Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoherence Puzzle in Measurements of Photons Originating from Electron-Positron Annihilation

Entanglement of photons originating from the electron-positron annihilation has not been proven experimentally. Though the independent experiments performed so far unanimously confirm that correlation between the linear polarizations of back-to-back photons from the electron-positron annihilation is consistent with the assumption that these photons are entangled in polarization. Yet, unexpectedly, recent experiments differ as regards the correlation of polarization direction of back-to-back photons after the decoherence induced by scattering of one of these photons on the electron in the scattering material. In one of the experiments, the correlation before and after the decoherence of the photon state is the same and in the other experiment the scattering of one photon leads to a significant decrease in this correlation. Here we discuss this puzzle. The decoherent states were ensured by forcing one of the annihilation photons through prior scattering before the Compton kinematics based measurement of the polarization correlation. A comparison between the experimental setups used for the different measurements and the results obtained are briefly discussed, highlighting the parameters that are important for performing such measurements. Finally, the main features of the J-PET detector are presented with the schemes for performing similar measurements so that the results can be used as conclusive remarks for solving this puzzle. Solving the decoherence puzzle will have crucial consequences for basic studies of entanglement, as well as for the proposed application of polarisation of photons in positron emission tomography. In case if the correlation of polarisation of back-to-back photons from the electron-positron annihilation is the same before and after the scattering of these photons, then it will not be useful for the reduction of the scatter fraction in PET diagnostics.
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery

Discovering out-of-domain (OOD) intent is important for developing new skills in task-oriented dialogue systems. The key challenges lie in how to transfer prior in-domain (IND) knowledge to OOD clustering, as well as jointly learn OOD representations and cluster assignments. Previous methods suffer from in-domain overfitting problem, and there is a natural gap between representation learning and clustering objectives. In this paper, we propose a unified K-nearest neighbor contrastive learning framework to discover OOD intents. Specifically, for IND pre-training stage, we propose a KCL objective to learn inter-class discriminative features, while maintaining intra-class diversity, which alleviates the in-domain overfitting problem. For OOD clustering stage, we propose a KCC method to form compact clusters by mining true hard negative samples, which bridges the gap between clustering and representation learning. Extensive experiments on three benchmark datasets show that our method achieves substantial improvements over the state-of-the-art methods.
LATE-TIME RADIO EMISSION FROM X-RAY-SELECTED TIDAL DISRUPTION EVENTS

We present new observations with the Karl G. Jansky Very Large Array of seven X-ray-selected tidal disruption events (TDEs). The radio observations were carried out between 9 and 22 years after the initial X-ray discovery, and thus probe the late-time formation of relativistic jets and jet interactions with the interstellar medium in these systems. We detect a compact radio source in the nucleus of the galaxy IC 3599 and a compact radio source that is a possible counterpart to RX J1420.4+5334. We find no radio counterparts for five other sources with flux density upper limits between 51 and 200 μJy (3σ). If the detections truly represent late radio emission associated with a TDE, then our results suggest that a fraction, ≳ 10%, of X-ray-detected TDEs are accompanied by relativistic jets. We explore several models for producing late radio emission, including interaction of the jet with gas in the circumnuclear environment (blast wave model), and emission from the core of the jet itself. Upper limits on the radio flux density from archival observations suggest that the jet formation may have been delayed for years after the TDE, possibly triggered by the accretion rate dropping below a critical threshold of ∼10−2–. The non-detections are also consistent with this scenario; deeper radio observations can determine whether relativistic jets are present in these systems. The emission from RX J1420.4+5334 is also consistent with the predictions of the blast wave model; however, the radio emission from IC 3599 is substantially underluminous, and its spectral slope is too flat, relative to the blast wave model expectations. Future radio monitoring of IC 3599 and RX J1420.4+5334 will help to better constrain the nature of the jets in these systems.
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation

Recent studies have demonstrated the superior performance of introducing ``scan-wise"contrast labels into contrastive learning for multi-organ segmentation on multi-phase computed tomography (CT). However, such scan-wise labels are limited: (1) a coarse classification, which could not capture the fine-grained ``organ-wise"contrast variations across all organs; (2) the label (i.e., contrast phase) is typically manually provided, which is error-prone and may introduce manual biases of defining phases. In this paper, we propose a novel data-driven contrastive loss function that adapts the similar/dissimilar contrast relationship between samples in each minibatch at organ-level. Specifically, as variable levels of contrast exist between organs, we hypothesis that the contrast differences in the organ-level can bring additional context for defining representations in the latent space. An organ-wise contrast correlation matrix is computed with mean organ intensities under one-hot attention maps. The goal of adapting the organ-driven correlation matrix is to model variable levels of feature separability at different phases. We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets. Compared to the state-of-the-art approaches, our proposed contrastive loss yields a substantial and significant improvement of 1.41% (from 0.923 to 0.936, p-value$<$0.01) and 2.02% (from 0.891 to 0.910, p-value$<$0.01) on mean Dice scores across all organs with respect to NCCT and CECT cohorts. We further assess the trained model performance with the MICCAI 2021 FLARE Challenge CECT datasets and achieve a substantial improvement of mean Dice score from 0.927 to 0.934 (p-value$<$0.01). The code is available at: https://github.com/MASILab/DCC_CL
The CO-to-H2 Conversion Factor

CO line emission represents the most accessible and widely used tracer of the molecular interstellar medium. This renders the translation of observed CO intensity into total H2 gas mass critical to understand star formation and the interstellar medium in our Galaxy and beyond. We review the theoretical underpinning, techniques, and results of efforts to estimate this CO-to-H2 “conversion factor,” XCO, in different environments. In the Milky Way disk, we recommend a conversion factor XCO = 2×10 20 cm −2 (K km s −1 ) −1 with ±30% uncertainty. Studies of other “normal galaxies” return similar values in Milky Way-like disks, but with greater scatter and systematic uncertainty. Departures from this Galactic conversion factor are both observed and expected. Dust-based determinations, theoretical arguments, and scaling relations all suggest that XCO increases with decreasing metallicity, turning up sharply below metallicity ≈ 1/3–1/2 solar in a manner consistent with model predictions that identify shielding as a key parameter. Based on spectral line modeling and dust observations, XCO appears to drop in the central, bright regions of some but not all galaxies, often coincident with regions of bright CO emission and high stellar surface density. This lower XCO is also present in the overwhelmingly molecular interstellar medium of starburst galaxies, where several lines of evidence point to a lower CO-to-H2 conversion factor. At high redshift, direct evidence regarding the conversion factor remains scarce; we review what is known based on dynamical modeling and other arguments. Subject headings: ISM: general — ISM: molecules — galaxies: ISM — radio lines: ISM
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition

Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. Previous methods solve this problem based on token-wise classification, which ignores the information of entity boundaries, and inevitably the performance is affected by the massive non-entity tokens. To this end, we propose a seminal span-based prototypical network (SpanProto) that tackles few-shot NER via a two-stage approach, including span extraction and mention classification. In the span extraction stage, we transform the sequential tags into a global boundary matrix, enabling the model to focus on the explicit boundary information. For mention classification, we leverage prototypical learning to capture the semantic representations for each labeled span and make the model better adapt to novel-class entities. To further improve the model performance, we split out the false positives generated by the span extractor but not labeled in the current episode set, and then present a margin-based loss to separate them from each prototype region. Experiments over multiple benchmarks demonstrate that our model outperforms strong baselines by a large margin.
Attention Regularized Laplace Graph for Domain Adaptation

In leveraging manifold learning in domain adaptation (DA), graph embedding-based DA methods have shown their effectiveness in preserving data manifold through the Laplace graph. However, current graph embedding DA methods suffer from two issues: 1). they are only concerned with preservation of the underlying data structures in the embedding and ignore sub-domain adaptation, which requires taking into account intra-class similarity and inter-class dissimilarity, thereby leading to negative transfer; 2). manifold learning is proposed across different feature/label spaces separately, thereby hindering unified comprehensive manifold learning. In this paper, starting from our previous DGA-DA, we propose a novel DA method, namely <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>ttention <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>egularized Laplace <inline-formula> <tex-math notation="LaTeX">${G}$ </tex-math></inline-formula>raph-based <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>omain <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>daptation (ARG-DA), to remedy the aforementioned issues. Specifically, by weighting the importance across different sub-domain adaptation tasks, we propose the <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>ttention <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>egularized Laplace Graph for class aware DA, thereby generating the attention regularized DA. Furthermore, using a specifically designed FEEL strategy, our approach dynamically unifies alignment of the manifold structures across different feature/label spaces, thus leading to comprehensive manifold learning. Comprehensive experiments are carried out to verify the effectiveness of the proposed DA method, which consistently outperforms the state of the art DA methods on 7 standard DA benchmarks, i.e., 37 cross-domain image classification tasks including object, face, and digit images. An in-depth analysis of the proposed DA method is also discussed, including sensitivity, convergence, and robustness.
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of scientific progress of the field. Beyond designing DRL methods for general intelligence, designing task-specific methods is becoming increasingly prominent for real-world applications. In these settings, the standard evaluation practice involves using a few instances of Markov Decision Processes (MDPs) to represent the task. However, many tasks induce a large family of MDPs owing to variations in the underlying environment, particularly in real-world contexts. For example, in traffic signal control, variations may stem from intersection geometries and traffic flow levels. The select MDP instances may thus inadvertently cause overfitting, lacking the statistical power to draw conclusions about the method's true performance across the family. In this article, we augment DRL evaluations to consider parameterized families of MDPs. We show that in comparison to evaluating DRL methods on select MDP instances, evaluating the MDP family often yields a substantially different relative ranking of methods, casting doubt on what methods should be considered state-of-the-art. We validate this phenomenon in standard control benchmarks and the real-world application of traffic signal control. At the same time, we show that accurately evaluating on an MDP family is nontrivial. Overall, this work identifies new challenges for empirical rigor in reinforcement learning, especially as the outcomes of DRL trickle into downstream decision-making.
On Trustworthy Decision-Making Process of Human Drivers From the View of Perceptual Uncertainty Reduction

Humans are experts at making decisions for challenging driving tasks with uncertainties. Many efforts have been made to model the decision-making process of human drivers at the behavior level. However, limited studies explain how human drivers actively make trustworthy sequential decisions to complete interactive driving tasks in an uncertain environment. This paper argues that human drivers intently search for actions to reduce the uncertainty of their perception of the environment, i.e., perceptual uncertainty, to a low level that allows them to make a trustworthy decision easily. This paper provides a proof-of-concept framework to empirically reveal that human drivers’ perceptual uncertainty decreases when executing interactive tasks with uncertainties. We first introduce an explainable-artificial intelligence approach (i.e., SHapley Additive exPlanation, SHAP) to determine the salient features on which human drivers base decisions. Then, we use entropy-based measures to quantify the drivers’ perceptual changes in these ranked salient features across the decision-making process, reflecting the changes in uncertainties. The validation and verification of our proposed method are conducted in the highway on-ramp merging scenario with congested traffic using the INTERACTION dataset. Experimental results support that human drivers intentionally seek information to reduce their perceptual uncertainties in the number and rank of salient features of their perception of environments to make a trustworthy decision.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years for this task, there has been no standard comprehensive setting for performance evaluation. Consequently, it has been difficult to understand which methods work well and when under a broad range of settings. To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights. (1) We benchmark the outlier detection performance of 14 methods ranging from classical matrix factorization to the latest graph neural networks. (2) Using nine real datasets, our benchmark assesses how the different detection methods respond to two major types of synthetic outliers and separately to"organic"(real non-synthetic) outliers. (3) Using an existing random graph generation technique, we produce a family of synthetically generated datasets of different graph sizes that enable us to compare the running time and memory usage of the different outlier detection algorithms. Based on our experimental results, we discuss the pros and cons of existing graph outlier detection algorithms, and we highlight opportunities for future research. Importantly, our code is freely available and meant to be easily extendable: https://github.com/pygod-team/pygod/tree/main/benchmark
Unit Selection: Learning Benefit Function from Finite Population Data

The unit selection problem is to identify a group of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if incentivized and a different way if not. The unit selection problem consists of evaluation and search subproblems. Li and Pearl defined the"benefit function"to evaluate the average payoff of selecting a certain individual with given characteristics. The search subproblem is then to design an algorithm to identify the characteristics that maximize the above benefit function. The hardness of the search subproblem arises due to the large number of characteristics available for each individual and the sparsity of the data available in each cell of characteristics. In this paper, we present a machine learning framework that uses the bounds of the benefit function that are estimable from the finite population data to learn the bounds of the benefit function for each cell of characteristics. Therefore, we could easily obtain the characteristics that maximize the benefit function.
Updated Low-temperature Gas Opacities with ÆSOPUS 2.0

This work introduces new low-temperature gas opacities in the range 3.2≲log(T/K)≲4.5 computed with the ÆSOPUS code under the assumption of thermodynamic equilibrium. In comparison to the previous version, ÆSOPUS 1.0, we updated and expanded molecular absorption to include 80 species, mostly using the recommended line lists currently available from the ExoMol and HITRAN databases. Furthermore, in light of a recent study, we revised the H− photodetachment cross section, added the free–free absorption of other negative ions of atoms and molecules, and updated the collision-induced absorption due to H2/H2, H2/H, H2/He, and H/He pairs. Using the new input physics, we computed tables of Rosseland mean opacities for several scaled-solar chemical compositions, including Magg et al.'s most recent one, as well as α-enhanced mixtures. The differences in opacity between the new ÆSOPUS 2.0 and the original ÆSOPUS 1.0 versions, as well as other sets of calculations, are discussed. The new opacities are released to the community via a dedicated webpage that includes both precomputed tables for widely used chemical compositions and a web interface for calculating opacities on the fly for any abundance distribution.
A General Rheology for Dense Fluid-Particle Mixtures

Granular-ﬂuid mixtures widely exist in nature, and with the increase of solid fractions, their rheology behaviors are still elusive in diﬀerent conditions. The challenge in the prediction of their dynamics is due to the unclear combined eﬀect of ﬂuid-solid and solid-solid interactions on the macroscopic behaviors. In this contribution, we generalize and unify the frictional rheologies of both dense suspensions and dry granular media using a dimensionless number based on the ratio between physically interpretable length scales, which covers a wide range of ﬂow regimes and uniﬁes the existing theories for dry and immersed granular ﬂows. These results support the power-law scaling behaviors near jamming reported in recent investigations. The ﬁndings establish a general constitutive framework for visco-inertial granular ﬂows and are important for a better understanding of granular-ﬂuid mixtures in both natural and engineering situations, and also provide a reliable method to understand the multi-phase, multi-scale ﬂows under complex force ﬁelds.
Perceptual-Score: A Psychophysical Measure for Assessing the Biological Plausibility of Visual Recognition Models

For the last decade, convolutional neural networks (CNNs) have vastly superseded their predecessors in nearly all vision tasks in artiﬁcial intelligence, including object recognition. However, in spite of abundant advancements, they continue to pale in comparison to biological vision. This chasm has prompted the development of biologically-inspired models that have attempted to mimic the human visual system, primarily at a neural-level, which are evaluated using standard dataset benchmarks. However, more work is needed to understand how these models actually perceive the visual world. This article proposes a state-of-the-art procedure that generates a new metric, Perceptual-Score, which is grounded in visual psychophysics, and is capable of reliably estimating perceptual responses across numerous models — representing a large range in complexity and biological inspiration. We perform the procedure on twelve models that vary in degree of biological inspiration and complexity, and compare the results against the aggregated results of 2,390 Amazon Mechanical Turk workers who together provided ∼ 2 . 7 million perceptual responses. Each model’s Perceptual-Score is compared against the state-of-the-art neural activity-based metric, Brain-Score. Our study indicates that models with high correlation to human perceptual behavior also have high correlation with the corresponding neural activity.
Learned Video Compression for YUV 4:2:0 Content Using Flow-based Conditional Inter-frame Coding

This paper proposes a learning-based video compression framework for variable-rate coding on YUV 4:2:0 content. Most existing learning-based video compression models adopt the traditional hybrid-based coding architecture, which involves temporal prediction followed by residual coding. However, recent studies have shown that residual coding is suboptimal from the information-theoretic perspective. In addition, most existing models are optimized with respect to RGB content. Furthermore, they require separate models for variable-rate coding. To address these issues, this work presents an attempt to incorporate the conditional inter-frame coding for YUV 4:2:0 content. We introduce a conditional flow-based inter-frame coder to improve the inter-frame coding efficiency. To adapt our codec to YUV 4:2:0 content, we adopt a simple strategy of using space-to-depth and depth-to-space conversions. Lastly, we employ a rate-adaption net to achieve variable-rate coding without training multiple models. Experimental results show that our model performs better than x265 on UVG and MCL-JCV datasets in terms of PSNR-YUV. However, on the more challenging datasets from ISCAS’22 GC, there is still ample room for improvement. This insufficient performance is due to the lack of inter-frame coding capability at a large GOP size and can be mitigated by increasing the model capacity and applying an error propagationaware training strategy.