequencies. Proposed fabrication process uses minimum number of masks and it makes the design much suits for batch production.
Efficient Simulations of Early Structure Formation and Reionization
Authors: Andrei Mesinger, Steven Furlanetto
Date: 2007-04-09 00:00:00

We present a method to construct semi-numerical ``simulations'', which can efficiently generate realizations of halo distributions and ionization maps at high redshifts. Our procedure combines an excursion-set approach with first-order Lagrangian perturbation theory and operates directly on the linear density and velocity fields. As such, the achievable dynamic range with our algorithm surpasses the current practical limit of N-body codes by orders of magnitude. This is particularly significant in studies of reionization, where the dynamic range is the principal limiting factor. We test our halo-finding and HII bubble-finding algorithms independently against N-body simulations with radiative transfer and obtain excellent agreement. We compute the size distributions of ionized and neutral regions in our maps. We find even larger ionized bubbles than do purely analytic models at the same volume-weighted mean hydrogen neutral fraction. We also generate maps and power spectra of 21-cm brightness temperature fluctuations, which for the first time include corrections due to gas bulk velocities. We find that velocities widen the tails of the temperature distributions and increase small-scale power, though these effects quickly diminish as reionization progresses. We also include some preliminary results from a simulation run with the largest dynamic range to date: a 250 Mpc box that resolves halos with masses M >~ 2.2 x10^8 M_sun. We show that accurately modeling the late stages of reionization requires such large scales. The speed and dynamic range provided by our semi-numerical approach will be extremely useful in the modeling of early structure formation and reionization.
Segment Anything for Microscopy
Authors: Anwai Archit, Sushmita Nair, Nabeel Khalid, Paul Hilt, Vikas Rajashekar, Marei Freitag, Sagnik Gupta, Andreas Dengel, Sheraz Ahmed, Constantin Pape
Date: 2023-08-22 00:00:00
Proceeding: bioRxiv 2023 8
Tasks: Image Segmentation, Instance Segmentation, Object Tracking, Segmentation, Semantic Segmentation

We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data. Our method is based on Segment Anything, a vision foundation model for image segmentation. We extend it by training specialized models for microscopy data that significantly improve segmentation quality for a wide range of imaging conditions. We also implement annotation tools for interactive (volumetric) segmentation and tracking, that speed up data annotation significantly compared to established tools. Our work constitutes the first application of vision foundation models to microscopy, laying the groundwork for solving image analysis problems in these domains with a small set of powerful deep learning architectures.
A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots
Authors: Hamed Rezaee, Student Member, and Farzaneh Abdollahi, Member, IEEE
Date: 2013-02-06 00:00:00
Proceeding: journal 2013 2

The problem of formation control of a team of mobile
robots based on the virtual and behavioral structures is considered
in this paper. In the virtual structure, each mobile robot ismodeled
by an electric charge. The mobile robots move toward a circle,
and due to repulsive forces between the identical charges, regular
polygon formations of the mobile robots will be realized. For
swarm formation, a virtual mobile robot is located at the center
of the circle, and other mobile robots follow it. In the introduced
approach, each mobile robot finds its position in the formation
autonomously, and the formation can change automatically in the
case of change in the number of the mobile robots. This paper
also proposes a technique for avoiding obstacles based on the
behavioral structure. In this technique, when a mobile robot gets
close to an obstacle, while moving toward its target, a rotational
potential field is applied to lead the mobile robot to avoid the
obstacle, without locating in local minimum positions.
Multi-View Action Recognition Using Contrastive Learning
Authors: Ketul Shah, Anshul Shah, Chun Pong Lau, Celso M. de Melo, Rama Chellappa
Date: 2023-01-03 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 1
Tasks: Action Recognition, Contrastive Learning, Domain Generalization

In this work, we present a method for RGB-based action recognition using multi-view videos. We present a supervised contrastive learning framework to learn a feature embedding robust to changes in viewpoint, by effectively leveraging multi-view data. We use an improved supervised contrastive loss and augment the positives with those coming from synchronized viewpoints. We also propose a new approach to use classifier probabilities to guide the selection of hard negatives in the contrastive loss, to learn a more discriminative representation. Negative samples from confusing classes based on posterior are weighted higher. We also show that our method leads to better domain generalization compared to the standard supervised training based on synthetic multi-view data. Extensive experiments on real (NTU-60, NTU-120, NUMA) and synthetic (RoCoG) data demonstrate the effectiveness of our approach.
Cross-modal transformers for infrared and visible image fusion
Authors: Seonghyun Park, An Gia Vien, Chul Lee
Date: 2023-06-26 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2023 6
Tasks: Cross-Modal Retrieval, Depth Estimation, Infrared And Visible Image Fusion, Monocular Depth Estimation, object-detection, Object Detection

Image fusion techniques aim to generate more informative images by merging multiple images of different modalities with complementary information. Despite significant fusion performance improvements of recent learning-based approaches, most fusion algorithms have been developed based on convolutional neural networks (CNNs), which stack deep layers to obtain a large receptive field for feature extraction. However, important details and contexts of the source images may be lost through a series of convolution layers. In this work, we propose a cross-modal transformer-based fusion (CMTFusion) algorithm for infrared and visible image fusion that captures global interactions by faithfully extracting complementary information from source images. Specifically, we first extract the multiscale feature maps of infrared and visible images. Then, we develop cross-modal transformers (CMTs) to retain complementary information in the source images by removing redundancies in both the spatial and channel domains. To this end, we design a gated bottleneck that integrates cross-domain interaction to consider the characteristics of the source images. Finally, a fusion result is obtained by exploiting spatial-channel information in refined feature maps using a fusion block. Experimental results on multiple datasets demonstrate that the proposed algorithm provides better fusion performance than state-of-the-art infrared and visible image fusion algorithms, both quantitatively and qualitatively. Furthermore, we show that the proposed algorithm can be used to improve the performance of computer vision tasks, e.g., object detection and monocular depth estimation.
Multiple transformation function estimation for image enhancement
Authors: Jaemin Park, An Gia Vien, Minhee Cha, Thuy Thi Pham, HanUl Kim, and Chul Lee
Date: 2023-09-01 00:00:00
Proceeding: Journal of Visual Communication and Image Representation 2023 9
Tasks: Image Enhancement, Image-to-Image Translation, Low-Light Image Enhancement, Underwater Image Restoration

Most deep learning-based image enhancement algorithms have been developed based on the image-to-image translation approach, in which enhancement processes are difficult to interpret. In this paper, we propose a novel interpretable image enhancement algorithm that estimates multiple transformation functions to describe complex color mapping. First, we develop a histogram-based multiple transformation function estimation network (HMTF-Net) to estimate multiple transformation functions by exploiting both the spatial and statistical information of the input images. Second, we estimate pixel-wise weight maps, which indicate the contribution of each transformation function at each pixel, based on the local structures of the input image and the transformed images obtained by each transformation function. Finally, we obtain the enhanced image as the weighted sum of the transformed images using the estimated weight maps. Extensive experiments confirm the effectiveness of the proposed approach and demonstrate that the proposed algorithm outperforms state-of-the-art image enhancement algorithms for different image enhancement tasks.
Application of Visual Communication in Image Enhancement and Optimization of Human–Computer Interface
Authors: Kexiang Ma, SungWon Lee, Hui Chen
Date: 2023-08-16 00:00:00
Proceeding: Springer 2023 8
Tasks: Image Enhancement

The visual plane of human–computer interaction has a great influence on the perception of color images. The author proposes
the use of visual communication in the development and optimization of human–computer interface graphics. The local
contrast of the plane vision image can be improved according to the relationship between the current and the pixel area, and
the full description of the plane vision image in human–computer interaction is better. The maximum time taken to clarify
image color using this system was 0.2 s less than the Binocular Vision Enhancement system and 0.18 s less than the Mark
Visibility Enhancement system; When this system is used to enhance the color of the composite image, the maximum time
consumption is 0.18 s, which is 0.37 s less than the binocular vision enhancement system. The system requires the shortest
time and has the best performance for processing color images. The design improves the color accuracy of the image plane
on the human–computer interaction page, and the processing speed is fast for processing simple and normal images and
complex images.
Synthetic Data Generation for Visual Detection of Flattened PET Bottles
Authors: Vitālijs Feščenko, Janis Arents, Roberts Kadikis
Date: 2022-12-29 00:00:00
Proceeding: Machine Learning and Knowledge Extraction 2022 12
Tasks: Synthetic Data Generation

Polyethylene terephthalate (PET) bottle recycling is a highly automated task; however, manual quality control is required due to inefficiencies of the process. In this paper, we explore automation of the quality control sub-task, namely visual bottle detection, using convolutional neural network (CNN)-based methods and synthetic generation of labelled training data. We propose a synthetic generation pipeline tailored for transparent and crushed PET bottle detection; however, it can also be applied to undeformed bottles if the viewpoint is set from above. We conduct various experiments on CNNs to compare the quality of real and synthetic data, show that synthetic data can reduce the amount of real data required and experiment with the combination of both datasets in multiple ways to obtain the best performance.
Attention-Based Spatial Interpolation for House Price Prediction
Authors: Darniton Viana, Luciano Barbosa
Date: 2021-10-21 00:00:00
Proceeding: International Conference on Advances in Geographic Information Systems 2021 10
Tasks: Prediction, Spatial Interpolation

Estimating the market price of a house is important for many busi-
nesses such as real estate and mortgage lending companies. The
price of a house depends not only on its structural features (e.g. area
and number of bedrooms) but also on the spatial context where it is
located. In this work we estimate the price of a house based solely
on its structural features and the characteristics and price of its
neighbors. For that, we propose a hybrid attention mechanism that
weights neighbors based on their similarity to the house in terms
of structural features and geographic location. For the structural
features, we apply an euclidean-based attention and, for the geo-
graphic location, we propose an attention layer based on a radial
basis function kernel. Those attention mechanisms are then used
by a neural network regressor to predict the price of a house and to
generate a vector representation of the house based on its implicit
context: the house embedding, which can be used as a feature set
by any regressor to perform house price prediction. We have per-
formed an extensive experimental evaluation on real-world datasets
that shows that: (1) regressors using house embedding obtained
the best results on all 4 datasets, outperforming baseline models;
(2) the learned house embedding improves the performance of the
evaluated regressors in almost all scenarios in comparison to raw
features; and (3) simple regressor models such as Linear Regres-
sion using house embedding achieved comparable results to more
competitive algorithms (e.g. Random Forest and Xgboost).
Optimized Deep Feature Selection for Pneumonia Detection: A Novel RegNet and XOR-Based PSO Approach
Authors: Fatemehsadat Ghanadi Ladani, Samaneh Hosseini Semnani
Date: 2023-08-31 00:00:00
Tasks: feature selection, Pneumonia Detection

Pneumonia remains a significant cause of child mortality, particularly in developing countries where resources and expertise are limited. The automated detection of Pneumonia can greatly assist in addressing this challenge. In this research, an XOR based Particle Swarm Optimization (PSO) is proposed to select deep features from the second last layer of a RegNet model, aiming to improve the accuracy of the CNN model on Pneumonia detection. The proposed XOR PSO algorithm offers simplicity by incorporating just one hyperparameter for initialization, and each iteration requires minimal computation time. Moreover, it achieves a balance between exploration and exploitation, leading to convergence on a suitable solution. By extracting 163 features, an impressive accuracy level of 98% was attained which demonstrates comparable accuracy to previous PSO-based methods. The source code of the proposed method is available in the GitHub repository.
OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
Authors: Zhening Huang, Xiaoyang Wu, Xi Chen, Hengshuang Zhao, Lei Zhu, Joan Lasenby
Date: 2023-09-01 00:00:00
Tasks: 3D Open-Vocabulary Instance Segmentation, 3D Open-Vocabulary Object Detection, Instance Segmentation, object-detection, Object Detection, Open Vocabulary Object Detection, Scene Understanding, Semantic Segmentation, Zero-shot 3D Point Cloud Classification

In this work, we introduce OpenIns3D, a new 3D-input-only framework for 3D open-vocabulary scene understanding. The OpenIns3D framework employs a "Mask-Snap-Lookup" scheme. The "Mask" module learns class-agnostic mask proposals in 3D point clouds, the "Snap" module generates synthetic scene-level images at multiple scales and leverages 2D vision-language models to extract interesting objects, and the "Lookup" module searches through the outcomes of "Snap" to assign category names to the proposed masks. This approach, yet simple, achieves state-of-the-art performance across a wide range of 3D open-vocabulary tasks, including recognition, object detection, and instance segmentation, on both indoor and outdoor datasets. Moreover, OpenIns3D facilitates effortless switching between different 2D detectors without requiring retraining. When integrated with powerful 2D open-world models, it achieves excellent results in scene understanding tasks. Furthermore, when combined with LLM-powered 2D models, OpenIns3D exhibits an impressive capability to comprehend and process highly complex text queries that demand intricate reasoning and real-world knowledge. Project page: https://zheninghuang.github.io/OpenIns3D/
Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair
Authors: Yuxiang Wei, Chunqiu Steven Xia, Lingming Zhang
Date: 2023-09-01 00:00:00
Tasks: Code Generation, Program Repair, valid

During Automated Program Repair (APR), it can be challenging to synthesize correct patches for real-world systems in general-purpose programming languages. Recent Large Language Models (LLMs) have been shown to be helpful "copilots" in assisting developers with various coding tasks, and have also been directly applied for patch synthesis. However, most LLMs treat programs as sequences of tokens, meaning that they are ignorant of the underlying semantics constraints of the target programming language. This results in plenty of statically invalid generated patches, impeding the practicality of the technique. Therefore, we propose Repilot, a general code generation framework to further copilot the AI "copilots" (i.e., LLMs) by synthesizing more valid patches during the repair process. Our key insight is that many LLMs produce outputs autoregressively (i.e., token by token), resembling human writing programs, which can be significantly boosted and guided through a Completion Engine. Repilot synergistically synthesizes a candidate patch through the interaction between an LLM and a Completion Engine, which 1) prunes away infeasible tokens suggested by the LLM and 2) proactively completes the token based on the suggestions provided by the Completion Engine. Our evaluation on a subset of the widely-used Defects4j 1.2 and 2.0 datasets shows that Repilot outperforms state-of-the-art techniques by fixing 27% and 47% more bugs, respectively. Moreover, Repilot produces more valid and correct patches than the base LLM with the same budget. While we focus on leveraging Repilot for APR in this work, the overall approach is also generalizable to other code generation tasks.