pwc-archive/papers-with-abstracts huggingface dataset:
InterTrack: Interaction Transformer for 3D Multi-Object Tracking
Authors: John Willes, Cody Reading, Steven L. Waslander
Date: 2022-08-17 00:00:00
Tasks: 3D Multi-Object Tracking, Autonomous Vehicles, Management, Motion Planning, Multi-Object Tracking, Object, Object Tracking

3D multi-object tracking (MOT) is a key problem for autonomous vehicles, required to perform well-informed motion planning in dynamic environments. Particularly for densely occupied scenes, associating existing tracks to new detections remains challenging as existing systems tend to omit critical contextual information. Our proposed solution, InterTrack, introduces the Interaction Transformer for 3D MOT to generate discriminative object representations for data association. We extract state and shape features for each track and detection, and efficiently aggregate global information via attention. We then perform a learned regression on each track/detection feature pair to estimate affinities, and use a robust two-stage data association and track management approach to produce the final tracks. We validate our approach on the nuScenes 3D MOT benchmark, where we observe significant improvements, particularly on classes with small physical sizes and clustered objects. As of submission, InterTrack ranks 1st in overall AMOTA among methods using CenterPoint detections.
LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction
Authors: Zhaoyun Jiang, Jiaqi Guo, Shizhao Sun, Huayu Deng, Zhongkai Wu, Vuksan Mijovic, Zijiang James Yang, Jian-Guang Lou, Dongmei Zhang
Date: 2022-08-17 00:00:00
Proceeding: CVPR 2023 1
Tasks: Decoder, Layout Generation

Conditional graphic layout generation, which generates realistic layouts according to user constraints, is a challenging task that has not been well-studied yet. First, there is limited discussion about how to handle diverse user constraints flexibly and uniformly. Second, to make the layouts conform to user constraints, existing work often sacrifices generation quality significantly. In this work, we propose LayoutFormer++ to tackle the above problems. First, to flexibly handle diverse constraints, we propose a constraint serialization scheme, which represents different user constraints as sequences of tokens with a predefined format. Then, we formulate conditional layout generation as a sequence-to-sequence transformation, and leverage encoder-decoder framework with Transformer as the basic architecture. Furthermore, to make the layout better meet user requirements without harming quality, we propose a decoding space restriction strategy. Specifically, we prune the predicted distribution by ignoring the options that definitely violate user constraints and likely result in low-quality layouts, and make the model samples from the restricted distribution. Experiments demonstrate that LayoutFormer++ outperforms existing approaches on all the tasks in terms of both better generation quality and less constraint violation.
Cross-Domain Few-Shot Classification via Inter-Source Stylization
Authors: Huali Xu, Shuaifeng Zhi, Li Liu
Date: 2022-08-17 00:00:00
Tasks: Classification, Cross-Domain Few-Shot, Domain Adaptation, Inductive Bias

The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets. Some existing approaches require labelled samples from multiple domains for model training. However, these methods fail when the sample labels are scarce. To overcome this challenge, this paper proposes a solution that makes use of multiple source domains without the need for additional labeling costs. Specifically, one of the source domains is completely tagged, while the others are untagged. An Inter-Source Stylization Network (ISSNet) is then introduced to enhance stylisation across multiple source domains, enriching data distribution and model's generalization capabilities. Experiments on 8 target datasets show that ISSNet leverages unlabelled data from multiple source data and significantly reduces the negative impact of domain gaps on classification performance compared to several baseline methods.
Deep Learning Enabled Time-Lapse 3D Cell Analysis
Authors: Jiaxiang Jiang, Amil Khan, S. Shailja, Samuel A. Belteton, Michael Goebel, Daniel B. Szymanski, B. S. Manjunath
Date: 2022-08-17 00:00:00
Tasks: Deep Learning

This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on Github and the method is available as a service through the BisQue portal.
Blind Users Accessing Their Training Images in Teachable Object Recognizers
Authors: Jonggi Hong, Jaina Gandhi, Ernest Essuah Mensah, Farnaz Zamiri Zeraati, Ebrima Haddy Jarjue, Kyungjun Lee, Hernisa Kacorri
Date: 2022-08-16 00:00:00

Iteration of training and evaluating a machine learning model is an important process to improve its performance. However, while teachable interfaces enable blind users to train and test an object recognizer with photos taken in their distinctive environment, accessibility of training iteration and evaluation steps has received little attention. Iteration assumes visual inspection of the training photos, which is inaccessible for blind users. We explore this challenge through MyCam, a mobile app that incorporates automatically estimated descriptors for non-visual access to the photos in the users' training sets. We explore how blind participants (N=12) interact with MyCam and the descriptors through an evaluation study in their homes. We demonstrate that the real-time photo-level descriptors enabled blind users to reduce photos with cropped objects, and that participants could add more variations by iterating through and accessing the quality of their training sets. Also, Participants found the app simple to use indicating that they could effectively train it and that the descriptors were useful. However, subjective responses were not reflected in the performance of their models, partially due to little variation in training and cluttered backgrounds.
TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments
Authors: Shubham Dokania, Anbumani Subramanian, Manmohan Chandraker, C. V. Jawahar
Date: 2022-08-16 00:00:00
Tasks: Diversity, Semantic Segmentation, Synthetic Data Generation

High-quality structured data with rich annotations are critical components in intelligent vehicle systems dealing with road scenes. However, data curation and annotation require intensive investments and yield low-diversity scenarios. The recently growing interest in synthetic data raises questions about the scope of improvement in such systems and the amount of manual work still required to produce high volumes and variations of simulated data. This work proposes a synthetic data generation pipeline that utilizes existing datasets, like nuScenes, to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation, mimicking real scene properties with high-fidelity, along with mechanisms to diversify samples in a physically meaningful way. We demonstrate improvements in mIoU metrics by presenting qualitative and quantitative experiments with real and synthetic data for semantic segmentation on the Cityscapes and KITTI-STEP datasets. All relevant code and data is released on github (https://github.com/shubham1810/trove_toolkit).
ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos
Authors: James Wensel, Hayat Ullah, Arslan Munir
Date: 2022-08-16 00:00:00
Tasks: Activity Recognition, Activity Recognition In Videos, Gesture Recognition, Human Activity Recognition

Human activity recognition is an emerging and important area in computer vision which seeks to determine the activity an individual or group of individuals are performing. The applications of this field ranges from generating highlight videos in sports, to intelligent surveillance and gesture recognition. Most activity recognition systems rely on a combination of convolutional neural networks (CNNs) to perform feature extraction from the data and recurrent neural networks (RNNs) to determine the time dependent nature of the data. This paper proposes and designs two transformer neural networks for human activity recognition: a recurrent transformer (ReT), a specialized neural network used to make predictions on sequences of data, as well as a vision transformer (ViT), a transformer optimized for extracting salient features from images, to improve speed and scalability of activity recognition. We have provided an extensive comparison of the proposed transformer neural networks with the contemporary CNN and RNN-based human activity recognition models in terms of speed and accuracy.
Casual Indoor HDR Radiance Capture from Omnidirectional Images
Authors: Pulkit Gera, Mohammad Reza Karimi Dastjerdi, Charles Renaud, P. J. Narayanan, Jean-François Lalonde
Date: 2022-08-16 00:00:00
Tasks: NeRF

We present PanoHDR-NeRF, a neural representation of the full HDR radiance field of an indoor scene, and a pipeline to capture it casually, without elaborate setups or complex capture protocols. First, a user captures a low dynamic range (LDR) omnidirectional video of the scene by freely waving an off-the-shelf camera around the scene. Then, an LDR2HDR network uplifts the captured LDR frames to HDR, which are used to train a tailored NeRF++ model. The resulting PanoHDR-NeRF can render full HDR images from any location of the scene. Through experiments on a novel test dataset of real scenes with the ground truth HDR radiance captured at locations not seen during training, we show that PanoHDR-NeRF predicts plausible HDR radiance from any scene point. We also show that the predicted radiance can synthesize correct lighting effects, enabling the augmentation of indoor scenes with synthetic objects that are lit correctly. Datasets and code are available at https://lvsn.github.io/PanoHDR-NeRF/.
Adapting TimeML to Basque: Event Annotation
Authors: Begoña Altuna, María Jesús Aranzabe, Arantza Díaz de Ilarraza
Date: 2018-03-21 00:00:00
Proceeding: Computational Linguistics and Intelligent Text Processing 2018 3

In this paper we present an event annotation effort following EusTimeML, a temporal mark-up language for Basque based on TimeML. For this, we first describe events and their main ontological and grammatical features. We base our analysis on Basque grammars and TimeML mark-up language classification of events. Annotation guidelines have been created to address the event information annotation for Basque and an annotation experiment has been conducted. A first round has served to evaluate the preliminary guidelines and decisions on event annotation have been taken according to annotations and inter-annotator agreement results. Then a guideline tuning period has followed. In the second round, we have created a manually-annotated gold standard corpus for event annotation in Basque. Event analysis and annotation experiment are part of a complete temporal information analysis and corpus creation work.
Safety Assessment for Autonomous Systems' Perception Capabilities
Authors: John Molloy, John McDermid
Date: 2022-08-17 00:00:00
Tasks: Decision Making, Scene Understanding

Autonomous Systems (AS) are increasingly proposed, or used, in Safety Critical (SC) applications. Many such systems make use of sophisticated sensor suites and processing to provide scene understanding which informs the AS' decision-making. The sensor processing typically makes use of Machine Learning (ML) and has to work in challenging environments, further the ML-algorithms have known limitations,e.g., the possibility of false-negatives or false-positives in object classification. The well-established safety-analysis methods developed for conventional SC systems are not well-matched to AS, ML, or the sensing systems used by AS. This paper proposes an adaptation of well-established safety-analysis methods to address the specifics of perception-systems for AS, including addressing environmental effects and the potential failure-modes of ML, and provides a rationale for choosing particular sets of guidewords, or prompts, for safety-analysis. It goes on to show how the results of the analysis can be used to inform the design and verification of the AS and illustrates the new method by presenting a partial analysis of a road vehicle. Illustrations in the paper are primarily based on optical sensing, however the paper discusses the applicability of the method to other sensing modalities and its role in a wider safety process addressing the overall capabilities of AS.
Disentangled Speaker Representation Learning via Mutual Information Minimization
Authors: Sung Hwan Mun, Min Hyun Han, Minchan Kim, Dongjune Lee, Nam Soo Kim
Date: 2022-08-17 00:00:00
Tasks: Disentanglement, Representation Learning, Speaker Recognition, Speaker Verification, valid

Domain mismatch problem caused by speaker-unrelated feature has been a major topic in speaker recognition. In this paper, we propose an explicit disentanglement framework to unravel speaker-relevant features from speaker-unrelated features via mutual information (MI) minimization. To achieve our goal of minimizing MI between speaker-related and speaker-unrelated features, we adopt a contrastive log-ratio upper bound (CLUB), which exploits the upper bound of MI. Our framework is constructed in a 3-stage structure. First, in the front-end encoder, input speech is encoded into shared initial embedding. Next, in the decoupling block, shared initial embedding is split into separate speaker-related and speaker-unrelated embeddings. Finally, disentanglement is conducted by MI minimization in the last stage. Experiments on Far-Field Speaker Verification Challenge 2022 (FFSVC2022) demonstrate that our proposed framework is effective for disentanglement. Also, to utilize domain-unknown datasets containing numerous speakers, we pre-trained the front-end encoder with VoxCeleb datasets. We then fine-tuned the speaker embedding model in the disentanglement framework with FFSVC 2022 dataset. The experimental results show that fine-tuning with a disentanglement framework on a existing pre-trained model is valid and can further improve performance.
Mental health concerns prelude the Great Resignation: Evidence from Social Media
Authors: R. Maria del Rio-Chanona, Alejandro Hermida-Carrillo, Melody Sepahpour-Fard, Luning Sun, Renata Topinkova, Ljubica Nedelkoska
Date: 2022-08-16 00:00:00

To study the causes of the 2021 Great Resignation, we use text analysis to investigate the changes in work- and quit-related posts between 2018 and 2021 on Reddit. We find that the Reddit discourse evolution resembles the dynamics of the U.S. quit and layoff rates. Furthermore, when the COVID-19 pandemic started, conversations related to working from home, switching jobs, work-related distress, and mental health increased. We distinguish between general work-related and specific quit-related discourse changes using a difference-in-differences method. Our main finding is that mental health and work-related distress topics disproportionally increased among quit-related posts since the onset of the pandemic, likely contributing to the Great Resignation. Along with better labor market conditions, some relief came beginning-to-mid-2021 when these concerns decreased. Our study validates the use of forums such as Reddit for studying emerging economic phenomena in real time, complementing traditional labor market surveys and administrative data.
Computationally Efficient Robust Model Predictive Control for Uncertain System Using Causal State-Feedback Parameterization
Authors: Anastasis Georgiou, Furqan Tahir, Imad M. Jaimoukha, Simos A. Evangelou
Date: 2022-08-17 00:00:00
Tasks: Model Predictive Control

This paper investigates the problem of robust model predictive control (RMPC) of linear-time-invariant (LTI) discrete-time systems subject to structured uncertainty and bounded disturbances. Typically, the constrained RMPC problem with state-feedback parameterizations is nonlinear (and nonconvex) with a prohibitively high computational burden for online implementation. To remedy this, a novel approach is proposed to linearize the state-feedback RMPC problem, with minimal conservatism, through the use of semidefinite relaxation techniques. The proposed algorithm computes the state-feedback gain and perturbation online by solving a linear matrix inequality (LMI) optimization that, in comparison to other schemes in the literature is shown to have a substantially reduced computational burden without adversely affecting the tracking performance of the controller. Additionally, an offline strategy that provides initial feasibility on the RMPC problem is presented. The effectiveness of the proposed scheme is demonstrated through numerical examples from the literature.
Temporal Relaxation of Signal Temporal Logic Specifications for Resilient Control Synthesis
Authors: Ali Tevfik Buyukkocak, Derya Aksaray
Date: 2022-08-17 00:00:00

We introduce a metric that can quantify the temporal relaxation of Signal Temporal Logic (STL) specifications and facilitate resilient control synthesis in the face of infeasibilities. The proposed metric quantifies a cumulative notion of relaxation among the subtasks, and minimizing it yields to structural changes in the original STL specification by i) modifying time-intervals, ii) removing subtasks entirely if needed. To this end, we formulate an optimal control problem that extracts state and input sequences by minimally violating the temporal requirements while achieving the desired predicates. We encode this problem in the form of a computationally efficient mixed-integer program. We show some theoretical results on the properties of the new metric. Finally, we present a case study of a robot that minimally violates the time constraints of desired tasks in the face of an infeasibility.
Energy-grade double pricing mechanism for a combined heat and power system using the asynchronous dispatch method
Authors: Xinyi Yi, Ye Guo, Hongbin Sun, Qiuwei Wu, Li Xiao
Date: 2022-08-17 00:00:00

The problem of heat and electricity pricing in combined heat and power systems regarding the time scales of electricity and heat, as well as thermal energy quality, is studied. Based on the asynchronous coordinated dispatch of the combined heat and power system, an energy-grade double pricing mechanism is proposed. Under the pricing mechanism, the resulting merchandise surplus of the heat system operator at each heat dispatch interval can be decomposed into interpretable parts and its revenue adequacy can be guaranteed for all heat dispatch intervals. And the electric power system operator's resulting merchandise surplus is composed of non-negative components at each electricity dispatch interval, also ensuring its revenue adequacy. In addition, the effects of different time scales and cogeneration are analyzed in different kinds of combined heat and power units' pricing.
Implementation of Multi-channel Active Noise Control based on Back-propagation Mechanism
Authors: Zhengding Luo, Dongyuan Shi, Junwei Ji, Woon-Seng Gan
Date: 2022-08-17 00:00:00

Active noise control (ANC) systems can efficiently attenuate low-frequency noises by introducing anti-noises to combine with the unwanted noises. In ANC systems, the filtered-x least mean square (FxLMS) and filtered-X normalized least-mean-square (FxNLMS) algorithm are well-known algorithms for adaptively adjusting control filters. Multi-channel ANC systems are typically required to attenuate unwanted noises in a large space. However, open-source implementations of the multi-channel FxLMS (McFxLMS) and multi-channel FxNLMS (McFxNLMS) algorithm continue to be scarce. Therefore, this paper proposes a simple and effective implementation approach of the McFxLMS and McFxNLMS algorithm. Motivated by the back-propagation process during neural network training, the McFxLMS and McFxNLMS algorithm can be implemented via automatic derivation mechanism. We implemented the two algorithms using the automatic derivation mechanism in PyTorch and made the source code available on GitHub. This implementation method can improve the practicality of multi-channel ANC systems, which is expected to be widely used in ANC applications.
Deep Learning based Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Systems
Authors: Mingjian Tuo, Xingpeng Li
Date: 2022-08-17 00:00:00

With the goal of electricity system decarbonization, conventional synchronous generators are gradually replaced by converter-interfaced renewable generations. Such transition is causing concerns over system frequency and rate-of-change-of-frequency (RoCoF) security due to significant reduction in system inertia. Existing efforts are mostly derived from uniform system frequency response model which may fail to capture all characteristics of the systems. To ensure the locational frequency security, this paper presents a deep neural network (DNN) based RoCoF-constrained unit commitment (DNN-RCUC) model. RoCoF predictor is trained to predict the highest locational RoCoF based on a high-fidelity simulation dataset. Training samples are generated from models over various scenarios, which can avoid simulation divergence and system instability. The trained network is then reformulated into a set of mixed-integer linear constraints representing the locational RoCoF-limiting constraints in unit commitment. The proposed DNN-RCUC model is studied on the IEEE 24-bus system. Time domain simulation results on PSS/E demonstrate the effectiveness of the proposed algorithm.
Vehicle Electrification Solutions: review and open challenges
Authors: Youssef El Amrani, Saad Motahhir, Abdelaziz El Ghzizal
Date: 2022-08-16 00:00:00
Tasks: Management

An Electric Vehicle usually refers to any vehicle that is partially or fully powered by a battery that can be directly plugged into the mains. Therefore, the new vehicles provide various benefits, including convenience, efficiency, sustainability, and economy. The present study concerns a comprehensive review of vehicle electrification solutions. Indeed, the major electric vehicle technologies are presented. Moreover, based on several research works from the literature, the main electrification solutions are illustrated, including degree of electrification, battery system management, onboard chargers, and power converters. In addition, these solutions, as well as the open challenges, are discussed and evaluated.