pwc-archive/papers-with-abstracts huggingface dataset:
Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks
Authors: Jingchi Jiang, Rujia Shen, Boran Wang, Yi Guan
Date: 2024-05-23 00:00:00
Tasks: counterfactual, Counterfactual Inference, Reinforcement Learning (RL)

Type 1 diabetes mellitus (T1D) is characterized by insulin deficiency and blood glucose (BG) control issues. The state-of-the-art solution for continuous BG control is reinforcement learning (RL), where an agent can dynamically adjust exogenous insulin doses in time to maintain BG levels within the target range. However, due to the lack of action guidance, the agent often needs to learn from randomized trials to understand misleading correlations between exogenous insulin doses and BG levels, which can lead to instability and unsafety. To address these challenges, we propose an introspective RL based on Counterfactual Invertible Neural Networks (CINN). We use the pre-trained CINN as a frozen introspective block of the RL agent, which integrates forward prediction and counterfactual inference to guide the policy updates, promoting more stable and safer BG control. Constructed based on interpretable causal order, CINN employs bidirectional encoders with affine coupling layers to ensure invertibility while using orthogonal weight normalization to enhance the trainability, thereby ensuring the bidirectional differentiability of network parameters. We experimentally validate the accuracy and generalization ability of the pre-trained CINN in BG prediction and counterfactual inference for action. Furthermore, our experimental results highlight the effectiveness of pre-trained CINN in guiding RL policy updates for more accurate and safer BG control.
Flipcy: Efficient Pattern Redistribution for Enhancing MLC PCM Reliability and Storage Density
Authors: Muhammad Imran∗, Taehyun Kwon†, ‡, Jung Min You∗ and Joon-Sung Yang†
Date: 2019-12-27 00:00:00
Proceeding: 04-07 November 2019 12

Phase change memory (PCM) is a scalable, nonvolatile emerging memory. The storage density of the PCM can
be enhanced by using the multi-level cell (MLC) operation.
However, the MLC PCM suffers from low reliability due to
resistance drift. The rate of resistance drift is proportional to
the initial resistance of the cell with intermediate storage levels
being particularly vulnerable. Using heavy Error Correction
Codes (ECC) results in poor effective storage density (data bits
per cell) of the MLC PCM. The state-of-the-art Tri-level cell
technique improves reliability by using only three out of four
storage levels, thus eliminating the ECC overhead. However, its
storage density is much less than the ideal MLC PCM. Moreover,
its storage density is fixed even if practically the MLC PCM
reliability is improved. This paper introduces a more flexible
pattern redistribution technique, Flipcy, to improve the MLC
PCM reliability and effective storage density. The proposed
method proportions the data-patterns according to the rate of
resistance drift for different storage levels. A simple flip or
a complement operation is used to reduce the percentage of
the most error-prone pattern. The simulation results show up
to 107X reduction in the error rate and 31% improvement in
performance compared to the conventional MLC PCM. With
a reduced overhead of the auxiliary bits and ECC parity bits,
the proposed method can achieve about 25% improvement in
effective storage density over the Tri-level cell approach for a
similar level of reliability while incurring about 11% degradation
in performance compared to the Tri-level cell approach. This
performance degradation can be reduced when the proposed
method is accompanied with orthogonal techniques to improve
MLC PCM reliability and efficient scrubbing methods
Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks
Authors: Konstantin Grotov, Sergey Titov, Yaroslav Zharov, Timofey Bryksin
Date: 2024-03-26 00:00:00
Tasks: Bug fixing

Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an increased potential for bugs. There are many tools for bug fixing; however, they are generally targeted at the classical linear code. With the rise of code-fluent Large Language Models, a new stream of smart bug-fixing tools has emerged. However, the applicability of those tools is still problematic for non-linear computational notebooks. In this paper, we propose a potential solution for resolving errors in computational notebooks via an iterative LLM-based agent. We discuss the questions raised by this approach and share a novel dataset of computational notebooks containing bugs to facilitate the research of the proposed approach.
Sound transmission into a thick hollow cylinder with the fixed-end boundary condition
Authors: Noise reduction Fixed-end cylinder Uniform wave Monopole source Dipole source
Date: 2008-04-04 00:00:00
Proceeding: Applied Mathematical Modelling 2008 4

In this paper sound transmission through the air filled finite thick cylinders exposed to the
different incident acoustic wave is studied. The effect of end boundary conditions on the
noise reduction of finite cylinders is evaluated. The uniform incident wave and the wave
radiated from monopole and dipole sources are used in this study. Three positions are considered for the dipole source. Every position for the dipole source causes symmetric or antisymmetric pressure distributions on the external surface of the cylinder in tangential or
axial direction. For the purpose of sound transmission analysis the linear three-dimensional theory of elasticity utilizing the technique of variables separation for the infinite circular cylinders is used to analyze the vibration of finite circular cylinder. In these analyzes
the stress continuity condition on the inner and outer surfaces of the cylinder is satisfied
using orthogonalization technique and velocity continuity condition is exactly satisfied
on the interfacial surfaces. The sound transmission evaluation is carried out for cylinders
with various half-length to outer-radius ratios. The results show that in the case of the
fixed-end cylinder, the effect of boundary conditions on the noise reduction can be
neglected for the half-length to outer-radius ratio of more than 10. Comparing between
the obtained results from different acoustic sources shows that the obtained noise reductions from the uniform acoustic wave are less than those obtained from the monopole and
dipole sources.
A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation
Authors: Maohan Liang, Ryan Wen Liu, Ruobin Gao, Zhe Xiao, Xiaocai Zhang, Hua Wang
Date: 2024-07-13 00:00:00
Tasks: Anomaly Detection, Clustering, Survey, Trajectory Clustering, Trajectory Prediction

Vessel trajectory clustering, a crucial component of the maritime intelligent transportation systems, provides valuable insights for applications such as anomaly detection and trajectory prediction. This paper presents a comprehensive survey of the most prevalent distance-based vessel trajectory clustering methods, which encompass two main steps: trajectory similarity measurement and clustering. Initially, we conducted a thorough literature review using relevant keywords to gather and summarize pertinent research papers and datasets. Then, this paper discussed the principal methods of data pre-processing that prepare data for further analysis. The survey progresses to detail the leading algorithms for measuring vessel trajectory similarity and the main clustering techniques used in the field today. Furthermore, the various applications of trajectory clustering within the maritime context are explored. Finally, the paper evaluates the effectiveness of different algorithm combinations and pre-processing methods through experimental analysis, focusing on their impact on the performance of distance-based trajectory clustering algorithms. The experimental results demonstrate the effectiveness of various trajectory clustering algorithms and notably highlight the significant improvements that trajectory compression techniques contribute to the efficiency and accuracy of trajectory clustering. This comprehensive approach ensures a deep understanding of current capabilities and future directions in vessel trajectory clustering.
MoreFixes: A Large-Scale Dataset of CVE Fix Commits Mined through Enhanced Repository Discovery
Authors: Jafar Akhoundali, Sajad Rahim Nouri, Kristian Rietveld, and Olga Gadyatskaya
Date: 2024-07-10 00:00:00
Proceeding: Promise 2024 7
Tasks: Vulnerability Detection

Vulnerability datasets have become an important instrument in software security research, being used to develop automated, machine learning-based vulnerability detection and patching approaches. Yet, any limitations of these datasets may translate into inadequate performance of the developed solutions. For example, the limited size of a vulnerability dataset may restrict the applicability of deep learning techniques. In our work, we have designed and implemented a novel workflow with several heuristic methods to combine state-of-the-art methods related to CVE fix commits gathering. As a consequence of our improvements, we have been able to gather the largest programming language-independent real-world dataset of CVE vulnerabilities with the associated fix commits. Our dataset containing 26,617 unique CVEs coming from 6,945 unique GitHub projects is, to the best of our knowledge, by far the biggest CVE vulnerability dataset with fix commits available today. These CVEs are associated with 31,883 unique commits that fixed those vulnerabilities. Compared to prior work, our dataset brings about a 397% increase in CVEs, a 295% increase in covered open-source projects, and a 480% increase in commit fixes. Our larger dataset thus substantially improves over the current real-world vulnerability datasets and enables further progress in research on vulnerability detection and software security. We release to the community a 14GB PostgreSQL database that contains information on CVEs up to January 24, 2024, CWEs of each CVE, files and methods changed by each commit, and repository metadata. Additionally, patch files related to the fix commits are available as a separate package. Furthermore, we make our dataset collection tool also available to the community.
Floating-Point Quantization Analysis of Multi-Layer Perceptron Artificial Neural Networks
Authors: Hussein Al-Rikabi, Balázs Renczes
Date: 2024-03-18 00:00:00
Proceeding: Journal of Signal Processing Systems 2024 3
Tasks: Quantization

The impact of quantization in Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANNs) is presented in this paper. In this architecture, the constant increase in size and the demand to decrease bit precision are two factors that contribute to the significant enlargement of quantization errors. We introduce an analytical tool that models the propagation of Quantization Noise Power (QNP) in floating-point MLP ANNs. Contrary to the state-of-the-art approach, which compares the exact and quantized data experimentally, the proposed algorithm can predict the QNP theoretically when the effect of operation quantization and Coefficient Quantization Error (CQE) are considered. This supports decisions in determining the required precision during the hardware design. The algorithm is flexible in handling MLP ANNs of user-defined parameters, such as size and type of activation function. Additionally, a simulation environment is built that can perform each operation on an adjustable bit precision. The accuracy of the QNP calculation is verified with two publicly available benchmarked datasets, using the default precision simulation environment as a reference.
A Fast and Efficient Shape Blending by Stable and Analytically Invertible Finite Descriptors
Authors: Emna Ghorbel;Faouzi Ghorbel;Slim M’Hiri
Date: 2022-08-24 00:00:00
Proceeding: IEEE Transactions on Image Processing 2022 8

In a previous work, we have proposed a morphing method based on invertible and stable descriptors that are invariant to Euclidean transformations and to the starting point. The stability guarantees the closeness in the shape sense of the reconstructed intermediate contours. However, this set of descriptors is not defined by a general expression. Here, we propose several sets of stable and invertible finite descriptors expressed by the same formula. Its stability is proven for a subset of this family thanks to the finite-dimension of the invariant space. Such finite dimension results from the Discrete Fourier Transform model that is used instead of Fourier coefficients. Moreover, an analytical general inverse formula is established. The use of the inverse analytical formula and the double utilization of the Fast Fourier Transform ensure an effective blending while being computationally efficient. Finally, we propose a new quantitative criterion for shape morphing. The latter is based on the Euclidean distances between successive curves in a morphing sequence after applying a given registration. Therefore, it allows us to compare the blending results of each set of descriptors. Several experiments are conducted on KIMIA’99 and MPEG-7 datasets. The results highlight the concordance of this criterion with the morphing visual quality and indicate which set of descriptors generates the most appropriate blending.
GenRC: Generative 3D Room Completion from Sparse Image Collections
Authors: Ming-Feng Li, Yueh-Feng Ku, Hong-Xuan Yen, Chi Liu, Yu-Lun Liu, Albert Y. C. Chen, Cheng-Hao Kuo, Min Sun
Date: 2024-07-17 00:00:00

Sparse RGBD scene completion is a challenging task especially when considering consistent textures and geometries throughout the entire scene. Different from existing solutions that rely on human-designed text prompts or predefined camera trajectories, we propose GenRC, an automated training-free pipeline to complete a room-scale 3D mesh with high-fidelity textures. To achieve this, we first project the sparse RGBD images to a highly incomplete 3D mesh. Instead of iteratively generating novel views to fill in the void, we utilized our proposed E-Diffusion to generate a view-consistent panoramic RGBD image which ensures global geometry and appearance consistency. Furthermore, we maintain the input-output scene stylistic consistency through textual inversion to replace human-designed text prompts. To bridge the domain gap among datasets, E-Diffusion leverages models trained on large-scale datasets to generate diverse appearances. GenRC outperforms state-of-the-art methods under most appearance and geometric metrics on ScanNet and ARKitScenes datasets, even though GenRC is not trained on these datasets nor using predefined camera trajectories. Project page: https://minfenli.github.io/GenRC
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net
Authors: Akkidas Noel Prakash, Jahnvi Sai Ganta, Ramaswami Krishnadas, Tin A. Tunc, Satish K Panda
Date: 2024-07-18 00:00:00
Tasks: Denoising, Diagnostic, SSIM

Optical Coherence Tomography (OCT) imaging is pivotal in diagnosing ophthalmic conditions by providing detailed cross-sectional images of the anterior and posterior segments of the eye. Nonetheless, speckle noise and other imaging artifacts inherent to OCT impede the accuracy of diagnosis significantly. In this study, we proposed an enhanced denoising model using a Residual U-Net architecture that effectively diminishes noise and improves image clarity across both Anterior Segment OCT (ASOCT) and polarization-sensitive OCT (PSOCT) images. Our approach demonstrated substantial improvements in image quality metrics: the Peak Signal Noise Ratio (PSNR) was 34.343 $\pm$ 1.113 for PSOCT images, and Structural Similarity Index Measure (SSIM) values were 0.885 $\pm$ 0.030, indicating enhanced preservation of tissue integrity and textural details. For ASOCT images, we observed the PSNR to be 23.525 $\pm$ 0.872 dB and SSIM 0.407 $\pm$ 0.044, reflecting significant enhancements in visual quality and structural accuracy. These metrics substantiate the models efficacy in not only reducing noise but also in maintaining crucial anatomical features, thereby enabling more precise and efficient clinical evaluations. The dual functionality across both ASOCT and PSOCT modalities underscores the versatility and potential for broad application in clinical settings, optimizing diagnostic processes and reducing the necessity for prolonged imaging sessions.
TXL-PBC: a freely accessible labeled peripheral blood cell dataset
Authors: Lu Gan, Xi Li
Date: 2024-07-18 00:00:00
Tasks: 2D Object Detection

In a recent study, we found that publicly BCCD and BCD datasets have significant issues such as labeling errors, insufficient sample size, and poor data quality. To address these problems, we performed sample deletion, re-labeling, and integration of these two datasets. Additionally, we introduced the PBC and Raabin-WBC datasets, and ultimately created a high-quality, sample-balanced new dataset, which we named TXL-PBC. The dataset contains 1008 training sets, 288 validation sets, and 144 test sets. Firstly, The dataset underwent strict manual annotation, automatic annotation with YOLOv8n model, and manual audit steps to ensure the accuracy and consistency of annotations. Secondly, we addresses the blood cell mislabeling problem of the original datasets. The distribution of label boundary box areas and the number of labels are better than the BCCD and BCD datasets. Moreover, we used the YOLOv8n model to train these three datasets, the performance of the TXL-PBC dataset surpass the original two datasets. Finally, we employed YOLOv5n, YOLOv5s, YOLOv5l, YOLOv8s, YOLOv8m detection models as the baseline models for TXL-PBC. This study not only enhances the quality of the blood cell dataset but also supports researchers in improving models for blood cell target detection. We published our freely accessible TXL-PBC dataset at https://github.com/lugan113/TXL-PBC\_Dataset.
Vision Controlled Sensorized Prosthetic Hand
Authors: Md Abdul Baset Sarker, Juan Pablo S. Sola, Aaron Jones, Evan Laing, Ernesto Sola-Thomas, Masudul H. Imtiaz
Date: 2024-06-25 00:00:00
Tasks: Object

This paper presents a sensorized vision-enabled prosthetic hand aimed at replicating a natural hand's performance, functionality, appearance, and comfort. The design goal was to create an accessible substitution with a user-friendly interface requiring little to no training. Our mechanical hand uses a camera and embedded processors to perform most of these tasks. The interfaced pressure sensor is used to get pressure feedback and ensure a safe grasp of the object; an accelerometer is used to detect gestures and release the object. Unlike current EMG-based designs, the prototyped hand does not require personalized training. The details of the design, trade-offs, results, and informing the next iteration are presented in this paper.
Beyond KV Caching: Shared Attention for Efficient LLMs
Authors: Bingli Liao, Danilo Vasconcellos Vargas
Date: 2024-07-13 00:00:00

The efficiency of large language models (LLMs) remains a critical challenge, particularly in contexts where computational resources are limited. Traditional attention mechanisms in these models, while powerful, require significant computational and memory resources due to the necessity of recalculating and storing attention weights across different layers. This paper introduces a novel Shared Attention (SA) mechanism, designed to enhance the efficiency of LLMs by directly sharing computed attention weights across multiple layers. Unlike previous methods that focus on sharing intermediate Key-Value (KV) caches, our approach utilizes the isotropic tendencies of attention distributions observed in advanced LLMs post-pretraining to reduce both the computational flops and the size of the KV cache required during inference. We empirically demonstrate that implementing SA across various LLMs results in minimal accuracy loss on standard benchmarks. Our findings suggest that SA not only conserves computational resources but also maintains robust model performance, thereby facilitating the deployment of more efficient LLMs in resource-constrained environments.
HPix: Generating Vector Maps from Satellite Images
Authors: Aditya Taparia, Keshab Nath
Date: 2024-07-18 00:00:00

Vector maps find widespread utility across diverse domains due to their capacity to not only store but also represent discrete data boundaries such as building footprints, disaster impact analysis, digitization, urban planning, location points, transport links, and more. Although extensive research exists on identifying building footprints and road types from satellite imagery, the generation of vector maps from such imagery remains an area with limited exploration. Furthermore, conventional map generation techniques rely on labor-intensive manual feature extraction or rule-based approaches, which impose inherent limitations. To surmount these limitations, we propose a novel method called HPix, which utilizes modified Generative Adversarial Networks (GANs) to generate vector tile map from satellite images. HPix incorporates two hierarchical frameworks: one operating at the global level and the other at the local level, resulting in a comprehensive model. Through empirical evaluations, our proposed approach showcases its effectiveness in producing highly accurate and visually captivating vector tile maps derived from satellite images. We further extend our study's application to include mapping of road intersections and building footprints cluster based on their area.
Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformers
Authors: Edwin Arkel Rios, Min-Chun Hu, Bo-Cheng Lai
Date: 2024-07-17 00:00:00
Tasks: feature selection, Fine-Grained Image Recognition

Fine-grained recognition involves the classification of images from subordinate macro-categories, and it is challenging due to small inter-class differences. To overcome this, most methods perform discriminative feature selection enabled by a feature extraction backbone followed by a high-level feature refinement step. Recently, many studies have shown the potential behind vision transformers as a backbone for fine-grained recognition, but their usage of its attention mechanism to select discriminative tokens can be computationally expensive. In this work, we propose a novel and computationally inexpensive metric to identify discriminative regions in an image. We compare the similarity between the global representation of an image given by the CLS token, a learnable token used by transformers for classification, and the local representation of individual patches. We select the regions with the highest similarity to obtain crops, which are forwarded through the same transformer encoder. Finally, high-level features of the original and cropped representations are further refined together in order to make more robust predictions. Through extensive experimental evaluation we demonstrate the effectiveness of our proposed method, obtaining favorable results in terms of accuracy across a variety of datasets. Furthermore, our method achieves these results at a much lower computational cost compared to the alternatives. Code and checkpoints are available at: \url{https://github.com/arkel23/GLSim}.