Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the performance of ANN in predicting of electrofacies (estimated by SOM, AHC, and MRGC models)

ABSTRACT Log facies analysis is important for reservoir characterization, but is made particularly difficult by the problem of “dimensionality”: log space is not equivalent to geological space, and two points that are close to each other in log space may not always be similar geologically. Even with good visualization tools, performing classic method (two-step) manually in high-dimensional (>3) space is still difficult, slow, somewhat subjective, and requires a skill or expertise that is not always readily available. Recently, some novel methods are found such as multiregression graph-based clustering (MRGC), agglomerative hierarchical clustering (AHC), and self-organizing map (SOM). In comparison with the existing two-step tool, new models have been found to make the work much faster and easier, but they need porosity and permeability for training that requires skill and time. In this study a neural network-based electrofacies determination technique is presented and finally electrofacies that evaluated in new models were determined very fast by using some logs without any computing of porosity or shale volume.
Antithetic Riemannian Manifold And Quantum-Inspired Hamiltonian Monte Carlo

Markov Chain Monte Carlo inference of target posterior distributions in machine learning is predominately conducted via Hamiltonian Monte Carlo and its variants. This is due to Hamiltonian Monte Carlo based samplers ability to suppress random-walk behaviour. As with other Markov Chain Monte Carlo methods, Hamiltonian Monte Carlo produces auto-correlated samples which results in high variance in the estimators, and low effective sample size rates in the generated samples. Adding antithetic sampling to Hamiltonian Monte Carlo has been previously shown to produce higher effective sample rates compared to vanilla Hamiltonian Monte Carlo. In this paper, we present new algorithms which are antithetic versions of Riemannian Manifold Hamiltonian Monte Carlo and Quantum-Inspired Hamiltonian Monte Carlo. The Riemannian Manifold Hamiltonian Monte Carlo algorithm improves on Hamiltonian Monte Carlo by taking into account the local geometry of the target, which is beneficial for target densities that may exhibit strong correlations in the parameters. Quantum-Inspired Hamiltonian Monte Carlo is based on quantum particles that can have random mass. Quantum-Inspired Hamiltonian Monte Carlo uses a random mass matrix which results in better sampling than Hamiltonian Monte Carlo on spiky and multi-modal distributions such as jump diffusion processes. The analysis is performed on jump diffusion process using real world financial market data, as well as on real world benchmark classification tasks using Bayesian logistic regression.
CT Image Harmonization for Enhancing Radiomics Studies

While remarkable advances have been made in Computed Tomography (CT), most of the existing efforts focus on imaging enhancement while reducing radiation dose. How to normalize CT images acquired using non-standard protocols is vital for decision-making in cross-center large-scale radiomics studies but remains the boundary to explore. We develop a novel GAN-based image standardization algorithm called RadiomicGAN to mitigate the discrepancy caused by using non-standard acquisition protocols. In RadiomicGAN, a pre-trained U-Net has been adopted as part of the generator to learn radiomic feature distributions efficiently, and a novel training approach, called Window Training, has been developed to smoothly transform the pre-trained model to the medical imaging domain. In the experiments, we compared RadiomicGAN with four state-of-the-art CT image standardization approaches on both patient and phantom CT images acquired using three different reconstruction kernels. We objectively evaluated model performance based on more than 1,000 radiomic features. The results show that RadiomicGAN clearly outperforms the compared models. The source code, manual, and sample data are available at https://github.con selim-iitdu/radiomicGAN.
Minimum Wasserstein Distance Estimator under Finite Location-scale Mixtures

When a population exhibits heterogeneity, we often model it via a finite mixture: decompose it into several different but homogeneous subpopulations. Contemporary practice favors learning the mixtures by maximizing the likelihood for statistical efficiency and the convenient EM-algorithm for numerical computation. Yet the maximum likelihood estimate (MLE) is not well defined for the most widely used finite normal mixture in particular and for finite location-scale mixture in general. We hence investigate feasible alternatives to MLE such as minimum distance estimators. Recently, the Wasserstein distance has drawn increased attention in the machine learning community. It has intuitive geometric interpretation and is successfully employed in many new applications. Do we gain anything by learning finite location-scale mixtures via a minimum Wasserstein distance estimator (MWDE)? This paper investigates this possibility in several respects. We find that the MWDE is consistent and derive a numerical solution under finite location-scale mixtures. We study its robustness against outliers and mild model mis-specifications. Our moderate scaled simulation study shows the MWDE suffers some efficiency loss against a penalized version of MLE in general without noticeable gain in robustness. We reaffirm the general superiority of the likelihood based learning strategies even for the non-regular finite location-scale mixtures.
Test-Time Personalization with a Transformer for Human Pose Estimation

We propose to personalize a human pose estimator given a set of test images of a person without using any manual annotations. While there is a significant advancement in human pose estimation, it is still very challenging for a model to generalize to different unknown environments and unseen persons. Instead of using a fixed model for every test case, we adapt our pose estimator during test time to exploit person-specific information. We first train our model on diverse data with both a supervised and a self-supervised pose estimation objectives jointly. We use a Transformer model to build a transformation between the self-supervised keypoints and the supervised keypoints. During test time, we personalize and adapt our model by fine-tuning with the self-supervised objective. The pose is then improved by transforming the updated self-supervised keypoints. We experiment with multiple datasets and show significant improvements on pose estimations with our self-supervised personalization.
TrafPy: Benchmarking Data Centre Network Systems

Benchmarking is commonly used in research fields such as computer architecture design and machine learning as a powerful paradigm for rigorously assessing, comparing, and developing novel technologies. However, the data centre networking community lacks a standard open-access benchmark. Driving factors behind this include the proprietary nature of traffic traces, the limited detail and quantity of open-access network-level data sets, the high cost of real world experimentation, and the poor reproducibility and fidelity of synthetically generated traffic. This is curtailing the community’s understanding of existing systems and hindering the ability with which novel technologies can be developed, compared, and tested. We present TrafPy; an open-access framework for generating both realistic and custom data centre network traffic traces. TrafPy is compatible with any simulation, emulation, or experimentation environment, and can be used for standardised benchmarking and for investigating the properties and limitations of network systems such as schedulers, switches, routers, and resource managers. To demonstrate the efficacy of TrafPy, we use it to conduct a thorough investigation into the sensitivity of 4 canonical scheduling algorithms (shortest remaining processing time, fair share, first fit, and random) to varying traffic trace characteristics. We show how the fundamental scheduler performance insights revealed by these tests translate to 4 realistic data centre network types; University, Private Enterprise, Commercial Cloud, and Social Media Cloud. We then draw conclusions as to which types of scheduling policies are most suited to which types of network load conditions and traffic characteristics, leading to the possibility of application-informed decision making at the design stage and new dynamically adaptable scheduling policies. TrafPy is open-sourced via GitHub [43] and all data associated with this manuscript via RDR [44].
GraspME - Grasp Manifold Estimator

In this paper, we introduce a Grasp Manifold Estimator (GraspME) to detect grasp affordances for objects directly in 2D camera images. To perform manipulation tasks autonomously it is crucial for robots to have such graspability models of the surrounding objects. Grasp manifolds have the advantage of providing continuously infinitely many grasps, which is not the case when using other grasp representations such as predefined grasp points. For instance, this property can be leveraged in motion optimization to define goal sets as implicit surface constraints in the robot configuration space. In this work, we restrict ourselves to the case of estimating possible end-effector positions directly from 2D camera images. To this extend, we define grasp manifolds via a set of keypoints and locate them in images using a Mask R-CNN [1] backbone. Using learned features allows to generalize to different view angle, with potentially noisy images, and objects that were not part of the training set. We rely on simulation data only and perform experiments on simple and complex objects, including unseen ones. Our framework achieves an inference speed of 11.5 fps on a GPU, an average precision for keypoint estimation of 94.5% and a mean pixel distance of only 1.29. This shows that we can estimate the objects very well via bounding boxes and segmentation masks as well as approximate the correct grasp manifold’s keypoint coordinates.
Mirror Mirror on the Wall: Next-Generation Wireless Jamming Attacks Based on Software-Controlled Surfaces

—The intelligent reﬂecting surface (IRS) is a promising new paradigm in wireless communications to meet the growing demand for high-speed connectivity in next-generation mobile networks. IRS, also known as software-controlled metasurfaces, consist of an array of adjustable radio wave reﬂectors, enabling smart radio environments, e.g., for enhancing the signal-to-noise ratio (SNR) and spatial diversity of wireless channels. Research on IRS to date has been largely focused on constructive applications. In this work, we show for the ﬁrst time that the IRS provides a practical low-cost toolkit for attackers to easily perform complex signal manipulation attacks on the physical layer in real time. We introduce the environment reconﬁguration attack (ERA) as a novel class of jamming attacks in wireless radio networks. Here, an adversary leverages an IRS to rapidly vary the electromagnetic propagation environment to disturb legitimate receivers. The IRS gives the adversary a key advantage over traditional jamming: It no longer has to actively emit a jamming signal itself while the jamming signal is correlated to the legitimate communication signal. We thoroughly investigate the ERA using the popular orthogonal frequency division multiplexing (OFDM) modulation as an example. We show that the ERA allows to severely degrade the available data rates even in entire networks. We present insights to the attack through analytical analysis, simulations, as well as experiments. Our results highlight that the attack also works with reasonably small IRS sizes. Finally, we implement an attacker setup and demonstrate a practical ERA to slow down a Wi-Fi network.
CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Network

Source code summaries are short natural language descriptions of code snippets that help developers better understand and maintain source code. There has been a surge of work on automatic code summarization to reduce the burden of writing summaries manually. However, most contemporary approaches mainly leverage the information within the boundary of the method being summarized (i.e., local context), and ignore the broader context that could assist with code summarization. This paper explores two global contexts, namely intra-class and inter-class contexts, and proposes the model CoCoSUM: Contextual Code Summarization with Multi-Relational Graph Neural Networks. CoCoSUM first incorporates class names as the intra-class context to generate the class semantic embeddings. Then, relevant Unified Modeling Language (UML) class diagrams are extracted as inter-class context and are encoded into the class relational embeddings using a novel Multi-Relational Graph Neural Network (MRGNN). Class semantic embeddings and class relational embeddings, together with the outputs from code token encoder and AST encoder, are passed to a decoder armed with a two-level attention mechanism to generate high-quality, context-aware code summaries. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models. The experimental results show that CoCoSUM is effective and outperforms state-of-the-art methods. Our source code and experimental data are available in the supplementary materials and will be made publicly available.
COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models

This paper is responding to the MIA-COV19 challenge to classify COVID from non-COVID based on CT lung images. The COVID-19 virus has devastated the world in the last eighteen months by infecting more than 182 million people and causing over 3.9 million deaths. The overarching aim is to predict the diagnosis of the COVID-19 virus from chest radiographs, through the development of explainable vision transformer deep learning techniques, leading to population screening in a more rapid, accurate and transparent way. In this competition, there are 5381 three-dimensional (3D) datasets in total, including 1552 for training, 374 for evaluation and 3455 for testing. While most of the data volumes are in axial view, there are a number of subjects' data are in coronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3D data based classification is investigated, in this competition, 2D images remains the main focus. Two deep learning methods are studied, which are vision transformer (ViT) based on attention models and DenseNet that is built upon conventional convolutional neural network (CNN). Initial evaluation results based on validation datasets whereby the ground truth is known indicate that ViT performs better than DenseNet with F1 scores being 0.76 and 0.72 respectively. Codes are available at GitHub at <https://github/xiaohong1/COVID-ViT>.
Assessing Contribution of Treatment Phases through Tipping Point Analyses via Counterfactual Elicitation Using Rank Preserving Structural Failure Time Models

Abstract This article provides a novel approach to assess the importance of specific treatment phases within a treatment regimen through tipping point analyses (TPA) of a time-to-event endpoint using rank-preserving-structural-failure-time (RPSFT) modeling. In oncology clinical research, an experimental treatment is often added to the standard of care therapy in multiple treatment phases to improve patient outcomes. When the resulting new regimen provides a meaningful benefit over standard of care, gaining insights into the contribution of each treatment phase becomes important to properly guide clinical practice. New statistical approaches are needed since traditional methods are inadequate in answering such questions. RPSFT modeling is an approach for causal inference, typically used to adjust for treatment switching in randomized clinical trials with time-to-event endpoints. A tipping-point analysis is commonly used in situations where a statistically significant treatment effect is suspected to be an artifact of missing or unobserved data rather than a real treatment difference. The methodology proposed in this article is an amalgamation of these two ideas to investigate the contribution of a specific component of a regimen comprising multiple treatment phases. We provide different variants of the method and construct indices of contribution of a treatment phase to the overall benefit of a regimen that facilitates interpretation of results. The proposed approaches are illustrated with findings from a recently concluded, real-life phase 3 cancer clinical trial. We conclude with several considerations and recommendations for practical implementation of this new methodology.
Survey: Leakage and Privacy at Inference Time

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance since commercial and government applications of ML can draw on multiple sources of data, potentially including users’ and clients’ sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malicious leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malicious leakage, followed by description of currently available defences, assessment metrics, and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research.
Discovery of a New Gamma-Ray Source, LHAASO J0341+5258, with Emission up to 200 TeV

We report the discovery of a new unidentified extended $\gamma$-ray source in the Galactic plane named LHAASO J0341+5258 with a pre-trial significance of 8.2 standard deviations above 25 TeV. The best fit position is R.A. $=55.34^{\circ}\pm0.11^{\circ}$ and Dec$=52.97^{\circ}\pm0.07^{\circ}$. The angular size of LHAASO J0341+5258 is $0.29^\circ \pm 0.06^\circ_{stat} \pm0.02^\circ_{sys}$. The flux above 25 TeV is about $20\%$ of the flux of Crab Nebula. Although a power-law fit of the spectrum from 10 TeV to 200 TeV with the photon index $\alpha=2.98 \pm 0. 19_{stat} \pm 0. 02_{sys}$ is not excluded, the LHAASO data together with the flux upper limit at 10 GeV set by the Fermi LAT observation, indicate a noticeable steepening of an initially hard power-law spectrum %($\alpha \leq 1.75$) spectrum with a cutoff at $\approx 50$ TeV. We briefly discuss the origin of UHE gamma-rays. The lack of an energetic pulsar and a young SNR inside or in the vicinity of LHAASO J0341+5258 challenge, but do not exclude both the leptonic and hadronic scenarios of gamma-ray production.
Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation

Sentiment analysis is a costly yet necessary task for enterprises to study the opinions of their customers to improve their products and to determine optimal marketing strategies. Due to the existence of a wide range of domains across different products and services, cross-domain sentiment analysis methods have received significant attention. These methods mitigate the domain gap between different applications by training cross-domain generalizable classifiers which help to relax the need for data annotation for each domain. Most existing methods focus on learning domain-agnostic representations that are invariant with respect to both the source and the target domains. As a result, a classifier that is trained using the source domain annotated data would generalize well in a related target domain. We introduce a new domain adaptation method which induces large margins between different classes in an embedding space. This embedding space is trained to be domain-agnostic by matching the data distributions across the domains. Large intraclass margins in the source domain help to reduce the effect of"domain shift"on the classifier performance in the target domain. Theoretical and empirical analysis are provided to demonstrate that the proposed method is effective.
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks

Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions. Herein, we propose Neural-Symbolic Solver (NS-Solver) to explicitly and seamlessly incorporate different levels of symbolic constraints by auxiliary tasks. Our NS-Solver consists of a problem reader to encode problems, a programmer to generate symbolic equations, and a symbolic executor to obtain answers. Along with target expression supervision, our solver is also optimized via 4 new auxiliary objectives to enforce different symbolic reasoning: a) self-supervised number prediction task predicting both number quantity and number locations; b) commonsense constant prediction task predicting what prior knowledge (e.g. how many legs a chicken has) is required; c) program consistency checker computing the semantic loss between predicted equation and target equation to ensure reasonable equation mapping; d) duality exploiting task exploiting the quasi-duality between symbolic equation generation and problem’s part-of-speech generation to enhance the understanding ability of a solver. Besides, to provide a more realistic and challenging benchmark for developing a universal and scalable solver, we also construct a new largescale MWP benchmark CM17K consisting of 4 kinds of MWPs (arithmetic, one-unknown linear, one-unknown non-linear, equation set) with more than 17K samples. Extensive experiments on Math23K and our CM17k demonstrate the superiority of our NS-Solver compared to state-of-the-art methods.
Self-consistent interpretations of the multiwavelength gamma-ray spectrum of LHAASO 
J0621+3755

LHAASO J0621$+$3755 is a TeV gamma-ray halo newly identified by LHAASO-KM2A. It is likely to be generated by electrons trapped in a slow-diffusion zone around PSR J0622$+$3749 through inverse Compton scattering. When the gamma-ray spectrum of LHAASO-KM2A is fitted, the GeV fluxes derived by the commonly used one-zone normal diffusion model for electron propagation are significantly higher than the upper limits (ULs) of Fermi-LAT. In this work, we respectively adopt the one-zone superdiffusion and two-zone normal diffusion models to solve this conflict. For the superdiffusion scenario, we find that a model with superdiffusion index $\alpha\lesssim1.2$ can meet the constraints of Fermi-LAT observation. For the two-zone diffusion scenario, the size of the slow-diffusion zone is required to be smaller than $\sim50$ pc, which is consistent with theoretical expectations. Future precise measurements of the Geminga halo may further distinguish between these two scenarios for the electron propagation in pulsar halos.