Miracle-dz/UniFAR-FTUs huggingface dataset:
Community Notes vs. Snoping: How the Crowd Selects Fact-Checking Targets on Social Media

Deploying links to fact-checking websites (so-called"snoping") is a common intervention that can be used by social media users to refute misleading claims. However, its real-world effect may be limited as it suffers from low visibility and distrust towards professional fact-checkers. As a remedy, Twitter launched its community-based fact-checking system Community Notes on which fact-checks are carried out by actual Twitter users and directly shown on the fact-checked tweets. Yet, an understanding of how fact-checking via Community Notes differs from snoping is absent. In this study, we analyze differences in how contributors to Community Notes and Snopers select their targets when fact-checking social media posts. For this purpose, we analyze two unique datasets from Twitter: (a) 25,912 community-created fact-checks from Twitter's Community Notes platform; and (b) 52,505"snopes"that debunk tweets via fact-checking replies linking to professional fact-checking websites. We find that Notes contributors and Snopers focus on different targets when fact-checking social media content. For instance, Notes contributors tend to fact-check posts from larger accounts with higher social influence and are relatively less likely to endorse/emphasize the accuracy of not misleading posts. Fact-checking targets of Notes contributors and Snopers rarely overlap; however, those overlapping exhibit a high level of agreement in the fact-checking assessment. Moreover, we demonstrate that Snopers fact-check social media posts at a higher speed. Altogether, our findings imply that different fact-checking approaches -- carried out on the same social media platform -- can result in vastly different social media posts getting fact-checked. This has important implications for future research on misinformation, which should not rely on a single fact-checking approach when compiling misinformation datasets.
Planting a Lyman alpha forest on AbacusSummit

The full-shape correlations of the Lyman alpha (Ly α) forest contain a wealth of cosmological information through the Alcock–Paczyński effect. However, these measurements are challenging to model without robustly testing and verifying the theoretical framework used for analysing them. Here, we leverage the accuracy and volume of the N-body simulation suite AbacusSummit to generate high-resolution Ly α skewers and quasi-stellar object (QSO) catalogues. One of the main goals of our mocks is to aid in the full-shape Ly α analysis planned by the Dark Energy Spectroscopic Instrument (DESI) team. We provide optical depth skewers for six of the fiducial cosmology base-resolution simulations ($L_{\rm box} = 2\, h^{-1}\, {\rm Gpc}$, N = 69123) at z = 2.5. We adopt a simple recipe based on the Fluctuating Gunn–Peterson Approximation (FGPA) for constructing these skewers from the matter density in an N-body simulation and calibrate it against the 1D and 3D Ly α power spectra extracted from the hydrodynamical simulation IllustrisTNG (TNG; $L_{\rm box} = 205\, h^{-1}\, {\rm Mpc}$, N = 25003). As an important application, we study the non-linear broadening of the baryon acoustic oscillation (BAO) peak and show the cross-correlation between DESI-like QSOs and our Ly α forest skewers. We find differences on small scales between the Kaiser approximation prediction and our mock measurements of the Ly α × QSO cross-correlation, which would be important to account for in upcoming analyses. The AbacusSummit Ly α forest mocks open up the possibility for improved modelling of cross-correlations between Ly α and cosmic microwave background (CMB) lensing and Ly α and QSOs, and for forecasts of the 3-point Ly α correlation function. Our catalogues and skewers are publicly available on Globus via the National Energy Research Scientific Computing Center (NERSC) (full link under the section ‘Data Availability’).
M2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M2DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://github.com/PurdueDigitalTwin/M2DAR.
CB-HVT Net: A Channel-Boosted Hybrid Vision Transformer Network for Lymphocyte Detection in Histopathological Images

Detection of Tumor-Infiltrating Lymphocytes (TILs) has a high prognostic value in cancer diagnosis due to their ability to identify and kill cancer cells. However, this task is non-trivial due to their diverse morphology, overlapping boundaries, and presence of artifacts. Vision Transformers (ViTs) have the ability to capture long-range relationships, but they lack local correlation in the images and require large training datasets. In this work, we propose a Channel Boosted Hybrid Vision Transformer (CB-HVT) to detect lymphocytes in histopathological images. The proposed network constitutes: 1) channel generation module; 2) channel exploitation module; 3) channel merging module; 4) region-aware module; and 5) detection and segmentation head. The proposed CB-HVT exploits the learning capacity of both CNN and ViT-based architectures to capture lymphocytic diverse morphology. In addition, we developed a feature fusion block to systematically and gradually merge the diverse feature maps to improve the learning capability of the network. The attention mechanism in the fusion block retains the most contributing features. We evaluated the effectiveness of the proposed CB-HVT on two publicly available datasets for lymphocyte detection in histopathological images. The proposed network showed good results as compared to the existing architectures in terms of F-Score (LYSTO: 0.88 and NuClick: 0.82). In addition, the performance of the proposed CB-HVT on an unseen test set reveals its significance as a valuable tool for pathologists for real-time lymphocyte detection.
A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow

This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the magnetic resonance field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in-vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.
AMULET: Adaptive Matrix-Multiplication-Like Tasks

Many useful tasks in data science and machine learning applications can be written as simple variations of matrix multiplication. However, users have difficulty performing such tasks as existing matrix/vector libraries support only a limited class of computations hand-tuned for each unique hardware platform. Users can alternatively write the task as a simple nested loop but current compilers are not sophisticated enough to generate fast code for the task written in this way. To address these issues, we extend an open-source compiler to recognize and optimize these matrix multiplication-like tasks. Our framework, called Amulet, uses both database-style and compiler optimization techniques to generate fast code tailored to its execution environment on CPUs. Amulet achieves speedups on a variety of matrix multiplication-like tasks compared to existing compilers while handling a much broader class of computations compared to libraries.
Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors

The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants’ motor performance in an objective and scalable manner in out-of-hospital settings. This information could be used for developmental research and to support clinical decision-making, such as detection of developmental problems and guiding of their therapeutic interventions. MAIJU-based analyses rely fully on the classification of infant’s posture and movement; it is hence essential to study ways to increase the accuracy of such classifications, aiming to increase the reliability and robustness of the automated analysis. Here, we investigated how self-supervised pre-training improves performance of the classifiers used for analyzing MAIJU recordings, and we studied whether performance of the classifier models is affected by context-selective quality-screening of pre-training data to exclude periods of little infant movement or with missing sensors. Our experiments show that i) pre-training the classifier with unlabeled data leads to a robust accuracy increase of subsequent classification models, and ii) selecting context-relevant pre-training data leads to substantial further improvements in the classifier performance.Clinical relevance— This study showcases that self-supervised learning can be used to increase the accuracy of out-of-hospital evaluation of infants’ motor abilities via smart wearables.
Neural Program Repair with Program Dependence Analysis and Effective Filter Mechanism

Automated program repair is a crucial task for improving the efficiency of software developers. Recently, neural-based techniques have demonstrated significant promise in generating correct patches for buggy code snippets. However, most existing approaches arbitrarily treat the buggy context without any analysis to capture the semantic relationship between the buggy statement and its context. Additionally, we observe that existing neural models may output an unaltered patch consistent with the input buggy code snippet, which fails to be the correct human-written one for fixing the given bug. To address the aforementioned limitations, we present in this paper a novel neural program repair framework called \approach, which adapts the general pre-trained language model for fixing single-line Java bugs. We make the first attempt to use program slicing to extract contextual information directly related to the given buggy statement as repair ingredients from the corresponding program dependence graph and eliminate unaltered patches using an intuitive but effective filter mechanism. We demonstrate the effectiveness of \approach on five benchmarks when compared with state-of-the-art baselines.
Energy Barriers for Thermally Activated Magnetization Reversal in Perpendicularly Magnetized Nanodisks in a Transverse Field

Thermally-induced transitions between bistable magnetic states of magnetic tunnel junctions (MTJ) are of interest for generating random bitstreams and for applications in stochastic computing. An applied field transverse to the easy axis of a perpendicularly magnetized MTJ (pMTJ) can lower the energy barrier ($E_b$) to these transitions leading to faster fluctuations. In this study, we present analytical and numerical calculations of $E_b$ considering both coherent (macrospin) reversal and non-uniform wall-mediated magnetization reversal for a selection of nanodisk diameters and applied fields. Non-uniform reversal processes dominate for larger diameters, and our numerical calculations of $E_b$ using the String method show that the transition state has a sigmoidal magnetization profile. The latter can be described with an analytical expression that depends on only one spatial dimension, parallel to the applied field, which is also the preferred direction of profile motion during reversal. Our results provide nanodisk energy barriers as a function of the transverse field, nanodisk diameter, and material characteristics, which are useful for designing stochastic bitstreams.
Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction

While Generative Adversarial Networks (GANs) have recently found applications in image editing, most previous GAN-based image editing methods require largescale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images. Previous researchers have proposed EditGAN for high-quality, high-precision semantic image editing with limited semantic annotations by finding `editing vectors'. However, it is noticed that there are many features that are not highly associated with semantics, and EditGAN may fail on them. Based on the orthogonality of latent space observed by EditGAN, we propose a method to estimate editing vectors that do not rely on semantic segmentation nor differentiable feature estimation network. Our method assumes that there is a correlation between the intensity distribution of features and the distribution of hidden vectors, and estimates the relationship between the above distributions by sampling the feature intensity of the image corresponding to several hidden vectors. We modified Linear Discriminant Analysis (LDA) to deal with both binary feature editing and continuous feature editing. We then found that this method has a good effect in processing features such as clothing type and texture, skin color and hair.
InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning

Localization for autonomous robots in prior maps is crucial for their functionality. This paper offers a solution to this problem for indoor environments called InstaLoc, which operates on an individual lidar scan to localize it within a prior map. We draw on inspiration from how humans navigate and position themselves by recognizing the layout of distinctive objects and structures. Mimicking the human approach, InstaLoc identifies and matches object instances in the scene with those from a prior map. As far as we know, this is the first method to use panoptic segmentation directly inferring on 3D lidar scans for indoor localization. InstaLoc operates through two networks based on spatially sparse tensors to directly infer dense 3D lidar point clouds. The first network is a panoptic segmentation network that produces object instances and their semantic classes. The second smaller network produces a descriptor for each object instance. A consensus based matching algorithm then matches the instances to the prior map and estimates a six degrees of freedom (DoF) pose for the input cloud in the prior map. The significance of InstaLoc is that it has two efficient networks. It requires only one to two hours of training on a mobile GPU and runs in real-time at 1 Hz. Our method achieves between two and four times more detections when localizing, as compared to baseline methods, and achieves higher precision on these detections.
Progressive Translation: Improving Domain Robustness of Neural Machine Translation with Intermediate Sequences

Previous studies show that intermediate supervision signals benefit various Natural Language Processing tasks. However, it is not clear whether there exist intermediate signals that benefit Neural Machine Translation (NMT). Borrowing techniques from Statistical Machine Translation, we propose intermediate signals which are intermediate sequences from the"source-like"structure to the"target-like"structure. Such intermediate sequences introduce an inductive bias that reflects a domain-agnostic principle of translation, which reduces spurious correlations that are harmful to out-of-domain generalisation. Furthermore, we introduce a full-permutation multi-task learning to alleviate the spurious causal relations from intermediate sequences to the target, which results from exposure bias. The Minimum Bayes Risk decoding algorithm is used to pick the best candidate translation from all permutations to further improve the performance. Experiments show that the introduced intermediate signals can effectively improve the domain robustness of NMT and reduces the amount of hallucinations on out-of-domain translation. Further analysis shows that our methods are especially promising in low-resource scenarios.
Establishing Shared Query Understanding in an Open Multi-Agent System

We propose a method that allows to develop shared understanding between two agents for the purpose of performing a task that requires cooperation. Our method focuses on efficiently establishing successful task-oriented communication in an open multi-agent system, where the agents do not know anything about each other and can only communicate via grounded interaction. The method aims to assist researchers that work on human-machine interaction or scenarios that require a human-in-the-loop, by defining interaction restrictions and efficiency metrics. To that end, we point out the challenges and limitations of such a (diverse) setup, while also restrictions and requirements which aim to ensure that high task performance truthfully reflects the extent to which the agents correctly understand each other. Furthermore, we demonstrate a use-case where our method can be applied for the task of cooperative query answering. We design the experiments by modifying an established ontology alignment benchmark. In this example, the agents want to query each other, while representing different databases, defined in their own ontologies that contain different and incomplete knowledge. Grounded interaction here has the form of examples that consists of common instances, for which the agents are expected to have similar knowledge. Our experiments demonstrate successful communication establishment under the required restrictions, and compare different agent policies that aim to solve the task in an efficient manner.
MPI-RICAL: Data-Driven MPI Distributed Parallelism Assistance with Transformers

Computational science has made rapid progress in recent years, leading to ever increasing demand for supercomputing resources. For scientific applications that leverage such resources, Message Passing Interface (MPI) plays a crucial role in enabling distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, performing domain decomposition, is a challenging and error-prone task. In this paper, we address this problem by developing MPI-rical, a novel data-driven, programming-assistance tool that assists programmers in writing domain decomposition based distributed memory parallelization code using MPI. Specifically, we leverage Transformer architecture — the invention that led to advancements in the field of natural language processing (NLP) — with a supervised language model to suggest MPI functions and their proper locations in the code on the fly. In addition to the novel model for MPI-based parallel programming, in this paper, we also introduce MPICodeCorpus, the first publicly-available corpus of MPI-based parallel programs that is created by mining more than 15,000 open-source repositories on GitHub. Experimental results demonstrate the effectiveness of MPI-rical on both dataset from MPICodeCorpus and more importantly, on a compiled benchmark of MPI-based parallel programs for numerical computations that represent real-world scientific applications. Specifically, MPI-rical achieves F1 scores between 0.87-0.91 on these programs, demonstrating its accuracy in suggesting correct MPI functions at appropriate code locations. The source code used in this work, as well as other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCN/MPI-rical.
Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator

We propose an easily computed estimator of marginal likelihoods from posterior simulation output, via reciprocal importance sampling, combining earlier proposals of DiCiccio et al (1997) and Robert and Wraith (2009). This involves only the unnormalized posterior densities from the sampled parameter values, and does not involve additional simulations beyond the main posterior simulation, or additional complicated calculations. It is unbiased for the reciprocal of the marginal likelihood, consistent, has finite variance, and is asymptotically normal. It involves one user-specified control parameter, and we derive an optimal way of specifying this. We illustrate it with several numerical examples.
On CSI-Free Multiantenna Schemes for Massive Wireless-Powered Underground Sensor Networks

Radio-frequency wireless energy transfer (WET) is a promising technology to realize wireless-powered underground sensor networks (WPUSNs) and enable sustainable underground monitoring. However, due to the severe attenuation in harsh underground soil and the tight energy budget of the underground sensors, traditional WPUSNs relying on the channel state information (CSI) are highly inefficient, especially in massive WET scenarios. To address this challenge, we comparatively assess the feasibility of several state-of-the-art CSI-free multiantenna WET schemes for WPUSNs, under a given power budget. Moreover, to overcome the extremely low WET efficiency in underground channels, we propose a distributed CSI-free system, where multiple power beacons (PBs) simultaneously charge a large set of underground sensors without any CSI. We consider the position-aware $K$ -Means and the position-agnostic equally far-from-center (EFFC) approaches for the optimal deployment of the PBs. Our results evince that the performance of the proposed distributed CSI-free system can approach or even surpass that of a traditional full-CSI WET strategy, especially when adopting an appropriate CSI-free scheme, applying the advisable PBs deployment approach, and equipping the PBs with an appropriate number of antennas. Finally, we discuss the impact of underground parameters, i.e., the burial depth of devices and the volumetric water content of soil, on the system’s performance, and identify potential challenges and research opportunities for practical distributed CSI-free WPUSNs deployment.