pwc-archive/papers-with-abstracts huggingface dataset:
Recursive classification of satellite imaging time-series: An application to land cover mapping
Authors: Helena Calatrava, Bhavya Duvvuri, Haoqing Li, Ricardo Borsoi, Edward Beighley, Deniz Erdogmus, Pau Closas, Tales Imbiriba
Date: 2023-01-04 00:00:00
Tasks: Classification, Decision Making, Land Cover Classification, Time Series, Time Series Analysis

Despite the extensive body of literature focused on remote sensing applications for land cover mapping and the availability of high-resolution satellite imagery, methods for continuously updating classification maps in real-time remain limited, especially when training data is scarce. This paper introduces the Recursive Bayesian Classifier (RBC), which converts any instantaneous classifier into a robust online method through a probabilistic framework that is resilient to non-informative image variations. Three experiments are conducted using Sentinel-2 data: water mapping of the Oroville Dam in California and the Charles River basin in Massachusetts, and deforestation detection in the Amazon. RBC is applied to a Gaussian Mixture Model (GMM), Logistic Regression (LR), and our proposed Spectral Index Classifier (SIC). Results show that RBC significantly enhances classifier robustness in multitemporal settings under challenging conditions, such as cloud cover and cyanobacterial blooms. Specifically, balanced classification accuracy improves by up to 26.95% for SIC, 12.4% for GMM, and 13.81% for LR in water mapping, and by 15.25%, 14.17%, and 14.7% in deforestation detection. Moreover, without additional training data, RBC improves the performance of the state-of-the-art DeepWaterMap and WatNet algorithms by up to 9.62% and 11.03%. These benefits are provided by RBC while requiring minimal supervision and maintaining a low computational cost that remains constant for each time step regardless of the time-series length.
Identification of lung nodules CT scan using YOLOv5 based on convolution neural network
Authors: Haytham Al Ewaidat, Youness El Brag
Date: 2022-12-31 00:00:00
Tasks: Diagnostic

Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promising results by using convolutional neural network (CNN), especially for image recognition and it's one of the most used algorithm in computer vision. Approach: we use (CNN) building blocks based on YOLOv5 (you only look once) to learn the features representations for nodule detection labels, in this paper, we introduce a method for detecting lung cancer localization. Chest X-rays and low-dose computed tomography are also possible screening methods, When it comes to recognizing nodules in radiography, computer-aided diagnostic (CAD) system based on (CNN) have demonstrated their worth. One-stage detector YOLOv5 trained on 280 annotated CT SCAN from a public dataset LIDC-IDRI based on segmented pulmonary nodules. Results: we analyze the predictions performance of the lung nodule locations, and demarcates the relevant CT scan regions. In lung nodule localization the accuracy is measured as mean average precision (mAP). the mAP takes into account how well the bounding boxes are fitting the labels as well as how accurate the predicted classes for those bounding boxes, the accuracy we got 92.27%. Conclusion: this study was to identify the nodule that were developing in the lungs of the participants. It was difficult to find information on lung nodules in medical literature.
Simultaneously Transmitting and Reflecting (STAR) RIS Assisted Over-the-Air Computation Systems
Authors: Xiongfei Zhai, Guojun Han, Yunlong Cai, Yuanwei Liu, Lajos Hanzo
Date: 2023-01-05 00:00:00

The performance of over-the-air computation (AirComp) systems degrades due to the hostile channel conditions of wireless devices (WDs), which can be significantly improved by the employment of reconfigurable intelligent surfaces (RISs). However, the conventional RISs require that the WDs have to be located in the half-plane of the reflection space, which restricts their potential benefits. To address this issue, the novel family of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is considered in AirComp systems to improve the computation accuracy across a wide coverage area. To minimize the computation mean-squared-error (MSE) in STAR-RIS assisted AirComp systems, we propose a joint beamforming design for optimizing both the transmit power at the WDs, as well as the passive reflect and transmit beamforming matrices at the STAR-RIS, and the receive beamforming vector at the fusion center (FC). Specifically, in the updates of the passive reflect and transmit beamforming matrices, closed-form solutions are derived by introducing an auxiliary variable and exploiting the coupled binary phase-shift conditions. Moreover, by assuming that the number of antennas at the FC and that of elements at the STAR-RIS/RIS are sufficiently high, we theoretically prove that the STAR-RIS assisted AirComp systems provide higher computation accuracy than the conventional RIS assisted systems. Our numerical results show that the proposed beamforming design outperforms the benchmark schemes relying on random phase-shift constraints and the deployment of conventional RIS. Moreover, its performance is close to the lower bound achieved by the beamforming design based on the STAR-RIS dispensing with coupled phase-shift constraints.
Hardware Prototype of a Time-Encoding Sub-Nyquist ADC
Authors: Hila Naaman, Nimrod Glazer, Moshe Namer, Daniel Bilik, Shlomi Savariego, Yonina C. Eldar
Date: 2023-01-05 00:00:00

Analog-to-digital converters (ADCs) are key components of digital signal processing. Classical samplers in this framework are controlled by a global clock. At high sampling rates, clocks are expensive and power-hungry, thus increasing the cost and energy consumption of ADCs. It is, therefore, desirable to sample using a clock-less ADC at the lowest possible rate. An integrate-and-fire time-encoding machine (IF-TEM) is a time-based power-efficient asynchronous design that is not synced to a global clock. Finite-rate-of-innovation (FRI) signals, ubiquitous in various applications, have fewer degrees of freedom than the signal's Nyquist rate, enabling sub-Nyquist sampling signal models. This work proposes a power-efficient IF-TEM ADC architecture and demonstrates sub-Nyquist sampling and FRI signal recovery. Using an IF-TEM, we implement in hardware the first sub-Nyquist time-based sampler. We offer a feasible approach for accurately estimating the FRI parameters from IF-TEM data. The suggested hardware and reconstruction approach retrieves FRI parameters with an error of up to -25dB while operating at rates approximately 10 times lower than the Nyquist rate, paving the way to low-power ADC architectures.
Online Decomposition of Surface Electromyogram into Individual Motor Unit Activities Using Progressive FastICA Peel-off
Authors: Haowen Zhao, Xu Zhang, Maoqi Chen, Ping Zhou
Date: 2023-01-05 00:00:00

Surface electromyogram (SEMG) decomposition provides a promising tool for decoding and understanding neural drive information non-invasively. In contrast to previous SEMG decomposition methods mainly developed in offline conditions, there are few studies on online SEMG decomposition. A novel method for online decomposition of SEMG data is presented using the progressive FastICA peel-off (PFP) algorithm. The online method consists of an offline prework stage and an online decomposition stage. More specifically, a series of separation vectors are first initialized by the originally offline version of the PFP algorithm from SEMG data recorded in advance. Then they are applied to online SEMG data to extract motor unit spike trains precisely. The performance of the proposed online SEMG decomposition method was evaluated by both simulation and experimental approaches. It achieved an online decomposition accuracy of 98.53% when processing simulated SEMG data. For decomposing experimental SEMG data, the proposed online method was able to extract an average of 12.00 +- 3.46 MUs per trial, with a matching rate of 90.38% compared with results from the expert-guided offline decomposition. Our study provides a valuable way of online decomposition of SEMG data with advanced applications in movement control and health.
Stochastics of DNA Quantification
Authors: Abdoelnaser M Degoot, Wilfred Ndifon
Date: 2023-01-05 00:00:00

A common approach to quantifying DNA involves repeated cycles of DNA amplification. This approach, employed by the polymerase chain reaction (PCR), produces outputs that are corrupted by amplification noise, making it challenging to accurately back-calculate the amount of input DNA. Standard mathematical solutions to this back-calculation problem do not take adequate account of such noise and are error-prone. Here, we develop a parsimonious mathematical model of the stochastic mapping of input DNA onto experimental outputs that accounts, in a natural way, for amplification noise. We use the model to derive the probability density of the quantification cycle, a frequently reported experimental output, which can be fit to data to estimate input DNA. Strikingly, the model predicts that a sample with only one input DNA molecule has a $<$4% chance of testing positive, which is $>$25-fold lower than assumed by a standard method of interpreting PCR data. We provide formulae for calculating both the limit of detection and the limit of quantification, two important operating characteristics of DNA quantification methods that are frequently assessed by using ad-hoc mathematical techniques. Our results provide a mathematical foundation for the rigorous analysis of DNA quantification.
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications
Authors: Tara M. Pattilachan, Ugur Demir, Elif Keles, Debesh Jha, Derk Klatte, Megan Engels, Sanne Hoogenboom, Candice Bolan, Michael Wallace, Ulas Bagci
Date: 2022-12-14 00:00:00
Tasks: Data Augmentation, Medical Diagnosis

Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging. We hypothesize that sub-optimal data augmentations can easily distort or occlude medical images, leading to false positives or negatives during patient diagnosis, prediction, or therapy/surgery evaluation. In our experimental results, we found that utilizing commonly used intensity-based data augmentation distorts the MRI scans and leads to texture information loss, thus negatively affecting the overall performance of classification. Additionally, we observed that commonly used data augmentation methods cannot be used with a plug-and-play approach in medical imaging, and requires manual tuning and adjustment.
LONG RANGE ACOUSTIC AND DEEP FEATURES PERSPECTIVE ON ASVSPOOF 2019
Authors: Rohan Kumar Das, Jichen Yang and Haizhou Li
Date: 2020-02-20 00:00:00
Proceeding: 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2020 2
Tasks: Speaker Verification

To secure automatic speaker verification (ASV) systems from intruders, robust countermeasures for spoofing attack detection are required. The ASVspoof series of challenge provides a shared anti-spoofing task. The recent edition, ASVspoof 2019, focuses on attacks by both synthetic and replay speech that are referred to as logical and physical access attacks, respectively. In the ASVspoof 2019 submission, we considered novel countermeasures based on long range acoustic features, that are unique in many ways as they are derived using octave power spectrum and subbands, as opposed to the commonly used linear power spectrum. During the post-challenge study, we further investigate the use of deep features that enhances the discriminative ability between genuine and spoofed speech. In this paper, we summarize the findings from the perspective of long range acoustic and deep features for spoof detection. We make a comprehensive analysis on the nature of different kinds of spoofing attacks and system development.
DAtRNet: Disentangling Fashion Attribute Embedding for Substitute Item Retrieval
Authors: Gaurab Bhattacharya, Nikhil Kilari, Jayavardhana Gubbi, Bagya Lakshmi V ., Arpan Pal, Balamuralidhar P . TCS Research, India
Date: 2022-06-01 00:00:00
Proceeding: CVPR 2022 6
Tasks: Attribute, Retrieval

Interactive substitute recommendation for fashion products improves the online retail customer experience. Traditional fashion search platforms incorporate product metadata between the query products and the products to be retrieved. In this paper, we propose DAtRNet, an attribute representation network to disentangle the features in the query product. It is used to recommend attribute-aware substitute items based on the conditional similarity of the retrieved products. The proposed architecture relies on attribute-level similarity providing a fine-grained recommendation. In addition, a concurrent axial attention mechanism is proposed that generates global information embedding and adaptively re-calibrates the soft attention masks. Overall, the end-to-end framework enables the system to disentangle the attribute features and independently deals with them to enhance its flexibility towards one or multiple attributes. The proposed method outperforms the state-of-the-art by a significant margin.
TarViS: A Unified Approach for Target-based Video Segmentation
Authors: Ali Athar, Alexander Hermans, Jonathon Luiten, Deva Ramanan, Bastian Leibe
Date: 2023-01-06 00:00:00
Proceeding: CVPR 2023 1
Tasks: Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation, Video Instance Segmentation, Video Object Segmentation, Video Panoptic Segmentation, Video Segmentation, Video Semantic Segmentation

The general domain of video segmentation is currently fragmented into different tasks spanning multiple benchmarks. Despite rapid progress in the state-of-the-art, current methods are overwhelmingly task-specific and cannot conceptually generalize to other tasks. Inspired by recent approaches with multi-task capability, we propose TarViS: a novel, unified network architecture that can be applied to any task that requires segmenting a set of arbitrarily defined 'targets' in video. Our approach is flexible with respect to how tasks define these targets, since it models the latter as abstract 'queries' which are then used to predict pixel-precise target masks. A single TarViS model can be trained jointly on a collection of datasets spanning different tasks, and can hot-swap between tasks during inference without any task-specific retraining. To demonstrate its effectiveness, we apply TarViS to four different tasks, namely Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), Video Object Segmentation (VOS) and Point Exemplar-guided Tracking (PET). Our unified, jointly trained model achieves state-of-the-art performance on 5/7 benchmarks spanning these four tasks, and competitive performance on the remaining two. Code and model weights are available at: https://github.com/Ali2500/TarViS
Triple-stream Deep Metric Learning of Great Ape Behavioural Actions
Authors: Otto Brookes, Majid Mirmehdi, Hjalmar Kühl, Tilo Burghardt
Date: 2023-01-06 00:00:00
Tasks: Metric Learning, Optical Flow Estimation

We propose the first metric learning system for the recognition of great ape behavioural actions. Our proposed triple stream embedding architecture works on camera trap videos taken directly in the wild and demonstrates that the utilisation of an explicit DensePose-C chimpanzee body part segmentation stream effectively complements traditional RGB appearance and optical flow streams. We evaluate system variants with different feature fusion techniques and long-tail recognition approaches. Results and ablations show performance improvements of ~12% in top-1 accuracy over previous results achieved on the PanAf-500 dataset containing 180,000 manually annotated frames across nine behavioural actions. Furthermore, we provide a qualitative analysis of our findings and augment the metric learning system with long-tail recognition techniques showing that average per class accuracy -- critical in the domain -- can be improved by ~23% compared to the literature on that dataset. Finally, since our embedding spaces are constructed as metric, we provide first data-driven visualisations of the great ape behavioural action spaces revealing emerging geometry and topology. We hope that the work sparks further interest in this vital application area of computer vision for the benefit of endangered great apes.
Feedback-Gated Rectified Linear Units
Authors: Marco Kemmerling
Date: 2023-01-06 00:00:00

Feedback connections play a prominent role in the human brain but have not received much attention in artificial neural network research. Here, a biologically inspired feedback mechanism which gates rectified linear units is proposed. On the MNIST dataset, autoencoders with feedback show faster convergence, better performance, and more robustness to noise compared to their counterparts without feedback. Some benefits, although less pronounced and less consistent, can be observed when networks with feedback are applied on the CIFAR-10 dataset.
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads
Authors: Vincent Mai, Philippe Maisonneuve, Tianyu Zhang, Hadi Nekoei, Liam Paull, Antoine Lesage-Landry
Date: 2023-01-06 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation. Frequency regulation through demand response has the potential to coordinate temporally flexible loads, such as air conditioners, to counteract these variations. Existing approaches for discrete control with dynamic constraints struggle to provide satisfactory performance for fast timescale action selection with hundreds of agents. We propose a decentralized agent trained with multi-agent proximal policy optimization with localized communication. We explore two communication frameworks: hand-engineered, or learned through targeted multi-agent communication. The resulting policies perform well and robustly for frequency regulation, and scale seamlessly to arbitrary numbers of houses for constant processing times.
Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information
Authors: Dekai Zhu, Qadeer Khan, Daniel Cremers
Date: 2023-01-06 00:00:00
Tasks: Prediction, Trajectory Prediction

Traditional approaches to prediction of future trajectory of road agents rely on knowing information about their past trajectory. This work rather relies only on having knowledge of the current state and intended direction to make predictions for multiple vehicles at intersections. Furthermore, message passing of this information between the vehicles provides each one of them a more holistic overview of the environment allowing for a more informed prediction. This is done by training a neural network which takes the state and intent of the multiple vehicles to predict their future trajectory. Using the intention as an input allows our approach to be extended to additionally control the multiple vehicles to drive towards desired paths. Experimental results demonstrate the robustness of our approach both in terms of trajectory prediction and vehicle control at intersections. The complete training and evaluation code for this work is available here: \url{https://github.com/Dekai21/Multi_Agent_Intersection}.
"No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy
Authors: Yuchen Cui, Siddharth Karamcheti, Raj Palleti, Nidhya Shivakumar, Percy Liang, Dorsa Sadigh
Date: 2023-01-06 00:00:00
Tasks: Instruction Following

Systems for language-guided human-robot interaction must satisfy two key desiderata for broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction-following agents cannot adapt, lacking the ability to incorporate online natural language supervision, and even if they could, require hundreds of demonstrations to learn even simple policies. In this work, we address these problems by presenting Language-Informed Latent Actions with Corrections (LILAC), a framework for incorporating and adapting to natural language corrections - "to the right," or "no, towards the book" - online, during execution. We explore rich manipulation domains within a shared autonomy paradigm. Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot: language is an input to a learned model that produces a meaningful, low-dimensional control space that the human can use to guide the robot. Each real-time correction refines the human's control space, enabling precise, extended behaviors - with the added benefit of requiring only a handful of demonstrations to learn. We evaluate our approach via a user study where users work with a Franka Emika Panda manipulator to complete complex manipulation tasks. Compared to existing learned baselines covering both open-loop instruction following and single-turn shared autonomy, we show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users because of its reliability, precision, and ease of use.
SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm
Authors: Abdelrahman Kaseb, Mona Farouk
Date: 2023-01-06 00:00:00
Tasks: Dialect Identification, Language Modelling, Sarcasm Detection, Sentence, Sentence Embedding, Sentence-Embedding, Sentiment Analysis

Sentiment analysis becomes an essential part of every social network, as it enables decision-makers to know more about users' opinions in almost all life aspects. Despite its importance, there are multiple issues it encounters like the sentiment of the sarcastic text which is one of the main challenges of sentiment analysis. This paper tackles this challenge by introducing a novel system (SAIDS) that predicts the sentiment, sarcasm and dialect of Arabic tweets. SAIDS uses its prediction of sarcasm and dialect as known information to predict the sentiment. It uses MARBERT as a language model to generate sentence embedding, then passes it to the sarcasm and dialect models, and then the outputs of the three models are concatenated and passed to the sentiment analysis model. Multiple system design setups were experimented with and reported. SAIDS was applied to the ArSarcasm-v2 dataset where it outperforms the state-of-the-art model for the sentiment analysis task. By training all tasks together, SAIDS achieves results of 75.98 FPN, 59.09 F1-score and 71.13 F1-score for sentiment analysis, sarcasm detection, and dialect identification respectively. The system design can be used to enhance the performance of any task which is dependent on other tasks.