pwc-archive/papers-with-abstracts huggingface dataset:
Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks
Authors: Mona Nourbakhsh, Kristine Degn, Astrid Saksager, Matteo Tiberti, Elena Papaleo
Date: 2023-03-30 00:00:00
Tasks: Benchmarking

The vast amount of sequencing data presently available allow the scientific community to explore a range of genetic variables that may drive and progress cancer. A myriad of predictive tools has been proposed, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tool. However, such limitations forces researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases every day, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartment, mutational burden, or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive tools for discovery of cancer drivers. We introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving in the direction of integrative frameworks.
Synchrotron Science for Sustainability: Life Cycle of Metals in the Environment
Authors: Louisa Smieska, Mary Lou Guerinot, Karin Olson Hoal, Matthew Reid, Olena Vatamaniuk
Date: 2023-03-30 00:00:00

The movement of metals through the environment links together a wide range of scientific fields: from earth sciences and geology as weathering releases minerals; to environmental sciences as metals are mobilized and transformed, cycling through soil and water; to biology as living things take up metals from their surroundings. Studies of these fundamental processes all require quantitative analysis of metal concentrations, locations, and chemical states. Synchrotron x-ray tools can address these requirements with high sensitivity, high spatial resolution, and minimal sample preparation. This perspective describes the state of fundamental scientific questions in the lifecycle of metals, from rocks to ecosystems, from soils to plants, and from environment to animals. Key x-ray capabilities and facility infrastructure for future synchrotron-based analytical resources serving these areas are summarized, and potential opportunities for future experiments are explored.
Option pricing using a skew random walk pricing tree
Authors: Yuan Hu, W. Brent Lindquist, Svetlozar T. Rachev, Frank J. Fabozzi
Date: 2023-03-29 00:00:00

Motivated by the Corns-Satchell, continuous time, option pricing model, we develop a binary tree pricing model with underlying asset price dynamics following It\^o-Mckean skew Brownian motion. While the Corns-Satchell market model is incomplete, our discrete time market model is defined in the natural world; extended to the risk neutral world under the no-arbitrage condition where derivatives are priced under uniquely determined risk-neutral probabilities; and is complete. The skewness introduced in the natural world is preserved in the risk neutral world. Furthermore, we show that the model preserves skewness under the continuous-time limit. We provide numerical applications of our model to the valuation of European put and call options on exchange-traded funds tracking the S&P Global 1200 index.
CADM: Confusion Model-based Detection Method for Real-drift in Chunk Data Stream
Authors: Songqiao Hu, Zeyi Liu, Xiao He
Date: 2023-03-25 00:00:00
Tasks: Drift Detection, Fault Diagnosis

Concept drift detection has attracted considerable attention due to its importance in many real-world applications such as health monitoring and fault diagnosis. Conventionally, most advanced approaches will be of poor performance when the evaluation criteria of the environment has changed (i.e. concept drift), either can only detect and adapt to virtual drift. In this paper, we propose a new approach to detect real-drift in the chunk data stream with limited annotations based on concept confusion. When a new data chunk arrives, we use both real labels and pseudo labels to update the model after prediction and drift detection. In this context, the model will be confused and yields prediction difference once drift occurs. We then adopt cosine similarity to measure the difference. And an adaptive threshold method is proposed to find the abnormal value. Experiments show that our method has a low false alarm rate and false negative rate with the utilization of different classifiers.
A Generative Modeling Approach Using Quantum Gates
Authors: Soumyadip Sarkar
Date: 2023-03-25 00:00:00

In recent years, quantum computing has emerged as a promising technology for solving complex computational problems. Generative modeling is a technique that allows us to learn and generate new data samples similar to the original dataset. In this paper, we propose a generative modeling approach using quantum gates to generate new samples from a given dataset. We start with a brief introduction to quantum computing and generative modeling. Then, we describe our proposed approach, which involves encoding the dataset into quantum states and using quantum gates to manipulate these states to generate new samples. We also provide mathematical details of our approach and demonstrate its effectiveness through experimental results on various datasets.
On pitfalls (and advantages) of sophisticated large language models
Authors: Anna Strasser
Date: 2023-02-25 00:00:00
Tasks: Misinformation

Natural language processing based on large language models (LLMs) is a booming field of AI research. After neural networks have proven to outperform humans in games and practical domains based on pattern recognition, we might stand now at a road junction where artificial entities might eventually enter the realm of human communication. However, this comes with serious risks. Due to the inherent limitations regarding the reliability of neural networks, overreliance on LLMs can have disruptive consequences. Since it will be increasingly difficult to distinguish between human-written and machine-generated text, one is confronted with new ethical challenges. This begins with the no longer undoubtedly verifiable human authorship and continues with various types of fraud, such as a new form of plagiarism. This also concerns the violation of privacy rights, the possibility of circulating counterfeits of humans, and, last but not least, it makes a massive spread of misinformation possible.
The Full Rights Dilemma for A.I. Systems of Debatable Personhood
Authors: Eric Schwitzgebel
Date: 2023-02-21 00:00:00

An Artificially Intelligent system (an AI) has debatable personhood if it's epistemically possible either that the AI is a person or that it falls far short of personhood. Debatable personhood is a likely outcome of AI development and might arise soon. Debatable AI personhood throws us into a catastrophic moral dilemma: Either treat the systems as moral persons and risk sacrificing real human interests for the sake of entities without interests worth the sacrifice, or don't treat the systems as moral persons and risk perpetrating grievous moral wrongs against them. The moral issues become even more perplexing if we consider cases of possibly conscious AI that are subhuman, superhuman, or highly divergent from us in their morally relevant properties.
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding
Authors: Bo Xie, Xiaohui Jia, Xiawen Song, Hua Zhang, Bi Chen, Bo Jiang, Ye Wang, Yun Pan
Date: 2023-08-01 00:00:00
Proceeding: journal 2023 8
Tasks: Intent Detection, Multiple-choice, Question Answering, Reading Comprehension, Semantic Parsing, slot-filling, Slot Filling, Spoken Language Understanding

Spoken language understanding (SLU) plays a crucial role in the performance of dialogue systems. It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances. At present, researchers focus mainly on English SLU tasks, while such investigations on Chinese utterances are not sufficient. In this paper, we first propose a reading comprehension based multi-source information fusion network, called ReCoMIF for Chinese SFID tasks by transforming the SLU task into a multi-turn question answering procedure comprising multiple choice for intent detection and span extraction for slot filling. Moreover, we present a TCM_CLS module with a concise architecture composed of TextCNN, MaxPooling, and feed forward network plus the [CLS] representation. Such three TCM_CLS modules are stacked in the proposed ReCoMIF network that can serve as sufficient integration of multi-source information originating from contexts, reading comprehension based queries, and hidden representations concerning intent and slot semantics. Finally, experimental results and ablation studies on three Chinese SLU datasets show that our proposed model can effectively fuse intent and slot information achieved by\ state-of-the-art performance compared with other baseline methods.
BOUNDARY REGULARIZED BUILDING FOOTPRINT EXTRACTION FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS
Authors: Kang Zhao, Muhammad Kamran, Gunho Sohn
Date: 2020-06-23 00:00:00
Proceeding: arXiv 2020 6
Tasks: Object, object-detection, Object Detection, Object Localization

In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to
support a wide range of civil, public and military applications. One of the key information obtained from satellite imagery is to produce
and update spatial maps of built environment due to its wide coverage with high resolution data. However, reconstructing spatial maps
from satellite imagery is not a trivial vision task as it requires reconstructing a scene or object with high-level representation such as
primitives. For the last decade, significant advancement in object detection and representation using visual data has been achieved, but
the primitive-based object representation still remains as a challenging vision task. Thus, a high-quality spatial map is mainly produced
through complex labour-intensive processes. In this paper, we propose a novel deep neural network, which enables to jointly detect
building instance and regularize noisy building boundary shapes from a single satellite imagery. The proposed deep learning method
consists of a two-stage object detection network to produce region of interest (RoI) features and a building boundary extraction network
using graph models to learn geometric information of the polygon shapes. Extensive experiments show that our model can accomplish
multi-tasks of object localization, recognition, semantic labelling and geometric shape extraction simultaneously. In terms of building
extraction accuracy, computation efficiency and boundary regularization performance, our model outperforms the state-of-the-art
baseline models.
Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence
Authors: Quan Thanh Huynh, Phuc Hoang Nguyen, Hieu Xuan Le, Lua Thi Ngo, Nhu-Thuy Trinh, Mai Thi-Thanh Tran, Hoan Tam Nguyen, Nga Thi Vu, Anh Tam Nguyen, Kazuma Suda, Kazuhiro Tsuji, Tsuyoshi Ishii, Trung Xuan Ngo, Hoan Thanh Ngo
Date: 2022-08-03 00:00:00
Proceeding: Diagnostics 2022 8
Tasks: Acne Severity Grading, Medical Image Classification, Medical Object Detection, Object, object-detection, Object Detection

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types, including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems to help acne patients better understand their conditions and support doctors in acne diagnosis.
Keywords: deep learning; smartphone image; acne grading; acne object detection
A simple neural model for unlabelled data
Authors: Ünver Çiftçi
Date: 2020-03-13 00:00:00
Proceeding: Preprint 2020 3
Tasks: model

Neural networks are powerful tools for modelling big unstructured data. Advanced architectures and learning methods are used in applications. Nevertheless simpler models which use less data could be helpful. We propose a very simple learning algorithm in order to model the data distribution. Experiments show how effective and data-efficient our method is. Code is available at https://github.com/unverciftci/toUniform.
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework
Authors: Matteo Ciotola, Giuseppe Scarpa
Date: 2023-01-05 00:00:00
Proceeding: MDPI Remote Sensing 2023 1
Tasks: Image Super-Resolution, Pansharpening, satellite image super-resolution, Super-Resolution

In the last few years, there has been a renewed interest in data fusion techniques, and, in particular, in pansharpening due to a paradigm shift from model-based to data-driven approaches, supported by the recent advances in deep learning. Although a plethora of convolutional neural networks (CNN) for pansharpening have been devised, some fundamental issues still wait for answers. Among these, cross-scale and cross-datasets generalization capabilities are probably the most urgent ones since most of the current networks are trained at a different scale (reduced-resolution), and, in general, they are well-fitted on some datasets but fail on others. A recent attempt to address both these issues leverages on a target-adaptive inference scheme operating with a suitable full-resolution loss. On the downside, such an approach pays an additional computational overhead due to the adaptation phase. In this work, we propose a variant of this method with an effective target-adaptation scheme that allows for the reduction in inference time by a factor of ten, on average, without accuracy loss. A wide set of experiments carried out on three different datasets, GeoEye-1, WorldView-2 and WorldView-3, prove the computational gain obtained while keeping top accuracy scores compared to state-of-the-art methods, both model-based and deep-learning ones. The generality of the proposed solution has also been validated, applying the new adaptation framework to different CNN models.
STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network
Authors: Ryan Wen Liu, Maohan Liang, Jiangtian Nie, Yanli Yuan, Zehui Xiong, Member, IEEE, Han Yu
Date: 2022-04-08 00:00:00
Proceeding: IEEE Transactions on Industrial Informatics 2022 4
Tasks: Edge-computing, Management, Prediction, Trajectory Prediction

—The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base stations, are of considerable importance for promoting traffic situation awareness and vessel traffic services, etc. To guarantee traffic safety and efficiency, it is essential to robustly and accurately predict the AIS-based vessel trajectories (i.e., the future positions of vessels) in maritime IoT. In this work, we propose a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm. Our STMGCN is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to closest point of approach, and the size of surrounding vessels. These three graphs are then jointly embedded into the prediction framework by introducing the spatio-temporal multigraph convolutional layer. To further enhance the prediction performance, the self-attention temporal convolutional layer is proposed to further optimize STMGCN with fewer parameters. Owing to the high interpretability and powerful learning ability, STMGCN is able to achieve superior prediction performance in terms of both accuracy and robustness. The reliable prediction results are potentially beneficial for traffic safety management and intelligent vehicle navigation in MEC-enabled maritime IoT.
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise
Authors: Julian Wyatt, Adam Leach, Sebastian M. Schmon, Chris G. Willcocks
Date: 2022-06-30 00:00:00
Proceeding: CVPR 2022 6
Tasks: Anomaly Detection, Denoising, Unsupervised Anomaly Detection

Generative models have been shown to provide a powerful mechanism for anomaly detection by learning to model healthy or normal reference data which can subsequently be used as a baseline for scoring anomalies. In this work we consider denoising diffusion probabilistic models (DDPMs) for unsupervised anomaly detection. DDPMs have superior mode coverage over generative adversarial networks (GANs) and higher sample quality than variational autoencoders (VAEs). However, this comes at the expense of poor scalability and increased sampling times due to the long Markov chain sequences required. We observe that within reconstruction-based anomaly detection a full-length Markov chain diffusion is not required. This leads us to develop a novel partial diffusion anomaly detection strategy that scales to high-resolution imagery, named AnoDDPM. A secondary problem is that Gaussian diffusion fails to capture larger anomalies; therefore we develop a multi-scale simplex noise diffusion process that gives control over the target anomaly size. AnoDDPM with simplex noise is shown to significantly outperform both f-AnoGAN and Gaussian diffusion for the tumorous dataset of 22 T1-weighted MRI scans (CCBS Edinburgh) qualitatively and quantitatively (improvement of +25.5% Sorensen-Dice coefficient, +17.6% IoU and +7.4% AUC).
Deep Anatomical Context Feature Learning for Cephalometric Landmark Detection
Authors: Kanghan Oh, Il-Seok Oh, Van Nhat Thang Le, and Dae-Woo Lee
Date: 2020-06-05 00:00:00
Proceeding: IEEE Journal of Biomedical and Health Informatics 2020 6

In the past decade, anatomical context features have been widely used for cephalometric landmark detection and significant progress is still being made. However, most existing methods rely on handcrafted graphical models rather than incorporating anatomical context during training, leading to suboptimal performance. In this study, we present a novel framework that allows a Convolutional Neural Network (CNN) to learn richer anatomical context features during training. Our key idea consists of the Local Feature Perturbator (LFP) and the Anatomical Configuration loss (AC loss). When training the CNN, the LFP perturbs a cephalometric image based on prior anatomical distribution, forcing the CNN to gaze relevant features more globally. Then AC loss helps the CNN to learn the anatomical context based on spatial relationships between the landmarks. The experimental results demonstrate that the proposed framework makes the CNN learn richer anatomical representation, leading to increased performance. In the performance comparisons, the proposed scheme outperforms state-of-the-art methods on the ISBI 2015 Cephalometric X-ray Image Analysis Challenge.
Path Planning via an Improved DQN-Based Learning Policy
Authors: LIANGHENG LV, SUNJIE ZHANG, DERUI DING, AND YONGXIONG WANG
Date: 2019-05-24 00:00:00
Proceeding: IEEE Access 2019 5

The path planning technology is an important part of navigation, which is the core of robotics
research. Reinforcement learning is a fashionable algorithm that learns from experience by mimicking the
process of human learning skills. When learning new skills, the comprehensive and diverse experience help
to refine the grasp of new skills which are called as the depth and the breadth of experience. According to
the path planning, this paper proposes an improved learning policy based on the different demand of the
experience’s depth and breadth in different learning stages, where the deep Q-networks calculated Q-value
adopts the dense network framework. In the initial stage of learning, an experience value evaluation network
is created to increase the proportion of deep experience to understand the environmental rules more quickly.
When the path wandering phenomenon happens, the exploration of wandering point and other points are
taken into account to improve the breadth of the experience pool by using parallel exploration structure.
In addition, the network structure is improved by referring to the dense connection method, so the learning
and expressive abilities of the network are improved to some extent. Finally, the experimental results show
that our model has a certain improvement in convergence speed, planning success rate, and path accuracy.
Under the same experimental conditions, the method of this paper is compared with the conventional
intensive learning method via deep Q-networks. The results show that the indicators of this method are
significantly higher.