pwc-archive/papers-with-abstracts huggingface dataset:
Visuomotor feedback tuning in the absence of visual error information
Authors: Sae Franklin, David W. Franklin
Date: 2023-06-01 00:00:00

Large increases in visuomotor feedback gains occur during initial adaptation to novel dynamics, which we propose are due to increased internal model uncertainty. That is, large errors indicate increased uncertainty in our prediction of the environment, increasing feedback gains and co-contraction as a coping mechanism. Our previous work showed distinct patterns of visuomotor feedback gains during abrupt or gradual adaptation to a force field, suggesting two complementary processes: reactive feedback gains increasing with internal model uncertainty and the gradual learning of predictive feedback gains tuned to the environment. Here we further investigate what drives these changes visuomotor feedback gains in learning, by separating the effects of internal model uncertainty from visual error signal through removal of visual error information. Removing visual error information suppresses the visuomotor feedback gains in all conditions, but the pattern of modulation throughout adaptation is unaffected. Moreover, we find increased muscle co-contraction in both abrupt and gradual adaptation protocols, demonstrating that visuomotor feedback responses are independent from the level of co-contraction. Our result suggests that visual feedback benefits motor adaptation tasks through higher visuomotor feedback gains, but when it is not available participants adapt at a similar rate through increased co-contraction. We have demonstrated a direct connection between learning and predictive visuomotor feedback gains, independent from visual error signals. This further supports our hypothesis that internal model uncertainty drives initial increases in feedback gains.
Quantitative Methods for Optimizing Patient Outcomes in Liver Transplantation
Authors: Raja Al-Bahou, Julia Bruner, Helen Moore, Ali Zarrinpar
Date: 2023-05-31 00:00:00
Tasks: Management, Math

Liver transplantation continues to be the gold standard for treating patients with end-stage liver diseases. However, despite the huge success of liver transplantation in improving patient outcomes, long term graft survival continues to be a major problem. The current clinical practice in the management of liver transplant patients is centered around immunosuppressive multidrug regimens. Current research has been focusing on phenotypic personalized medicine as a novel approach in the optimization of immunosuppression, a regressional math modeling focusing on individual patient dose and response using specific markers like transaminases. A prospective area of study includes the development of a mechanistic computational math modeling for optimizing immunosuppression to improve patient outcomes and increase long-term graft survival by exploring the intricate immune/drug interactions to help us further our understanding and management of medical problems like transplants, autoimmunity, and cancer therapy. Thus, by increasing long-term graft survival, the need for redo transplants will decrease, which will free up organs and potentially help with the organ shortage problem promoting equity and equal opportunity for transplants, as well as decreasing the medical costs associated with additional testing and hospital admissions. Although long-term graft survival remains challenging, computational and quantitative methods have led to significant improvements. In this article, we review recent advances and remaining opportunities. We focus on the following topics: donor organ availability and allocation with a focus on equity, monitoring of patient and graft health, and optimization of immunosuppression dosing.
Digital contact tracing/notification for SARS-CoV-2: navigating six points of failure
Authors: Joanna Masel, James Petrie, Jason Bay, Wolfgang Ebbers, Aalekh Sharan, Scott Leibrand, Andreas Gebhard, Samuel Zimmerman
Date: 2023-06-01 00:00:00

Digital contact tracing/notification was initially hailed as a promising strategy to combat SARS-CoV-2, but in most jurisdictions it did not live up to its promise. To avert a given transmission event, both parties must have adopted the tech, it must detect the contact, the primary case must be promptly diagnosed, notifications must be triggered, and the secondary case must change their behavior to avoid the focal tertiary transmission event. If we approximate these as independent events, achieving a 26% reduction in R(t) would require 80% success rates at each of these six points of failure. Here we review the six failure rates experienced by a variety of digital contact tracing/notification schemes, including Singapore's TraceTogether, India's Aarogya Setu, and leading implementations of the Google Apple Exposure Notification system. This leads to a number of recommendations, e.g. that tracing/notification apps be multi-functional and integrated with testing, manual contact tracing, and the gathering of critical scientific data, and that the narrative be framed in terms of user autonomy rather than user privacy.
A Data-Driven Computational Model for Engineered Cardiac Microtissues
Authors: Javiera Jilberto, Samuel J. DePalma, Jason Lo, Hiba Kobeissi, Lani Quach, Emma Lejeune, Brendon M. Baker, David Nordsletten
Date: 2023-05-31 00:00:00

Engineered heart tissues (EHTs) present a potential solution to some of the current challenges in the treatment of heart disease; however, the development of mature, adult-like cardiac tissues remains elusive. Mechanical stimuli have been observed to improve whole-tissue function and cardiomyocyte (CM) maturation, although our ability to fully utilize these mechanisms is hampered, in part, by our incomplete understanding of the mechanobiology of EHTs. In this work, we leverage the experimental data produced by a mechanically tunable experimental setup to generate tissue-specific computational models of EHTs. Using imaging and functional data, our modeling pipeline generates models with tissue-specific ECM and myofibril structure, allowing us to estimate CM active stress. We use this experimental and modeling pipeline to study different mechanical environments, where we contrast the force output of the tissue with the computed active stress of CMs. We show that the significant differences in measured experimental forces can largely be explained by the levels of myofibril formation achieved by the CMs in the distinct mechanical environments, with active stress showing more muted variations across conditions. The presented model also enables us to dissect the relative contributions of myofibrils and extracellular matrix to tissue force output, a task difficult to address experimentally. These results highlight the importance of tissue-specific modeling to augment EHT experiments, providing deeper insights into the mechanobiology driving EHT function.
Optimal execution and speculation with trade signals
Authors: Peter Bank, Álvaro Cartea, Laura Körber
Date: 2023-06-01 00:00:00

We propose a price impact model where changes in prices are purely driven by the order flow in the market. The stochastic price impact of market orders and the arrival rates of limit and market orders are functions of the market liquidity process which reflects the balance of the demand and supply of liquidity. Limit and market orders mutually excite each other so that liquidity is mean reverting. We use the theory of Meyer-$\sigma$-fields to introduce a short-term signal process from which a trader learns about imminent changes in order flow. Her trades impact the market through the same mechanism as other orders. With a novel version of Marcus-type SDEs we efficiently describe the intricate timing of market dynamics at moments when her orders concur with that of others. In this setting, we examine an optimal execution problem and derive the Hamilton--Jacobi--Bellman (HJB) equation for the value function of the trader. The HJB equation is solved numerically and we illustrate how the trader uses the signals to enhance the performance of execution problems and to execute speculative strategies.
The Cost of Misspecifying Price Impact
Authors: Natascha Hey, Jean-Philippe Bouchaud, Iacopo Mastromatteo, Johannes Muhle-Karbe, Kevin Webster
Date: 2023-06-01 00:00:00

Portfolio managers' orders trade off return and trading cost predictions. Return predictions rely on alpha models, whereas price impact models quantify trading costs. This paper studies what happens when trades are based on an incorrect price impact model, so that the portfolio either over- or under-trades its alpha signal. We derive tractable formulas for these misspecification costs and illustrate them on proprietary trading data. The misspecification costs are naturally asymmetric: underestimating impact concavity or impact decay shrinks profits, but overestimating concavity or impact decay can even turn profits into losses.
A Two-Stage Decoder for Efficient ICD Coding
Authors: Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap, Stefan Winkler
Date: 2023-05-27 00:00:00
Tasks: Classification, Decoder, Multilabel Text Classification, text-classification, Text Classification

Clinical notes in healthcare facilities are tagged with the International Classification of Diseases (ICD) code; a list of classification codes for medical diagnoses and procedures. ICD coding is a challenging multilabel text classification problem due to noisy clinical document inputs and long-tailed label distribution. Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. However, most of them do not reflect how human coders generate the code: first, the coders select general code categories and then look for specific subcategories that are relevant to a patient's condition. Inspired by this, we propose a two-stage decoding mechanism to predict ICD codes. Our model uses the hierarchical properties of the codes to split the prediction into two steps: At first, we predict the parent code and then predict the child code based on the previous prediction. Experiments on the public MIMIC-III data set show that our model performs well in single-model settings without external data or knowledge.
Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender
Authors: Shuai Yang, Lixin Zhang, Feng Xia, Leyu Lin
Date: 2023-05-29 00:00:00
Tasks: Diversity, Recommendation Systems, Reranking, Retrieval

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level diversity are both important for industrial recommender systems. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity. Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is deployed to increase individual-level diverisity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate attributes. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diveristy. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Offline simulations and online A/B tests show our solution can effectively improve both user engagement and system revenue.
DeepVAT: A Self-Supervised Technique for Cluster Assessment in Image Datasets
Authors: Alokendu Mazumder, Tirthajit Baruah, Akash Kumar Singh, Pagadla Krishna Murthy, Vishwajeet Pattanaik, Punit Rathore
Date: 2023-05-29 00:00:00
Tasks: Clustering, Deep Clustering

Estimating the number of clusters and cluster structures in unlabeled, complex, and high-dimensional datasets (like images) is challenging for traditional clustering algorithms. In recent years, a matrix reordering-based algorithm called Visual Assessment of Tendency (VAT), and its variants have attracted many researchers from various domains to estimate the number of clusters and inherent cluster structure present in the data. However, these algorithms face significant challenges when dealing with image data as they fail to effectively capture the crucial features inherent in images. To overcome these limitations, we propose a deep-learning-based framework that enables the assessment of cluster structure in complex image datasets. Our approach utilizes a self-supervised deep neural network to generate representative embeddings for the data. These embeddings are then reduced to 2-dimension using t-distributed Stochastic Neighbour Embedding (t-SNE) and inputted into VAT based algorithms to estimate the underlying cluster structure. Importantly, our framework does not rely on any prior knowledge of the number of clusters. Our proposed approach demonstrates superior performance compared to state-of-the-art VAT family algorithms and two other deep clustering algorithms on four benchmark image datasets, namely MNIST, FMNIST, CIFAR-10, and INTEL.
ROSARL: Reward-Only Safe Reinforcement Learning
Authors: Geraud Nangue Tasse, Tamlin Love, Mark Nemecek, Steven James, Benjamin Rosman
Date: 2023-05-31 00:00:00
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Safe Reinforcement Learning

An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common solution is for a human expert to define either a penalty in the reward function or a cost to be minimised when reaching unsafe states. However, this is non-trivial, since too small a penalty may lead to agents that reach unsafe states, while too large a penalty increases the time to convergence. Additionally, the difficulty in designing reward or cost functions can increase with the complexity of the problem. Hence, for a given environment with a given set of unsafe states, we are interested in finding the upper bound of rewards at unsafe states whose optimal policies minimise the probability of reaching those unsafe states, irrespective of task rewards. We refer to this exact upper bound as the "Minmax penalty", and show that it can be obtained by taking into account both the controllability and diameter of an environment. We provide a simple practical model-free algorithm for an agent to learn this Minmax penalty while learning the task policy, and demonstrate that using it leads to agents that learn safe policies in high-dimensional continuous control environments.
Learning Constrained Structured Spaces with Application to Multi-Graph Matching
Authors: Hedda Cohen Indelman, Tamir Hazan
Date: 2022-05-03 00:00:00
Proceeding: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022 5
Tasks: Graph Matching, Structured Prediction

Multi-graph matching is a prominent structured prediction task, in which the predicted label is constrained to the space of cycle-consistent matchings. While direct loss minimization is an effective method for learning predictors over structured label spaces, it cannot be applied efficiently to the problem at hand, since executing a specialized solver across sets of matching predictions is computationally prohibitive. Moreover,
there’s no supervision on the ground-truth matchings over cycle-consistent prediction sets.
Our key insight is to strictly enforce the matching constraints in pairwise matching predictions and softly enforce the cycle-consistency constraints
by casting them as weighted loss terms, such that the severity of inconsistency with global predictions is tuned by a penalty parameter.
Inspired by the classic penalty method, we prove that our method theoretically recovers the optimal multi-graph matching constrained solution.
Our method's advantages are brought to light in experimental results on the popular keypoint matching task on the Pascal VOC and the Willow ObjectClass datasets.
CT2: Colorization Transformer via Color Tokens
Authors: Shuchen Weng, Jimeng Sun, Yu Li, Si Li, Boxin Shi
Date: 2022-10-23 00:00:00
Proceeding: ECCV 2022 10
Tasks: Colorization, Image Colorization

Automatic image colorization is an ill-posed problem with multi-modal uncertainty, and there remains two main challenges with previous methods: incorrect semantic colors and under-saturation. In this paper, we propose an end-to-end transformer-based model to overcome these challenges. Benefited from the long-range context extraction of transformer and our holistic architecture, our method could colorize images with more diverse colors. Besides, we introduce color tokens into our approach and treat the colorization task as a classification problem, which increases the saturation of results. We also propose a series of modules to make image features interact with color tokens, and restrict the range of possible color candidates, which makes our results visually pleasing and reasonable. In addition, our method does not require any additional external priors, which ensures its well generalization capability. Extensive experiments and user studies demonstrate that our method achieves superior performance than previous works.
Disentangled Image Colorization via Global Anchors
Authors: Menghan Xia, WenBo Hu, Tien-Tsin Wong, Jue Wang
Date: 2022-11-30 00:00:00
Proceeding: SIGGRAPH 2022 11
Tasks: Colorization, Image Colorization

Colorization is multimodal by nature and challenges existing frameworks to achieve colorful and structurally consistent results. Even the sophisticated autoregressive model struggles to maintain long-distance color consistency due to the fragility of sequential dependence. To overcome this challenge, we propose a novel colorization framework that disentangles color multimodality and structure consistency through global color anchors, so that both aspects could be learned effectively. Our key insight is that several carefully located anchors could approximately represent the color distribution of an image, and conditioned on the anchor colors, we can predict the image color in a deterministic manner by utilizing internal correlation. To this end, we construct a colorization model with dual branches, where the color modeler predicts the color distribution for anchor color representation, and the color generator predicts the pixel colors by referring the sampled anchor colors. Importantly, the anchors are located under two principles: color independence and global coverage, which is realized with clustering analysis on the deep color features. To simplify the computation, we creatively adopt soft superpixel segmentation to reduce the image primitives, which still nicely reserves the reversibility to pixel-wise representation. Extensive experiments show that our method achieves notable superiority over various mainstream frameworks in perceptual quality. Thanks to anchor-based color representation, our model has the flexibility to support diverse and controllable colorization as well.
Performance Analysis of Two Hop Amplify-and-Forward Systems with Interference at the Relay
Authors: Himal A. Suraweera, Member, HariK.Garg, and A. Nallanathan, Senior Member, IEEE
Date: 2010-08-01 00:00:00
Proceeding: journal 2010 8

Abstract—We analyze the performance of a two hop channel state information (CSI)-assisted amplify-and-forward system, with co-channel interference at the relay. The system’s outage probability and the average bit error rate (BER) in the presence of Rayleigh faded multiple interferers are investigated. We derive an exact expression for the outage probability and an accurate bound for the system’s average BER. Simulation results show the validity of the analysis and point out the effect of interference.
Chest X-Ray Images Classification with CNN
Authors: Ahmet Ekiz, Kaplan KAPLAN, Erdal Ayvaz
Date: 2022-12-31 00:00:00
Proceeding: International Marmara Sciences Congress 2022 12
Tasks: Classification, Diagnostic, Image Classification, Medical Image Classification, Transfer Learning

Chest radiography or called Chest X-ray(CXR) is common and one of the most cost-effective diagnostic
procedures done in medical facilities. Interpretation of the chest X-ray is important in the diagnosis and
detection of diseases such as pneumonia, pneumothorax, interstitial lung disease, heart failure, bone
fracture, tuberculosis, pneumoconiosis, COVID-19, and even early lung cancer, as it contains a lot of
information about the patient's medical condition. In practice, radiologists or consultants review these
images and diagnose diseases, but this process can lead to misdiagnoses due to human error and
expertise. Also, interpretation takes time. Assistive systems can be developed to reduce the impact of
events that challenge health systems, such as the Covid-19 pandemic, to alleviate the workload and to
reduce human error. For this purpose, chest X-ray images of a total of 336, Covid-19, Infiltrative and
healthy patients collected from Derince Education and Research Hospital were collected. In this study,
experiments were conducted to classify these images with Convolutional Neural Networks. Our CNN
network with Residual Blocks and CNN with ResNet backbone by transfer learning were compared. As
a result, the CNN model achieved 86.76% accuracy on test data, and (K=5) K-fold cross-validation
84.84% accuracy. The CNN with ResNet backbone by transfer learning achieved 89.71% accuracy on
test data, and (K=5) K-fold cross-validation 84.23% accuracy.