pwc-archive/papers-with-abstracts huggingface dataset:
A Real-time Junk Food Recognition System based on Machine Learning
Authors: Sirajum Munira Shifat, Takitazwar Parthib, Sabikunnahar Talukder Pyaasa, Nila Maitra Chaity, Niloy Kumar, Md. Kishor Morol
Date: 2022-03-22 00:00:00
Tasks: BIG-bench Machine Learning, Food Recognition, object-detection, Object Detection, Object Recognition

$ $As a result of bad eating habits, humanity may be destroyed. People are constantly on the lookout for tasty foods, with junk foods being the most common source. As a consequence, our eating patterns are shifting, and we're gravitating toward junk food more than ever, which is bad for our health and increases our risk of acquiring health problems. Machine learning principles are applied in every aspect of our lives, and one of them is object recognition via image processing. However, because foods vary in nature, this procedure is crucial, and traditional methods like ANN, SVM, KNN, PLS etc., will result in a low accuracy rate. All of these issues were defeated by the Deep Neural Network. In this work, we created a fresh dataset of 10,000 data points from 20 junk food classifications to try to recognize junk foods. All of the data in the data set was gathered using the Google search engine, which is thought to be one-of-a-kind in every way. The goal was achieved using Convolution Neural Network (CNN) technology, which is well-known for image processing. We achieved a 98.05\% accuracy rate throughout the research, which was satisfactory. In addition, we conducted a test based on a real-life event, and the outcome was extraordinary. Our goal is to advance this research to the next level, so that it may be applied to a future study. Our ultimate goal is to create a system that would encourage people to avoid eating junk food and to be health-conscious. \keywords{ Machine Learning \and junk food \and object detection \and YOLOv3 \and custom food dataset.}
Focal Modulation Networks
Authors: Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao
Date: 2022-03-22 00:00:00
Tasks: image-classification, Image Classification, Object Detection, Panoptic Segmentation, Segmentation, Semantic Segmentation

We propose focal modulation networks (FocalNets in short), where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation. Specifically, FocalNets with tiny and base size achieve 82.3% and 83.9% top-1 accuracy on ImageNet-1K. After pretrained on ImageNet-22K in 224 resolution, it attains 86.5% and 87.3% top-1 accuracy when finetuned with resolution 224 and 384, respectively. When transferred to downstream tasks, FocalNets exhibit clear superiority. For object detection with Mask R-CNN, FocalNet base trained with 1\times outperforms the Swin counterpart by 2.1 points and already surpasses Swin trained with 3\times schedule (49.0 v.s. 48.5). For semantic segmentation with UPerNet, FocalNet base at single-scale outperforms Swin by 2.4, and beats Swin at multi-scale (50.5 v.s. 49.7). Using large FocalNet and Mask2former, we achieve 58.5 mIoU for ADE20K semantic segmentation, and 57.9 PQ for COCO Panoptic Segmentation. Using huge FocalNet and DINO, we achieved 64.3 and 64.4 mAP on COCO minival and test-dev, respectively, establishing new SoTA on top of much larger attention-based models like Swinv2-G and BEIT-3. Code and checkpoints are available at https://github.com/microsoft/FocalNet.
Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos
Authors: Tomáš Souček, Jean-Baptiste Alayrac, Antoine Miech, Ivan Laptev, Josef Sivic
Date: 2022-03-22 00:00:00
Proceeding: CVPR 2022 1
Tasks: Object

Human actions often induce changes of object states such as "cutting an apple", "cleaning shoes" or "pouring coffee". In this paper, we seek to temporally localize object states (e.g. "empty" and "full" cup) together with the corresponding state-modifying actions ("pouring coffee") in long uncurated videos with minimal supervision. The contributions of this work are threefold. First, we develop a self-supervised model for jointly learning state-modifying actions together with the corresponding object states from an uncurated set of videos from the Internet. The model is self-supervised by the causal ordering signal, i.e. initial object state $\rightarrow$ manipulating action $\rightarrow$ end state. Second, to cope with noisy uncurated training data, our model incorporates a noise adaptive weighting module supervised by a small number of annotated still images, that allows to efficiently filter out irrelevant videos during training. Third, we collect a new dataset with more than 2600 hours of video and 34 thousand changes of object states, and manually annotate a part of this data to validate our approach. Our results demonstrate substantial improvements over prior work in both action and object state-recognition in video.
Self-training semi-supervised classification based on density peaks of data
Authors: Di wu, Mingsheng Shang, Xin Luo a, Ji Xu, Huyong Yan, Weihui Deng, Guoyin Wang
Date: 2017-06-01 00:00:00
Proceeding: Neurocomputing 2017 6

Having a multitude of unlabeled data and few labeled ones is a common problem in many practical ap- plications. A successful methodology to tackle this problem is self-training semi-supervised classification. In this paper, we introduce a method to discover the structure of data space based on find of density peaks. Then, a framework for self-training semi-supervised classification, in which the structure of data space is integrated into the self-training iterative process to help train a better classifier, is proposed. A series of experiments on both artificial and real datasets are run to evaluate the performance of our proposed framework. Experimental results clearly demonstrate that our proposed framework has better performance than some previous works in general on both artificial and real datasets, especially when the distribution of data is non-spherical. Besides, we also find that the support vector machine is particularly suitable for our proposed framework to play the role of base classifier.
Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization
Authors: Yu Zhan, Fenghai Li, Renliang Weng, Wongun Choi
Date: 2022-03-22 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models. Our code and the synthetic dataset are available at https://github.com/YxZhxn/Ray3D .
Learning from All Vehicles
Authors: Dian Chen, Philipp Krähenbühl
Date: 2022-03-22 00:00:00
Proceeding: CVPR 2022 1
Tasks: All, Autonomous Driving, CARLA longest6

In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of other agents to create more diverse driving scenarios without collecting additional data. The main difficulty in learning from other vehicles is that there is no sensor information. We use a set of supervisory tasks to learn an intermediate representation that is invariant to the viewpoint of the controlling vehicle. This not only provides a richer signal at training time but also allows more complex reasoning during inference. Learning how all vehicles drive helps predict their behavior at test time and can avoid collisions. We evaluate this system in closed-loop driving simulations. Our system outperforms all prior methods on the public CARLA Leaderboard by a wide margin, improving driving score by 25 and route completion rate by 24 points. Our method won the 2021 CARLA Autonomous Driving challenge. Code and data are available at https://github.com/dotchen/LAV.
Multigrid-augmented deep learning preconditioners for the Helmholtz equation
Authors: Yael Azulay, Eran Treister
Date: 2022-03-14 00:00:00
Proceeding: NeurIPS Workshop DLDE 2021 12
Tasks: Data Augmentation, Deep Learning

In this paper, we present a data-driven approach to iteratively solve the discrete heterogeneous Helmholtz equation at high wavenumbers. In our approach, we combine classical iterative solvers with convolutional neural networks (CNNs) to form a preconditioner which is applied within a Krylov solver. For the preconditioner, we use a CNN of type U-Net that operates in conjunction with multigrid ingredients. Two types of preconditioners are proposed 1) U-Net as a coarse grid solver, and 2) U-Net as a deflation operator with shifted Laplacian V-cycles. Following our training scheme and data-augmentation, our CNN preconditioner can generalize over residuals and a relatively general set of wave slowness models. On top of that, we also offer an encoder-solver framework where an "encoder" network generalizes over the medium and sends context vectors to another "solver" network, which generalizes over the right-hand-sides. We show that this option is more robust and efficient than the stand-alone variant. Lastly, we also offer a mini-retraining procedure, to improve the solver after the model is known. This option is beneficial when solving multiple right-hand-sides, like in inverse problems. We demonstrate the efficiency and generalization abilities of our approach on a variety of 2D problems.
Exascale Grid Optimization (ExaGO) toolkit: An open-source high-performance package for solving large-scale grid optimization problems
Authors: Shrirang Abhyankar, Slaven Peles, Tamara Becejac, Jesse Holzer, Asher Mancinelli, Cameron Rutherford
Date: 2022-03-20 00:00:00

This paper introduces the Exascale Grid Optimization (ExaGO) toolkit, a library for solving large-scale alternating current optimal power flow (ACOPF) problems including stochastic effects, security constraints and multi-period constraints. ExaGO can run on parallel distributed memory platforms, including massively parallel hardware accelerators such as graphical processing units (GPUs). We present the details of the ExaGO library including its architecture, formulations, modeling details, and its performance for several optimization applications.
Status Updating with an Energy Harvesting Sensor under Partial Battery Knowledge
Authors: Mohammad Hatami, Markus Leinonen, Marian Codreanu
Date: 2022-03-19 00:00:00

We consider status updating under inexact knowledge of the battery level of an energy harvesting (EH) sensor that sends status updates about a random process to users via a cache-enabled edge node. More precisely, the control decisions are performed by relying only on the battery level knowledge captured from the last received status update packet. Upon receiving on-demand requests for fresh information from the users, the edge node uses the available information to decide whether to command the sensor to send a status update or to retrieve the most recently received measurement from the cache. We seek for the best actions of the edge node to minimize the average AoI of the served measurements, i.e., average on-demand AoI. Accounting for the partial battery knowledge, we model the problem as a partially observable Markov decision process (POMDP), and, through characterizing its key structures, develop a dynamic programming algorithm to obtain an optimal policy. Simulation results illustrate the threshold-based structure of an optimal policy and show the gains obtained by the proposed optimal POMDP-based policy compared to a request-aware greedy (myopic) policy.
Epidemic Propagation under Evolutionary Behavioral Dynamics: Stability and Bifurcation Analysis
Authors: Abhisek Satapathi, Narendra Kumar Dhar, Ashish R. Hota, Vaibhav Srivastava
Date: 2022-03-19 00:00:00

We consider the class of SIS epidemic models in which a large population of individuals chooses whether to adopt protection or to remain unprotected as the epidemic evolves. For a susceptible individual, adopting protection reduces the probability of becoming infected but it comes with a cost that is weighed with the instantaneous risk of becoming infected. An infected individual adopting protection transmits a new infection with a smaller probability compared to an unprotected infected individual. We focus on the replicator evolutionary dynamics to model the evolution of protection decisions by susceptible and infected subpopulations. We completely characterize the existence and local stability of the equilibria of the resulting coupled epidemic and replicator dynamics. We further show how the stability of different equilibrium points gets exchanged as certain parameters change. Finally, we investigate the system behavior under timescale separation between the epidemic and the evolutionary dynamics.
Incentive Compatibility in Two-Stage Repeated Stochastic Games
Authors: Bharadwaj Satchidanandan, Munther A. Dahleh
Date: 2022-03-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

We address the problem of mechanism design for two-stage repeated stochastic games -- a novel setting using which many emerging problems in next-generation electricity markets can be readily modeled. Repeated playing affords the players a large class of strategies that adapt a player's actions to all past observations and inferences obtained therefrom. In other settings such as iterative auctions or dynamic games where a large strategy space of this sort manifests, it typically has an important implication for mechanism design: It may be impossible to obtain truth-telling as a dominant strategy equilibrium. Consequently, in such scenarios, it is common to settle for mechanisms that render truth-telling only a Nash equilibrium, or variants thereof, even though Nash equilibria are known to be poor models of real-world behavior. This is owing to each player having to make overly specific assumptions about the behaviors of the other players to employ their Nash equilibrium strategy, which they may not make. In general, the lesser the burden of speculation in an equilibrium, the more plausible it is that it models real-world behavior. Guided by this maxim, we introduce a new notion of equilibrium called Dominant Strategy Non-Bankrupting Equilibrium (DNBE) which requires the players to make very little assumptions about the behavior of the other players to employ their equilibrium strategy. Consequently, a mechanism that renders truth-telling a DNBE as opposed to only a Nash equilibrium could be quite effective in molding real-world behavior along truthful lines. We present a mechanism for two-stage repeated stochastic games that renders truth-telling a Dominant Strategy Non-Bankrupting Equilibrium. The mechanism also guarantees individual rationality and maximizes social welfare. Finally, we describe an application of the mechanism to design demand response markets.
Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies
Authors: Patricia Wollstadt, Daniel L. Rathbun, W. Martin Usrey and, André Moraes Bastos, Michael Lindner, Viola Priesemann, Michael Wibral
Date: 2022-03-21 00:00:00

Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in neural dynamics. A prime example are studies on predictive coding. These often ask if confirmed predictions about inputs or predictions errors between internal predictions and inputs are passed on in a hierarchical neural system--while at the same time looking for the neural correlates of coding for errors and predictions. If we do not know exactly what a neural system predicts at any given moment, this results in a circular analysis--as has been criticized correctly. To circumvent such circular analysis, we propose to express information processing strategies (such as predictive coding) by local information-theoretic quantities, such that they can be estimated directly from neural data. We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy. We define testable hypotheses on the relationship of both quantities to identify which of the assumed strategies was used. We demonstrate our approach on spiking data from the retinogeniculate synapse of the cat. Applying our local information dynamics framework, we are able to show that the synapse codes for predictable rather than surprising input. To support our findings, we apply measures from partial information decomposition, which allow to differentiate if the transferred information is primarily bottom-up sensory input or information transferred conditionally on the current state of the synapse. Supporting our local information-theoretic results, we find that the synapse preferentially transfers bottom-up information.
Multi-View Dreaming: Multi-View World Model with Contrastive Learning
Authors: Akira Kinose, Masashi Okada, Ryo Okumura, Tadahiro Taniguchi
Date: 2022-03-15 00:00:00
Tasks: Contrastive Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming. Most current reinforcement learning method assumes a single-view observation space, and this imposes limitations on the observed data, such as lack of spatial information and occlusions. This makes obtaining ideal observational information from the environment difficult and is a bottleneck for real-world robotics applications. In this paper, we use contrastive learning to train a shared latent space between different viewpoints, and show how the Products of Experts approach can be used to integrate and control the probability distributions of latent states for multiple viewpoints. We also propose Multi-View DreamingV2, a variant of Multi-View Dreaming that uses a categorical distribution to model the latent state instead of the Gaussian distribution. Experiments show that the proposed method outperforms simple extensions of existing methods in a realistic robot control task.
Learning to Reduce False Positives in Analytic Bug Detectors
Authors: Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin, Xiaoyu Liu, Xin Shi, Colin Clement, Neel Sundaresan
Date: 2022-03-08 00:00:00

Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software. In response, programmers rely on static analysis tools to regularly scan their codebases and find potential bugs. In order to maximize coverage, however, these tools generally tend to report a significant number of false positives, requiring developers to manually verify each warning. To address this problem, we propose a Transformer-based learning approach to identify false positive bug warnings. We demonstrate that our models can improve the precision of static analysis by 17.5%. In addition, we validated the generalizability of this approach across two major bug types: null dereference and resource leak.
Collective Decision Making using Attractive and Repulsive Forces in Markovian Opinion Dynamics
Authors: Carl-Johan Heiker, Paolo Falcone
Date: 2022-03-21 00:00:00
Tasks: Autonomous Vehicles, Decision Making

In this paper, we model a decision-making process involving a set of interacting agents. We use Markovian opinion dynamics, where each agent switches between decisions according to a continuous time Markov chain. Existing opinion dynamics models are extended by introducing attractive and repulsive forces that act within and between groups of agents, respectively. Such an extension enables the resemblance of behaviours emerging in networks where agents make decisions that depend both on their own preferences and the decisions of specific groups of surrounding agents. The considered modeling problem and the contributions in this paper are inspired by the interaction among road users (RUs) at traffic junctions, where each RU has to decide whether to go or to yield.
Identification of Friction Models for MPC-based Control of a PowerCube Serial Robot
Authors: Jörg Fehr, Arnim Kargl, Hannes Eschmann
Date: 2022-03-21 00:00:00
Tasks: Friction, Model Predictive Control

For model-based control, an accurate and in its complexity suitable representation of the real system is a decisive prerequisite for high and robust control quality. In a structured step-by-step procedure, a model predictive control (MPC) scheme for a Schunk PowerCube robot is derived. Neweul-M$^2$ provides the necessary nonlinear model in symbolical and numerical form. To handle the heavy online computational burden involved with the derived nonlinear model, a linear time-varying MPC scheme is developed based on linearizing the nonlinear system concerning the desired trajectory and the a priori known corresponding feed-forward controller. To improve the identification of the nonlinear friction models of the joints, a nonlinear regression method and the Sparse Identification of Nonlinear Dynamics (SINDy) are compared with each other concerning robustness, online adaptivity, and necessary preprocessing of the input data. Everything is implemented on a slim, low-cost control system with a standard laptop PC.