Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring on joint associations from marginal associations and a reference sample

We present a method to infer on joint regression coefficients obtained from marginal regressions using a reference panel. This type of scenario is common in genetic fine-mapping, where the estimated marginal associations are reported in genomewide association studies (GWAS), and a reference panel is used for inference on the association in a joint regression model. We show that ignoring the uncertainty due to the use of a reference panel instead of the original design matrix, can lead to a severe inflation of false discoveries and a lack of replicable findings. We derive the asymptotic distribution of the estimated coefficients in the joint regression model, and show how it can be used to produce valid inference. We address two settings: inference within regions that are pre-selected, as well as within regions that are selected based on the same data. By means of real data examples and simulations we demonstrate the usefulness of our suggested methodology.
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning

Communication is supposed to improve multi-agent collaboration and overall performance in cooperative Multi-agent reinforcement learning (MARL). However, such improvements are prevalently limited in practice since most existing communication schemes ignore communication overheads (e.g., communication delays). In this paper, we demonstrate that ignoring communication delays has detrimental effects on collaborations, especially in delay-sensitive tasks such as autonomous driving. To mitigate this impact, we design a delay-aware multi-agent communication model (DACOM) to adapt communication to delays. Specifically, DACOM introduces a component, TimeNet, that is responsible for adjusting the waiting time of an agent to receive messages from other agents such that the uncertainty associated with delay can be addressed. Our experiments reveal that DACOM has a non-negligible performance improvement over other mechanisms by making a better trade-off between the benefits of communication and the costs of waiting for messages.
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization

Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Summarizing a narrative is challenging as it requires an understanding of event causality and character behaviors. To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset. It contains 122K narrative documents, which are collected from plot descriptions of movies and TV episodes with diverse genres, and their corresponding abstractive summaries. Experiments show that there is a large performance gap between humans and the state-of-the-art summarization models on NarraSum. We hope that this dataset will promote future research in summarization, as well as broader studies of natural language understanding and generation. The dataset is available at https://github.com/zhaochaocs/narrasum.
Angular-Rate Estimation Using Delayed Quaternion Measurements

This paper presents algorithms for estimating the angular-rate vector of satellites using quaternion measurements. Two approaches are compared one that uses differentiated quaternion measurements to yield coarse rate measurements, which are then fed into two different estimators. In the other approach the raw quaternion measurements themselves are fed directly into the two estimators. The two estimators rely on the ability to decompose the non-linear part of the rotas rotational dynamics equation of a body into a product of an angular-rate dependent matrix and the angular-rate vector itself. This non unique decomposition, enables the treatment of the nonlinear spacecraft (SC) dynamics model as a linear one and, thus, the application of a PseudoLinear Kalman Filter (PSELIKA). It also enables the application of a special Kalman filter which is based on the use of the solution of the State Dependent Algebraic Riccati Equation (SDARE) in order to compute the gain matrix and thus eliminates the need to compute recursively the filter covariance matrix. The replacement of the rotational dynamics by a simple Markov model is also examined. In this paper special consideration is given to the problem of delayed quaternion measurements. Two solutions to this problem are suggested and tested. Real Rossi X-Ray Timing Explorer (RXTE) data is used to test these algorithms, and results are presented.
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.
In-context Examples Selection for Machine Translation

Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used to describe a task to the model. For Machine Translation (MT), these examples are typically randomly sampled from the development dataset with a similar distribution as the evaluation set. However, it is unclear how the choice of these in-context examples and their ordering impacts the output translation quality. In this work, we aim to understand the properties of good in-context examples for MT in both in-domain and out-of-domain settings. We show that the translation quality and the domain of the in-context examples matter and that 1-shot noisy unrelated example can have a catastrophic impact on output quality. While concatenating multiple random examples reduces the effect of noise, a single good prompt optimized to maximize translation quality on the development dataset can elicit learned information from the pre-trained language model. Adding similar examples based on an n-gram overlap with the test source significantly and consistently improves the translation quality of the outputs, outperforming a strong kNN-MT baseline in 2 out of 4 out-of-domain datasets.
Kinematic-chemical Analysis and Time Tagging for the Diagonal Ridge Structure of the Galactic Outer Disk with LAMOST Red-giant Branch Stars

We investigate the kinematic-chemical distribution of red-giant branch stars from the LAMOST survey crossed matched with Gaia DR2 proper motions, and present time tagging for the well-known ridge structures (diagonal distributions for V R in the R, V ϕ plane) in the range of Galactocentric distance R = 8 to 15 kpc. We detect six ridge structures, including five ridges apparent in the radial velocity distribution and three ridges apparent in the vertical velocity, the sensitive time of which to the perturbations are from young population (0–3 Gyr) to old population (9–14 Gyr). Based on an analysis of the evolution of angular momentum distribution, we find that four ridges are relatively stationary, while another is evolving with time, which is confirmed by the difference analysis at different populations and supporting that there might be two kinds of dynamical origins. Furthermore, ridge features are also vividly present in the chemical properties ([Fe/H], [α/Fe]). The comparison between the north and south hemispheres of the Galaxy does show some differences and the ridge features are asymmetrical. Moreover, we find that diagonal ridge structures may affect the shape of the rotation curve, which is manifested as fluctuations and undulations on top of a smooth profile. Finally we speculate that the bar dynamics should be not enough to explain all ridge properties including the break feature in the V Z –L Z plane.
Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes

Abstract Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuations may arise from the intrinsic neural circuit dynamics and subsequently affect the computational properties of neural population activity remains poorly understood. The main difficulty lies in resolving the nonlinear coupling between correlated fluctuations with the overall dynamics of the system. In this study, we investigate the emergence of synergistic neural population codes from the intrinsic dynamics of correlated neural fluctuations in a neural circuit model capturing realistic nonlinear noise coupling of spiking neurons. We show that a rich repertoire of spatial correlation patterns naturally emerges in a bump attractor network and further reveals the dynamical regime under which the interplay between differential and noise correlations leads to synergistic codes. Moreover, we find that negative correlations may induce stable bound states between two bumps, a phenomenon previously unobserved in firing rate models. These noise-induced effects of bump attractors lead to a number of computational advantages including enhanced working memory capacity and efficient spatiotemporal multiplexing and can account for a range of cognitive and behavioral phenomena related to working memory. This study offers a dynamical approach to investigating realistic correlated neural fluctuations and insights to their roles in cortical computations.
The Radial Variation of the Solar Wind Turbulence Spectra near the Kinetic Break Scale from Parker Solar Probe Measurements

In this study we examine the radial dependence of the inertial and dissipation range indices, as well as the spectral break separating the inertial and dissipation range in power density spectra of interplanetary magnetic field fluctuations using Parker Solar Probe data from the fifth solar encounter between ∼0.1 and ∼0.7 au. The derived break wavenumber compares reasonably well with previous estimates at larger radial distances and is consistent with gyro-resonant damping of Alfvénic fluctuations by thermal protons. We find that the inertial scale power-law index varies between approximately −1.65 and −1.45. This is consistent with either the Kolmogorov (−5/3) or Iroshnikov–Kraichnan (−3/2) values, and has a very weak radial dependence with a possible hint that the spectrum becomes steeper closer to the Sun. The dissipation range power-law index, however, has a clear dependence on radial distance (and turbulence age), decreasing from −3 near 0.7 au (4 days) to −4 [±0.3] at 0.1 au (0.75 days) closer to the Sun.
Polarized Maser Emission with In-source Faraday Rotation

We discuss studies of polarization in astrophysical masers with particular emphasis on the case where the Zeeman splitting is small compared to the Doppler profile, resulting in a blend of the transitions between magnetic substates. A semiclassical theory of the molecular response is derived, and coupled to radiative transfer solutions for 1 and 2-beam linear masers, resulting in a set of nonlinear, algebraic equations for elements of the molecular density matrix. The new code, PRISM, implements numerical methods to compute these solutions. Using PRISM, we demonstrate a smooth transfer between this case and that of wider splitting. For a J = 1–0 system, with parameters based on the v = 1, J = 1–0 transition of SiO, we investigate the behavior of linear and circular polarization as a function of the angle between the propagation axis and the magnetic field, and with the optical depth, or saturation state, of the model. We demonstrate how solutions are modified by the presence of Faraday rotation, generated by various abundances of free electrons, and that strong Faraday rotation leads to additional angles where the Stokes Q changes sign. We compare our results to a number of previous models, from the analytical limits derived by Goldreich, Keeley, and Kwan in 1973, through computational results by W. Watson and coauthors, to the recent work by Lankhaar and Vlemmings in 2019. We find that our results are generally consistent with those of other authors given the differences in the approach and the approximations made.
Pandora’s Problem with Nonobligatory Inspection: Optimal Structure and a PTAS

Weitzman (1979) introduced Pandora’s box problem as a mathematical model of sequential search with inspection costs, in which a searcher is allowed to select a prize from one of n alternatives. Several decades later, Doval (2018) introduced a close version of the problem, where the searcher does not need to incur the inspection cost of an alternative, and can select it uninspected. Unlike the original problem, the optimal solution to the nonobligatory inspection variant is proved to need adaptivity by Doval (2018), and by recent work of Fu Li and Liu (2022), finding the optimal solution is NP-hard. Our first main result is a structural characterization of the optimal policy: We show there exists an optimal policy that follows only two different pre-determined orders of inspection, and transitions from one to the other at most once. Our second main result is a polynomial time approximation scheme (PTAS). Our proof involves a novel reduction to a framework developed by Fu Li and Xu (2018), utilizing our optimal two-phase structure. Furthermore, we show Pandora’s problem with nonobligatory inspection belongs to class NP, which by using the hardness result of Fu Li and Liu (2022), settles the computational complexity class of the problem. Finally, we provide a tight 0.8 approximation and a novel proof for committing policies (informally, the set of nonadaptive policies) for general classes of distributions, which was previously shown only for discrete and finite distributions in Guha, Munagala and Sarka (2008).
Fast Point Cloud Generation with Straight Flows

Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D real-world. To address this limitation, we propose Point Straight Flow (PSF), a model that exhibits impressive performance using one step. Our idea is based on the reformulation of the standard diffusion model, which optimizes the curvy learning trajectory into a straight path. Further, we develop a distillation strategy to shorten the straight path into one step without a performance loss, enabling applications to 3D real-world with latency constraints. We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods. On real-world applications such as point cloud completion and training-free text-guided generation in a low-latency setup, PSF performs favorably.
RHO ($ρ$): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding

Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce hallucinated responses not supported by the input source, which greatly hinders their application. The heterogeneity between external knowledge and dialogue context challenges representation learning and source integration, and further contributes to unfaithfulness. To handle this challenge and generate more faithful responses, this paper presents RHO ($\rho$) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG). We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism. In addition, we devise a response re-ranking technique based on walks over KG sub-graphs for better conversational reasoning. Experimental results on OpenDialKG show that our approach significantly outperforms state-of-the-art methods on both automatic and human evaluation by a large margin, especially in hallucination reduction (17.54% in FeQA).
Electromagnetic proton cyclotron ring instability: Threshold and saturation

This paper describes a theoretical and computational study of the threshold and saturation conditions of the electromagnetic proton cyclotron ring instability. The initial proton velocity distribution is modeled as a Maxwellian in the direction parallel to the ambient magnetic field and as a relatively cold ring in the plane perpendicular to the field. Linear Vlasov theory and one-dimensional hybrid simulations are used to examine and confirm previously derived scaling laws for the maximum instability growth rate and maximum value of the fluctuating magnetic field energy density. Linear theory yields a new instability threshold condition relating the dimensionless ring perpendicular speed and the dimensionless ring parallel temperature. Simulation results show that cessation of fluctuation growth is due to the heating of the ring protons in the parallel direction and to the reduction of the perpendicular kinetic energy of the ring; at saturation these parameters typically lie near values predicted by a linear threshold condition. The threshold condition is predicted to provide an upper bound for proton ring kinetic energies observed in space plasmas.
The Morley-type virtual element method for the Navier-Stokes equations in stream-function form on general meshes

The nonconforming Morley-type virtual element method for the incompressible Navier-Stokes equations formulated in terms of the stream-function on simply connected polygonal domains (not necessarily convex) is designed. A rigorous analysis by using a new enriching operator is developed. More precisely, by employing such operator, we provide novel discrete Sobolev embeddings, which allow to establish the well-posedness of the discrete scheme and obtain optimal error estimates in broken $H^2$-, $H^1$- and $L^2$-norms under minimal regularity condition on the weak solution. The velocity and vorticity fields are recovered via a postprocessing formulas. Furthermore, a new algorithm for pressure recovery based on a Stokes complex sequence is presented. Optimal error estimates are obtained for all the postprocessed variables. Finally, the theoretical error bounds and the good performance of the method are validated through several benchmark tests.
Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer

Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lingual transfer from high-resource ones. In this work, we try to better understand how such models, specifically mT5, transfer *any* linguistic and semantic knowledge across languages, even though no explicit cross-lingual signals are provided during pre-training. Rather, only unannotated texts from each language are presented to the model separately and independently of one another, and the model appears to implicitly learn cross-lingual connections. This raises several questions that motivate our study, such as: Are the cross-lingual connections between every language pair equally strong? What properties of source and target language impact the strength of cross-lingual transfer? Can we quantify the impact of those properties on the cross-lingual transfer? In our investigation, we analyze a pre-trained mT5 to discover the attributes of cross-lingual connections learned by the model. Through a statistical interpretation framework over 90 language pairs across three tasks, we show that transfer performance can be modeled by a few linguistic and data-derived features. These observations enable us to interpret cross-lingual understanding of the mT5 model. Through these observations, one can favorably choose the best source language for a task, and can anticipate its training data demands. A key finding of this work is that similarity of syntax, morphology and phonology are good predictors of cross-lingual transfer, significantly more than just the lexical similarity of languages. For a given language, we are able to predict zero-shot performance, that increases on a logarithmic scale with the number of few-shot target language data points.
A Multi-Layered GaGeTe Electro-Optic Device Integrated in Silicon Photonics

Electrically tunable devices contribute significantly to key functions of photonics integrated circuits. Here, we demonstrate the tuning of the optical index of refraction based on hybrid integration of multi-layered anisotropic GaGeTe on a silicon micro-ring resonator (Si-MRR). Under static applied (DC) bias and transverse-electric (TE) polarization, the device exhibits a linear resonance shift without any amplitude modulation. However, for the transverse-magnetic (TM) polarization, both amplitude and phase modulation is observed. The corresponding wavelength shift and half-wave voltage length product (${V}_\pi .\ l$) for the TE polarization are −1.78 pm/V and 1.24 V.cm, respectively. These values are enhanced for the TM polarizations and correspond to −6.65 pm/V and 0.28 V.cm, respectively. The dynamic radio frequency (RF) response of the devices was also tested at different bias conditions. Remarkably, the device exhibits a 1.6 MHz and 2.1 MHz response at 0 V and 7 V bias, respectively. Based on these findings, the integration of 2D GaGeTe on the silicon photonics platform has great potential for the next generation of integrated photonic applications such as switches and phase shifters.
What Matters to Individual Investors: Price Setting in Online Auctions of P2P Consumer Loans

: We analyze how retail investors price the credit risk of online P2P consumer loans in a reverse auction framework where personal interaction is absent. The explained interest rate variance is considerably larger than in comparable studies using bank loan data. This is unexpected, given the difference in experience and the limited set of information provided to the investor. Factors representing economic status significantly influence lender evaluations of the borrower’s credit risk. Previous studies on gender discrimination found mixed results. We show that this is likely due to simplified specifications and find surprising results: male borrowers are charged a higher interest rates, conditional on being married and having children. Overall, our results indicate that retail investors exhibit a strong degree of predictability in this weakly regulated market for online P2P consumer loans.