pwc-archive/papers-with-abstracts huggingface dataset:
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming
Authors: Ximing Qiao, Hai Li
Date: 2022-08-26 00:00:00
Tasks: Bayesian Inference, Probabilistic Programming

We consider learning and compositionality as the key mechanisms towards simulating human-like intelligence. While each mechanism is successfully achieved by neural networks and symbolic AIs, respectively, it is the combination of the two mechanisms that makes human-like intelligence possible. Despite the numerous attempts on building hybrid neuralsymbolic systems, we argue that our true goal should be unifying learning and compositionality, the core mechanisms, instead of neural and symbolic methods, the surface approaches to achieve them. In this work, we review and analyze the strengths and weaknesses of neural and symbolic methods by separating their forms and meanings (structures and semantics), and propose Connectionist Probabilistic Program (CPPs), a framework that connects connectionist structures (for learning) and probabilistic program semantics (for compositionality). Under the framework, we design a CPP extension for small scale sequence modeling and provide a learning algorithm based on Bayesian inference. Although challenges exist in learning complex patterns without supervision, our early results demonstrate CPP's successful extraction of concepts and relations from raw sequential data, an initial step towards compositional learning.
Learning energy-efficient driving behaviors by imitating experts
Authors: Abdul Rahman Kreidieh, Zhe Fu, Alexandre M. Bayen
Date: 2022-06-28 00:00:00
Tasks: Imitation Learning

The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are expected to serve as congestion-dampeners, mitigating the presence of instabilities that arise from various sources. However, in many applications, such maneuvers rely heavily on non-local sensing or coordination by interacting AVs, thereby rendering their adaptation to real-world settings a particularly difficult challenge. To address this challenge, this paper examines the role of imitation learning in bridging the gap between such control strategies and realistic limitations in communication and sensing. Treating one such controller as an "expert", we demonstrate that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations. Results and code are available online at https://sites.google.com/view/il-traffic/home.
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environment
Authors: Taekeun Yoon, Seon Woong Kim, Hosung Byun, Younsik Kim, Campbell D. Carter, Hyungrok Do
Date: 2022-07-29 00:00:00
Tasks: Denoising, Property Prediction

A deep learning strategy is developed for fast and accurate gas property measurements using flame emission spectroscopy (FES). Particularly, the short-gated fast FES is essential to resolve fast-evolving combustion behaviors. However, as the exposure time for capturing the flame emission spectrum gets shorter, the signal-to-noise ratio (SNR) decreases, and characteristic spectral features indicating the gas properties become relatively weaker. Then, the property estimation based on the short-gated spectrum is difficult and inaccurate. Denoising convolutional neural networks (CNN) can enhance the SNR of the short-gated spectrum. A new CNN architecture including a reversible down- and up-sampling (DU) operator and a loss function based on proper orthogonal decomposition (POD) coefficients is proposed. For training and testing the CNN, flame chemiluminescence spectra were captured from a stable methane-air flat flame using a portable spectrometer (spectral range: 250 - 850 nm, resolution: 0.5 nm) with varied equivalence ratio (0.8 - 1.2), pressure (1 - 10 bar), and exposure time (0.05, 0.2, 0.4, and 2 s). The long exposure (2 s) spectra were used as the ground truth when training the denoising CNN. A kriging model with POD is trained by the long-gated spectra for calibration, and then the prediction of the gas properties taking the denoised short-gated spectrum as the input: The property prediction errors of pressure and equivalence ratio were remarkably lowered in spite of the low SNR attendant with reduced exposure.
Understanding Deep Learning using Topological Dynamical Systems, Index Theory, and Homology
Authors: Bill Basener
Date: 2022-07-25 00:00:00

In this paper we investigate Deep Learning Models using topological dynamical systems, index theory, and computational homology. These mathematical machinery was invented initially by Henri Poincare around 1900 and developed over time to understand shapes and dynamical systems whose structure and behavior is too complicated to solve for analytically but can be understood via global relationships. In particular, we show how individual neurons in a neural network can correspond to simplexes in a simplicial complex manifold approximation to the decision surface learned by the NN, and how these simplexes can be used to compute topological invariants from algebraic topology for the decision manifold with an explicit computation of homology groups by hand in a simple case. We also show how the gradient of the probability density function learned by the NN creates a dynamical system, which can be analyzed by a myriad of topological tools such as Conley Index Theory, Morse Theory, and Stable Manifolds. We solve analytically for associated the differential equation for a trained NN with a single hidden layer of 256 Neurons applied to the MINST digit dataset, and approximately numerically that it a sink and basin of attraction for each of the 10 classes, but the sinks and strong attracting manifolds lie in regions not corresponding to images of actual digits. Index theory implies the existence of saddles. Level sets of the probability functions are 783-dimensional manifolds which can only change topology at critical points of the dynamical system, and these changes in topology can be investigated with Morse Theory.
A machine learning approach to predict the structural and magnetic properties of Heusler alloy families
Authors: Srimanta Mitra, Aquil Ahmad, Sajib Biswas, Amal Kumar Das
Date: 2022-08-07 00:00:00
Tasks: Feature Importance

Random forest (RF) regression model is used to predict the lattice constant, magnetic moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler alloys and quaternary Heusler alloys based on existing as well as indigenously prepared databases. Prior analysis was carried out to check the distribution of the data points of the response variables and found that in most of the cases, the data is not normally distributed. The outcome of the RF model performance is sufficiently accurate to predict the response variables on the test data and also shows its robustness against overfitting, outliers, multicollinearity and distribution of data points. The parity plots between the machine learning predicted values against the computed values using density functional theory (DFT) shows linear behavior with adjusted R2 values lying in the range of 0.80 to 0.94 for all the predicted properties for different types of Heusler alloys. Feature importance analysis shows that the valence electron numbers plays an important feature role in the prediction for most of the predicted outcomes. Case studies with one full Heusler alloy and one quaternary Heusler alloy were also mentioned comparing the machine learning predicted results with our earlier theoretical calculated values and experimentally measured results, suggesting high accuracy of the model predicted results.
Distributed Cooperative Control and Optimization of Connected Automated Vehicles Platoon Against Cut-in Behaviors of Social Drivers
Authors: Bohui Wang, Rong Su
Date: 2022-08-29 00:00:00
Tasks: Decision Making, Management, Trajectory Planning

Connected automated vehicles (CAVs) have brought new opportunities to improve traffic throughput and reduce energy consumption. However, the uncertain lane-change behaviors (LCBs) of surrounding vehicles (SVs) as an uncontrollable factor significantly threaten the driving safety and the consistent movement of a group of platoon CAVs. How to ensure safe, efficient, and fuel economic platoon control poses a key challenge faced by researchers in complex traffic environments. This study proposes a dynamic platoon management and cooperative driving framework for a mixed traffic flow consisting of multiple CAVs and possible human-driven vehicles (HDVs) as the SVs on unsignalized roads. In the proposed framework, the leader CAV of the platoon provides a high-level automatic driving decision to the follower CAVs by developing an optimal trajectory estimation of the HDVs while distributed observers and tracking controllers are properly implemented by the follower CAVs. Specifically, the proposed framework consists of three stages. At the observation stage, the cruising information of all the SVs will be collected by the leader CAV through the Cellular-Vehicle-to-X (C-V2X) infrastructure, while an automatic decision-making driving assistance system (ADMDSS) is constructed to determine the driving states of the platoon. When the HDVs approach the communication range of the platoon, in the prediction stage, the trajectories of the HDVs as the target vehicles will be estimated and the reference trajectory planning for the leader CAV and the cooperative controller design for the follower CAVs will be respectively activated by using C-V2X infrastructure. Simulation cases are presented to illustrate the effectiveness of the proposed approaches.
Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens
Authors: Yao Chen, Samuel S. Streeter, Brady Hunt, Hira S. Sardar, Jason R. Gunn, Laura J. Tafe, Joseph A. Paydarfar, Brian W. Pogue, Keith D. Paulsen, Kimberley S. Samkoe
Date: 2022-08-29 00:00:00

In this study, a radiomics approach was extended to optical fluorescence molecular imaging data for tissue classification, termed 'optomics'. Fluorescence molecular imaging is emerging for precise surgical guidance during head and neck squamous cell carcinoma (HNSCC) resection. However, the tumor-to-normal tissue contrast is confounded by intrinsic physiological limitations of heterogeneous expression of the target molecule, epidermal growth factor receptor (EGFR). Optomics seek to improve tumor identification by probing textural pattern differences in EGFR expression conveyed by fluorescence. A total of 1,472 standardized optomic features were extracted from fluorescence image samples. A supervised machine learning pipeline involving a support vector machine classifier was trained with 25 top-ranked features selected by minimum redundancy maximum relevance criterion. Model predictive performance was compared to fluorescence intensity thresholding method by classifying testing set image patches of resected tissue with histologically confirmed malignancy status. The optomics approach provided consistent improvement in prediction accuracy on all test set samples, irrespective of dose, compared to fluorescence intensity thresholding method (mean accuracies of 89% vs. 81%; P = 0.0072). The improved performance demonstrates that extending the radiomics approach to fluorescence molecular imaging data offers a promising image analysis technique for cancer detection in fluorescence-guided surgery.
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization
Authors: Rui Zhong, Masaharu Munetomo
Date: 2022-08-29 00:00:00
Tasks: Combinatorial Optimization

In this paper, we propose a variable grouping method based on cooperative coevolution for large-scale multi-objective problems (LSMOPs), named Linkage Measurement Minimization (LMM). And for the sub-problem optimization stage, a hybrid NSGA-II with a Gaussian sampling operator based on an estimated convergence point is proposed. In the variable grouping stage, according to our previous research, we treat the variable grouping problem as a combinatorial optimization problem, and the linkage measurement function is designed based on linkage identification by the nonlinearity check on real code (LINC-R). We extend this variable grouping method to LSMOPs. In the sub-problem optimization stage, we hypothesize that there is a higher probability of existing better solutions around the Pareto Front (PF). Based on this hypothesis, we estimate a convergence point at every generation of optimization and perform Gaussian sampling around the convergence point. The samples with good objective value will participate in the optimization as elites. Numerical experiments show that our variable grouping method is better than some popular variable grouping methods, and hybrid NSGA-II has broad prospects for multi-objective problem optimization.
Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis
Authors: Shishuai Hu, Zehui Liao, Yong Xia
Date: 2022-08-29 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR) images remains challenging, due to limited annotations and heterogeneous arteries. In this paper, we propose a semi-supervised label propagation framework to segment lumen, normal vessel walls, and atherosclerotic vessel wall on 3D MR images. By interpolating the provided annotations, we get 3D continuous labels for training 3D segmentation model. With the trained model, we generate pseudo labels for unlabeled slices to incorporate them for model training. Then we use the whole MR scans and the propagated labels to re-train the segmentation model and improve its robustness. We evaluated the label propagation framework on the CarOtid vessel wall SegMentation and atherosclerOsis diagnosiS (COSMOS) Challenge dataset and achieved a QuanM score of 83.41\% on the testing dataset, which got the 1-st place on the online evaluation leaderboard. The results demonstrate the effectiveness of the proposed framework.
Boundary-Aware Network for Kidney Parsing
Authors: Shishuai Hu, Yiwen Ye, Zehui Liao, Yong Xia
Date: 2022-08-29 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable tumor sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Kidney PArsing (KiPA) Challenge dataset and achieved an average Dice score of 89.65$\%$ for kidney structure segmentation on CTA scans using 4-fold cross-validation. The results demonstrate the effectiveness of the BA-Net.
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A Human
Authors: Benyamin Ghojogh
Date: 2022-08-29 00:00:00
Tasks: Dimensionality Reduction, Metric Learning, Supervised dimensionality reduction

After the development of different machine learning and manifold learning algorithms, it may be a good time to put them together to make a powerful mind for machine. In this work, we propose affective manifolds as components of a machine's mind. Every affective manifold models a characteristic group of mind and contains multiple states. We define the machine's mind as a set of affective manifolds. We use a learning model for mapping the input signals to the embedding space of affective manifold. Using this mapping, a machine or a robot takes an input signal and can react emotionally to it. We use deep metric learning, with Siamese network, and propose a loss function for affective manifold learning. We define margins between states based on the psychological and philosophical studies. Using triplets of instances, we train the network to minimize the variance of every state and have the desired distances between states. We show that affective manifolds can have various applications for machine-machine and human-machine interactions. Some simulations are also provided for verification of the proposed method. It is possible to have as many affective manifolds as required in machine's mind. More affective manifolds in the machine's mind can make it more realistic and effective. This paper opens the door; we invite the researchers from various fields of science to propose more affective manifolds to be inserted in machine's mind.
Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis
Authors: Wan-Cyuan Fan, Yen-Chun Chen, Dongdong Chen, Yu Cheng, Lu Yuan, Yu-Chiang Frank Wang
Date: 2022-08-29 00:00:00
Tasks: Conditional Image Generation, Denoising, Image Generation, Representation Learning

Diffusion models (DMs) have shown great potential for high-quality image synthesis. However, when it comes to producing images with complex scenes, how to properly describe both image global structures and object details remains a challenging task. In this paper, we present Frido, a Feature Pyramid Diffusion model performing a multi-scale coarse-to-fine denoising process for image synthesis. Our model decomposes an input image into scale-dependent vector quantized features, followed by a coarse-to-fine gating for producing image output. During the above multi-scale representation learning stage, additional input conditions like text, scene graph, or image layout can be further exploited. Thus, Frido can be also applied for conditional or cross-modality image synthesis. We conduct extensive experiments over various unconditioned and conditional image generation tasks, ranging from text-to-image synthesis, layout-to-image, scene-graph-to-image, to label-to-image. More specifically, we achieved state-of-the-art FID scores on five benchmarks, namely layout-to-image on COCO and OpenImages, scene-graph-to-image on COCO and Visual Genome, and label-to-image on COCO. Code is available at https://github.com/davidhalladay/Frido.
Bayesian Continual Learning via Spiking Neural Networks
Authors: Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Date: 2022-08-29 00:00:00
Tasks: Continual Learning, Management, Uncertainty Quantification

Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engineering has been thus far mostly driven by the goal of implementing energy-efficient machines that take inspiration from the time-based computing paradigm of biological brains. In this paper, we take steps towards the design of neuromorphic systems that are capable of adaptation to changing learning tasks, while producing well-calibrated uncertainty quantification estimates. To this end, we derive online learning rules for spiking neural networks (SNNs) within a Bayesian continual learning framework. In it, each synaptic weight is represented by parameters that quantify the current epistemic uncertainty resulting from prior knowledge and observed data. The proposed online rules update the distribution parameters in a streaming fashion as data are observed. We instantiate the proposed approach for both real-valued and binary synaptic weights. Experimental results using Intel's Lava platform show the merits of Bayesian over frequentist learning in terms of capacity for adaptation and uncertainty quantification.
Categorical semantics of compositional reinforcement learning
Authors: Georgios Bakirtzis, Michail Savvas, Ufuk Topcu
Date: 2022-08-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Compositional knowledge representations in reinforcement learning (RL) facilitate modular, interpretable, and safe task specifications. However, generating compositional models requires the characterization of minimal assumptions for the robustness of the compositionality feature, especially in the case of functional decompositions. Using a categorical point of view, we develop a knowledge representation framework for a compositional theory of RL. Our approach relies on the theoretical study of the category $\mathsf{MDP}$, whose objects are Markov decision processes (MDPs) acting as models of tasks. The categorical semantics models the compositionality of tasks through the application of pushout operations akin to combining puzzle pieces. As a practical application of these pushout operations, we introduce zig-zag diagrams that rely on the compositional guarantees engendered by the category $\mathsf{MDP}$. We further prove that properties of the category $\mathsf{MDP}$ unify concepts, such as enforcing safety requirements and exploiting symmetries, generalizing previous abstraction theories for RL.
Chosen methods of improving small object recognition with weak recognizable features
Authors: Magdalena Stachoń, Marcin Pietroń
Date: 2022-08-29 00:00:00
Tasks: Diversity, Object, object-detection, Object Detection, Object Recognition, Small Object Detection

Many object detection models struggle with several problematic aspects of small object detection including the low number of samples, lack of diversity and low features representation. Taking into account that GANs belong to generative models class, their initial objective is to learn to mimic any data distribution. Using the proper GAN model would enable augmenting low precision data increasing their amount and diversity. This solution could potentially result in improved object detection results. Additionally, incorporating GAN-based architecture inside deep learning model can increase accuracy of small objects recognition. In this work the GAN-based method with augmentation is presented to improve small object detection on VOC Pascal dataset. The method is compared with different popular augmentation strategies like object rotations, shifts etc. The experiments are based on FasterRCNN model.