pwc-archive/papers-with-abstracts huggingface dataset:
Phage family classification under Caudoviricetes: a review of current tools using the latest ICTV classification framework
Authors: Yilin Zhu, Jiayu Shang, Cheng Peng, Yanni Sun
Date: 2022-09-05 00:00:00
Tasks: Classification

Bacteriophages, which are viruses infecting bacteria, are the most ubiquitous and diverse entities in the biosphere. There is accumulating evidence revealing their important roles in shaping the structure of various microbiomes. Thanks to (viral) metagenomic sequencing, a large number of new bacteriophages have been discovered. However, lacking a standard and automatic virus classification pipeline, the taxonomic characterization of new viruses seriously lag behind the sequencing efforts. In particular, according to the latest version of ICTV, several large phage families in the previous classification system are removed. Therefore, a comprehensive review and comparison of taxonomic classification tools under the new standard are needed to establish the state-of-the-art. In this work, we retrained and tested four recently published tools on newly labeled databases. We demonstrated their utilities and tested them on multiple datasets, including the RefSeq, short contigs, simulated metagenomic datasets, and low-similarity datasets. This study provides a comprehensive review of phage family classification in different scenarios and a practical guidance for choosing appropriate taxonomic classification pipelines. To our best knowledge, this is the first review conducted under the new ICTV classification framework. The results show that the new family classification framework overall leads to better-conserved groups and thus makes family-level classification more feasible.
Capsule Networks as Generative Models
Authors: Alex B. Kiefer, Beren Millidge, Alexander Tschantz, Christopher L. Buckley
Date: 2022-09-06 00:00:00
Tasks: Scene Recognition

Capsule networks are a neural network architecture specialized for visual scene recognition. Features and pose information are extracted from a scene and then dynamically routed through a hierarchy of vector-valued nodes called 'capsules' to create an implicit scene graph, with the ultimate aim of learning vision directly as inverse graphics. Despite these intuitions, however, capsule networks are not formulated as explicit probabilistic generative models; moreover, the routing algorithms typically used are ad-hoc and primarily motivated by algorithmic intuition. In this paper, we derive an alternative capsule routing algorithm utilizing iterative inference under sparsity constraints. We then introduce an explicit probabilistic generative model for capsule networks based on the self-attention operation in transformer networks and show how it is related to a variant of predictive coding networks using Von-Mises-Fisher (VMF) circular Gaussian distributions.
A descriptive analysis of olfactory sensation and memory in Drosophila and its relation to artificial neural networks
Authors: Chris Rohlfs
Date: 2022-09-03 00:00:00
Tasks: Descriptive

This article provides a background and descriptive analysis of insect memory and the coding of olfactory sensation in Drosophila, presenting graphs and summary statistics from a large dataset of neurons and synapses that was recently made publicly available and also discussing findings from the existing empirical literature. Some general principles from Drosophila olfaction are discussed as they apply to the design of analogous systems in artificial neural networks: (1) the networks used for coding are shallow; (2) the level of connectedness varies widely across neurons in the same layer; (3) much communication is between neurons in the same layer; (4) in most olfactory learning, the manner in which sensory inputs are represented in stored memory is largely fixed, and the learning process involves developing positive or negative associations with existing categories of inputs.
The arrow of evolution when the offspring variance is large
Authors: Guocheng Wang, Qi Su, Long Wang, Joshua B. Plotkin
Date: 2022-09-06 00:00:00

The concept of fitness is central to evolution, but it quantifies only the expected number of offspring an individual will produce. The actual number of offspring is also subject to noise, arising from environmental or demographic stochasticity. In nature, individuals who are more fecund tend to have greater variance in their offspring number -- sometimes far greater than the Poisson variance assumed in classical models of population genetics. Here, we develop a model for the evolution of two types reproducing in a population of non-constant size. The frequency-dependent fitness of each type is determined by pairwise interactions in a prisoner's dilemma game, but the offspring number is subject to an exogenously controlled variance that may depend upon the mean. Whereas defectors are preferred by natural selection in classical well-mixed populations, since they always have greater fitness than cooperators, we show that large offspring variance can reverse the direction of evolution and favor cooperation. Reproductive over-dispersion produces qualitatively new dynamics for other types of social interactions, as well, which cannot arise in populations with a fixed size or Poisson offspring variance.
Evolutionary Dynamics Within and Among Competing Groups
Authors: Daniel B. Cooney, Simon A. Levin, Yoichiro Mori, Joshua B. Plotkin
Date: 2022-09-05 00:00:00
Tasks: Management

Biological and social systems are structured at multiple scales, and the incentives of individuals who interact in a group may diverge from the collective incentive of the group as a whole. Mechanisms to resolve this tension are responsible for profound transitions in evolutionary history, including the origin of cellular life, multi-cellular life, and even societies. Here we synthesize a growing literature that extends evolutionary game theory to describe multilevel evolutionary dynamics, using nested birth-death processes and partial differential equations to model natural selection acting on competition within and among groups of individuals. We apply this theory to analyze how mechanisms known to promote cooperation within a single group -- including assortment, reciprocity, and population structure -- alter evolutionary outcomes in the presence of competition among groups. We find that population structures most conducive to cooperation in multi-scale systems may differ from those most conducive within a single group. Likewise, for competitive interactions with a continuous range of strategies we find that among-group selection may fail to produce socially optimal outcomes, but it can nonetheless produce second-best solutions that balance individual incentives to defect with the collective incentives for cooperation. We conclude by describing the broad applicability of multi-scale evolutionary models to problems ranging from the production of diffusible metabolites in microbes to the management of common-pool resources in human societies.
Pattern-Based Prediction of Population Outbreaks
Authors: Gabriel R. Palma, Wesley A. C. Godoy, Eduardo Engel, Douglas Lau, Edgar Galvan, Oliver Mason, Charles Markham, Rafael A. Moral
Date: 2022-09-06 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Insect outbreaks are biotic disturbances in forests and agroecosystems that cause economic and ecological damage. This phenomenon depends on a variety of biological and physical factors. The complexity and practical importance of the issue have made the problem of predicting outbreaks a focus of recent research. Here, we propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It is based on the Alert Zone Procedure, combined with elements from machine learning. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be useful when monitoring pest species. We illustrate the methodology using simulated datasets and real time series data obtained by monitoring aphids in wheat crops in Southern Brazil. We obtained an average test accuracy of $84.6\%$ in the simulation studies implemented with stochastic models, and $95.0\%$ for predicting outbreaks using the real dataset. This shows the feasibility of the PBP method in predicting outbreaks in population dynamics. We benchmarked our results against established state-of-the-art machine learning methods, namely Support Vector Machines, Deep Neural Networks, Long Short Term Memory and Random Forests. The PBP method yielded a competitive performance, associated with higher true-positive rates in most comparisons, while being able to provide interpretability rather than being a black-box method. This is an improvement over current state-of-the-art machine learning tools, especially when being used by non-specialists, such as ecologists aiming to use a quantitative approach for pest monitoring. We provide open-source code to implement the PBP method in Python, through the \texttt{pypbp} package, which may be directly downloaded from the Python Package Index server or accessed through \url{https://pypbp-documentation.readthedocs.io}
Periodicity Intensity of the 24 h Circadian Rhythm in Newborn Calves Show Indicators of Herd Welfare
Authors: Victoria Rhodes, Maureen Maguire, Meghana Shetty, Conor McAloon, Alan F. Smeaton
Date: 2022-08-08 00:00:00
Tasks: Rhythm

Circadian rhythms are a process of the sleep-wake cycle that regulates the physical, mental and behavioural changes in all living beings with a period of roughly 24 h. Wearable accelerometers are typically used in livestock applications to record animal movement from which we can estimate the activity type. Here, we use the overall movement recorded by accelerometers worn on the necks of newborn calves for a period of 8 weeks. From the movement data, we calculate 24 h periodicity intensities corresponding to circadian rhythms, from a 7-day window that slides through up to 8-weeks of data logging. The strength or intensity of the 24 h periodicity is computed at intervals as the calves become older, which is an indicator of individual calf welfare. We observe that the intensities of these 24 h periodicities for individual calves, derived from movement data, increase and decrease synchronously in a herd of 19 calves. Our results show that external factors affecting the welfare of the herd can be observed by processing and visualising movement data in this way and our method reveals insights that are not observable from movement data alone.
Assessing the different aspects of consuming fashion and the role of self-confidence on the buying behaviour of fashion consumers in the clothing market as a mediator
Authors: Milad Zam, Mohammadhosein Tavakoli, Hasan Ramezanian, Amin Rezasoltani
Date: 2022-07-28 00:00:00
Tasks: Decision Making

As a mediator variable, self-confidence is one of the most effective elements of the decision-making process of consumer behaviour. This research has studied the effects of different aspects of consuming fashion on the self-confidence and behaviour of consumers in Tehran's clothing market. This study has considered the acceptance of new products, interest in mode and fashion, utilitarianism, and personal taste in its analysis. This research aims to understand the fashion buying behaviour amongst Iranian consumers in consideration of their attitude towards self-confidence and aspects of fashion consumption. The statistic sample is 400 consumers from Tehran's clothing market who have been chosen based on the random availability procedure. The primary tool in this research was a questionary used to testify the assumptions and a model fit created by using structural equations and factor analysis. This research showed that the interest in mode and fashion, personal taste, utilitarianism, and new products positively impact self-confidence. In addition, the positive impact of self-confidence on fashion buying behaviour was confirmed.
Liquidity Provision Payoff on Automated Market Makers
Authors: Jin Hong Kuan
Date: 2022-09-04 00:00:00

The standard approach for compensating liquidity providers on many decentralized exchanges (DEX) for serving as counter-party to swaps is through charging a small percentage of fees. The expected payoff from the cash flow of this mode of market making has yet to be mathematically formulated in terms of volatility in the existing literature. We provide here a preliminary derivation of the payoff formula, by making the standard set of assumptions for efficient markets, namely geometric Brownian price movements and zero arbitrage. Trading volume, conventionally taken as an exogenous variable for fees calculation, becomes a function of volatility and available liquidity in this formulation. In doing so, we show that it is a near-linear function of the volatility of the underlying risky asset. Since hedging instruments with such a property are highly sought after, we discuss the potential of securitizing the cash flow of liquidity fees to serve as a volatility product in its own right.
MangoLeafBD: A Comprehensive Image Dataset to Classify Diseased and Healthy Mango Leaves
Authors: Sarder Iftekhar Ahmed, Muhammad Ibrahim, Md. Nadim, Md. Mizanur Rahman, Maria Mehjabin Shejunti, Taskeed Jabid, Md. Sawkat Ali
Date: 2022-08-27 00:00:00

Agriculture is of one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full benefit of these powerful computational predictive tools and techniques. To improve this scenario, we develop, to the best of our knowledge, the first-ever standard, ready-to-use, and publicly available dataset of mango leaves. The images are collected from four mango orchards of Bangladesh, one of the top mango-growing countries of the world. The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango yield. This dataset is expected to draw wide attention from machine learning researchers and practitioners in the field of automated agriculture.
Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots
Authors: Sahar Salimpour, Jorge Peña Queralta, Tomi Westerlund
Date: 2022-08-26 00:00:00
Tasks: Anomaly Detection, Change Detection, Fault Detection, object-detection, Object Detection

Automatic detection of visual anomalies and changes in the environment has been a topic of recurrent attention in the fields of machine learning and computer vision over the past decades. A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. The majority of existing approaches focus on anomaly or fault detection in a specific class of images or environments, while general purpose visual anomaly detection algorithms are more scarce in the literature. In this paper, we propose a comprehensive deep learning framework for detecting anomalies and changes in a priori unknown environments after a reference dataset is gathered, and without need for retraining the model. We use the SuperPoint and SuperGlue feature extraction and matching methods to detect anomalies based on reference images taken from a similar location and with partial overlapping of the field of view. We also introduce a self-calibrating method for the proposed model in order to address the problem of sensitivity to feature matching thresholds and environmental conditions. To evaluate the proposed framework, we have used a ground robot system for the purpose of reference and query data collection. We show that high accuracy can be obtained using the proposed method. We also show that the calibration process enhances changes and foreign object detection performance
MARTI-4: new model of human brain, considering neocortex and basal ganglia -- learns to play Atari game by reinforcement learning on a single CPU
Authors: Igor Pivovarov, Sergey Shumsky
Date: 2022-08-18 00:00:00
Tasks: CPU, OpenAI Gym, reinforcement-learning, Reinforcement Learning (RL)

We present Deep Control - new ML architecture of cortico-striatal brain circuits, which use whole cortical column as a structural element, instead of a singe neuron. Based on this architecture, we present MARTI - new model of human brain, considering neocortex and basal ganglia. This model is de-signed to implement expedient behavior and is capable to learn and achieve goals in unknown environments. We introduce a novel surprise feeling mechanism, that significantly improves reinforcement learning process through inner rewards. We use OpenAI Gym environment to demonstrate MARTI learning on a single CPU just in several hours.
ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completion
Authors: Mojtaba Moattari, Sahar Vahdati, Farhana Zulkernine
Date: 2022-08-15 00:00:00
Tasks: Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graphs, Text Generation

Knowledge Graph Embedding (KGE) methods have gained enormous attention from a wide range of AI communities including Natural Language Processing (NLP) for text generation, classification and context induction. Embedding a huge number of inter-relationships in terms of a small number of dimensions, require proper modeling in both cognitive and computational aspects. Recently, numerous objective functions regarding cognitive and computational aspects of natural languages are developed. Among which are the state-of-the-art methods of linearity, bilinearity, manifold-preserving kernels, projection-subspace, and analogical inference. However, the major challenge of such models lies in their loss functions that associate the dimension of relation embeddings to corresponding entity dimension. This leads to inaccurate prediction of corresponding relations among entities when counterparts are estimated wrongly. ProjE KGE, published by Bordes et al., due to low computational complexity and high potential for model improvement, is improved in this work regarding all translative and bilinear interactions while capturing entity nonlinearity. Experimental results on benchmark Knowledge Graphs (KGs) such as FB15K and WN18 show that the proposed approach outperforms the state-of-the-art models in entity prediction task using linear and bilinear methods and other recent powerful ones. In addition, a parallel processing structure is proposed for the model in order to improve the scalability on large KGs. The effects of different adaptive clustering and newly proposed sampling approaches are also explained which prove to be effective in improving the accuracy of knowledge graph completion.
Overview of the GermEval 2022 Shared Task on Text Complexity Assessment of German Text
Authors: Salar Mohtaj, Babak Naderi, Sebastian Möller
Date: 2022-09-01 00:00:00
Proceeding: GermEval 2022 9

In this paper we present the GermEval 2022 shared task on Text Complexity Assessment of German text. Text forms an integral part of exchanging information and interacting with the world, correlating with quality and experience of life. Text complexity is one of the factors which affects a reader’s understanding of a text. The mapping of a body of text to a mathematical unit quantifying the degree of readability is the basis of complexity assessment. As readability might be influenced by representation, we only target the text complexity for readers in this task. We designed the task as text regression in which participants developed models to predict complexity of pieces of text for a German learner in a range from 1 to 7. The shared task is organized in two phases; the development and the test phases. Among 24 participants who registered for the shared task, ten teams submitted their results on the test data.
Everybody likes short sentences - A Data Analysis for the Text Complexity DE Challenge 2022
Authors: Ulf A. Hamster
Date: 2022-09-01 00:00:00
Proceeding: GermEval 2022 9
Tasks: Feature Engineering, regression, Sensitivity, Sentence

The German Text Complexity Assessment Shared Task in KONVENS 2022 explores how to predict a complexity score for sentence examples from language learners’ perspective. Our modeling approach for this shared task utilizes off-the-shelf NLP tools for feature engineering and a Random Forest regression model. We identified the text length, or resp. the logarithm of a sentence’s string length, as the most important feature to predict the complexity score. Further analysis showed that the Pearson correlation between text length and complexity score is about \rho ≈ 0.777. A sensitivity analysis on the loss function revealed that semantic SBert features impact the complexity score as well.
HIIG at GermEval 2022: Best of Both Worlds Ensemble for Automatic Text Complexity Assessment
Authors: Hadi Asghari, Freya Hewett
Date: 2022-09-01 00:00:00
Proceeding: GermEval 2022 9
Tasks: Sentence

In this paper we explain HIIG’s contribution to the shared task Text Complexity DE Challenge 2022. Our best-performing model for the task of automatically determining the complexity level of a German-language sentence is a combination of a transformer model and a classic feature-based model, which achieves a mapped root square mean error of 0.446 on the test data.
TUM Social Computing at GermEval 2022: Towards the Significance of Text Statistics and Neural Embeddings in Text Complexity Prediction
Authors: Miriam Anschütz, Georg Groh
Date: 2022-09-01 00:00:00
Proceeding: GermEval 2022 9

In this paper, we describe our submission to the GermEval 2022 Shared Task on Text Complexity Assessment of German Text. It addresses the problem of predicting the complexity of German sentences on a continuous scale. While many related works still rely on handcrafted statistical features, neural networks have emerged as state-of-the-art in other natural language processing tasks. Therefore, we investigate how both can complement each other and which features are most relevant for text complexity prediction in German. We propose a fine-tuned German DistilBERT model enriched with statistical text features that achieved fourth place in the shared task with a RMSE of 0.481 on the competition’s test data.
HHUplexity at Text Complexity DE Challenge 2022
Authors: David Arps, Jan Kels, Florian Krämer, Yunus Renz, Regina Stodden, Wiebke Petersen
Date: 2022-09-01 00:00:00
Proceeding: GermEval 2022 9
Tasks: regression

In this paper, we describe our submission to the ‘Text Complexity DE Challenge 2022’ shared task on predicting the complexity of German sentences. We compare performance of different feature-based regression architectures and transformer language models. Our best candidate is a fine-tuned German Distilbert model that ignores linguistic features of the sentences. Our model ranks 7th place in the shared task.
Tackling Data Drift with Adversarial Validation: An Application for German Text Complexity Estimation
Authors: Alejandro Mosquera
Date: 2022-09-01 00:00:00
Proceeding: GermEval 2022 9
Tasks: Feature Engineering, regression

This paper describes the winning approach in the first automated German text complexity assessment shared task as part of KONVENS 2022. To solve this difficult problem, the evaluated system relies on an ensemble of regression models that successfully combines both traditional feature engineering and pre-trained resources. Moreover, the use of adversarial validation is proposed as a method for countering the data drift identified during the development phase, thus helping to select relevant models and features and avoid leaderboard overfitting. The best submission reached 0.43 mapped RMSE on the test set during the final phase of the competition.