pwc-archive/papers-with-abstracts huggingface dataset:
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models
Authors: Yuchi Qiu, Guo-Wei Wei
Date: 2023-07-27 00:00:00
Tasks: Drug Discovery, Protein Structure Prediction, Topological Data Analysis

Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.
Variability in the local and global composition of human T-cell receptor repertoires during thymic development across cell types and individuals
Authors: Giulio Isacchini, Valentin Quiniou, Hélène Vantomme, Paul Stys, Encarnita Mariotti-Ferandiz, David Klatzmann, Aleksandra M. Walczak, Thierry Mora, Armita Nourmohammad
Date: 2023-07-25 00:00:00
Tasks: Diversity

The adaptive immune response relies on T cells that combine phenotypic specialization with diversity of T cell receptors (TCRs) to recognize a wide range of pathogens. TCRs are acquired and selected during T cell maturation in the thymus. Characterizing TCR repertoires across individuals and T cell maturation stages is important for better understanding adaptive immune responses and for developing new diagnostics and therapies. Analyzing a dataset of human TCR repertoires from thymocyte subsets, we find that the variability between individuals generated during the TCR V(D)J recombination is maintained through all stages of T cell maturation and differentiation. The inter-individual variability of repertoires of the same cell type is of comparable magnitude to the variability across cell types within the same individual. To zoom in on smaller scales than whole repertoires, we defined a distance measuring the relative overlap of locally similar sequences in repertoires. We find that the whole repertoire models correctly predict local similarity networks, suggesting a lack of forbidden T cell receptor sequences. The local measure correlates well with distances calculated using whole repertoire traits and carries information about cell types.
Fodor and Pylyshyn's Critique of Connectionism and the Brain as Basis of the Mind
Authors: Christoph von der Malsburg
Date: 2023-07-27 00:00:00

To this day there is no satisfactory answer to the question how mental patterns correspond to physical states of our brain. For more than six decades, progress has been held up by the logjam between two traditions, one inspired by neuroscience, the other by digital computing. This logjam is well illuminated by Fodor and Pylyshyn's article of 1988, which is mainly devoted to a critique of what they call Connectionism, but also lays bare weaknesses of the Classical approach which they defend. As recent machine learning breakthroughs may be expected to through new light on the issue, it seems time to arrive at a synthesis of the connectionist neural approach and the classical stance based on symbol processing. I will present and discuss an attempt at such synthesis in the form of structured, self-organized neural networks.
Prevalence and Associated Factors of Human Papillomavirus Infection among Iraqi Women
Authors: Maitham G. Yousif, Fadhil G. Al-Amran, Alaa M. Sadeq, Nasser Ghaly Yousif
Date: 2023-07-27 00:00:00

Human papillomavirus (HPV) is a significant public health concern, as it is a leading cause of cervical cancer in women. However, data on the prevalence of HPV infection among Iraqi women is scarce. This study aimed to estimate the prevalence of HPV infection and its associated factors among Iraqi women aged 15-50 attending health centers. In this cross-sectional study, 362 female participants aged 15-50 were recruited from health centers in Iraq. Serological tests were used to screen for HPV infection. Sociodemographic information, obstetric history, and contraceptive use were collected. Pap smears were performed to assess cervical changes related to HPV infection. Of the 362 participants, 65 (17.96%) tested positive for HPV. The majority of HPV-positive women were aged 30-35 years, housewives, and belonged to lower social classes. Among HPV-positive women, 30% had abnormal Pap smears, with 55% diagnosed with cervical intraepithelial neoplasia grade 1 (CIN1), 25% with CIN2, and 15% with CIN3. Biopsy confirmed the diagnosis in 5% of cases. No significant association was found between HPV infection and contraceptive use. Most HPV-positive women were multiparous. This study reveals a considerable prevalence of HPV infection among Iraqi women attending health centers, particularly in the age group of 30-35 years and among housewives. These findings highlight the need for targeted public health interventions to increase HPV awareness, promote regular screening, and improve access to healthcare services for women, especially those from lower social classes. Further research is warranted to better understand the factors contributing to HPV transmission in Iraq and to develop effective prevention strategies.
Decoding Microbial Enigmas: Unleashing the Power of Artificial Intelligence in Analyzing Antibiotic-Resistant Pathogens and their Impact on Human Health
Authors: Maitham G. Yousif
Date: 2023-07-27 00:00:00

In this research, medical information from 1200 patients across various hospitals in Iraq was collected over a period of 3 years, from February 3, 2018, to March 5, 2021. The study encompassed several infections, including urinary tract infections, wound infections, tonsillitis, prostatitis, endometritis, endometrial lining infections, burns infections, pneumonia, and bloodstream infections in children. Multiple bacterial pathogens were identified, and their resistance to various antibiotics was recorded. The data analysis revealed significant patterns of antibiotic resistance among the identified bacterial pathogens. Resistance was observed to several commonly used antibiotics, highlighting the emerging challenge of antimicrobial resistance in Iraq. These findings underscore the importance of implementing effective antimicrobial stewardship programs and infection control measures in healthcare settings to mitigate the spread of antibiotic-resistant infections and ensure optimal patient outcomes. This study contributes valuable insights into the prevalence and patterns of antibiotic resistance in microbial infections, which can guide healthcare practitioners and policymakers in formulating targeted interventions to combat the growing threat of antimicrobial resistance in Iraq's healthcare landscape.
Irreversible evolution, obstacles in fitness landscapes and persistent drug resistance
Authors: Kristina Crona
Date: 2023-07-27 00:00:00

We use fitness graphs, or directed cube graphs, for analyzing evolutionary reversibility. The main application is antimicrobial drug resistance. Reversible drug resistance has been observed both clinically and experimentally. If drug resistance depends on a single point mutation, then a possible scenario is that the mutation reverts back to the wild-type codon after the drug has been discontinued, so that susceptibility is fully restored. In general, a drug pause does not automatically imply fast elimination of drug resistance. Also if drug resistance is reversible, the threshold concentration for reverse evolution may be lower than for forward evolution. For a theoretical understanding of evolutionary reversibility, including threshold asymmetries, it is necessary to analyze obstacles in fitness landscapes. We compare local and global obstacles, obstacles for forward and reverse evolution, and conjecture that favorable landscapes for forward evolution correlate with evolution being reversible. Both suboptimal peaks and plateaus are analyzed with some observations on the impact of redundancy and dimensionality. Our findings are compared with laboratory studies on irreversible malarial drug resistance.
Nonlinear behavior of coupled Evolutionary Games -- Epidemiological Models
Authors: Eliza Maria Ferreira, Ricardo Edem Ferreira, Chiara Mocenni
Date: 2023-07-26 00:00:00

Epidemiological models are an important tool in coping with epidemics, as they offer a forecast, even if often simplistic, of the behavior of the disease in the population. This allows responsible health agencies to organize themselves and adopt strategies to minimize and postpone the population's infection peaks. While during an epidemic outbreak, the available model can be used to describe the behavior of the disease in order to aim for fast and efficient forecasts of the epidemiological scenario, once the epidemiological emergency is over, the objective of the subsequent works is to extend models by integrating new facts and information, thus providing more efficient tools to face future epidemics. In this sense, we present an epidemiological model that takes into account on one hand, the creation of a vaccine during an epidemic outbreak (as we saw happen in the case of COVID-19) and that, on the other hand, considers the impact of the cooperative behavioral choices of individuals.
Biological Modelling with Nonlocal Advection Diffusion Equations
Authors: Kevin J Painter, Thomas Hillen, Jonathan R Potts
Date: 2023-07-26 00:00:00

The employment of nonlocal PDE models to describe biological aggregation and other phenomena has gained considerable traction in recent years. For cell populations, these methods grant a means of accommodating essential elements such as cell adhesion, critical to the development and structure of tissues. For animals, they can be used to describe how the nearby presence of conspecifics and/or heterospecifics influence movement behaviour. In this review, we will focus on classes of biological movement models in which the advective (or directed) component to motion is governed by an integral term that accounts for how the surrounding distribution(s) of the population(s) impact on a member's movement. We recount the fundamental motivation for these models: the intrinsic capacity of cell populations to self-organise and spatially sort within tissues; the wide-ranging tendency of animals towards spatial structuring, from the formations of herds and swarms to territorial segregation. We examine the derivation of these models from an individual level, illustrating in the process methods that allow models to be connected to data. We explore a growing analytical literature, including methods of stability and bifurcation analysis, and existence results. We conclude with a short section that lays out some future challenges and connections to the modelling of sociological phenomena including opinion dynamics.
Microbial Engineering to Mitigate Methane Emissions in Ruminant Livestock -- A Review
Authors: Rehema Iddi Mrutu, Kabir Mustapha Umar, Adnan Abdulhamid, Morris Agaba, Abdussamad Muhammad Abdussamad
Date: 2023-07-25 00:00:00

The most recent and promising strategies for mitigating methane emissions in ruminants are reviewed highlighting the potential of reductive acetogenesis as a viable alternative to methanogenesis. The emergence of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, and its exceptional precision in genome editing, further enhances the prospects of exploring this avenue. Indeed, research in ruminant methane mitigation has been extensive, and over the years has resulted in the development of a wide variety of mitigation strategies. There is no doubt that the concepts of meat alternatives like lab-meat, microbial proteins and plant proteins may produce equivalent emissions. Reducing methane intensity through breeding and diet has been limited by our inability to phenotype ruminants in a high-throughput manner and the intensification of feed-food competition. Although chemical inhibitors have demonstrated effectiveness in manipulating the rumen microbiota to reduce net emissions, their success is constrained in terms of duration and feasibility in grazing system. Progress in making acetogenesis the dominant hydrogen sink in the rumen has been hampered by the thermodynamic cost of the reaction and the limited hydrogen levels in the rumen environment. We believe that CRISPR may allow the dominance of acetogenesis by converting methanogens into acetogens. We propose Methanobrevibacter ruminantium to be targeted because it is the dominant methane producer in the rumen.
Machine Learning-powered Pricing of the Multidimensional Passport Option
Authors: Josef Teichmann, Hanna Wutte
Date: 2023-07-27 00:00:00
Tasks: Board Games, Deep Reinforcement Learning

Introduced in the late 90s, the passport option gives its holder the right to trade in a market and receive any positive gain in the resulting traded account at maturity. Pricing the option amounts to solving a stochastic control problem that for $d>1$ risky assets remains an open problem. Even in a correlated Black-Scholes (BS) market with $d=2$ risky assets, no optimal trading strategy has been derived in closed form. In this paper, we derive a discrete-time solution for multi-dimensional BS markets with uncorrelated assets. Moreover, inspired by the success of deep reinforcement learning in, e.g., board games, we propose two machine learning-powered approaches to pricing general options on a portfolio value in general markets. These approaches prove to be successful for pricing the passport option in one-dimensional and multi-dimensional uncorrelated BS markets.
Pruning Distorted Images in MNIST Handwritten Digits
Authors: Amarnath R, Vinay Kumar V
Date: 2023-05-26 00:00:00

Recognizing handwritten digits is a challenging task primarily due to the diversity of writing styles and the presence of noisy images. The widely used MNIST dataset, which is commonly employed as a benchmark for this task, includes distorted digits with irregular shapes, incomplete strokes, and varying skew in both the training and testing datasets. Consequently, these factors contribute to reduced accuracy in digit recognition. To overcome this challenge, we propose a two-stage deep learning approach. In the first stage, we create a simple neural network to identify distorted digits within the training set. This model serves to detect and filter out such distorted and ambiguous images. In the second stage, we exclude these identified images from the training dataset and proceed to retrain the model using the filtered dataset. This process aims to improve the classification accuracy and confidence levels while mitigating issues of underfitting and overfitting. Our experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy rate of over 99.5% on the testing dataset. This significant improvement showcases the potential of our method in enhancing digit classification accuracy. In our future work, we intend to explore the scalability of this approach and investigate techniques to further enhance accuracy by reducing the size of the training data.
General Image-to-Image Translation with One-Shot Image Guidance
Authors: Bin Cheng, Zuhao Liu, Yunbo Peng, Yue Lin
Date: 2023-07-20 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Generation, Image-to-Image Translation, Translation

Large-scale text-to-image models pre-trained on massive text-image pairs show excellent performance in image synthesis recently. However, image can provide more intuitive visual concepts than plain text. People may ask: how can we integrate the desired visual concept into an existing image, such as our portrait? Current methods are inadequate in meeting this demand as they lack the ability to preserve content or translate visual concepts effectively. Inspired by this, we propose a novel framework named visual concept translator (VCT) with the ability to preserve content in the source image and translate the visual concepts guided by a single reference image. The proposed VCT contains a content-concept inversion (CCI) process to extract contents and concepts, and a content-concept fusion (CCF) process to gather the extracted information to obtain the target image. Given only one reference image, the proposed VCT can complete a wide range of general image-to-image translation tasks with excellent results. Extensive experiments are conducted to prove the superiority and effectiveness of the proposed methods. Codes are available at https://github.com/CrystalNeuro/visual-concept-translator.
Learned Gridification for Efficient Point Cloud Processing
Authors: Putri A. van der Linden, David W. Romero, Erik J. Bekkers
Date: 2023-07-22 00:00:00

Neural operations that rely on neighborhood information are much more expensive when deployed on point clouds than on grid data due to the irregular distances between points in a point cloud. In a grid, on the other hand, we can compute the kernel only once and reuse it for all query positions. As a result, operations that rely on neighborhood information scale much worse for point clouds than for grid data, specially for large inputs and large neighborhoods. In this work, we address the scalability issue of point cloud methods by tackling its root cause: the irregularity of the data. We propose learnable gridification as the first step in a point cloud processing pipeline to transform the point cloud into a compact, regular grid. Thanks to gridification, subsequent layers can use operations defined on regular grids, e.g., Conv3D, which scale much better than native point cloud methods. We then extend gridification to point cloud to point cloud tasks, e.g., segmentation, by adding a learnable de-gridification step at the end of the point cloud processing pipeline to map the compact, regular grid back to its original point cloud form. Through theoretical and empirical analysis, we show that gridified networks scale better in terms of memory and time than networks directly applied on raw point cloud data, while being able to achieve competitive results. Our code is publicly available at https://github.com/computri/gridifier.
Strictly Low Rank Constraint Optimization -- An Asymptotically $\mathcal{O}(\frac{1}{t^2})$ Method
Authors: Mengyuan Zhang, Kai Liu
Date: 2023-07-04 00:00:00

We study a class of non-convex and non-smooth problems with \textit{rank} regularization to promote sparsity in optimal solution. We propose to apply the proximal gradient descent method to solve the problem and accelerate the process with a novel support set projection operation on the singular values of the intermediate update. We show that our algorithms are able to achieve a convergence rate of $O(\frac{1}{t^2})$, which is exactly same as Nesterov's optimal convergence rate for first-order methods on smooth and convex problems. Strict sparsity can be expected and the support set of singular values during each update is monotonically shrinking, which to our best knowledge, is novel in momentum-based algorithms.
Long Short Term Memory Networks for Anomaly Detection in Time Series
Authors: Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Puneet Agarwal
Date: 2015-04-22 00:00:00
Proceeding: ESANN 2015 4
Tasks: Anomaly Detection, Fault Detection, Time Series, Time Series Anomaly Detection

Long Short Term Memory (LSTM) networks have been demonstrated to be particularly useful for learning sequences containing longer term patterns of unknown length, due to their ability to maintain long term memory. Stacking recurrent hidden layers in such networks also enables the learning of higher level temporal features, for faster learning with sparser representations. In this paper, we use stacked LSTM networks for anomaly/fault detection in time series. A network is trained on non-anomalous data and used as a predictor over a number of time steps. The resulting prediction errors are modeled as a multivariate Gaussian distribution, which is used to assess the likelihood of anomalous behavior. The efficacy of this approach is demonstrated on four datasets: ECG, space shuttle, power demand, and multi-sensor engine dataset.
UNTAG: LEARNING GENERIC FEATURES FOR UNSUPERVISED TYPE-AGNOSTIC DEEPFAKE DETECTION
Authors: Nesryne Mejri, Enjie Ghorbel, Djamila Aouada
Date: 2023-06-04 00:00:00
Proceeding: ICASSP 2023 6
Tasks: DeepFake Detection, Face Swapping, One-Class Classification, One-class classifier

This paper introduces a novel framework for unsupervised type-agnostic deepfake detection called UNTAG. Existing methods are generally trained in a supervised manner at the classification level, focusing on detecting at most two types of forgeries; thus, limiting their generalization capability across different deepfake types. To handle that, we reformulate the deepfake detection problem as a one-class classification supported by a self-supervision mechanism. Our intuition is that by estimating the distribution of real data in a discriminative feature space, deepfakes can be detected as outliers regardless of their type. UNTAG involves two sequential steps. First, deep representations are learned based on a self-supervised pretext task focusing on manipulated regions. Second, a one-class classifier fitted on authentic image embeddings is used to detect deepfakes. The results reported on several datasets show the effectiveness of UNTAG and the relevance of the proposed new paradigm. The code is publicly available.
Multi Label Image Classification using Adaptive Graph Convolutional Networks (ML-AGCN)
Authors: Inder Pal Singh, Enjie Ghorbel, Oyebade Oyedotun, Djamila Aouada
Date: 2022-10-01 00:00:00
Proceeding: ICIP 2022 10
Tasks: image-classification, Image Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Image Classification

In this paper, a novel graph-based approach for multi-label image classification called Multi-Label Adaptive Graph Convolutional Network (ML-AGCN) is introduced. Graph-based methods have shown great potential in the field of multi-label classification. However, these approaches heuristically fix the graph topology for modeling label dependencies, which might be not optimal. To handle that, we propose to learn the topology in an end-to-end manner. Specifically, we incorporate an attention-based mechanism for estimating the pairwise importance between graph nodes and a similarity-based mechanism for conserving the feature similarity between different nodes. This offers a more flexible way for adaptively modeling the graph. Experimental results are reported on two well-known datasets, namely, MS-COCO and VG-500. Results show that ML-AGCN outperforms state-of-the-art methods while reducing the number of model parameter