pwc-archive/papers-with-abstracts huggingface dataset:
MIK2 is a candidate gene of the S-locus for sporophytic self-incompatibility (SSI) in chicory (Cichorium intybus, Asteraceae)
Authors: Fabio Palumbo, Samela Draga, Gabriele Magon, Giovanni Gabelli, Alessandro Vannozzi, Silvia Farinati, Francesco Scariolo, Margherita Lucchin, Gianni Barcaccia
Date: 2023-04-13 00:00:00

The Cichorium genus offers a unique opportunity to study the sporophytic self incompatibility (SSI) system, being composed of species characterized by highly efficient SI (C. intybus) and complete self compatibility (C. endivia). The chicory genome was used to map 7 previously identified SSI locus-associated markers. The region containing the S locus was restricted to an 4 M bp window on chromosome 5. Among the genes predicted in this region, MDIS1 INTERACTING RECEPTOR LIKE KINASE 2 (MIK2) was promising as a candidate for SSI. Its ortholog in Arabidopsis is involved in pollen stigma recognition reactions, and its protein structure is similar to that of S-receptor kinase (SRK), a key component of the SSI in the Brassica genus. The sequencing of MIK2 in chicory and endive accessions revealed two contrasting scenarios. In C. endivia, MIK2 was fully conserved even comparing different botanical varieties (smooth and curly). In C. intybus, 387 SNPs and 3 INDELs were identified when comparing accessions of different biotypes from the same botanical variety (radicchio). The SNP distribution throughout the gene was uneven, with hypervariable domains preferentially localized in the LRR-rich extracellular region, putatively identified as the receptor domain. The gene was hypothesized to be under positive selection, as the nonsynonymous mutations were more than double the synonymous ones (dN / dS = 2.17). An analogous situation was observed analyzing the first 500 bp of the MIK2 promoter: no SNPs were observed among the endive samples, whereas 44 SNPs and 6 INDELs were detected among the chicory samples. Further analyses are needed to confirm the role of MIK2 in SSI and to demonstrate whether the 23 species-specific nonsynonymous SNPs in the CDS and/or the species-specific 10 bp INDEL found in a CCAAT box region of the promoter are responsible for the contrasting sexual behaviors of the two species.
Inferring Population Dynamics in Macaque Cortex
Authors: Ganga Meghanath, Bryan Jimenez, Joseph G. Makin
Date: 2023-04-05 00:00:00

The proliferation of multi-unit cortical recordings over the last two decades, especially in macaques and during motor-control tasks, has generated interest in neural "population dynamics": the time evolution of neural activity across a group of neurons working together. A good model of these dynamics should be able to infer the activity of unobserved neurons within the same population and of the observed neurons at future times. Accordingly, Pandarinath and colleagues have introduced a benchmark to evaluate models on these two (and related) criteria: four data sets, each consisting of firing rates from a population of neurons, recorded from macaque cortex during movement-related tasks. Here we show that simple, general-purpose architectures based on recurrent neural networks (RNNs) outperform more "bespoke" models, and indeed outperform all published models on all four data sets in the benchmark. Performance can be improved further still with a novel, hybrid architecture that augments the RNN with self-attention, as in transformer networks. But pure transformer models fail to achieve this level of performance, either in our work or that of other groups. We argue that the autoregressive bias imposed by RNNs is critical for achieving the highest levels of performance. We conclude, however, by proposing that the benchmark be augmented with an alternative evaluation of latent dynamics that favors generative over discriminative models like the ones we propose in this report.
Deterministic epidemic models overestimate the basic reproduction number of observed outbreaks
Authors: Wajid Ali, Christopher E. Overton, Robert R. Wilkinson, Kieran J. Sharkey
Date: 2023-04-13 00:00:00

The basic reproduction number, $R_0$, is a well-known quantifier of epidemic spread. However, a class of existing methods for estimating $R_0$ from incidence data early in the epidemic can lead to an over-estimation of this quantity. In particular, when fitting deterministic models to estimate the rate of spread, we do not account for the stochastic nature of epidemics and that, given the same system, some outbreaks may lead to epidemics and some may not. Typically, an observed epidemic that we wish to control is a major outbreak. This amounts to implicit selection for major outbreaks which leads to the over-estimation problem. We formally characterised the split between major and minor outbreaks by using Otsu's method which provides us with a working definition. We show that by conditioning a `deterministic' model on major outbreaks, we can more reliably estimate the basic reproduction number from an observed epidemic trajectory.
Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates
Authors: Alexandra Blenkinsop, Lysandros Sofocleous, Francesco Di Lauro, Evangelia Georgia Kostaki, Ard van Sighem, Daniela Bezemer, Thijs van de Laar, Peter Reiss, Godelieve de Bree, Nikos Pantazis, Oliver Ratmann
Date: 2023-04-13 00:00:00

In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This prompted us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the approach to estimate age-specific sources of HIV infection in Amsterdam MSM transmission networks between 2010-2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models.
Mutation enhances cooperation in direct reciprocity
Authors: Josef Tkadlec, Christian Hilbe, Martin A. Nowak
Date: 2023-04-12 00:00:00
Tasks: Diversity

Direct reciprocity is a powerful mechanism for evolution of cooperation based on repeated interactions between the same individuals. But high levels of cooperation evolve only if the benefit-to-cost ratio exceeds a certain threshold that depends on memory length. For the best-explored case of one-round memory, that threshold is two. Here we report that intermediate mutation rates lead to high levels of cooperation, even if the benefit-to-cost ratio is only marginally above one, and even if individuals only use a minimum of past information. This surprising observation is caused by two effects. First, mutation generates diversity which undermines the evolutionary stability of defectors. Second, mutation leads to diverse communities of cooperators that are more resilient than homogeneous ones. This finding is relevant because many real world opportunities for cooperation have small benefit-to-cost ratios, which are between one and two, and we describe how direct reciprocity can attain cooperation in such settings. Our result can be interpreted as showing that diversity, rather than uniformity, promotes evolution of cooperation.
Quantitative Trading using Deep Q Learning
Authors: Soumyadip Sarkar
Date: 2023-04-03 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is a subfield of machine learning that has been used in many fields, such as robotics, gaming, and autonomous systems. There has been growing interest in using RL for quantitative trading, where the goal is to make trades that generate profits in financial markets. This paper presents the use of RL for quantitative trading and reports a case study based on an RL-based trading algorithm. The results show that RL can be a useful tool for quantitative trading and can perform better than traditional trading algorithms. The use of reinforcement learning for quantitative trading is a promising area of research that can help develop more sophisticated and efficient trading systems. Future research can explore the use of other reinforcement learning techniques, the use of other data sources, and the testing of the system on a range of asset classes. Together, our work shows the potential in the use of reinforcement learning for quantitative trading and the need for further research and development in this area. By developing the sophistication and efficiency of trading systems, it may be possible to make financial markets more efficient and generate higher returns for investors.
Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakes
Authors: Ali Borji
Date: 2023-03-29 00:00:00
Tasks: Image Generation, Video Generation

The ability of image and video generation models to create photorealistic images has reached unprecedented heights, making it difficult to distinguish between real and fake images in many cases. However, despite this progress, a gap remains between the quality of generated images and those found in the real world. To address this, we have reviewed a vast body of literature from both academic publications and social media to identify qualitative shortcomings in image generation models, which we have classified into five categories. By understanding these failures, we can identify areas where these models need improvement, as well as develop strategies for detecting deep fakes. The prevalence of deep fakes in today's society is a serious concern, and our findings can help mitigate their negative impact.
A Deep Learning Approach Towards Generating High-fidelity Diverse Synthetic Battery Datasets
Authors: Janamejaya Channegowda, Vageesh Maiya, Chaitanya Lingaraj
Date: 2023-04-09 00:00:00
Tasks: Data Augmentation

Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the number of vehicles powered by fossil fuels. Lithium-ion based batteries are presently dominating the electric automotive sector. Energy research efforts are also focussed on accurate computation of State-of-Charge of such batteries to provide reliable vehicle range estimates. Although such estimation algorithms provide precise estimates, all such techniques available in literature presume availability of superior quality battery datasets. In reality, gaining access to proprietary battery usage datasets is very tough for battery scientists. Moreover, open access datasets lack the diverse battery charge/discharge patterns needed to build generalized models. Curating battery measurement data is time consuming and needs expensive equipment. To surmount such limited data scenarios, we introduce few Deep Learning-based methods to synthesize high-fidelity battery datasets, these augmented synthetic datasets will help battery researchers build better estimation models in the presence of limited data. We have released the code and dataset used in the present approach to generate synthetic data. The battery data augmentation techniques introduced here will alleviate limited battery dataset challenges.
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
Authors: Mohamad Alansari, Oussama Abdul Hay, Sajid Javed, Abdulhadi Shoufan, Yahya Zweiri, Naoufel Werghi
Date: 2023-04-10 00:00:00
Proceeding: IEEE Access 2023 4
Tasks: Face Identification, Face Recognition, Face Verification, Lightweight Face Recognition, model

The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge. Previous approaches to this problem have not prioritized the reduction of feature map redundancy, but the introduction of Ghost modules represents a major innovation in this area. Ghost modules use a series of inexpensive linear transformations to extract additional feature maps from a set of intrinsic features, allowing for a more comprehensive representation of the underlying information. GhostNetV1 and GhostNetV2, both of which are based on Ghost modules, serve as the foundation for a group of lightweight face recognition models called GhostFaceNets. GhostNetV2 expands upon the original GhostNetV1 by adding an attention mechanism to capture long-range dependencies. Evaluation of GhostFaceNets using various benchmarks reveals that these models offer superior performance while requiring a computational complexity of approximately 60–275 MFLOPs. This is significantly lower than that of State-Of-The-Art (SOTA) big convolutional neural network (CNN) models, which can require hundreds of millions of FLOPs. GhostFaceNets trained with the ArcFace loss on the refined MS-Celeb-1M dataset demonstrate SOTA performance on all benchmarks. In comparison to previous SOTA mobile CNNs, GhostFaceNets greatly improve efficiency for face verification tasks. The GhostFaceNets code is available at: https://github.com/HamadYA/GhostFaceNets .
Deep representation learning for individualized treatment effect estimation using electronic health records
Authors: Peipei Chen, Wei Dong, Xudong, Uzay Kaymak, Kunlun He, Zhengxing Huang
Date: 2019-12-01 00:00:00
Proceeding: Journal of Biomedical Informatics 2019 12
Tasks: Causal Inference, counterfactual, Representation Learning

Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a challenging task, as confounding inevitably exists in clinical data. Most of the existing models for ITE estimation tackle this problem by creating unbiased estimators of the treatment effects. Although valuable, learning a balanced representation is sometimes directly opposed to the objective of learning an effective and discriminative model for ITE estimation. We propose a novel hybrid model bridging multi-task deep learning and K-nearest neighbors (KNN) for ITE estimation. In detail, the proposed model firstly adopts multi-task deep learning to extract both outcome-predictive and treatment-specific latent representations from Electronic Health Records (EHR), by jointly performing the outcome prediction and treatment category classification. Thereafter, we estimate counterfactual outcomes by KNN based on the learned hidden representations. We validate the proposed model on a widely used semi-simulated dataset, i.e. IHDP, and a real-world clinical dataset consisting of 736 heart failure (HF) patients. The performance of our model remains robust and reaches 1.7 and 0.23 in terms of Precision in the estimation of heterogeneous effect (PEHE) and average treatment effect (ATE), respectively, on IHDP dataset, and 0.703 and 0.796 in terms of accuracy and F1 score respectively, on HF dataset. The results demonstrate that the proposed model achieves competitive performance over state-of-the-art models. In addition, the results reveal several findings which are consistent with existing medical domain knowledge, and discover certain suggestive hypotheses that could be validated through further investigations in the clinical domain.
Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation
Authors: Qian Yu; Lei Qi; Yang Gao; Wuzhang Wang; Yinghuan Shi
Date: 2022-09-08 00:00:00
Proceeding: IEEE Transactions on Image Processing 2022 9
Tasks: Decoder, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges caused by various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architectures have been proposed and widely used, but their performance remains unsatisfactory. To further address these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations. (1) Since the discrimination of the features learned via square convolutional kernels needs to be further improved, we propose utilizing nonsquare vertical and horizontal convolutional kernels in a double-branch encoder so that the features learned by both branches can be expected to complement each other. (2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation of small-sized targets. With the above two schemes, we develop a novel double-branch encoder-based segmentation framework for medical image segmentation, namely, Crosslink-Net, and validate its effectiveness on five datasets with experiments. The code is released at https://github.com/Qianyu1226/Crosslink-Net .
Adaptive hybrid activation function for deep neural networks
Authors: Yevgeniy Bodyanskiy, Serhii Kostiuk
Date: 2022-04-25 00:00:00
Proceeding: System research and information technologies 2022 4
Tasks: Activation Function Synthesis, image-classification, Image Classification, Learning Theory, Neural Architecture Search

The adaptive hybrid activation function (AHAF) is proposed that combines the properties of the rectifier units and the squashing functions. The proposed function can be used as a drop-in replacement for ReLU, SiL and Swish activations for deep neural networks and can evolve to one of such functions during the training. The effectiveness of the function was evaluated on the image classification task using the Fashion-MNIST and CIFAR-10 datasets. The evaluation shows that the neural networks with AHAF activations achieve better classification accuracy comparing to their base implementations that use ReLU and SiL. A double-stage parameter tuning process for training the neural networks with AHAF is proposed. The proposed approach is sufficiently simple from the implementation standpoint and provides high performance for the neural network training process.
Deep neural network based on F-neurons and its learning
Authors: Yevgeniy Bodyanskiy, Serhii Kostiuk
Date: 2022-11-03 00:00:00
Proceeding: Research Square (pre-print) 2022 11
Tasks: Activation Function Synthesis, image-classification, Image Classification, Neural Architecture Search

Artificial neural networks are widely used in data processing and Data Mining. In contrast to traditional artificial neural networks, deep neural networks employ many artificial neuron blocks and more than three layers. An increase in the number of neuron blocks and layers improves the approximation capabilities but leads to the effects of exploding and vanishing gradients. Deep neural networks often employ piece-wise activation functions like ReLU to overcome the effects of exploding and vanishing gradients. We propose F-neuron as an adaptive alternative to piece-wise activation functions. Like ReLU, F-neuron does not suffer from the effects of exploding and vanishing gradient. F-neuron changes its form during the training, can approximate any currently used function, and synthesize new task-specific functions. We show that F-neuron can synthesize new task-specific functions and achieve higher approximation quality in existing neural network architectures. We evaluate the performance of the F-neuron on two image classification datasets (Fashion-MNIST and CIFAR-10) in two different architectures (LeNet-5 and KerasNet).
Neo-fuzzy-neuron based new approach to system modeling, with application to actual system
Authors: E. Uchino, T. Yamakawa
Date: 1994-11-01 00:00:00
Proceeding: Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94 1994 11

This paper introduces a new approach to system modeling by using a neo-fuzzy-neuron. The system of concern is modeled adaptively by simply feeding to the neo-fuzzy-neuron, the basic principle of which was proposed by the authors in 1992, the input and the output data of the objective system. Firstly, the neo-fuzzy-neuron is applied to the restoration of a saturated and/or intermittent speech or chaotic signal to show its actual effectiveness. It is then extended in order to get a better generalization capability. An adaptive fuzzy modeling with use of a piece-wise linear membership function is also introduced. The experimental results have provided substantial proofs for their practical use.
Learn from Each Other to Classify Better: Cross-layer Mutual Attention Learning for Fine-grained Visual Classification
Authors: Dichao Liu, Longjiao Zhao, Yu Wang, Jien Kato
Date: 2023-03-22 00:00:00
Proceeding: Pattern Recognition 2023 3
Tasks: Fine-Grained Image Classification

Fine-grained visual classification (FGVC) is valuable yet challenging. The difficulty of FGVC mainly lies in its intrinsic inter-class similarity, intra-class variation, and limited training data. Moreover, with the popularity of deep convolutional neural networks, researchers have mainly used deep, abstract, semantic information for FGVC, while shallow, detailed information has been neglected. This work proposes a cross-layer mutual attention learning network (CMAL-Net) to solve the above problems. Specifically, this work views the shallow to deep layers of CNNs as “experts” knowledgeable about different perspectives. We let each expert give a category prediction and an attention region indicating the found clues. Attention regions are treated as information carriers among experts, bringing three benefits: (ⅰ) helping the model focus on discriminative regions; (ⅱ) providing more training data; (ⅲ) allowing experts to learn from each other to improve the overall performance. CMAL-Net achieves state-of-the-art performance on three competitive datasets: FGVC-Aircraft, Stanford Cars, and Food-11.