pwc-archive/papers-with-abstracts huggingface dataset:
Thirty years of molecular dynamics simulations on posttranslational modifications of proteins
Authors: Austin T. Weigle, Jiangyan Feng, Diwakar Shukla
Date: 2022-06-24 00:00:00

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.
The Promise of Cross-Species Coexpression Analysis in Studying the Coevolution and Ecology of Host-Symbiont Interactions
Authors: Amanda K Hund, Peter Tiffin, Jean-Gabriel Young, Daniel I Bolnick
Date: 2022-06-25 00:00:00
Tasks: Diversity

Measuring gene expression simultaneously in both hosts and symbionts offers a powerful approach to explore the biology underlying species interactions. Such dual or simultaneous RNAseq approaches have primarily been used to gain insight into gene function in model systems, but there is opportunity to expand and apply these tools in new ways to understand ecological and evolutionary questions. By incorporating genetic diversity in both hosts and symbionts and studying how gene expression is correlated between partner species, we can gain new insight into host-symbiont coevolution and the ecology of species interactions. In this perspective, we explore how these relatively new tools could be applied to study such questions. We review the mechanisms that could be generating patterns of cross-species gene coexpression, including indirect genetic effects and selective filters, how these tools could be applied across different biological and temporal scales, and outline other methodological considerations and experiment possibilities.
Algebraic study of receptor-ligand systems: a dose-response analysis
Authors: Léa Sta, Michael Adamer, Carmen Molina-París
Date: 2022-06-27 00:00:00

The study of a receptor-ligand system generally relies on the analysis of its dose-response (or concentration-effect) curve, which quantifies the relation between ligand concentration and the biological effect (or cellular response) induced when binding its specific cell surface receptor. Mathematical models of receptor-ligand systems have been developed to compute a dose-response curve under the assumption that the biological effect is proportional to the number of ligand-bound receptors. Given a dose-response curve, two quantities (or metrics) have been defined to characterise the properties of the ligand-receptor system under consideration: amplitude and potency (or half-maximal effective concentration, and denoted by EC$_{50}$). Both the amplitude and the EC$_{50}$ are key quantities commonly used in pharmaco-dynamic modelling, yet a comprehensive mathematical investigation of the behaviour of these two metrics is still outstanding; for a large (and important) family of receptors, called cytokine receptors, we still do not know how amplitude and EC$_{50}$ depend on receptor copy numbers. Here we make use of algebraic approaches (Gr\"obner basis) to study these metrics for a large class of receptor-ligand models, with a focus on cytokine receptors. In particular, we introduce a method, making use of two motivating examples based on the interleukin-7 (IL-7) receptor, to compute analytic expressions for the amplitude and the EC$_{50}$. We then extend the method to a wider class of receptor-ligand systems, sequential receptor-ligand systems with extrinsic kinase, and provide some examples.
Personalized rTMS for Depression: A Review
Authors: Juha Gogulski1 Jessica Ross, Austin Talbot, Christopher Cline, Francesco L Donati, Saachi Munot, Naryeong Kim, Ciara Gibbs, Nikita Bastin, Jessica Yang, Chris Minasi, Manjima Sarkar, Jade Truong, Corey J Keller
Date: 2022-06-27 00:00:00
Tasks: Anatomy

Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, where focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) are used to modulate brain circuits and alleviate clinical symptoms. rTMS is well-tolerated and clinically effective for treatment-resistant depression (TRD) and other neuropsychiatric disorders. However, despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (~50%). In this review, we examine components of rTMS that can be optimized to account for inter-individual variability in neural function and anatomy. We discuss current treatment options for TRD, the neural mechanisms thought to underlie treatment, differences in FDA-cleared devices, targeting strategies, stimulation parameter selection, and adaptive closed-loop rTMS to improve treatment outcomes. We suggest that better understanding of the wide and modifiable parameter space of rTMS will greatly improve clinical outcome.
The Temporal Evolution of Modality-Independent Representations of Conceptual Categories
Authors: Julien Dirani, Liina Pylkkänen
Date: 2022-06-24 00:00:00

To what extent does language production activate amodal conceptual representations? In picture naming, we view specific exemplars of concepts and then name them with a category label, like 'dog'. In contrast, in overt reading, the written word expresses the category (dog), not an exemplar. Here we used a decoding approach with magnetoencephalography to address whether picture naming and overt word reading involve shared representations of semantic categories. This addresses a fundamental question about the modality-generality of conceptual representations and their temporal evolution. Crucially, we do this using a language production task that does not require explicit categorization judgment and that controls for word form properties across semantic categories. We trained our models to classify the animal/tool distinction using MEG data of one modality at each time point and then tested the generalization of those models on the other modality. We obtained evidence for the automatic activation of modality-independent semantic category representations for both pictures and words starting at ~150ms and lasting until about 500ms. The time course of lexical activation was also assessed revealing that semantic category is represented before lexical access for pictures but after lexical access for words. Notably, this earlier activation of semantic category in pictures occurred simultaneously with visual representations. We thus show evidence for the spontaneous activation of modality-independent semantic categories in picture naming and word reading, supporting theories in which amodal conceptual representations exist. Together, these results serve to anchor a more comprehensive spatio-temporal delineation of the semantic feature space during production planning.
A Phylogenetic Model of the Evolution of Discrete Matrices for the Joint Inference of Lexical and Phonological Language Histories
Authors: Grégoire Clarté, Robin J. Ryder
Date: 2022-06-24 00:00:00

We propose a model of the evolution of a matrix along a phylogenetic tree, in which transformations affect either entire rows or columns of the matrix. This represents the change of both lexical and phonological aspects of linguistic data, by allowing for new words to appear and for systematic phonological changes to affect the entire vocabulary. We implement a Sequential Monte Carlo method to sample from the posterior distribution, and infer jointly the phylogeny, model parameters, and latent variables representing cognate births and phonological transformations. We successfully apply this method to synthetic and real data of moderate size.
Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk
Authors: Anand Deo, Karthyek Murthy, Tirtho Sarker
Date: 2022-06-26 00:00:00
Tasks: Computational Efficiency

This paper investigates the use of retrospective approximation solution paradigm in solving risk-averse optimization problems effectively via importance sampling (IS). While IS serves as a prominent means for tackling the large sample requirements in estimating tail risk measures such as Conditional Value at Risk (CVaR), its use in optimization problems driven by CVaR is complicated by the need to tailor the IS change of measure differently to different optimization iterates and the circularity which arises as a consequence. The proposed algorithm overcomes these challenges by employing a univariate IS transformation offering uniform variance reduction in a retrospective approximation procedure well-suited for tuning the IS parameter choice. The resulting simulation based approximation scheme enjoys both the computational efficiency bestowed by retrospective approximation and logarithmically efficient variance reduction offered by importance sampling
Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety
Authors: Amit Sheth, Manas Gaur, Kaushik Roy, Revathy Venkataraman, Vedant Khandelwal
Date: 2022-06-09 00:00:00
Tasks: Food recommendation, Management

AI systems have been widely adopted across various domains in the real world. However, in high-value, sensitive, or safety-critical applications such as self-management for personalized health or food recommendation with a specific purpose (e.g., allergy-aware recipe recommendations), their adoption is unlikely. Firstly, the AI system needs to follow guidelines or well-defined processes set by experts; the data alone will not be adequate. For example, to diagnose the severity of depression, mental healthcare providers use Patient Health Questionnaire (PHQ-9). So if an AI system were to be used for diagnosis, the medical guideline implied by the PHQ-9 needs to be used. Likewise, a nutritionist's knowledge and steps would need to be used for an AI system that guides a diabetic patient in developing a food plan. Second, the BlackBox nature typical of many current AI systems will not work; the user of an AI system will need to be able to give user-understandable explanations, explanations constructed using concepts that humans can understand and are familiar with. This is the key to eliciting confidence and trust in the AI system. For such applications, in addition to data and domain knowledge, the AI systems need to have access to and use the Process Knowledge, an ordered set of steps that the AI system needs to use or adhere to.
Constrained Mass Optimal Transport
Authors: Said Kerrache, Yasushi Nakauchi
Date: 2022-06-05 00:00:00
Tasks: Content-Based Image Retrieval, Geophysics, Image Registration, Image Retrieval, Retrieval

Optimal mass transport, also known as the earth mover's problem, is an optimization problem with important applications in various disciplines, including economics, probability theory, fluid dynamics, cosmology and geophysics to cite a few. Optimal transport has also found successful applications in image registration, content-based image retrieval, and more generally in pattern recognition and machine learning as a way to measure dissimilarity among data. This paper introduces the problem of constrained optimal transport. The time-dependent formulation, more precisely, the fluid dynamics approach is used as a starting point from which the constrained problem is defined by imposing a soft constraint on the density and momentum fields or restricting them to a subset of curves that satisfy some prescribed conditions. A family of algorithms is introduced to solve a class of constrained saddle point problems, which has convexly constrained optimal transport on closed convex subsets of the Euclidean space as a special case. Convergence proofs and numerical results are presented.
Transformer with Tree-order Encoding for Neural Program Generation
Authors: Klaudia-Doris Thellmann, Bernhard Stadler, Ricardo Usbeck, Jens Lehmann
Date: 2022-05-30 00:00:00
Tasks: Code Generation, Decoder, Semantic Parsing

While a considerable amount of semantic parsing approaches have employed RNN architectures for code generation tasks, there have been only few attempts to investigate the applicability of Transformers for this task. Including hierarchical information of the underlying programming language syntax has proven to be effective for code generation. Since the positional encoding of the Transformer can only represent positions in a flat sequence, we have extended the encoding scheme to allow the attention mechanism to also attend over hierarchical positions in the input. Furthermore, we have realized a decoder based on a restrictive grammar graph model to improve the generation accuracy and ensure the well-formedness of the generated code. While we did not surpass the state of the art, our findings suggest that employing a tree-based positional encoding in combination with a shared natural-language subword vocabulary improves generation performance over sequential positional encodings.
UI Layers Merger: Merging UI layers via Visual Learning and Boundary Prior
Authors: Yun-nong Chen, Yan-kun Zhen, Chu-ning Shi, Jia-zhi Li, Liu-qing Chen, Ze-jian Li, Ling-yun Sun, Ting-ting Zhou, Yan-fang Chang
Date: 2022-06-18 00:00:00
Tasks: Code Generation, Data Augmentation

With the fast-growing GUI development workload in the Internet industry, some work on intelligent methods attempted to generate maintainable front-end code from UI screenshots. It can be more suitable for utilizing UI design drafts that contain UI metadata. However, fragmented layers inevitably appear in the UI design drafts which greatly reduces the quality of code generation. None of the existing GUI automated techniques detects and merges the fragmented layers to improve the accessibility of generated code. In this paper, we propose UI Layers Merger (UILM), a vision-based method, which can automatically detect and merge fragmented layers into UI components. Our UILM contains Merging Area Detector (MAD) and a layers merging algorithm. MAD incorporates the boundary prior knowledge to accurately detect the boundaries of UI components. Then, the layers merging algorithm can search out the associated layers within the components' boundaries and merge them into a whole part. We present a dynamic data augmentation approach to boost the performance of MAD. We also construct a large-scale UI dataset for training the MAD and testing the performance of UILM. The experiment shows that the proposed method outperforms the best baseline regarding merging area detection and achieves a decent accuracy regarding layers merging.
Deep reinforced active learning for multi-class image classification
Authors: Emma Slade, Kim M. Branson
Date: 2022-06-20 00:00:00
Tasks: Active Learning, Classification, Deep Reinforcement Learning, image-classification, Image Classification, Medical Image Classification, Multi-class Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

High accuracy medical image classification can be limited by the costs of acquiring more data as well as the time and expertise needed to label existing images. In this paper, we apply active learning to medical image classification, a method which aims to maximise model performance on a minimal subset from a larger pool of data. We present a new active learning framework, based on deep reinforcement learning, to learn an active learning query strategy to label images based on predictions from a convolutional neural network. Our framework modifies the deep-Q network formulation, allowing us to pick data based additionally on geometric arguments in the latent space of the classifier, allowing for high accuracy multi-class classification in a batch-based active learning setting, enabling the agent to label datapoints that are both diverse and about which it is most uncertain. We apply our framework to two medical imaging datasets and compare with standard query strategies as well as the most recent reinforcement learning based active learning approach for image classification.
Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network
Authors: Lam Pham, Khoa Tran, Dat Ngo, Jasmin Lampert, Alexander Schindler
Date: 2022-06-20 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, object-detection, Object Detection, Remote Sensing Image Classification, Scene Classification, Transfer Learning

The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has taken the important role in a wide range of applications such as urban planning, natural hazards detection, environment monitoring,vegetation mapping, or geospatial object detection. During the past years, research community focusing on RSISC task has shown significant effort to publish diverse datasets as well as propose different approaches to deal with the RSISC challenges. Recently, almost proposed RSISC systems base on deep learning models which prove powerful and outperform traditional approaches using image processing and machine learning. In this paper, we also leverage the power of deep learning technology, evaluate a variety of deep neural network architectures, indicate main factors affecting the performance of a RSISC system. Given the comprehensive analysis, we propose a deep learning based framework for RSISC, which makes use of the transfer learning technique and multihead attention scheme. The proposed deep learning framework is evaluated on the benchmark NWPU-RESISC45 dataset and achieves the best classification accuracy of 94.7% which shows competitive to the state-of-the-art systems and potential for real-life applications.
Uncovering variability in human driving behavior through automatic extraction of similar traffic scenes from large naturalistic datasets
Authors: Olger Siebinga, Arkady Zgonnikov, David Abbink
Date: 2022-06-17 00:00:00

Recently, multiple naturalistic traffic datasets of human-driven trajectories have been published (e.g., highD, NGSim, and pNEUMA). These datasets have been used in studies that investigate variability in human driving behavior, for example for scenario-based validation of autonomous vehicle (AV) behavior, modeling driver behavior, or validating driver models. Thus far, these studies focused on the variability on an operational level (e.g., velocity profiles during a lane change), not on a tactical level (i.e., to change lanes or not). Investigating the variability on both levels is necessary to develop driver models and AVs that include multiple tactical behaviors. To expose multi-level variability, the human responses to the same traffic scene could be investigated. However, no method exists to automatically extract similar scenes from datasets. Here, we present a four-step extraction method that uses the Hausdorff distance, a mathematical distance metric for sets. We performed a case study on the highD dataset that showed that the method is practically applicable. The human responses to the selected scenes exposed the variability on both the tactical and operational levels. With this new method, the variability in operational and tactical human behavior can be investigated, without the need for costly and time-consuming driving-simulator experiments.
Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames
Authors: Evlampios Apostolidis, Georgios Balaouras, Vasileios Mezaris, Ioannis Patras
Date: 2022-06-29 00:00:00
Proceeding: ACM ICMR 2022 6
Tasks: Benchmarking, Diversity, Unsupervised Video Summarization, Video Summarization

In this work, we describe a new method for unsupervised video summarization. To overcome limitations of existing unsupervised video summarization approaches, that relate to the unstable training of Generator-Discriminator architectures, the use of RNNs for modeling long-range frames' dependencies and the ability to parallelize the training process of RNN-based network architectures, the developed method relies solely on the use of a self-attention mechanism to estimate the importance of video frames. Instead of simply modeling the frames' dependencies based on global attention, our method integrates a concentrated attention mechanism that is able to focus on non-overlapping blocks in the main diagonal of the attention matrix, and to enrich the existing information by extracting and exploiting knowledge about the uniqueness and diversity of the associated frames of the video. In this way, our method makes better estimates about the significance of different parts of the video, and drastically reduces the number of learnable parameters. Experimental evaluations using two benchmarking datasets (SumMe and TVSum) show the competitiveness of the proposed method against other state-of-the-art unsupervised summarization approaches, and demonstrate its ability to produce video summaries that are very close to the human preferences. An ablation study that focuses on the introduced components, namely the use of concentrated attention in combination with attention-based estimates about the frames' uniqueness and diversity, shows their relative contributions to the overall summarization performance.
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
Authors: Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg, Ulugbek S. Kamilov
Date: 2021-02-09 00:00:00
Tasks: Image Reconstruction

We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements. Unlike traditional DL methods that learn a mapping from the measurements to the desired image, CoIL trains a multilayer perceptron (MLP) to encode the complete measurement field by mapping the coordinates of the measurements to their responses. CoIL is a self-supervised method that requires no training examples besides the measurements of the test object itself. Once the MLP is trained, CoIL generates new measurements that can be used within a majority of image reconstruction methods. We validate CoIL on sparse-view computed tomography using several widely-used reconstruction methods, including purely model-based methods and those based on DL. Our results demonstrate the ability of CoIL to consistently improve the performance of all the considered methods by providing high-fidelity measurement fields.