pwc-archive/papers-with-abstracts huggingface dataset:
Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization
Authors: Yuki Shirai, Devesh K. Jha, Arvind Raghunathan, Diego Romeres
Date: 2022-03-22 00:00:00
Tasks: Bilevel Optimization, Friction

Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interaction with uncertainty in physical properties of the object. In this paper, we study robust optimization for control of pivoting manipulation in the presence of uncertainties. We present insights about how friction can be exploited to compensate for the inaccuracies in the estimates of the physical properties during manipulation. In particular, we derive analytical expressions for stability margin provided by friction during pivoting manipulation. This margin is then used in a bilevel trajectory optimization algorithm to design a controller that maximizes this stability margin to provide robustness against uncertainty in physical properties of the object. We demonstrate our proposed method using a 6 DoF manipulator for manipulating several different objects.
Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis
Authors: Shaoru Chen, Victor M. Preciado, Manfred Morari, Nikolai Matni
Date: 2022-03-21 00:00:00
Tasks: model, Model Predictive Control

We propose a robust model predictive control (MPC) method for discrete-time linear systems with polytopic model uncertainty and additive disturbances. Optimizing over linear time-varying (LTV) state feedback controllers has been successfully used for robust MPC when only additive disturbances are present. However, it is challenging to design LTV state feedback controllers in the face of model uncertainty whose effects are difficult to bound. To address this issue, we propose a novel approach to over-approximate the effects of both model uncertainty and additive disturbances by a filtered additive disturbance signal. Using the System Level Synthesis framework, we jointly search for robust LTV state feedback controllers and the bounds on the effects of uncertainty online, which allows us to reduce the conservatism and minimize an upper bound on the worst-case cost in robust MPC. We provide a comprehensive numerical comparison of our method and representative robust MPC methods from the literature. Numerical examples demonstrate that our proposed method can significantly reduce the conservatism over a wide range of uncertainty parameters with comparable computational effort as the baseline methods.
Sensitivity of Single-Pulse Radar Detection to Radar State Uncertainty
Authors: Austin Costley, Randall Christensen, Robert C. Leishman, Greg Droge
Date: 2022-03-21 00:00:00
Tasks: Position, Sensitivity

Mission planners for aircraft operating under threat of detection from ground-based radar systems are often concerned with the probability of detection. Current approaches to path planning in such environments consider the radar state (i.e. radar position and parameters) to be deterministic and known. In practice, there is uncertainty in the radar state which induces uncertainty in the probability of detection. This paper presents a method to incorporate the uncertainty of the radar state in a single-pulse radar detection model. The method linearizes the radar detection model with respect to the the radar state and uses the linearized models to estimate, to the first order, the variance of the probability of detection. The results in this paper validate the linearization using Monte Carlo analysis and illustrate the sensitivity of the probability of detection to radar state uncertainty.
Alarm-Based Root Cause Analysis in Industrial Processes Using Deep Learning
Authors: Negin Javanbakht, Amir Neshastegaran, Iman Izadi
Date: 2022-03-21 00:00:00
Tasks: Fault Detection, Management

Alarm management systems have become indispensable in modern industry. Alarms inform the operator of abnormal situations, particularly in the case of equipment failures. Due to the interconnections between various parts of the system, each fault can affect other sections of the system operating normally. As a result, the fault propagates through faultless devices, increasing the number of alarms. Hence, the timely detection of the major fault that triggered the alarm by the operator can prevent the following consequences. However, due to the complexity of the system, it is often impossible to find precise relations between the underlying fault and the alarms. As a result, the operator needs support to make an appropriate decision immediately. Modeling alarms based on the historical alarm data can assist the operator in determining the root cause of the alarm. This research aims to model the relations between industrial alarms using historical alarm data in the database. Firstly, alarm data is collected, and alarm tags are sequenced. Then, these sequences are converted to numerical vectors using word embedding. Next, a self-attention-based BiLSTM-CNN classifier is used to learn the structure and relevance between historical alarm data. After training the model, this model is used for online fault detection. Finally, as a case study, the proposed model is implemented in the well-known Tennessee Eastman process, and the results are presented.
Assessing trade-offs among electrification and grid decarbonization in a clean energy transition: Application to New York State
Authors: Terence Conlon, Michael Waite, Yuezi Wu, Vijay Modi
Date: 2022-03-21 00:00:00

A modeling framework is presented to investigate trade-offs among decarbonization from increased low-carbon electricity generation and electrification of heating and vehicles. The model is broadly applicable but relies on high-fidelity parameterization of existing infrastructure and anticipated electrified loads; this study applies it to New York State where detailed data is available. Trade-offs are investigated between end use electrification and renewable energy deployment in terms of supply costs, generation and storage capacities, renewable resource mix, and system operation. Results indicate that equivalent emissions reductions can be achieved at lower costs to the grid by prioritizing electrification with 40-70% low-carbon electricity supply instead of aiming for complete grid decarbonization. With 60% electrification and 50% low-carbon electricity, approximately 1/3 emissions reductions can be achieved at current supply costs; with only 20% electrification, 90% low-carbon electricity is required to achieve the same emissions reductions, resulting in 43% higher grid costs. In addition, three primary cost drivers are identified for a system undergoing decarbonization: (1) decreasing per-unit costs of existing infrastructure with increasing electrified demand, (2) higher in-state generation costs from low-carbon sources relative to gas-based and hydropower generation, and (3) increasing integration costs at high percentages of low-carbon electricity.
Planar Rooted Phylogenetic Networks
Authors: Vincent Moulton, Taoyang Wu
Date: 2022-03-18 00:00:00

A rooted phylogenetic network is a directed acyclic graph with a single root, whose sinks correspond to a set of species. As such networks are useful for representing the evolution of species that have undergone reticulate evolution, there has been great interest in developing the theory behind and algorithms for constructing them. However, unlike evolutionary trees, these networks can be highly non-planar, which can make them difficult to visualise and interpret. Here we investigate properties of planar rooted phylogenetic networks and algorithms for deciding whether or not rooted networks have certain special planarity properties. In particular, we introduce three natural subclasses of planar rooted phylogenetic networks and show that they form a hierarchy. In addition, for the well-known level-k networks, we show that level-1, -2, -3 networks are always outer, terminal, and upward planar, respectively, and that level-4 networks are not necessarily planar. Finally, we show that a regular network is terminal planar if and only if it is pyramidal. Our results make use of the highly developed field of planar digraphs, and we believe that the link between phylogenetic networks and planar graphs should prove useful in future for developing new approaches to both construct and visualise phylogenetic networks.
Dataset Distillation by Matching Training Trajectories
Authors: George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu
Date: 2022-03-22 00:00:00
Proceeding: CVPR 2022 1
Tasks: Dataset Distillation, Dataset Distillation - 1IPC

Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this paper, we propose a new formulation that optimizes our distilled data to guide networks to a similar state as those trained on real data across many training steps. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset. Our method handily outperforms existing methods and also allows us to distill higher-resolution visual data.
What can we Learn Even From the Weakest? Learning Sketches for Programmatic Strategies
Authors: Leandro C. Medeiros, David S. Aleixo, Levi H. S. Lelis
Date: 2022-03-22 00:00:00

In this paper we show that behavioral cloning can be used to learn effective sketches of programmatic strategies. We show that even the sketches learned by cloning the behavior of weak players can help the synthesis of programmatic strategies. This is because even weak players can provide helpful information, e.g., that a player must choose an action in their turn of the game. If behavioral cloning is not employed, the synthesizer needs to learn even the most basic information by playing the game, which can be computationally expensive. We demonstrate empirically the advantages of our sketch-learning approach with simulated annealing and UCT synthesizers. We evaluate our synthesizers in the games of Can't Stop and MicroRTS. The sketch-based synthesizers are able to learn stronger programmatic strategies than their original counterparts. Our synthesizers generate strategies of Can't Stop that defeat a traditional programmatic strategy for the game. They also synthesize strategies that defeat the best performing method from the latest MicroRTS competition.
GradViT: Gradient Inversion of Vision Transformers
Authors: Ali Hatamizadeh, Hongxu Yin, Holger Roth, Wenqi Li, Jan Kautz, Daguang Xu, Pavlo Molchanov
Date: 2022-03-22 00:00:00
Proceeding: CVPR 2022 1
Tasks: Scheduling

In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. During this attack, the original data batch is reconstructed given model weights and the corresponding gradients. We introduce a method, named GradViT, that optimizes random noise into naturally looking images via an iterative process. The optimization objective consists of (i) a loss on matching the gradients, (ii) image prior in the form of distance to batch-normalization statistics of a pretrained CNN model, and (iii) a total variation regularization on patches to guide correct recovery locations. We propose a unique loss scheduling function to overcome local minima during optimization. We evaluate GadViT on ImageNet1K and MS-Celeb-1M datasets, and observe unprecedentedly high fidelity and closeness to the original (hidden) data. During the analysis we find that vision transformers are significantly more vulnerable than previously studied CNNs due to the presence of the attention mechanism. Our method demonstrates new state-of-the-art results for gradient inversion in both qualitative and quantitative metrics. Project page at https://gradvit.github.io/.
Insights From the NeurIPS 2021 NetHack Challenge
Authors: Eric Hambro, Sharada Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, DaeJin Jo, Anssi Kanervisto, Jongmin Kim, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michał Sypetkowski
Date: 2022-03-22 00:00:00
Tasks: NetHack, Reinforcement Learning (RL)

In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' in) the popular dungeon-crawler game of NetHack by interacting with the NetHack Learning Environment (NLE), a scalable, procedurally generated, and challenging Gym environment for reinforcement learning (RL). The challenge showcased community-driven progress in AI with many diverse approaches significantly beating the previously best results on NetHack. Furthermore, it served as a direct comparison between neural (e.g., deep RL) and symbolic AI, as well as hybrid systems, demonstrating that on NetHack symbolic bots currently outperform deep RL by a large margin. Lastly, no agent got close to winning the game, illustrating NetHack's suitability as a long-term benchmark for AI research.
Open-Vocabulary DETR with Conditional Matching
Authors: Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, Chen Change Loy
Date: 2022-03-22 00:00:00
Tasks: Language Modelling, object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection

Open-vocabulary object detection, which is concerned with the problem of detecting novel objects guided by natural language, has gained increasing attention from the community. Ideally, we would like to extend an open-vocabulary detector such that it can produce bounding box predictions based on user inputs in form of either natural language or exemplar image. This offers great flexibility and user experience for human-computer interaction. To this end, we propose a novel open-vocabulary detector based on DETR -- hence the name OV-DETR -- which, once trained, can detect any object given its class name or an exemplar image. The biggest challenge of turning DETR into an open-vocabulary detector is that it is impossible to calculate the classification cost matrix of novel classes without access to their labeled images. To overcome this challenge, we formulate the learning objective as a binary matching one between input queries (class name or exemplar image) and the corresponding objects, which learns useful correspondence to generalize to unseen queries during testing. For training, we choose to condition the Transformer decoder on the input embeddings obtained from a pre-trained vision-language model like CLIP, in order to enable matching for both text and image queries. With extensive experiments on LVIS and COCO datasets, we demonstrate that our OV-DETR -- the first end-to-end Transformer-based open-vocabulary detector -- achieves non-trivial improvements over current state of the arts.
A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework
Authors: Usha Lokala, Aseem Srivastava, Triyasha Ghosh Dastidar, Tanmoy Chakraborty, Md Shad Akthar, Maryam Panahiazar, Amit Sheth
Date: 2022-03-22 00:00:00
Tasks: Multi-Task Learning, Transfer Learning

Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of $150k$ items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel task-adaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adapt a knowledge-assisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models. Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task.
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Authors: Jakob Hoydis, Sebastian Cammerer, Fayçal Ait Aoudia, Avinash Vem, Nikolaus Binder, Guillermo Marcus, Alexander Keller
Date: 2022-03-22 00:00:00
Tasks: Benchmarking, GPU

Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. It enables the rapid prototyping of complex communication system architectures and provides native support for the integration of neural networks. Sionna implements a wide breadth of carefully tested state-of-the-art algorithms that can be used for benchmarking and end-to-end performance evaluation. This allows researchers to focus on their research, making it more impactful and reproducible, while saving time implementing components outside their area of expertise. This white paper provides a brief introduction to Sionna, explains its design principles and features, as well as future extensions, such as integrated ray tracing and custom CUDA kernels. We believe that Sionna is a valuable tool for research on next-generation communication systems, such as 6G, and we welcome contributions from our community.
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments
Authors: Antonis Maronikolakis, Axel Wisiorek, Leah Nann, Haris Jabbar, Sahana Udupa, Hinrich Schuetze
Date: 2022-03-22 00:00:00
Proceeding: Findings (ACL) 2022 5

Building on current work on multilingual hate speech (e.g., Ousidhoum et al. (2019)) and hate speech reduction (e.g., Sap et al. (2020)), we present XTREMESPEECH, a new hate speech dataset containing 20,297 social media passages from Brazil, Germany, India and Kenya. The key novelty is that we directly involve the affected communities in collecting and annotating the data - as opposed to giving companies and governments control over defining and combatting hate speech. This inclusive approach results in datasets more representative of actually occurring online speech and is likely to facilitate the removal of the social media content that marginalized communities view as causing the most harm. Based on XTREMESPEECH, we establish novel tasks with accompanying baselines, provide evidence that cross-country training is generally not feasible due to cultural differences between countries and perform an interpretability analysis of BERT's predictions.
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
Authors: Liang Gao, Huazhu Fu, Li Li, YingWen Chen, Ming Xu, Cheng-Zhong Xu
Date: 2022-03-22 00:00:00
Proceeding: CVPR 2022 1
Tasks: Federated Learning, image-classification, Image Classification

Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity among their local data distributions, which would cause inconsistent optimized local models on the client-side. To address this fundamental dilemma, we propose a novel federated learning algorithm with local drift decoupling and correction (FedDC). Our FedDC only introduces lightweight modifications in the local training phase, in which each client utilizes an auxiliary local drift variable to track the gap between the local model parameter and the global model parameters. The key idea of FedDC is to utilize this learned local drift variable to bridge the gap, i.e., conducting consistency in parameter-level. The experiment results and analysis demonstrate that FedDC yields expediting convergence and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous clients.
The Stanford Drone Dataset is More Complex than We Think: An Analysis of Key Characteristics
Authors: Joshua Andle, Nicholas Soucy, Simon Socolow, Salimeh Yasaei Sekeh
Date: 2022-03-22 00:00:00
Tasks: Autonomous Navigation, Trajectory Prediction

Several datasets exist which contain annotated information of individuals' trajectories. Such datasets are vital for many real-world applications, including trajectory prediction and autonomous navigation. One prominent dataset currently in use is the Stanford Drone Dataset (SDD). Despite its prominence, discussion surrounding the characteristics of this dataset is insufficient. We demonstrate how this insufficiency reduces the information available to users and can impact performance. Our contributions include the outlining of key characteristics in the SDD, employment of an information-theoretic measure and custom metric to clearly visualize those characteristics, the implementation of the PECNet and Y-Net trajectory prediction models to demonstrate the outlined characteristics' impact on predictive performance, and lastly we provide a comparison between the SDD and Intersection Drone (inD) Dataset. Our analysis of the SDD's key characteristics is important because without adequate information about available datasets a user's ability to select the most suitable dataset for their methods, to reproduce one another's results, and to interpret their own results are hindered. The observations we make through this analysis provide a readily accessible and interpretable source of information for those planning to use the SDD. Our intention is to increase the performance and reproducibility of methods applied to this dataset going forward, while also clearly detailing less obvious features of the dataset for new users.
CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation
Authors: Feng Wang, Huiyu Wang, Chen Wei, Alan Yuille, Wei Shen
Date: 2022-03-22 00:00:00
Tasks: Contrastive Learning, Representation Learning, Segmentation, Semantic Segmentation

Recent advances in self-supervised contrastive learning yield good image-level representation, which favors classification tasks but usually neglects pixel-level detailed information, leading to unsatisfactory transfer performance to dense prediction tasks such as semantic segmentation. In this work, we propose a pixel-wise contrastive learning method called CP2 (Copy-Paste Contrastive Pretraining), which facilitates both image- and pixel-level representation learning and therefore is more suitable for downstream dense prediction tasks. In detail, we copy-paste a random crop from an image (the foreground) onto different background images and pretrain a semantic segmentation model with the objective of 1) distinguishing the foreground pixels from the background pixels, and 2) identifying the composed images that share the same foreground.Experiments show the strong performance of CP2 in downstream semantic segmentation: By finetuning CP2 pretrained models on PASCAL VOC 2012, we obtain 78.6% mIoU with a ResNet-50 and 79.5% with a ViT-S.