pwc-archive/papers-with-abstracts huggingface dataset:
C-CNN: Contourlet Convolutional Neural Networks
Authors: Mengkun Liu, Licheng Jiao, Xu Liu, Lingling Li, Fang Liu, Shuyuan Yang
Date: 2020-07-21 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2020 7
Tasks: Classification, Texture Classification

Extracting effective features is always a challenging problem for texture classification because of the uncertainty of scales and the clutter of textural patterns. For texture classification, spectral analysis is traditionally employed in the frequency domain. Recent studies have shown the potential of convolutional neural networks (CNNs) when dealing with the texture classification task in the spatial domain. In this article, we try combining both approaches in different domains for more abundant information and proposed a novel network architecture named contourlet CNN (C-CNN). The network aims to learn sparse and effective feature representations for images. First, the contourlet transform is applied to get the spectral features from an image. Second, the spatial-spectral feature fusion strategy is designed to incorporate the spectral features into CNN architecture. Third, the statistical features are integrated into the network by the statistical feature fusion. Finally, the results are obtained by classifying the fusion features. We also investigated the behavior of the parameters in contourlet decomposition. Experiments on the widely used three texture data sets (kth-tips2-b, DTD, and CUReT) and five remote sensing data sets (UCM, WHU-RS, AID, RSSCN7, and NWPU-RESISC45) demonstrate that the proposed approach outperforms several well-known classification methods in terms of classification accuracy with fewer trainable parameters.
Fuzzy Pooling
Authors: D.E. Diamantis, D.K. Iakovidis
Date: 2020-11-01 00:00:00
Proceeding: IEEE Transactions on Fuzzy Systems 2020 11
Tasks: Classification, Dimensionality Reduction, image-classification, Image Classification

Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pooling, which implements dimensionality reduction. The impact of pooling in the classification performance of the CNNs has been highlighted in several previous works, and a variety of alternative pooling operators have been proposed. However, only a few of them tackle with the uncertainty that is naturally propagated from the input layer to the feature maps of the hidden layers through convolutions. In this paper we present a novel pooling operation based on (type-1) fuzzy sets to cope with the local imprecision of the feature maps, and we investigate its performance in the context of image classification. Fuzzy pooling is performed by fuzzification, aggregation and defuzzification of feature map neighborhoods. It is used for the construction of a fuzzy pooling layer that can be applied as a drop-in replacement of the current, crisp, pooling layers of CNN architectures. Several experiments using publicly available datasets show that the proposed approach can enhance the classification performance of a CNN. A comparative evaluation shows that it outperforms state-of-the-art pooling approaches.
Fault-Tolerant Control of Quadcopter UAVs Using Robust Adaptive Sliding Mode Approach
Authors: Ngoc Phi Nguyen and Sung Kyung Hong *
Date: 2018-12-28 00:00:00
Proceeding: Energies 2018 12

In this paper, a fault-tolerant control method is proposed for quadcopter unmanned aerial
vehicles (UAV) to account for system uncertainties and actuator faults. A mathematical model of the
quadcopter UAV is first introduced when faults occur in actuators. A normal adaptive sliding mode
control (NASMC) approach is proposed as a baseline controller to handle the chattering problem
and system uncertainties, which does not require information of the upper bound. To improve
the performance of the NASMC scheme, radial basis function neural networks are combined
with an adaptive scheme to make a quick compensation in presence of system uncertainties and
actuator faults. The Lyapunov theory is applied to verify the stability of the proposed methods.
The effectiveness of modified ASMC algorithm is compared with that of NASMC using numerical
examples under different faulty conditions.
Expectations-driven cycles in the housing market
Authors: Luisa Lambertinia, Caterina Mendicinob, Maria Teresa Punzi
Date: 2017-01-01 00:00:00
Proceeding: Economic Modelling Volume 60, Pages 297-312 2017 1

This paper explores the transmission of “news shocks” in a model of the housing market and shows that anticipated signals or beliefs of future macroeconomic developments can generate boom-bust cycles in the housing market and lead to business cycle fluctuations. Anticipated monetary policy and inflationary shocks that turn out to be wrong can also lead to subsequent macroeconomic recessions. Credit frictions also play an important role in generating boom-bust cycle dynamics in the housing market. In particular, favorable credit conditions that are expected to be reversed in the near future generate an housing boom. The active use of the loan-to-value ratio as a policy tool aimed at dampening the severity of expectations-driven cycles effectively reduces the volatility of household debt, aggregate consumption and GDP.
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization
Authors: Runlong Zhou, Zelin He, Yuandong Tian, Yi Wu, Simon S. Du
Date: 2022-02-11 00:00:00
Tasks: Combinatorial Optimization, Reinforcement Learning (RL)

Over the recent years, reinforcement learning (RL) starts to show promising results in tackling combinatorial optimization (CO) problems, in particular when coupled with curriculum learning to facilitate training. Despite emerging empirical evidence, theoretical study on why RL helps is still at its early stage. This paper presents the first systematic study on policy optimization methods for online CO problems. We show that online CO problems can be naturally formulated as latent Markov Decision Processes (LMDPs), and prove convergence bounds on natural policy gradient (NPG) for solving LMDPs. Furthermore, our theory explains the benefit of curriculum learning: it can find a strong sampling policy and reduce the distribution shift, a critical quantity that governs the convergence rate in our theorem. For a canonical online CO problem, the Best Choice Problem (BCP), we formally prove that distribution shift is reduced exponentially with curriculum learning even if the curriculum is a randomly generated BCP on a smaller scale. Our theory also shows we can simplify the curriculum learning scheme used in prior work from multi-step to single-step. Lastly, we provide extensive experiments on the Best Choice Problem, Online Knapsack, and AdWords to verify our findings.
SafePicking: Learning Safe Object Extraction via Object-Level Mapping
Authors: Kentaro Wada, Stephen James, Andrew J. Davison
Date: 2022-02-11 00:00:00
Tasks: Motion Planning, Object, Object Recognition, Scene Understanding

Robots need object-level scene understanding to manipulate objects while reasoning about contact, support, and occlusion among objects. Given a pile of objects, object recognition and reconstruction can identify the boundary of object instances, giving important cues as to how the objects form and support the pile. In this work, we present a system, SafePicking, that integrates object-level mapping and learning-based motion planning to generate a motion that safely extracts occluded target objects from a pile. Planning is done by learning a deep Q-network that receives observations of predicted poses and a depth-based heightmap to output a motion trajectory, trained to maximize a safety metric reward. Our results show that the observation fusion of poses and depth-sensing gives both better performance and robustness to the model. We evaluate our methods using the YCB objects in both simulation and the real world, achieving safe object extraction from piles.
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
Authors: Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
Date: 2022-02-11 00:00:00
Tasks: Logical Reasoning

Machine learning systems perform well on pattern matching tasks, but their ability to perform algorithmic or logical reasoning is not well understood. One important reasoning capability is algorithmic extrapolation, in which models trained only on small/simple reasoning problems can synthesize complex strategies for large/complex problems at test time. Algorithmic extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems. We observe that this approach fails to scale to highly complex problems because behavior degenerates when many iterations are applied -- an issue we refer to as "overthinking." We propose a recall architecture that keeps an explicit copy of the problem instance in memory so that it cannot be forgotten. We also employ a progressive training routine that prevents the model from learning behaviors that are specific to iteration number and instead pushes it to learn behaviors that can be repeated indefinitely. These innovations prevent the overthinking problem, and enable recurrent systems to solve extremely hard extrapolation tasks.
CLIPasso: Semantically-Aware Object Sketching
Authors: Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo, Roman Christian Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, Ariel Shamir
Date: 2022-02-11 00:00:00
Tasks: Object

Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings. Abstraction entails identifying the essential visual properties of an object or scene, which requires semantic understanding and prior knowledge of high-level concepts. Abstract depictions are therefore challenging for artists, and even more so for machines. We present CLIPasso, an object sketching method that can achieve different levels of abstraction, guided by geometric and semantic simplifications. While sketch generation methods often rely on explicit sketch datasets for training, we utilize the remarkable ability of CLIP (Contrastive-Language-Image-Pretraining) to distill semantic concepts from sketches and images alike. We define a sketch as a set of B\'ezier curves and use a differentiable rasterizer to optimize the parameters of the curves directly with respect to a CLIP-based perceptual loss. The abstraction degree is controlled by varying the number of strokes. The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual components of the subject drawn.
PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?
Authors: Arnaud Huaulmé, Kanako Harada, Quang-Minh Nguyen, Bogyu Park, Seungbum Hong, Min-Kook Choi, Michael Peven, Yunshuang Li, Yonghao Long, Qi Dou, Satyadwyoom Kumar, Seenivasan Lalithkumar, Ren Hongliang, Hiroki Matsuzaki, Yuto Ishikawa, Yuriko Harai, Satoshi Kondo, Mamoru Mitsuishi, Pierre Jannin
Date: 2022-02-11 00:00:00
Tasks: Kinematic Based Workflow Recognition, Segmentation Based Workflow Recognition, Semantic Segmentation, Video Based Workflow Recognition, Video & Kinematic Base Workflow Recognition, Video, Kinematic & Segmentation Base Workflow Recognition

This paper presents the design and results of the "PEg TRAnsfert Workflow recognition" (PETRAW) challenge whose objective was to develop surgical workflow recognition methods based on one or several modalities, among video, kinematic, and segmentation data, in order to study their added value. The PETRAW challenge provided a data set of 150 peg transfer sequences performed on a virtual simulator. This data set was composed of videos, kinematics, semantic segmentation, and workflow annotations which described the sequences at three different granularity levels: phase, step, and activity. Five tasks were proposed to the participants: three of them were related to the recognition of all granularities with one of the available modalities, while the others addressed the recognition with a combination of modalities. Average application-dependent balanced accuracy (AD-Accuracy) was used as evaluation metric to take unbalanced classes into account and because it is more clinically relevant than a frame-by-frame score. Seven teams participated in at least one task and four of them in all tasks. Best results are obtained with the use of the video and the kinematics data with an AD-Accuracy between 93% and 90% for the four teams who participated in all tasks. The improvement between video/kinematic-based methods and the uni-modality ones was significant for all of the teams. However, the difference in testing execution time between the video/kinematic-based and the kinematic-based methods has to be taken into consideration. Is it relevant to spend 20 to 200 times more computing time for less than 3% of improvement? The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.
The HaMSE Ontology: Using Semantic Technologies to support Music Representation Interoperability and Musicological Analysis
Authors: Andrea Poltronieri, Aldo Gangemi
Date: 2022-02-11 00:00:00

The use of Semantic Technologies - in particular the Semantic Web - has revealed to be a great tool for describing the cultural heritage domain and artistic practices. However, the panorama of ontologies for musicological applications seems to be limited and restricted to specific applications. In this research, we propose HaMSE, an ontology capable of describing musical features that can assist musicological research. More specifically, HaMSE proposes to address sues that have been affecting musicological research for decades: the representation of music and the relationship between quantitative and qualitative data. To do this, HaMSE allows the alignment between different music representation systems and describes a set of musicological features that can allow the music analysis at different granularity levels.
Investigating Power laws in Deep Representation Learning
Authors: Arna Ghosh, Arnab Kumar Mondal, Kumar Krishna Agrawal, Blake Richards
Date: 2022-02-11 00:00:00
Tasks: Representation Learning, Scene Recognition, Self-Supervised Learning

Representation learning that leverages large-scale labelled datasets, is central to recent progress in machine learning. Access to task relevant labels at scale is often scarce or expensive, motivating the need to learn from unlabelled datasets with self-supervised learning (SSL). Such large unlabelled datasets (with data augmentations) often provide a good coverage of the underlying input distribution. However evaluating the representations learned by SSL algorithms still requires task-specific labelled samples in the training pipeline. Additionally, the generalization of task-specific encoding is often sensitive to potential distribution shift. Inspired by recent advances in theoretical machine learning and vision neuroscience, we observe that the eigenspectrum of the empirical feature covariance matrix often follows a power law. For visual representations, we estimate the coefficient of the power law, $\alpha$, across three key attributes which influence representation learning: learning objective (supervised, SimCLR, Barlow Twins and BYOL), network architecture (VGG, ResNet and Vision Transformer), and tasks (object and scene recognition). We observe that under mild conditions, proximity of $\alpha$ to 1, is strongly correlated to the downstream generalization performance. Furthermore, $\alpha \approx 1$ is a strong indicator of robustness to label noise during fine-tuning. Notably, $\alpha$ is computable from the representations without knowledge of any labels, thereby offering a framework to evaluate the quality of representations in unlabelled datasets.
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
Authors: Nicolò Dal Fabbro, Subhrakanti Dey, Michele Rossi, Luca Schenato
Date: 2022-02-11 00:00:00
Tasks: Distributed Optimization, Federated Learning

There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is strongly heterogeneous in terms of communication resources (e.g., bandwidth) and data distribution. In these cases, communication between local machines (agents) and the central server (Master) is a main consideration. In this work, we present SHED, an original communication-constrained Newton-type (NT) algorithm designed to accelerate FL in such heterogeneous scenarios. SHED is by design robust to non i.i.d. data distributions, handles heterogeneity of agents' communication resources (CRs), only requires sporadic Hessian computations, and achieves super-linear convergence. This is possible thanks to an incremental strategy, based on eigendecomposition of the local Hessian matrices, which exploits (possibly) outdated second-order information. The proposed solution is thoroughly validated on real datasets by assessing (i) the number of communication rounds required for convergence, (ii) the overall amount of data transmitted and (iii) the number of local Hessian computations. For all these metrics, the proposed approach shows superior performance against state-of-the art techniques like GIANT and FedNL.
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system
Authors: Haoyang Cao, Xin Guo, Guan Wang
Date: 2022-02-11 00:00:00
Tasks: Anomaly Detection, Meta-Learning, SSIM

Anomaly detection has been an active research area with a wide range of potential applications. Key challenges for anomaly detection in the AI era with big data include lack of prior knowledge of potential anomaly types, highly complex and noisy background in input data, scarce abnormal samples, and imbalanced training dataset. In this work, we propose a meta-learning framework for anomaly detection to deal with these issues. Within this framework, we incorporate the idea of generative adversarial networks (GANs) with appropriate choices of loss functions including structural similarity index measure (SSIM). Experiments with limited labeled data for high-speed rail inspection demonstrate that our meta-learning framework is sharp and robust in identifying anomalies. Our framework has been deployed in five high-speed railways of China since 2021: it has reduced more than 99.7% workload and saved 96.7% inspection time.
Answer Set Planning: A Survey
Authors: Tran Cao Son, Enrico Pontelli, Marcello Balduccini, Torsten Schaub
Date: 2022-02-11 00:00:00
Tasks: Survey

Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, i.e., solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.
Multi-Modal Knowledge Graph Construction and Application: A Survey
Authors: Xiangru Zhu, Zhixu Li, Xiaodan Wang, Xueyao Jiang, Penglei Sun, Xuwu Wang, Yanghua Xiao, Nicholas Jing Yuan
Date: 2022-02-11 00:00:00
Tasks: graph construction, Knowledge Graphs, Multi-modal Knowledge Graph, Survey

Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strength and weakness of different solutions. We finalize this survey with open research problems relevant to MMKGs.
Visualising Multiplayer Game Spaces
Authors: James Goodman, Diego Perez-Liebana, Simon Lucas
Date: 2022-02-11 00:00:00
Tasks: Dimensionality Reduction

We compare four different `game-spaces' in terms of their usefulness in characterising multi-player tabletop games, with a particular interest in any underlying change to a game's characteristics as the number of players changes. In each case we take a 16-dimensional feature space, and reduce it to a 2-dimensional visualizable landscape. We find that a space obtained from optimization of parameters in Monte Carlo Tree Search (MCTS) is the most directly interpretable to characterise our set of games in terms of the relative importance of imperfect information, adversarial opponents and reward sparsity. These results do not correlate with a space defined using attributes of the game-tree. This dimensionality reduction does not show any general effect as the number of players. We therefore consider the question using the original features to classify the games into two sets; those for which the characteristics of the game changes significantly as the number of players changes, and those for which there is no such effect.
Constrained Optimization with Dynamic Bound-scaling for Effective NLPBackdoor Defense
Authors: Guangyu Shen, Yingqi Liu, Guanhong Tao, QiuLing Xu, Zhuo Zhang, Shengwei An, Shiqing Ma, Xiangyu Zhang
Date: 2022-02-11 00:00:00

We develop a novel optimization method for NLPbackdoor inversion. We leverage a dynamically reducing temperature coefficient in the softmax function to provide changing loss landscapes to the optimizer such that the process gradually focuses on the ground truth trigger, which is denoted as a one-hot value in a convex hull. Our method also features a temperature rollback mechanism to step away from local optimals, exploiting the observation that local optimals can be easily deter-mined in NLP trigger inversion (while not in general optimization). We evaluate the technique on over 1600 models (with roughly half of them having injected backdoors) on 3 prevailing NLP tasks, with 4 different backdoor attacks and 7 architectures. Our results show that the technique is able to effectively and efficiently detect and remove backdoors, outperforming 4 baseline methods.