Miracle-dz/UniFAR-FTUs huggingface dataset:
Predict and Interpret Health Risk using EHR through Typical Patients

Predicting health risks from electronic health records (EHR) is a topic of recent interest. Deep learning models have achieved success by modeling temporal and feature interaction. However, these methods learn insufficient representations and lead to poor performance when it comes to patients with few visits or sparse records. Inspired by the fact that doctors may compare the patient with typical patients and make decisions from similar cases, we propose a Progressive Prototypical Network (PPN) to select typical patients as prototypes and utilize their information to enhance the representation of the given patient. In particular, a progressive prototype memory and two prototype separation losses are proposed to update prototypes. Besides, a novel integration is introduced for better fusing information from patients and prototypes. Experiments on three real-world datasets demonstrate that our model brings improvement on all metrics. To make our results better understood by physicians, we developed an application at http://ppn.ai-care.top. Our code is released at https://github.com/yzhHoward/PPN.
Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering

We present Paint-it, a text-driven high-fidelity texture map synthesis method for 3D meshes via neural re-parameterized texture optimization. Paint-it synthesizes texture maps from a text description by synthesis-through-optimization, exploiting the Score-Distillation Sampling (SDS). We observe that directly applying SDS yields undesirable texture quality due to its noisy gradients. We reveal the importance of texture parameterization when using SDS. Specifically, we propose Deep Convolutional Physically-Based Rendering (DC-PBR) parameterization, which re-parameterizes the physically-based rendering (PBR) texture maps with randomly initialized convolution-based neural kernels, instead of a standard pixel-based parameterization. We show that DC-PBR inherently schedules the optimization curriculum according to texture frequency and naturally filters out the noisy signals from SDS. In experiments, Paint-it obtains remarkable quality PBR texture maps within 15 min., given only a text description. We demonstrate the generalizability and practicality of Paint-it by synthesizing high-quality texture maps for large-scale mesh datasets and showing test-time applications such as relighting and material control using a popular graphics engine. Project page: https://kim-youwang.github. io/paint-it
FightTracker: Real-time predictive analytics for Mixed Martial Arts bouts

Mixed martial arts (MMA) has been one of the fastest-growing sports in recent years and has become a mainstream sport on the global stage. The growth of MMA has been driven by the Ultimate Fighting Championship (UFC), which is currently the largest MMA promotion organization in the world. However, data collection and statistical modeling in MMA are still in their infancy. We developed FightTracker, a data-driven solution that delivers real-time predictions for UFC fights. We first conducted regression analyses on the data provided by the UFC and MMA Decisions and built two predictive models of UFC fight outcomes. One model predicts the judges' majority score by round while the other predicts whether the red fighter will win the fight or not in 3-round fights that go beyond the second round (53% of all UFC fights). Both models use in-round fight statistics as explanatory variables and achieve 80% accuracy. We then designed an R shiny app that delivers these two predictions in real-time based on the ESPN live data. This information is valuable for fans, coaches, athletes, and especially bettors. Indeed, a live betting strategy based on FightTracker proved to generate large profits over an 8-week period against the bookmaker Unibet (90.17% ROI).
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations

Transferring adversarial examples (AEs) from surrogate machine-learning (ML) models to target models is commonly used in black-box adversarial robustness evaluation. Attacks leveraging certain data augmentation, such as random resizing, have been found to help AEs generalize from surrogates to targets. Yet, prior work has explored limited augmentations and their composition. To fill the gap, we systematically studied how data augmentation affects transferability. Particularly, we explored 46 augmentation techniques of seven categories originally proposed to help ML models generalize to unseen benign samples, and assessed how they impact transferability, when applied individually or composed. Performing exhaustive search on a small subset of augmentation techniques and genetic search on all techniques, we identified augmentation combinations that can help promote transferability. Extensive experiments with the ImageNet and CIFAR-10 datasets and 18 models showed that simple color-space augmentations (e.g., color to greyscale) outperform the state of the art when combined with standard augmentations, such as translation and scaling. Additionally, we discovered that composing augmentations impacts transferability mostly monotonically (i.e., more methods composed $\rightarrow$ $\ge$ transferability). We also found that the best composition significantly outperformed the state of the art (e.g., 93.7% vs. $\le$ 82.7% average transferability on ImageNet from normally trained surrogates to adversarially trained targets). Lastly, our theoretical analysis, backed up by empirical evidence, intuitively explain why certain augmentations help improve transferability.
Cardiac and extracardiac discharge diagnosis prediction from emergency department ECGs using deep learning

Current deep learning algorithms designed for automatic ECG analysis have exhibited notable accuracy. However, akin to traditional electrocardiography, they tend to be narrowly focused and typically address a singular diagnostic condition. In this study, we specifically demonstrate the capability of a single model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a sole ECG collected in the emergency department. Among the 1,076 hierarchically structured ICD codes considered, our model achieves an AUROC exceeding 0.8 in 439 of them. This underscores the models proficiency in handling a wide array of diagnostic scenarios. We emphasize the potential of utilizing this model as a screening tool, potentially integrated into a holistic clinical decision support system for efficiently triaging patients in the emergency department. This research underscores the remarkable capabilities of comprehensive ECG analysis algorithms and the extensive range of possibilities facilitated by the open MIMIC-IV-ECG dataset. Finally, our data may play a pivotal role in revolutionizing the way ECG analysis is performed, marking a significant advancement in the field.
Satellite Captioning: Large Language Models to Augment Labeling

With the growing capabilities of modern object detection networks and datasets to train them, it has gotten more straightforward and, importantly, less laborious to get up and running with a model that is quite adept at detecting any number of various objects. However, while image datasets for object detection have grown and continue to proliferate (the current most extensive public set, ImageNet, contains over 14m images with over 14m instances), the same cannot be said for textual caption datasets. While they have certainly been growing in recent years, caption datasets present a much more difficult challenge due to language differences, grammar, and the time it takes for humans to generate them. Current datasets have certainly provided many instances to work with, but it becomes problematic when a captioner may have a more limited vocabulary, one may not be adequately fluent in the language, or there are simple grammatical mistakes. These difficulties are increased when the images get more specific, such as remote sensing images. This paper aims to address this issue of potential information and communication shortcomings in caption datasets. To provide a more precise analysis, we specify our domain of images to be remote sensing images in the RSICD dataset and experiment with the captions provided here. Our findings indicate that ChatGPT grammar correction is a simple and effective way to increase the performance accuracy of caption models by making data captions more diverse and grammatically correct.
Target detection using underwater acoustic communication links

Underwater monitoring and surveillance systems are essential for underwater target detection, localization and classification. The aim of this work is to investigate the possibility of target detection by using data transmission between communication nodes in an underwater acoustic (UWA) network, i.e, re-using acoustic communication signals for target detection. A new target detection method based on estimation of the time-varying channel impulse response between the communication transmitter(s) and receiver(s) is proposed and investigated. This is based on a lake experiment and numerical experiments using a simulator developed for modeling the time-varying UWA channel in the presence of a moving target. The proposed detection method provides a clear indication of a target crossing the communication link. A good similarity between results obtained in the numerical and lake experiments is observed.
AE-NeRF: Audio Enhanced Neural Radiance Field for Few Shot Talking Head Synthesis

Audio-driven talking head synthesis is a promising topic with wide applications in digital human, film making and virtual reality. Recent NeRF-based approaches have shown superiority in quality and fidelity compared to previous studies. However, when it comes to few-shot talking head generation, a practical scenario where only few seconds of talking video is available for one identity, two limitations emerge: 1) they either have no base model, which serves as a facial prior for fast convergence, or ignore the importance of audio when building the prior; 2) most of them overlook the degree of correlation between different face regions and audio, e.g., mouth is audio related, while ear is audio independent. In this paper, we present Audio Enhanced Neural Radiance Field (AE-NeRF) to tackle the above issues, which can generate realistic portraits of a new speaker with few-shot dataset. Specifically, we introduce an Audio Aware Aggregation module into the feature fusion stage of the reference scheme, where the weight is determined by the similarity of audio between reference and target image. Then, an Audio-Aligned Face Generation strategy is proposed to model the audio related and audio independent regions respectively, with a dual-NeRF framework. Extensive experiments have shown AE-NeRF surpasses the state-of-the-art on image fidelity, audio-lip synchronization, and generalization ability, even in limited training set or training iterations.
An Interpretable Deep Learning Approach for Skin Cancer Categorization

Skin cancer is a serious worldwide health issue, precise and early detection is essential for better patient outcomes and effective treatment. In this research, we use modern deep learning methods and explainable artificial intelligence (XAI) approaches to address the problem of skin cancer detection. To categorize skin lesions, we employ four cutting-edge pre-trained models: XceptionNet, EfficientNetV2S, InceptionResNetV2, and EfficientNetV2M. Image augmentation approaches are used to reduce class imbalance and improve the generalization capabilities of our models. Our models decision-making process can be clarified because of the implementation of explainable artificial intelligence (XAI). In the medical field, interpretability is essential to establish credibility and make it easier to implement AI driven diagnostic technologies into clinical workflows. We determined the XceptionNet architecture to be the best performing model, achieving an accuracy of 88.72%. Our study shows how deep learning and explainable artificial intelligence (XAI) can improve skin cancer diagnosis, laying the groundwork for future developments in medical image analysis. These technologies ability to allow for early and accurate detection could enhance patient care, lower healthcare costs, and raise the survival rates for those with skin cancer. Source Code: https://github.com/Faysal-MD/An-Interpretable-Deep-Learning? Approach-for-Skin-Cancer-Categorization-IEEE2023
Neighborhoods and Boundaries of Urban Mobility (preprint)

Barriers of urban mobility may contribute to the segregation of neighborhoods. However, the empirical knowledge about this issue is limited. Here, we present a methodological framework to assess the importance of barriers to urban mobility along their hierarchy, over time and across neighborhoods. Using GPS mobility data, we construct a network of blocks from the sequence of individual visits. A community finding algorithm allows us to evaluate the fit of the neighborhoods detected in this mobility network to characteristic urban barriers by adjusting the resolution parameter of the Louvain method to decrease the dominance of mobility hubs. Using the Symmetric Area Difference index, that quantifies the overlap between the polygons separated by urban barriers and the network communities, we find that the match of detected neighborhoods and urban barriers improves as the resolution parameter increases. However, at high values of the resolution parameter, this fit gets better for lower rank administrative or road barriers than for their higher rank pairs. Although the role of urban barriers in hindering mobility became stronger over the COVID-19 restrictions, their fit with detected neighborhoods deteriorated for most analyzed barriers. Exceptions are primary roads, to which the fit of neighborhoods is stable over periods. By increasing the resolution parameter, we find emergent communities of local mobility flows that cross primary roads and a consequent optimal value of the resolution parameter. The Barrier Crossing Ratio, the fraction of barrier crossings that are network community crossings as well, demonstrates that the barriers' impact on mobility are significantly different across urban areas. These results contribute to the ongoing discourse on urban segregation, emphasizing the importance of barriers to urban mobility in shaping neighborhood interactions and mixing.
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation

In mineral processing plants, grinding is a crucial step, accounting for approximately 50 percent of the total mineral processing costs. Semi-autogenous grinding mills are extensively employed in the grinding circuit of mineral processing plants. Maximizing SAG mill throughput is of significant importance considering its profound financial outcomes. However, the optimum process parameter setting aimed at achieving maximum mill throughput remains an uninvestigated domain in prior research. This study introduces a hybrid intelligent framework leveraging expert knowledge, machine learning techniques, and evolutionary algorithms to address this research need. In this study, we utilize an extensive industrial dataset comprising 36743 records and select relevant features based on the insights of industry experts. Following the removal of erroneous data, a comprehensive evaluation of 17 diverse machine learning models is undertaken to identify the most accurate predictive model. To improve the model performance, feature selection and outlier detection are executed. The resultant optimal model, trained with refined features, serves as the objective function within three distinct evolutionary algorithms. These algorithms are employed to identify parameter configurations that maximize SAG mill throughput while adhering to the working limits of input parameters as constraints. Notably, our analysis revealed that CatBoost, as an ensemble model, stands out as the most accurate predictor. Furthermore, differential evolution emerges as the preferred optimization algorithm, exhibiting superior performance in both achieving the highest mill throughput predictions and ensuring robustness in predictions, surpassing alternative methods.
Superhorizon entanglement from inflationary particle production

We investigate entanglement generation between the sub- and super-Hubble modes of inflaton fluctuations, in the context of particle production from perturbations during inflation. We consider a large-field inflationary scenario where inflation is driven by a vacuum energy symmetry breaking potential and the scalar inflaton field is nonminimally coupled to spacetime curvature. In particular, we focus on the slow-roll phase, adopting a quasi-de Sitter scale factor to properly account for the presence of perturbations and computing the pair production probability associated to the coupling between the inflaton and spacetime inhomogeneities. The interaction Lagrangian at first order is constructed from inhomogeneities induced by the inflaton dynamics, and the initial Bunch-Davies vacuum state of the field evolves under the action of such Lagrangian. In this framework, we quantify the total amount of entanglement via the von Neumann entropy of the reduced density operator for superhorizon modes, tracing out sub-Hubble degrees of freedom. We then compare these outcomes with entanglement production for quadratic chaotic inflation and for a small-field quadratic hilltop scenario, preserving field-curvature coupling in both cases and pointing out the main differences between large and small-field approaches. We show that the amount of entanglement entropy arising from such geometric production grows rapidly in slow-roll regime and that it is typically higher in large-field scenarios. We also discuss our outcomes in light of recent findings for the squeezing entropy of cosmological perturbations and cubic nonlinearities in de Sitter space.
Appearance-based Refinement for Object-Centric Motion Segmentation

The goal of this paper is to discover, segment, and track independently moving objects in complex visual scenes. Previous approaches have explored the use of optical flow for motion segmentation, leading to imperfect predictions due to partial motion, background distraction, and object articulations and interactions. To address this issue, we introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a simple selection mechanism that identifies accurate flow-predicted masks as exemplars, and an object-centric architecture that refines problematic masks based on exemplar information. The model is pre-trained on synthetic data and then adapted to real-world videos in a self-supervised manner, eliminating the need for human annotations. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTubeVOS, SegTrackv2, and FBMS-59. We achieve competitive performance on single-object segmentation, while significantly outperforming existing models on the more challenging problem of multi-object segmentation. Finally, we investigate the benefits of using our model as a prompt for a per-frame Segment Anything Model.
Learning to Act without Actions

Pre-training large models on vast amounts of web data has proven to be an effective approach for obtaining powerful, general models in domains such as language and vision. However, this paradigm has not yet taken hold in reinforcement learning. This is because videos, the most abundant form of embodied behavioral data on the web, lack the action labels required by existing methods for imitating behavior from demonstrations. We introduce Latent Action Policies (LAPO), a method for recovering latent action information, and thereby latent-action policies, world models, and inverse dynamics models, purely from videos. LAPO is the first method able to recover the structure of the true action space just from observed dynamics, even in challenging procedurally-generated environments. LAPO enables training latent-action policies that can be rapidly fine-tuned into expert-level policies, either offline using a small action-labeled dataset, or online with rewards. LAPO takes a first step towards pre-training powerful, generalist policies and world models on the vast amounts of videos readily available on the web.
When can you trust feature selection? -- II: On the effects of random data on condition in statistics and optimisation

In Part I, we defined a LASSO condition number and developed an algorithm -- for computing support sets (feature selection) of the LASSO minimisation problem -- that runs in polynomial time in the number of variables and the logarithm of the condition number. The algorithm is trustworthy in the sense that if the condition number is infinite, the algorithm will run forever and never produce an incorrect output. In this Part II article, we demonstrate how finite precision algorithms (for example algorithms running floating point arithmetic) will fail on open sets when the condition number is large -- but still finite. This augments Part I's result: If an algorithm takes inputs from an open set that includes at least one point with an infinite condition number, it fails to compute the correct support set for all inputs within that set. Hence, for any finite precision algorithm working on open sets for the LASSO problem with random inputs, our LASSO condition number -- as a random variable -- will estimate the probability of success/failure of the algorithm. We show that a finite precision version of our algorithm works on traditional Gaussian data for LASSO with high probability. The algorithm is trustworthy, specifically, in the random cases where the algorithm fails, it will not produce an output. Finally, we demonstrate classical random ensembles for which the condition number will be large with high probability, and hence where any finite precision algorithm on open sets will fail. We show numerically how commercial software fails on these cases.
ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton

Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e.g., pedestrian predictions and marketing bidding. Badminton represents a fascinating example of a multifaceted turn-based sport, requiring both sophisticated tactic developments and alternate-dependent decision-making. Recent deep learning approaches for player tactic forecasting in badminton show promising performance partially attributed to effective reasoning about rally-player interactions. However, a critical obstacle lies in the unclear functionality of which features are learned for simulating players' behaviors by black-box models, where existing explainers are not equipped with turn-based and multi-output attributions. To bridge this gap, we propose a turn-based feature attribution approach, ShuttleSHAP, for analyzing forecasting models in badminton based on variants of Shapley values. ShuttleSHAP is a model-agnostic explainer that aims to quantify contribution by not only temporal aspects but also player aspects in terms of multifaceted cues. Incorporating the proposed analysis tool into the state-of-the-art turn-based forecasting model on the benchmark dataset reveals that it is, in fact, insignificant to reason about past strokes, while conventional sequential models have greater impacts. Instead, players' styles influence the models for the future simulation of a rally. On top of that, we investigate and discuss the causal analysis of these findings and demonstrate the practicability with local analysis.