Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSES OF THE VARIABILITY ASYMMETRY OF KEPLER AGNs

The high-quality light curves from the Kepler space telescope make it possible to analyze the optical variability of active galactic nuclei (AGNs) with unprecedented time resolution. Studying the asymmetry in variations could provide independent constraints on physical models for AGN variability. In this paper, we use Kepler observations of 19 sources to perform analyses of the variability asymmetry of AGNs. We apply smoothing correction to light curves to deduct their bias toward high-frequency variability asymmetry caused by long-term variations that have been poorly sampled due to the limited length of light curves. A parameter β based on structure functions is introduced to quantitively describe the asymmetry and its uncertainty is measured using extensive Monte Carlo simulations. Individual sources show no evidence of asymmetry at timescales of 1 ∼ 20 ? > days and there is no general trend toward positive or negative asymmetry over the whole sample. Stacking the data from all 19 AGNs, we derive an averaged β ¯ ? > of 0.00 ± 0.03 and −0.02 ± 0.04 over timescales of 1 ∼ 5 days and 5 ∼ 20 days, respectively, which are statistically consistent with zero. Quasars and Seyfert galaxies show similar asymmetry parameters. Our results indicate that short-term optical variations in AGNs are highly symmetric.
LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting

This paper proposes a hardware-efﬁcient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized speciﬁcally for low-power processor units like microcontrollers. ML operators exhibit heteroge-neous efﬁciency proﬁles on power-efﬁcient hardware. Given the exact theoretical computation cost, int8 operators are more computation-effective than ﬂoat operators, and linear layers are often more efﬁcient than other layers. The proposed LiCo-Net is a dual-phase system that uses the efﬁcient int 8 linear operators at the inference phase and applies streaming convolutions at the training phase to maintain a high model capacity. The experimental results show that LiCo-Net outperforms single-value decomposition ﬁlter (SVDF) on hardware efﬁciency with on-par detection performance. Compared to SVDF, LiCo-Net reduces cycles by 40% on HiFi4 DSP.
Internal DLA on mated-CRT maps

We prove a shape theorem for internal diffusion limited aggregation on mated-CRT maps, a family of random planar maps which approximate Liouville quantum gravity (LQG) surfaces. The limit is an LQG harmonic ball, which we constructed in a companion paper. We also prove an analogous result for the divisible sandpile.
fPINNs: Fractional Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) are effective in solving integer-order partial differential equations (PDEs) based on scattered and noisy data. PINNs employ standard feedforward neural networks (NNs) with the PDEs explicitly encoded into the NN using automatic differentiation, while the sum of the mean-squared PDE-residuals and the mean-squared error in initial/boundary conditions is minimized with respect to the NN parameters. We extend PINNs to fractional PINNs (fPINNs) to solve space-time fractional advection-diffusion equations (fractional ADEs), and we demonstrate their accuracy and effectiveness in solving multi-dimensional forward and inverse problems with forcing terms whose values are only known at randomly scattered spatio-temporal coordinates (black-box forcing terms). A novel element of the fPINNs is the hybrid approach that we introduce for constructing the residual in the loss function using both automatic differentiation for the integer-order operators and numerical discretization for the fractional operators. We consider 1D time-dependent fractional ADEs and compare white-box (WB) and black-box (BB) forcing. We observe that for the BB forcing fPINNs outperform FDM. Subsequently, we consider multi-dimensional time-, space-, and space-time-fractional ADEs using the directional fractional Laplacian and we observe relative errors of $10^{-4}$. Finally, we solve several inverse problems in 1D, 2D, and 3D to identify the fractional orders, diffusion coefficients, and transport velocities and obtain accurate results even in the presence of significant noise.
High Jaynes-Cummings pseudospins eigenstates in the homogeneous Tavis-Cummings model

We show that a set of N identical qubits coupled to a single cavity resonator can be decomposed into a set of independent subsystems, which can be thought of as higher pseudospin generalisations of the Jaynes-Cummings model. We derive and analyse the solutions to the equations of motion and demonstrate unusual beating behaviour resulting from a new form of square-root-n-type non-linearity appearing within a pseudospin ladder. Furthermore, we propose a relative phase shift transformation which allows one to switch/initiate the multi-qubit state in a desired pseudospin configuration, and show how such transformations can be used to undo spontaneous single qubit decay. We discuss the conditions which justify the validity of the rotating wave approximation in an N-qubit system.
Local Structure Matters Most in Most Languages

Many recent perturbation studies have found unintuitive results on what does and does not matter when performing Natural Language Understanding (NLU) tasks in English. Coding properties, such as the order of words, can often be removed through shuffling without impacting downstream performances. Such insight may be used to direct future research into English NLP models. As many improvements in multilingual settings consist of wholesale adaptation of English approaches, it is important to verify whether those studies replicate or not in multilingual settings. In this work, we replicate a study on the importance of local structure, and the relative unimportance of global structure, in a multilingual setting. We find that the phenomenon observed on the English language broadly translates to over 120 languages, with a few caveats.
Workload Forecasting of a Logistic Node Using Bayesian Neural Networks

Purpose: Traffic volume in empty container depots has been highly volatile due to external factors. Forecasting the expected container truck traffic along with having a dynamic module to foresee the future workload plays a critical role in improving the work efficiency. This paper studies the relevant literature and designs a forecasting model addressing the aforementioned issues. Methodology: The paper develops a forecasting model to predict hourly work and traffic volume of container trucks in an empty container depot using a Bayesian Neural Network based model. Furthermore, the paper experiments with datasets with different characteristics to assess the model’s fo recasting range for various data sources. Findings: The real data of an empty container depot is utilized to develop a forecasting model and to later verify the capabilities of the model. The findings show the performance validity of the model and provide the groundwork to build an effective traffic and workload planning system for the empty container depot in question. Originality: This paper proposes a Bayesian deep learning-based forecasting model for traffic and workload of an empty container depot using real-world data. This designed and implemented forecasting model offers a solution with which every actor in the container truck transportation benefits from the optimized workload.
Gaussian Process Learning-Based Model Predictive Control for Safe Interactions of a Platoon of Autonomous and Human-Driven Vehicles

—With the continued integration of autonomous vehicles (AVs) into public roads, a mixed trafﬁc environment with large-scale human-driven vehicles (HVs) and AVs interactions is imminent. In challenging trafﬁc scenarios, such as emergency braking, it is crucial to account for the reactive and uncertain behavior of HVs when developing control strategies for AVs. This paper studies the safe control of a platoon of AVs interacting with a human-driven vehicle in longitudinal car-following scenarios. We ﬁrst propose the use of a model that combines a ﬁrst-principles model (nominal model) with a Gaussian process (GP) learning-based component for predicting behaviors of the human-driven vehicle when it interacts with AVs. The modeling accuracy of the proposed method shows a 9% reduction in root mean square error (RMSE) in predicting a HV’s velocity compared to the nominal model. Exploiting the properties of this model, we design a model predictive control (MPC) strategy for a platoon of AVs to ensure a safe distance between each vehicle, as well as a (probabilistic) safety of the human-driven car following the platoon. Compared to a baseline MPC that uses only a nominal model for HVs, our method achieves better velocity-tracking performance for the autonomous vehicle platoon and more robust constraint satisfaction control for a platoon of mixed vehicles system. Simulation studies demonstrate a 4 . 2% decrease in the control cost and an approximate 1 m increase in the minimum distance between autonomous and human-driven vehicles to better guarantee safety in challenging trafﬁc scenarios.
Stochastic gravitational waves associated with the formation of primordial black holes

Primordial black hole (PBH) mergers have been proposed as an explanation for the gravitational wave events detected by the LIGO collaboration. Such PBHs may be formed in the early Universe as a result of the collapse of extremely rare high-sigma peaks of primordial fluctuations on small scales, as long as the amplitude of primordial perturbations on small scales is enhanced significantly relative to the amplitude of perturbations observed on large scales. One consequence of these small-scale perturbations is generation of stochastic gravitational waves that arise at second order in scalar perturbations, mostly before the formation of the PBHs. These induced gravitational waves have been shown, assuming gaussian initial conditions, to be comparable to the current limits from the European Pulsar Timing Array, severely restricting this scenario. We show, however, that models with enhanced fluctuation amplitudes typically involve non-gaussian initial conditions. With such initial conditions, the current limits from pulsar timing can be evaded. The amplitude of the induced gravitational-wave background can be larger or smaller than the stochastic gravitational-wave background from supermassive black hole binaries.
Energy transfers and magnetic energy growth in small-scale dynamo

In this letter we investigate the dynamics of magnetic energy growth in small-scale dynamo by studying energy transfers, mainly energy fluxes and shell-to-shell energy transfers. We perform dynamo simulations for the magnetic Prandtl number on 10243 grid using the pseudospectral method. We demonstrate that the magnetic energy growth is caused by nonlocal energy transfers from the large-scale or forcing-scale velocity field to small-scale magnetic field. The peak of these energy transfers moves towards lower wave numbers as dynamo evolves, which is the reason why the integral scale of the magnetic field increases with time. The energy transfers U2U (velocity to velocity) and B2B (magnetic to magnetic) are forward and local.
VO-KOREL: A Fourier Disentangling Service of the Virtual Observatory

Abstract VO-KOREL is a web service exploiting the technology of the Virtual Observatory for providing astronomers with the intuitive graphical front-end and distributed computing back-end running the most recent version of the Fourier disentangling code KOREL. The system integrates the ideas of the e-shop basket, conserving the privacy of every user by transfer encryption and access authentication, with features of laboratory notebook, allowing the easy housekeeping of both input parameters and final results, as well as it explores a newly emerging technology of cloud computing. While the web-based front-end allows the user to submit data and parameter files, edit parameters, manage a job list, resubmit or cancel running jobs and mainly watching the text and graphical results of a disentangling process, the main part of the back-end is a simple job queue submission system executing in parallel multiple instances of the FORTRAN code KOREL. This may be easily extended for GRID-based deployment on massively parallel computing clusters. The short introduction into underlying technologies is given, briefly mentioning advantages as well as bottlenecks of the design used.
What is Wrong with Language Models that Can Not Tell a Story?

In this position paper, we contend that advancing our understanding of narrative and the effective generation of longer, subjectively engaging texts is crucial for progress in modern Natural Language Processing (NLP) and potentially the broader field of Artificial Intelligence. We highlight the current lack of appropriate datasets, evaluation methods, and operational concepts necessary for initiating work on narrative processing.
Almost Tight Error Bounds on Differentially Private Continual Counting

The first large-scale deployment of private federated learning uses differentially private counting in the continual release model as a subroutine (Google AI blog titled"Federated Learning with Formal Differential Privacy Guarantees"). In this case, a concrete bound on the error is very relevant to reduce the privacy parameter. The standard mechanism for continual counting is the binary mechanism. We present a novel mechanism and show that its mean squared error is both asymptotically optimal and a factor 10 smaller than the error of the binary mechanism. We also show that the constants in our analysis are almost tight by giving non-asymptotic lower and upper bounds that differ only in the constants of lower-order terms. Our algorithm is a matrix mechanism for the counting matrix and takes constant time per release. We also use our explicit factorization of the counting matrix to give an upper bound on the excess risk of the private learning algorithm of Denisov et al. (NeurIPS 2022). Our lower bound for any continual counting mechanism is the first tight lower bound on continual counting under approximate differential privacy. It is achieved using a new lower bound on a certain factorization norm, denoted by $\gamma_F(\cdot)$, in terms of the singular values of the matrix. In particular, we show that for any complex matrix, $A \in \mathbb{C}^{m \times n}$, \[ \gamma_F(A) \geq \frac{1}{\sqrt{m}}\|A\|_1, \] where $\|\cdot \|$ denotes the Schatten-1 norm. We believe this technique will be useful in proving lower bounds for a larger class of linear queries. To illustrate the power of this technique, we show the first lower bound on the mean squared error for answering parity queries.
Perceived personality state estimation in dyadic and small group interaction with deep learning methods

Dyadic and small group collaboration is an evolutionary advantageous behaviour and the need for such collaboration is a regular occurrence in day to day life. In this paper we estimate the perceived personality traits of individuals in dyadic and small groups over thin-slices of interaction on four multimodal datasets. We ﬁnd that our transformer based predictive model performs similarly to human annotators tasked with predicting the perceived big-ﬁve personality traits of participants. Using this model we analyse the estimated perceived personality traits of individuals performing tasks in small groups and dyads. Permutation analysis shows that in the case of small groups undergoing collaborative tasks, the perceived personality of group members clusters, this is also observed for dyads in a collaborative problem solving task, but not in dyads under non-collaborative task settings. Additionally, we ﬁnd that the group level average perceived personality traits provide a better predictor of group performance than the group level average self-reported personality traits.
MACSum: Controllable Summarization with Mixed Attributes

Abstract Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing work has to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on most metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.
Colloquium: Atomic quantum gases in periodically driven optical lattices

Dynamics of quantum many-body systems is one of the most complex problems in physics since it involves the time evolution of a large number of particles that interact with each other under the influence of external forces. With ultracold atoms in optical atomic lattices it can be done in a controlled environment by applying a periodic force. This Colloquium covers the experimental and theoretical developments in this exciting field of physics.
Detecting Euphemisms with Literal Descriptions and Visual Imagery

This paper describes our two-stage system for the Euphemism Detection shared task hosted by the 3rd Workshop on Figurative Language Processing in conjunction with EMNLP 2022. Euphemisms tone down expressions about sensitive or unpleasant issues like addiction and death. The ambiguous nature of euphemistic words or expressions makes it challenging to detect their actual meaning within a context. In the first stage, we seek to mitigate this ambiguity by incorporating literal descriptions into input text prompts to our baseline model. It turns out that this kind of direct supervision yields remarkable performance improvement. In the second stage, we integrate visual supervision into our system using visual imageries, two sets of images generated by a text-to-image model by taking terms and descriptions as input. Our experiments demonstrate that visual supervision also gives a statistically significant performance boost. Our system achieved the second place with an F1 score of 87.2%, only about 0.9% worse than the best submission.
Null boundary gravitational charges from local Lorentz symmetries

In this paper, we revisit the null boundary gravitational charge in the Newman-Penrose formalism with special emphasis on the charges from local Lorentz transformations. We find that there is one more charge derived from the local Lorentz transformation and the new charge is purely from the Holst term. This reveals a remarkable fact that trivial terms which do not change classical equations of motion can not only affect the boundary degrees of freedom through their contributions to the boundary charges but also have their own rights to create new boundary degrees of freedom.
SPICA: the next generation Infrared Space Telescope

We present an overview of SPICA, the Space Infrared Telescope for Cosmology and Astrophysics , a world-class space observatory optimized for mid- and far-IR astronomy (from 5 to ~210 μ m) with a cryogenically cooled ~3.2 m telescope ( > 30μ m wavelength). In the present design, SPICA will carry mid-IR camera, spectrometers and coronagraph (by JAXA institutes) and a far-IR imager FTS-spectrometer, SAFARI (~34–210 μ m, provided by an European/Canadian consortium lead by SRON). Complementary instruments such as a far-IR/submm spectrometer (proposed by NASA) are also being discussed. SPICA will be the only space observatory of its era to bridge the far–IR wavelength gap between JWST and ALMA, and carry out unique science not achievable at visible or submm wavelengths. In this contribution we summarize some of the scientific advances that will be made possible by the large increase in sensitivity compared to previous infrared space missions.
The association between gas and galaxies – III. The cross-correlation of galaxies and Lyα absorbers at z∼ 1

We have measured the two-dimensional two-point correlation function, ξ AG , between low-column-density Lyα absorbers and galaxies at a redshift z ~ 1. We measured Lyαabsorbers between redshifts z = 0.68 and 1.51 over a total redshift path length of Δz= 1.08 from Hubble Space Telescope Space Telescope Imaging Spectrograph E230M absorption spectra towards the quasars HE 1122-1648 (z = 2.4) and PKS 1127-145 (z = 1.187). The column density of the Lyαabsorbers ranged from 13.2 ≤log 10 [N HI (cm -2 )] ≤ 17.4, with a median column density of log 10 [N HI (cm -2 )] = 14.0. A total of 193 galaxy redshifts within the surrounding 6.8 x 5.7 arcmin 2 field of view of both quasars were identified in a R-magnitude-limited survey (21.5 ≤ R vega ≤ 24.5) using the Focal Reducer and Low Dispersion Spectrograph (FORS2) at the Very Large Telescope, of which 95 were higher than the minimum redshift of z = 0.68 to be used in the correlation function. A 3σ upper limit of ξ AG = 2.8 was found when 145 Lyα absorber-galaxy pairs were binned in redshift space, in a bin of size Δσ = 1.0, Δπ = 2.0 h -1 70 Mpc along the projected separation and line-of-sight distances, respectively. The upper limit in the cross-correlation was found to be 5. 4σ lower than the central peak in the galaxy autocorrelation within the same redshift range, ξ GG , which was in our data equal to 10.7 ± 1.4. Thus, we have shown for the first time that the clustering between low-column-density absorbers and galaxies at a redshift of 1 is weaker than that between galaxies at the same redshift.
Ultralight scalar fields and the growth of structure in the Universe

Ultralight scalar fields, with masses of between m=10{sup -33} eV and m=10{sup -22} eV, can affect the growth of structure in the Universe. We identify the different regimes in the evolution of ultralight scalar fields, how they affect the expansion rate of the Universe, and how they affect the growth rate of cosmological perturbations. We find a number of interesting effects, discuss how they might arise in realistic scenarios of the early universe, and comment on how they might be observed.
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes

New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society. For example: the COVID-19 pandemic highlighted disparities in the availability of broadband service and its role in the digital divide; the environmental justice movement in the United States has raised awareness to health implications for minority populations stemming from historical redlining practices; and studies have found varying quality and coverage in the collection and sharing of open-source geospatial data. Despite the extensive literature on machine learning (ML) fairness, few algorithmic strategies have been proposed to mitigate such biases. In this paper we highlight the unique challenges for quantifying and addressing spatio-temporal biases, through the lens of use cases presented in the scientific literature and media. We envision a roadmap of ML strategies that need to be developed or adapted to quantify and overcome these challenges -- including transfer learning, active learning, and reinforcement learning techniques. Further, we discuss the potential role of ML in providing guidance to policy makers on issues related to spatial fairness.