pwc-archive/papers-with-abstracts huggingface dataset:
Selection-recombination-mutation dynamics: Gradient, limit cycle, and closed invariant curve
Authors: Suman Chakraborty, Sagar Chakraborty
Date: 2023-04-01 00:00:00

In this paper, the replicator dynamics of the two-locus two-allele system under weak mutation and weak selection is investigated in a generation-wise non-overlapping unstructured population of individuals mating at random. Our main finding is that the dynamics is gradient-like when the point mutations at the two loci are independent. This is in stark contrast to the case of one-locus multi-allele where the existence gradient behaviour is contingent on a specific relationship between the mutation rates. When the mutations are not independent in the two-locus two-allele system, there is the possibility of non-convergent outcomes, like asymptotically stable oscillations, through either the Hopf bifurcation or the Neimark--Sacker bifurcation depending on the strength of the weak selection. The results can be straightforwardly extended for multi-locus two-allele systems.
A study of a deterministic model for meningitis epidemic
Authors: S. J Yaga, F. W. O Saporu
Date: 2023-03-31 00:00:00

A compartmental deterministic model that allows (1) immunity from two stages of infection and carriage, and (2) disease induced death, is used in studying the dynamics of meningitis epidemic process in a closed population. It allows for difference in the transmission rate of infection to a susceptible by a carrier and an infective. It is generalized to allow a proportion ({\phi}) of those susceptibles infected to progress directly to infectives in stage I. Both models are used in this study. The threshold conditions for the spread of carrier and infectives in stage I are derived for the two models. Sensitivity analysis is performed on the reproductive number derived from the next generation matrix. The case-carrier ratio profile for various parameters and threshold values are shown. So also are the graphs of the total number ever infected as influenced by {\epsilon} and {\phi}. The infection transmission rate (\b{eta}), the odds in favor of a carrier, over an infective, in transmitting an infection to a susceptible ({\epsilon}) and the carrier conversion rate ({\phi}) to an infective in stage I, are identified as key parameters that should be subject of attention for any control intervention strategy. The case-carrier ratio profiles provide evidence of a critical case-carrier ratio attained before the number of reported cases grows to an epidemic level. They also provide visual evidence of epidemiological context, in this case, epidemic incidence (in later part of dry season) and endemic incidence (during rainy season). Results from total proportion ever infected suggest that the model, in which {\phi}=0 obtained, can adequately represent, in essence, the generalized model for this study.
Effectiveness of iNTS vaccination in Sub-Saharan Africa
Authors: Daniele Cassese, Nicola Dimitri, Gianluca Breghi, Tiziana Spadafina
Date: 2023-03-31 00:00:00

Invasive non-Typhoidal Salmonella (iNTS) is one of the leading causes of blood stream infections in Sub-Saharan Africa, especially among children. iNTS can be difficult to diagnose, particularly in areas where malaria is endemic and difficult to treat, partly because of the emergence of antibiotic resistance. We developed a mathematical model to evaluate the impact of a vaccine for iNTS in 49 countries of sub-Saharan Africa. Without vaccination we estimate 9.2 million new iNTS cases among children below 5 years old in these 49 countries from 2022 to 2038, 6.2 million of which between 2028 and 2038. The introduction of a 85% (95%) efficacy vaccine in 2028 would prevent 2.6 (2.9) million of these new infections. We provide the country-specific impact of a iNTS vaccine considering the different age structures and vaccine coverage levels.
Population size estimation with capture-recapture in presence of individual misidentification and low recapture
Authors: Rémi Fraysse, Rémi Choquet, Carlo Costantini, Roger Pradel
Date: 2023-04-03 00:00:00

While non-invasive sampling is more and more commonly used in capture-recapture (CR) experiments, it carries a higher risk of misidentifications than direct observations. As a consequence, one must screen the data to retain only the reliable data before applying a classical CR model. This procedure is unacceptable when too few data would remain. Models able to deal with misidentifications have been proposed but are barely used. Three objectives are pursued in this paper. First, we present the Latent Multinomial Model of Link et al. (2010) where estimates of the model are obtained from a Monte Carlo Markov Chain (MCMC). Second we show the impact of the use of an informative prior over the estimations when the capture rate is low. Finally we extend the model to the multistate paradigm as an example of its flexibility. We showed that, without prior information, with capture rate at 0.2 or lower, parameters of the model are difficult to estimate i.e. either the MCMC does not converge or the estimates are biased. In that case, we show that adding an informative prior on the identification probability solves the identifiability problem of the model and allow for convergence. It also allows for good quality estimates of population size, although when the capture rate is 0.1 it underestimates it of about 10%. A similar approach on the multistate extension show good quality estimates of the population size and transition probabilities with a capture rate of 0.3 or more.
Company Competition Graph
Authors: Yanci Zhang, Yutong Lu, Haitao Mao, Jiawei Huang, Cien Zhang, Xinyi Li, Rui Dai
Date: 2023-04-01 00:00:00
Tasks: Knowledge Graphs

Financial market participants frequently rely on numerous business relationships to make investment decisions. Investors can learn about potential risks and opportunities associated with other connected entities through these corporate connections. Nonetheless, human annotation of a large corpus to extract such relationships is highly time-consuming, not to mention that it requires a considerable amount of industry expertise and professional training. Meanwhile, we have yet to observe means to generate reliable knowledge graphs of corporate relationships due to the lack of impartial and granular data sources. This study proposes a system to process financial reports and construct the public competitor graph to fill the void. Our method can retrieve more than 83\% competition relationship of the S\&P 500 index companies. Based on the output from our system, we construct a knowledge graph with more than 700 nodes and 1200 edges. A demo interactive graph interface is available.
Determination of cutting positions of honeycomb blocks using computer vision
Authors: Alexander Razumovsky, Yakov Pikalov, Mikhail Saramud
Date: 2023-01-11 00:00:00

The article discusses a method for automating the process of cutting a honeycomb block, and specifically obtaining points and cutting angles for the required faces. The following requirements are taken into account in the calculations: the allowable location of the cut plane is 0.4 of the length of the cell face, the cut plane must be perpendicular to the cell wall. The algorithm itself consists of two main stages: determining the honeycomb structure and searching for cut points. In the absence of significant defects in honeycomb blocks (deformation of the cell profile and a dent on the edges of the cells), the structure determination algorithm works without significant inaccuracies. The results of the cut point search algorithm can be considered satisfactory.
Beyond Interpretable Benchmarks: Contextual Learning through Cognitive and Multimodal Perception
Authors: Nick DiSanto
Date: 2022-12-04 00:00:00

With state-of-the-art models achieving high performance on standard benchmarks, contemporary research paradigms continue to emphasize general intelligence as an enduring objective. However, this pursuit overlooks the fundamental disparities between the high-level data perception abilities of artificial and natural intelligence systems. This study questions the Turing Test as a criterion of generally intelligent thought and contends that it is misinterpreted as an attempt to anthropomorphize computer systems. Instead, it emphasizes tacit learning as a cornerstone of general-purpose intelligence, despite its lack of overt interpretability. This abstract form of intelligence necessitates contextual cognitive attributes that are crucial for human-level perception: generalizable experience, moral responsibility, and implicit prioritization. The absence of these features yields undeniable perceptual disparities and constrains the cognitive capacity of artificial systems to effectively contextualize their environments. Additionally, this study establishes that, despite extensive exploration of potential architecture for future systems, little consideration has been given to how such models will continuously absorb and adapt to contextual data. While conventional models may continue to improve in benchmark performance, disregarding these contextual considerations will lead to stagnation in human-like comprehension. Until general intelligence can be abstracted from task-specific domains and systems can learn implicitly from their environments, research standards should instead prioritize the disciplines in which AI thrives.
Disentangling Domain Ontologies
Authors: Mayukh Bagchi, Subhashis Das
Date: 2023-03-21 00:00:00
Tasks: Disentanglement

In this paper, we introduce and illustrate the novel phenomenon of Conceptual Entanglement which emerges due to the representational manifoldness immanent while incrementally modelling domain ontologies step-by-step across the following five levels: perception, labelling, semantic alignment, hierarchical modelling and intensional definition. In turn, we propose Conceptual Disentanglement, a multi-level conceptual modelling strategy which enforces and explicates, via guiding principles, semantic bijections with respect to each level of conceptual entanglement (across all the above five levels) paving the way for engineering conceptually disentangled domain ontologies. We also briefly argue why state-of-the-art ontology development methodologies and approaches are insufficient with respect to our characterization.
Neuro-Symbolic Execution of Generic Source Code
Authors: Yaojie Hu, Jin Tian
Date: 2023-03-23 00:00:00
Tasks: Variable misuse

Can a Python program be executed statement-by-statement by neural networks composed according to the source code? We formulate the Neuro-Symbolic Execution Problem and introduce Neural Interpretation (NI), the first neural model for the execution of generic source code that allows missing definitions. NI preserves source code structure, where every variable has a vector encoding, and every function executes a neural network. NI is a novel neural model of computers with a compiler architecture that can assemble neural layers "programmed" by source code. NI is the first neural model capable of executing Py150 dataset programs, including library functions without concrete inputs, and it can be trained with flexible code understanding objectives. We demonstrate white-box execution without concrete inputs for variable misuse localization and repair.
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free Evaluation
Authors: Nassim Mokhtari, Alexis Nédélec, Marlène Gilles, Pierre De Loor
Date: 2022-07-18 00:00:00
Proceeding: International Joint Conference on Neural Networks (IJCNN) 2022 7
Tasks: Neural Architecture Search, valid

Neural Architecture Search (NAS) algorithms are used to automate the design of deep neural networks. Finding the best architecture for a given dataset can be time consuming since these algorithms have to explore a large number of networks, and score them according to their performances to choose the most appropriate one. In this work, we propose a novel metric that uses the Intra-Cluster Distance (ICD) score to evaluate the ability of an untrained model to distinguish between data in order to approximate its quality. We also use an improved version of the FireFly algorithm, more robust to the local optimums problem than the baseline FireFly algorithm, as a search technique to find the best neural network model adapted to a specific dataset. Experimental results on the different NAS Benchmarks show that our metric is valid for either scoring CNNs and RNNs, and that our proposed FireFly algorithm can improve the result obtained by the state-of-art training-free methods.
Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection
Authors: Wencong Wu, Shicheng Liao, Guannan Lv, Peng Liang, Yungang Zhang
Date: 2023-04-04 00:00:00
Tasks: Denoising, Image Denoising, Noise Estimation

In recent years, deep convolutional neural networks have shown fascinating performance in the field of image denoising. However, deeper network architectures are often accompanied with large numbers of model parameters, leading to high training cost and long inference time, which limits their application in practical denoising tasks. In this paper, we propose a novel dual convolutional blind denoising network with skip connection (DCBDNet), which is able to achieve a desirable balance between the denoising effect and network complexity. The proposed DCBDNet consists of a noise estimation network and a dual convolutional neural network (CNN). The noise estimation network is used to estimate the noise level map, which improves the flexibility of the proposed model. The dual CNN contains two branches: a u-shaped sub-network is designed for the upper branch, and the lower branch is composed of the dilated convolution layers. Skip connections between layers are utilized in both the upper and lower branches. The proposed DCBDNet was evaluated on several synthetic and real-world image denoising benchmark datasets. Experimental results have demonstrated that the proposed DCBDNet can effectively remove gaussian noise in a wide range of levels, spatially variant noise and real noise. With a simple model structure, our proposed DCBDNet still can obtain competitive denoising performance compared to the state-of-the-art image denoising models containing complex architectures. Namely, a favorable trade-off between denoising performance and model complexity is achieved. Codes are available at https://github.com/WenCongWu/DCBDNet.
DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising
Authors: Wencong Wu, Guannan Lv, Yingying Duan, Peng Liang, Yungang Zhang, Yuelong Xia
Date: 2023-04-04 00:00:00
Tasks: Denoising, Image Denoising, Noise Estimation

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet.
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
Authors: Jaeill Kim, Suhyun Kang, Duhun Hwang, Jungwook Shin, Wonjong Rhee
Date: 2023-04-04 00:00:00
Proceeding: CVPR 2023 1
Tasks: Disentanglement, Domain Generalization, Few-Shot Image Classification, General Classification, Image Classification, Meta-Learning, Object Detection, Representation Learning, Self-Supervised Image Classification, Self-Supervised Learning, Semi-Supervised Image Classification

Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of representation. However, manipulating such properties can be challenging in terms of implementational effectiveness and general applicability. To address these limitations, we propose to regularize von Neumann entropy~(VNE) of representation. First, we demonstrate that the mathematical formulation of VNE is superior in effectively manipulating the eigenvalues of the representation autocorrelation matrix. Then, we demonstrate that it is widely applicable in improving state-of-the-art algorithms or popular benchmark algorithms by investigating domain-generalization, meta-learning, self-supervised learning, and generative models. In addition, we formally establish theoretical connections with rank, disentanglement, and isotropy of representation. Finally, we provide discussions on the dimension control of VNE and the relationship with Shannon entropy. Code is available at: https://github.com/jaeill/CVPR23-VNE.
EGC: Image Generation and Classification via a Diffusion Energy-Based Model
Authors: Qiushan Guo, Chuofan Ma, Yi Jiang, Zehuan Yuan, Yizhou Yu, Ping Luo
Date: 2023-04-04 00:00:00
Proceeding: ICCV 2023 1
Tasks: Denoising, image-classification, Image Classification, Image Generation

Learning image classification and image generation using the same set of network parameters is a challenging problem. Recent advanced approaches perform well in one task often exhibit poor performance in the other. This work introduces an energy-based classifier and generator, namely EGC, which can achieve superior performance in both tasks using a single neural network. Unlike a conventional classifier that outputs a label given an image (i.e., a conditional distribution $p(y|\mathbf{x})$), the forward pass in EGC is a classifier that outputs a joint distribution $p(\mathbf{x},y)$, enabling an image generator in its backward pass by marginalizing out the label $y$. This is done by estimating the energy and classification probability given a noisy image in the forward pass, while denoising it using the score function estimated in the backward pass. EGC achieves competitive generation results compared with state-of-the-art approaches on ImageNet-1k, CelebA-HQ and LSUN Church, while achieving superior classification accuracy and robustness against adversarial attacks on CIFAR-10. This work represents the first successful attempt to simultaneously excel in both tasks using a single set of network parameters. We believe that EGC bridges the gap between discriminative and generative learning.
Multi-Level Contrastive Learning for Dense Prediction Task
Authors: Qiushan Guo, Yizhou Yu, Yi Jiang, Jiannan Wu, Zehuan Yuan, Ping Luo
Date: 2023-04-04 00:00:00
Tasks: Contrastive Learning, Prediction, Self-Supervised Learning

In this work, we present Multi-Level Contrastive Learning for Dense Prediction Task (MCL), an efficient self-supervised method for learning region-level feature representation for dense prediction tasks. Our method is motivated by the three key factors in detection: localization, scale consistency and recognition. To explicitly encode absolute position and scale information, we propose a novel pretext task that assembles multi-scale images in a montage manner to mimic multi-object scenarios. Unlike the existing image-level self-supervised methods, our method constructs a multi-level contrastive loss that considers each sub-region of the montage image as a singleton. Our method enables the neural network to learn regional semantic representations for translation and scale consistency while reducing pre-training epochs to the same as supervised pre-training. Extensive experiments demonstrate that MCL consistently outperforms the recent state-of-the-art methods on various datasets with significant margins. In particular, MCL obtains 42.5 AP$^\mathrm{bb}$ and 38.3 AP$^\mathrm{mk}$ on COCO with the 1x schedule fintuning, when using Mask R-CNN with R50-FPN backbone pre-trained with 100 epochs. In comparison to MoCo, our method surpasses their performance by 4.0 AP$^\mathrm{bb}$ and 3.1 AP$^\mathrm{mk}$. Furthermore, we explore the alignment between pretext task and downstream tasks. We extend our pretext task to supervised pre-training, which achieves a similar performance to self-supervised learning. This result demonstrates the importance of the alignment between pretext task and downstream tasks, indicating the potential for wider applicability of our method beyond self-supervised settings.
Risk-Aware Distributed Multi-Agent Reinforcement Learning
Authors: Abdullah Al Maruf, Luyao Niu, Bhaskar Ramasubramanian, Andrew Clark, Radha Poovendran
Date: 2023-04-04 00:00:00
Tasks: Decision Making, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Autonomous cyber and cyber-physical systems need to perform decision-making, learning, and control in unknown environments. Such decision-making can be sensitive to multiple factors, including modeling errors, changes in costs, and impacts of events in the tails of probability distributions. Although multi-agent reinforcement learning (MARL) provides a framework for learning behaviors through repeated interactions with the environment by minimizing an average cost, it will not be adequate to overcome the above challenges. In this paper, we develop a distributed MARL approach to solve decision-making problems in unknown environments by learning risk-aware actions. We use the conditional value-at-risk (CVaR) to characterize the cost function that is being minimized, and define a Bellman operator to characterize the value function associated to a given state-action pair. We prove that this operator satisfies a contraction property, and that it converges to the optimal value function. We then propose a distributed MARL algorithm called the CVaR QD-Learning algorithm, and establish that value functions of individual agents reaches consensus. We identify several challenges that arise in the implementation of the CVaR QD-Learning algorithm, and present solutions to overcome these. We evaluate the CVaR QD-Learning algorithm through simulations, and demonstrate the effect of a risk parameter on value functions at consensus.