pwc-archive/papers-with-abstracts huggingface dataset:
Exploring Geometric Deep Learning For Precipitation Nowcasting
Authors: Shan Zhao, Sudipan Saha, Zhitong Xiong, Niklas Boers, Xiao Xiang Zhu
Date: 2023-09-11 00:00:00
Tasks: Deep Learning

Precipitation nowcasting (up to a few hours) remains a challenge due to the highly complex local interactions that need to be captured accurately. Convolutional Neural Networks rely on convolutional kernels convolving with grid data and the extracted features are trapped by limited receptive field, typically expressed in excessively smooth output compared to ground truth. Thus they lack the capacity to model complex spatial relationships among the grids. Geometric deep learning aims to generalize neural network models to non-Euclidean domains. Such models are more flexible in defining nodes and edges and can effectively capture dynamic spatial relationship among geographical grids. Motivated by this, we explore a geometric deep learning-based temporal Graph Convolutional Network (GCN) for precipitation nowcasting. The adjacency matrix that simulates the interactions among grid cells is learned automatically by minimizing the L1 loss between prediction and ground truth pixel value during the training procedure. Then, the spatial relationship is refined by GCN layers while the temporal information is extracted by 1D convolution with various kernel lengths. The neighboring information is fed as auxiliary input layers to improve the final result. We test the model on sequences of radar reflectivity maps over the Trento/Italy area. The results show that GCNs improves the effectiveness of modeling the local details of the cloud profile as well as the prediction accuracy by achieving decreased error measures.
The SLED (Shelf Life Expiration Date) Tracking System: Using Machine Learning Algorithms to Combat Food Waste and Food Borne Illnesses
Authors: Srilekha Mamidala
Date: 2023-09-05 00:00:00

The issue of food waste is a major problem contributing to the emission of greenhouse gases into the environment in addition to causing illness in humans. This research aimed to develop a correlation between the amount of time until a food spoiled and dates on food labels in conjunction with sensory observations. Sensory observations are more accurate as they are immediate observations that are specific to the food. This experiment observed bananas, bread, milk, eggs, and leafy greens over a period of time using characteristics specific to the food to quantify food spoilage. It was shown that the actual time until spoilage for all foods was longer than that of the best by date and that sensory observations proved to be a more accurate factor in determining spoilage. From this data, a machine learning algorithm was trained to predict if food was spoiled or not, in addition to the number of days until spoilage. This was presented to the consumer as an app, where the user can track foods and are reminded to check on them to prevent wastage. In addition, the experimental procedures were incorporated into a test kit for the consumer to take instructed observations to assess the spoilage of their food, which are then entered into the app to improve the algorithm. This paper discusses the individual effects of sensorial observations on each food and examines the shifting of consumer habits through an app and test kit to combat environmental consequences of food waste.
PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models
Authors: Li Chen, Mengyi Zhao, Yiheng Liu, Mingxu Ding, Yangyang Song, Shizun Wang, Xu Wang, Hao Yang, Jing Liu, Kang Du, Min Zheng
Date: 2023-09-11 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Personalized text-to-image generation has emerged as a powerful and sought-after tool, empowering users to create customized images based on their specific concepts and prompts. However, existing approaches to personalization encounter multiple challenges, including long tuning times, large storage requirements, the necessity for multiple input images per identity, and limitations in preserving identity and editability. To address these obstacles, we present PhotoVerse, an innovative methodology that incorporates a dual-branch conditioning mechanism in both text and image domains, providing effective control over the image generation process. Furthermore, we introduce facial identity loss as a novel component to enhance the preservation of identity during training. Remarkably, our proposed PhotoVerse eliminates the need for test time tuning and relies solely on a single facial photo of the target identity, significantly reducing the resource cost associated with image generation. After a single training phase, our approach enables generating high-quality images within only a few seconds. Moreover, our method can produce diverse images that encompass various scenes and styles. The extensive evaluation demonstrates the superior performance of our approach, which achieves the dual objectives of preserving identity and facilitating editability. Project page: https://photoverse2d.github.io/
Adaptive User-centered Neuro-symbolic Learning for Multimodal Interaction with Autonomous Systems
Authors: Amr Gomaa, Michael Feld
Date: 2023-09-11 00:00:00
Tasks: Incremental Learning, multimodal interaction, object-detection, Object Detection

Recent advances in machine learning, particularly deep learning, have enabled autonomous systems to perceive and comprehend objects and their environments in a perceptual subsymbolic manner. These systems can now perform object detection, sensor data fusion, and language understanding tasks. However, there is a growing need to enhance these systems to understand objects and their environments more conceptually and symbolically. It is essential to consider both the explicit teaching provided by humans (e.g., describing a situation or explaining how to act) and the implicit teaching obtained by observing human behavior (e.g., through the system's sensors) to achieve this level of powerful artificial intelligence. Thus, the system must be designed with multimodal input and output capabilities to support implicit and explicit interaction models. In this position paper, we argue for considering both types of inputs, as well as human-in-the-loop and incremental learning techniques, for advancing the field of artificial intelligence and enabling autonomous systems to learn like humans. We propose several hypotheses and design guidelines and highlight a use case from related work to achieve this goal.
Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environments
Authors: Shadan Golestan, Omid Ardakanian, Pierre Boulanger
Date: 2023-09-11 00:00:00
Tasks: Activity Recognition, Bayesian Optimization, Indoor Localization

Optimizing the configuration and placement of sensors is crucial for reliable fall detection, indoor localization, and activity recognition in assisted living spaces. We propose a novel, sample-efficient approach to find a high-quality sensor placement in an arbitrary indoor space based on grey-box Bayesian optimization and simulation-based evaluation. Our key technical contribution lies in capturing domain-specific knowledge about the spatial distribution of activities and incorporating it into the iterative selection of query points in Bayesian optimization. Considering two simulated indoor environments and a real-world dataset containing human activities and sensor triggers, we show that our proposed method performs better compared to state-of-the-art black-box optimization techniques in identifying high-quality sensor placements, leading to accurate activity recognition in terms of F1-score, while also requiring a significantly lower (51.3% on average) number of expensive function queries.
Social \textit{vs.} individual age-dependent costs of imperfect vaccination
Authors: Fabio A. C. C. Chalub, Paulo Doutor, Paula Patrício, Maria do Céu Soares
Date: 2023-09-12 00:00:00

In diseases with long-term immunity, vaccination is known to increase the average age at infection as a result of the decrease in the pathogen circulation. This implies that a vaccination campaign can have negative effects when a disease is more costly (financial or health-related costs) for higher ages. This work considers an age-structured population transmission model with imperfect vaccination. We aim to compare the social and individual costs of vaccination, assuming that disease costs are age-dependent, while the disease's dynamic is age-independent. A model coupling pathogen deterministic dynamics for a population consisting of juveniles and adults, assumed to be rational agents, is introduced. The parameter region for which vaccination has a positive social impact is fully characterized and the Nash equilibrium of the vaccination game is obtained. Finally, collective strategies designed to promote voluntary vaccination, without compromising social welfare, are discussed.
The canonical equation of adaptive dynamics in individual-based models with power law mutation rates
Authors: Tobias Paul
Date: 2023-09-05 00:00:00

In this paper, we consider an individual-based model with power law mutation probability. In this setting, we use the large population limit with a subsequent ``small mutations'' limit to derive the canonical equation of adaptive dynamics. For a one-dimensional trait space this corresponds to well established results and we can formulate a criterion for evolutionary branching in the spirit of Champagnat and M\'el\'eard (2011). In higher dimensional trait spaces, we find that the speed at which the solution of the canonical equation moves through space is reduced due to mutations being restricted to the underlying grid on the trait space. However, as opposed to the canonical equation with rare mutations, we can explicitly calculate the path which the dominant trait will take without having to solve the equation itself.
Effect of Feedback between Environment and Finite Population
Authors: Jia-Xu Han, Rui-Wu Wang
Date: 2023-09-05 00:00:00

Natural selection imply that any organisms including human being will evolve to improve its fitness advantage and the selected genotype or phenotype in equilibrium state will not vary over the time. However, evolutionary process of biological organisms in reality is greatly affected by the environmental change and historical accidents. In this research, we construct a co-evolutionary system to investigate the impact of species-environment feedback. When we talk about an invasion species or mutation, positive feedback is detrimental to the success of the invasion because positive feedback benefits a large number of individuals, whereas negative feedback benefits the invasion because negative feedback disadvantages a large number of individuals. In the case of a competition between two species with initially equal numbers of individuals, both positive and negative feedback will favor the species with low fitness, increasing its chances of taking over the whole population. The reason for this is that feedback allows initially inferior species to have greater fitness than initially dominating species in the early stages, emphasizing the importance of early random accident. Our findings emphasize the significance of the evolutionary path driven by species-environment feedback.
Fruit harvesting: A potential threat to the persistence, spatial distribution, and establishment of plants
Authors: Mozzamil Mohammed, Åke Brännström, Pietro Landi, Ulf Dieckmann
Date: 2023-09-04 00:00:00

Plant-frugivore interactions play a central role for plant persistence and spatial distribution by promoting the long-range dispersal of seeds by frugivores. However, plant-frugivore interactions are increasingly being threatened by anthropogenic activities. An important anthropogenic threat that could expose plant-frugivore systems to extinction risk is fruit harvesting. Here, we develop an individual-based and a pair-approximation model of plant-frugivore-human interactions to elucidate the effects of human harvesting of fruits on plant establishment, persistence, and spatial distribution. Our results show that frugivores strongly affect global density of plants and gradually shift their spatial distribution from aggregated to random, depending on the attack rate and dispersal efficiency of frugivores. We find that, in the absence of frugivores, plants experiencing intense fruit harvesting cannot persist even if their fecundity is high. In the presence of frugivores, fruit harvesting profoundly affects the global dispersal of seeds and thus changes the spatial distributions of plants from random to aggregated, potentially causing plant extinction. Our results demonstrate that sufficiently efficient frugivores mitigate the negative impact of fruit harvesting on plant populations and enable plant establishment precluded by harvesting. Taken together, these results draw attention to previously underappreciated impacts of fruit harvesting in plant-frugivore-human interactions.
Anomaly zones for uniformly sampled gene trees under the gene duplication and loss model
Authors: Brandon Legried
Date: 2023-09-04 00:00:00
Tasks: Mathematical Proofs

Recently, there has been interest in extending long-known results about the multispecies coalescent tree to other models of gene trees. Results about the gene duplication and loss (GDL) tree have mathematical proofs, including species tree identifiability, estimability, and sample complexity of popular algorithms like ASTRAL. Here, this work is continued by characterizing the anomaly zones of uniformly sampled gene trees. The anomaly zone for species trees is the set of parameters where some discordant gene tree occurs with the maximal probability. The detection of anomalous gene trees is an important problem in phylogenomics, as their presence renders effective estimation methods to being positively misleading. Under the multispecies coalescent, anomaly zones are known to exist for rooted species trees with as few as four species. The gene duplication and loss process is a generalization of the generalized linear-birth death process to the rooted species tree, where each edge is treated as a single timeline with exponential-rate duplication and loss. The methods and results come from a detailed probabilistic analysis of trajectories observed from this stochastic process. It is shown that anomaly zones do not exist for rooted GDL balanced trees on four species, but do exist for rooted caterpillar trees, as with the multispecies coalescent.
Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis
Authors: Ilya Auslender, Lorenzo Pavesi
Date: 2023-09-12 00:00:00

In this paper we present a computational model which decodes the spatio-temporal data from electro-physiological measurements of neuronal networks and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units. The model is based on reservoir computing network (RCN) approach, where experimental data is used as training and validation data. Consequently, the model can be used to study the functionality of different neuronal cultures and simulate the network response to external stimuli.
A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information
Authors: Divya Ramakrishnan, Leon Jekel, Saahil Chadha, Anastasia Janas, Harrison Moy, Nazanin Maleki, Matthew Sala, Manpreet Kaur, Gabriel Cassinelli Petersen, Sara Merkaj, Marc von Reppert, Ujjwal Baid, Spyridon Bakas, Claudia Kirsch, Melissa Davis, Khaled Bousabarah, Wolfgang Holler, MingDe Lin, Malte Westerhoff, Sanjay Aneja, Fatima Memon, Mariam S. Aboian
Date: 2023-09-10 00:00:00

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow.
A compendium of data sources for data science, machine learning, and artificial intelligence
Authors: Paul Bilokon, Oleksandr Bilokon, Saeed Amen
Date: 2023-09-10 00:00:00
Tasks: Drug Discovery

Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are application-specific, and it is impossible to produce an exhaustive list of such data sources, it seems that a comprehensive, rather than complete, list would still benefit data scientists and machine learning experts of all levels of seniority. The goal of this publication is to provide just such an (inevitably incomplete) list -- or compendium -- of data sources across multiple areas of applications, including finance and economics, legal (laws and regulations), life sciences (medicine and drug discovery), news sentiment and social media, retail and ecommerce, satellite imagery, and shipping and logistics, and sports.
Computational Approaches for Predicting Drug-Disease Associations: A Comprehensive Review
Authors: Chunyan Ao, Zhichao Xiao, Lixin Guan, Liang Yu
Date: 2023-09-10 00:00:00

In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been suggested for predicting the relationship between drugs and diseases through drug repositioning, aiming to reduce the cost, development cycle, and risks associated with developing new drugs. Researchers have explored different computational methods to predict drug-disease associations, including drug side effects-disease associations, drug-target associations, and miRNAdisease associations. In this comprehensive review, we focus on recent advances in predicting drug-disease association methods for drug repositioning. We first categorize these methods into several groups, including neural network-based algorithms, matrixbased algorithms, recommendation algorithms, link-based reasoning algorithms, and text mining and semantic reasoning. Then, we compare the prediction performance of existing drug-disease association prediction algorithms. Lastly, we delve into the present challenges and future prospects concerning drug-disease associations.
Cryo-Electron Ptychography: Applications and Potential in Biological Characterisation
Authors: Chen Huang, Judy S. Kim, Angus I. Kirkland
Date: 2023-09-09 00:00:00
Tasks: 3D Reconstruction, Single Particle Analysis

There is a clear need for developments in characterisation techniques that provide detailed information about structure-function relationships in biology. Using electron microscopy to achieve high resolution while maintaining a broad field of view remains a challenge, particularly for radiation sensitive specimens where the signal-to-noise ratio required to maintain structural integrity is limited by low electron fluence. In this review, we explore the potential of cryogenic electron ptychography as an alternative method for characterisation of biological systems under low fluence conditions. Using this method with increased information content from multiple sampled regions of interest, potentially allows 3D reconstruction with far fewer particles than required in conventional cryo-electron microscopy. This is important for achieving higher resolution for systems where distributions of homogeneous single particles are difficult to obtain. We discuss the progress, limitations and potential areas for future development of this approach for both single particle analysis and in applications to heterogeneous large objects.
ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing
Authors: Michael T. Kuczynski, Nathan J. Neeteson, Kathryn S. Stok, Andrew J. Burghardt, Michelle A. Espinosa Hernandez, Jared Vicory, Justin J. Tse, Pholpat Durongbhan, Serena Bonaretti, Andy Kin On Wong, Steven K. Boyd, Sarah L. Manske
Date: 2023-09-08 00:00:00

High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique capable of imaging trabecular bone in-vivo. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases, assess epidemiological associations, and evaluate the effects of pharmaceutical interventions. Processing HR-pQCT images has largely been supported using the scanner manufacturer scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The ORMIR_XCT Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows.
Large Language Model for Science: A Study on P vs. NP
Authors: Qingxiu Dong, Li Dong, Ke Xu, Guangyan Zhou, Yaru Hao, Zhifang Sui, Furu Wei
Date: 2023-09-11 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

In this work, we use large language models (LLMs) to augment and accelerate research on the P versus NP problem, one of the most important open problems in theoretical computer science and mathematics. Specifically, we propose Socratic reasoning, a general framework that promotes in-depth thinking with LLMs for complex problem-solving. Socratic reasoning encourages LLMs to recursively discover, solve, and integrate problems while facilitating self-evaluation and refinement. Our pilot study on the P vs. NP problem shows that GPT-4 successfully produces a proof schema and engages in rigorous reasoning throughout 97 dialogue turns, concluding "P $\neq$ NP", which is in alignment with (Xu and Zhou, 2023). The investigation uncovers novel insights within the extensive solution space of LLMs, shedding light on LLM for Science.