Miracle-dz/UniFAR-FTUs huggingface dataset:
Ice Features of Low-luminosity Protostars in Near-infrared Spectra of AKARI/IRC

We present near-infrared spectra of three low-luminosity protostars and one background star in the Perseus molecular cloud, acquired using the infrared camera on board the AKARI space telescope. For the comparison with different star-forming environments, we also present spectra of the massive protostar AFGL 7009S, where the protostellar envelope is heated significantly, and the low-mass protostar RNO 91, which is suspected to be undergoing an episodic burst. We detected ice absorption features of H2O, CO2, and CO in all spectra around the wavelengths of 3.05, 4.27, and 4.67 μm, respectively. For at least two low-luminosity protostars, we also detected the XCN ice feature at 4.62 μm. The presence of the crystalline H2O ice and XCN ice components indicates that the low-luminosity protostars experienced a hot phase via accretion bursts during the past mass accretion process. We compared the ice abundances of the low-luminosity protostars with those of embedded low-mass protostars and the dense molecular clouds and cores, suggesting that their ice abundances reflect the strength of prior bursts and the timescale after the last burst.
Controlled unidirectional reflectionlessness by coupling strength in a non-Hermitian waveguide quantum electrodynamics system

Unidirectional reflectionlessness is investigated in a waveguide quantum electrodynamics system that consists of a cavity and a [Formula: see text]-type three-level quantum dot coupled to a one-dimensional plasmonic waveguide. Analytical expressions of transmission and reflection coefficients are derived and discussed for both resonant and off-resonant couplings. By appropriately modulating the coupling strength, phase shift and Rabi frequency, unidirectional reflectionlessness is observed at the exceptional points (EPs). And unidirectional coherent perfect absorption is exhibited at the vicinity of the EP. These results might find applications in designing quantum devices of photons, such as optical switches and single-photon transistors.
Joint NMF for Identification of Shared Features in Datasets and a Dataset Distance Measure

—In this paper, we derive a new method for determin- ing shared features of datasets by employing joint non-negative matrix factorization and analyzing the resulting factorizations. Our approach uses the joint factorization of two dataset matrices X 1 , X 2 into non-negative matrices X 1 = AS 1 , X 2 = AS 2 to derive a similarity measure that determines how well a shared basis for X 1 , X 2 approximates each dataset. We also propose a dataset distance measure built upon this method and the learned factorization. Our method is able to successfully identity differences in structure in both image and text datasets. Potential applications include classiﬁcation, detecting plagiarism or other manipulation, and learning relationships between data sets.
CosmicNet II: emulating extended cosmologies with efficient and accurate neural networks

In modern analysis pipelines, Einstein-Boltzmann Solvers (EBSs) are an invaluable tool for obtaining CMB and matter power spectra. To significantly accelerate the computation of these observables, the CosmicNet strategy is to replace the usual bottleneck of an EBS, which is the integration of a system of differential equations for linear cosmological perturbations, by trained neural networks. This strategy offers several advantages compared to the direct emulation of the final observables, including very small networks that are easy to train in high-dimensional parameter spaces, and which do not depend by construction on primordial spectrum parameters nor observation-related quantities such as selection functions. In this second CosmicNet paper, we present a more efficient set of networks that are already trained for extended cosmologies beyond ΛCDM, with massive neutrinos, extra relativistic degrees of freedom, spatial curvature, and dynamical dark energy. We publicly release a new branch of the class code, called classnet, which automatically uses networks within a region of trusted accuracy. We demonstrate the accuracy and performance of classnet by presenting several parameter inference runs from Planck, BAO and supernovae data, performed with classnet and the cobaya inference package. We have eliminated the perturbation module as a bottleneck of the EBS, with a speedup that is even more remarkable in extended cosmologies, where the usual approach would have been more expensive while the network's performance remains the same. We obtain a speedup factor of order 150 for the emulated perturbation module of class. For the whole code, this translates into an overall speedup factor of order 3 when computing CMB harmonic spectra (now dominated by the highly parallelizable and further optimizable line-of-sight integration), and of order 50 when computing matter power spectra (less than 0.1 seconds even in extended cosmologies).
Lip-Listening: Mixing Senses to Understand Lips using Cross Modality Knowledge Distillation for Word-Based Models

In this work, we propose a technique to transfer speech recognition capabilities from audio speech recognition systems to visual speech recognizers, where our goal is to utilize audio data during lipreading model training. Impressive progress in the domain of speech recognition has been exhibited by audio and audio-visual systems. Nevertheless, there is still much to be explored with regards to visual speech recognition systems due to the visual ambiguity of some phonemes. To this end, the development of visual speech recognition models is crucial given the instability of audio models. The main contributions of this work are i) building on recent state-of-the-art word-based lipreading models by integrating sequence-level and frame-level Knowledge Distillation (KD) to their systems; ii) leveraging audio data during training visual models, a feat which has not been utilized in prior word-based work; iii) proposing the Gaussian-shaped averaging in frame-level KD, as an efficient technique that aids the model in distilling knowledge at the sequence model encoder. This work proposes a novel and competitive architecture for lip-reading, as we demonstrate a noticeable improvement in performance, setting a new benchmark equals to 88.64% on the LRW dataset.
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting

In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes correlations among the predicted trajectories of multiple agents; 2) how to rank the multiple predictions and select the optimal predicted trajectory. In order to handle the aforementioned challenges, this work first proposes a novel concept, collaborative uncertainty (CU), which models the uncertainty resulting from interaction modules. Then we build a general CU-aware regression framework with an original permutation-equivariant uncertainty estimator to do both tasks of regression and uncertainty estimation. Furthermore, we apply the proposed framework to current SOTA multi-agent multi-modal forecasting systems as a plugin module, which enables the SOTA systems to: 1) estimate the uncertainty in the multi-agent multi-modal trajectory forecasting task; 2) rank the multiple predictions and select the optimal one based on the estimated uncertainty. We conduct extensive experiments on a synthetic dataset and two public large-scale multi-agent trajectory forecasting benchmarks. Experiments show that: 1) on the synthetic dataset, the CU-aware regression framework allows the model to appropriately approximate the ground-truth Laplace distribution; 2) on the multi-agent trajectory forecasting benchmarks, the CU-aware regression framework steadily helps SOTA systems improve their performances. Especially, the proposed framework helps VectorNet improve by 262 cm regarding the Final Displacement Error of the chosen optimal prediction on the nuScenes dataset; 3) in multi-agent multi-modal trajectory forecasting, prediction uncertainty is proportional to future stochasticity; 4) the estimated CU values are highly related to the interactive information among agents. The proposed framework can guide the development of more reliable and safer forecasting systems in the future.
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization

Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance, to make an agent's interaction with users more engaging, or improve the robustness of a policy to an unexpected perturbance. We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task. Unlike prior work, it achieves this with a shared policy network trained over a single run. Specifically, we design an intrinsic reward based on an information-theoretic diversity objective. Our final objective alternately constraints on the diversity of the strategies and on the extrinsic reward. We solve the constrained optimization problem by casting it as a probabilistic inference task and use policy iteration to maximize the derived lower bound. Experimental results show that our method efficiently discovers diverse strategies in a wide variety of reinforcement learning tasks. Compared to baseline methods, DGPO achieves comparable rewards, while discovering more diverse strategies, and often with better sample efficiency.
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis

Survival analysis, time-to-event analysis, is an important problem in healthcare since it has a wide-ranging impact on patients and palliative care. Many survival analysis methods have assumed that the survival data is centrally available either from one medical center or by data sharing from multi-centers. However, the sensitivity of the patient attributes and the strict privacy laws have increasingly forbidden sharing of healthcare data. To address this challenge, the research community has looked at the solution of decentralized training and sharing of model parameters using the Federated Learning (FL) paradigm. In this paper, we study the utilization of FL for performing survival analysis on distributed healthcare datasets. Recently, the popular Cox proportional hazard (CPH) models have been adapted for FL settings; however, due to its linearity and proportional hazards assumptions, CPH models result in suboptimal performance, especially for non-linear, non-iid, and heavily censored survival datasets. To overcome the challenges of existing federated survival analysis methods, we leverage the predictive accuracy of the deep learning models and the power of pseudo values to propose a first-of-its-kind, pseudo value-based deep learning model for federated survival analysis (FSA) called FedPseudo. Furthermore, we introduce a novel approach of deriving pseudo values for survival probability in the FL settings that speeds up the computation of pseudo values. Extensive experiments on synthetic and real-world datasets show that our pseudo valued-based FL framework achieves similar performance as the best centrally trained deep survival analysis model. Moreover, our proposed FL approach obtains the best results for various censoring settings.
EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use

In audio classification, differentiable auditory filter-banks with few parameters cover the middle ground between hard-coded spectrograms and raw audio. LEAF [1], a Gabor-based filterbank combined with Per-Channel Energy Normalization (PCEN), has shown promising results, but is computationally expensive. With inhomogeneous convolution kernel sizes and strides, and by replacing PCEN with better parallelizable operations, we can reach similar results more efficiently. In experiments on six audio classification tasks, our frontend matches the accuracy of LEAF at 3% of the cost, but both fail to consistently outperform a fixed mel filterbank. The quest for learnable audio frontends is not solved.
Generalised spectral dimensions in non-perturbative quantum gravity

The seemingly universal phenomenon of scale-dependent effective dimensions in non-perturbative theories of quantum gravity has been shown to be a potential source of quantum gravity phenomenology. The scale-dependent effective dimension from quantum gravity has only been considered for scalar fields. It is, however, possible that the non-manifold like structures, that are expected to appear near the Planck scale, have an effective dimension that depends on the type of field under consideration. To investigate this question, we have studied the spectral dimension associated to the Laplace–Beltrami operator generalised to k-form fields on spatial slices of the non-perturbative model of quantum gravity known as causal dynamical triangulations. We have found that the two-form, tensor and dual scalar spectral dimensions exhibit a flow between two scales at which an effective dimension appears. However, the one-form and vector spectral dimensions show only a single effective dimension. The fact that the one-form and vector spectral dimension do not show a flow of the effective dimension can potentially be related to the absence of a dispersion relation for the electromagnetic field, but dynamically generated instead of as an assumption.
On Exact and Eﬃcient Inference for Many Normal Means

Inference about the unknown means θ = ( θ 1 , ., θ n ) (cid:48) ∈ R n in the sampling model X ∼ N n ( θ, I ) from the observed X , known as the many-normal-means problem, has proven to be fundamental and yet challenging inasmuch as a satisfactory understanding remains elusive. To tackle exact and eﬃcient inference about θ , in this paper we propose innovative formula-tions of Inferential Models for two kinds of this problem: the classic kind given as is and the empirical-Bayes kind where θ ’s are further assumed to be an unobservable sample from an unknown non-parametric distribution G . The formulation for the empirical-Bayes kind via numerical deconvolution allows for prior-free probabilistic inference with over-parameterization for the non-parametric model G , whereas the formation for the ﬁrst kind utilizes a latent random permutation and as a result provides a sound reasoning with uncertainty toward a deeper understanding. For uncertainty quantiﬁcation with the more familiar frequentist inference framework, the method of maximum plausibility estimation is used for point estimation. Exact but conservative interval estimation is obtained based on plausibility, with a Monte Carlo based adaptive-adjustment approach to constructing shorter conﬁdence intervals with targeted coverage. These methods are illustrated via simulation studies and a real-data example. The numerical results show that for interval estimation, adaptive intervals are satisfactory in both coverage and eﬃciency and that for point estimation, the proposed methods outperform the traditional James-Stein and Efron’s g-modeling in terms of mean square error. The paper concludes with a few remarks, including future developments and extensions of the proposed methods.
Accelerating astronomical and cosmological inference with preconditioned Monte Carlo

We introduce Preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non–trivial geometry. PMC utilises a Normalising Flow (NF) in order to decorrelate the parameters of the distribution and then proceeds by sampling from the preconditioned target distribution using an adaptive Sequential Monte Carlo (SMC) scheme. The results produced by PMC include samples from the posterior distribution and an estimate of the model evidence that can be used for parameter inference and model comparison respectively. The aforementioned framework has been thoroughly tested in a variety of challenging target distributions achieving state–of–the–art sampling performance. In the cases of primordial feature analysis and gravitational wave inference, PMC is approximately 50 and 25 times faster respectively than Nested Sampling (NS). We found that in higher dimensional applications the acceleration is even greater. Finally, PMC is directly parallelisable, manifesting linear scaling up to thousands of CPUs.
Deriving RIP sensing matrices for sparsifying dictionaries

Compressive sensing involves the inversion of a mapping $SD \in \mathbb{R}^{m \times n}$, where $m<n$, $S$ is a sensing matrix, and $D$ is a sparisfying dictionary. The restricted isometry property is a powerful sufficient condition for the inversion that guarantees the recovery of high-dimensional sparse vectors from their low-dimensional embedding into a Euclidean space via convex optimization. However, determining whether $SD$ has the restricted isometry property for a given sparisfying dictionary is an NP-hard problem, hampering the application of compressive sensing. This paper provides a novel approach to resolving this problem. We demonstrate that it is possible to derive a sensing matrix for any sparsifying dictionary with a high probability of retaining the restricted isometry property. In numerical experiments with sensing matrices for K-SVD, Parseval K-SVD, and wavelets, our recovery performance was comparable to that of benchmarks obtained using Gaussian and Bernoulli random sensing matrices for sparse vectors.
2022 Structural 130-K Phase Transition and Emergence of a Two-Ion Kondo State in HT-Ce 2 Rh 2 Ga Explored by 69 , 71 Ga Nuclear Quadrupole Resonance

We have studied the microscopic magnetic properties, the nature of the 130-K phase transition, and the ground state in the recently synthesized compound Ce 2 Rh 2 Ga by use of 69 , 71 Ga nuclear quadrupole resonance (NQR). The NQR spectra clearly show an unusual phase transition at T t ∼ 130 K yielding a splitting of the high-temperature single NQR line into two clearly resolved NQR lines, providing evidence for two crystallographically inequivalent Ga sites. The NQR frequencies are in good agreement with fully-relativistic calculations of the band structure. Our NQR results indicate the absence of magnetic or charge order down to 0.3 K. The temperature dependence of the spin-lattice relaxation rate, 1/ T 1 , shows three distinct regimes, with onset temperatures at T t and 2 K. The temperature-independent 1/ T 1 , observed between T t and 2 K, crosses over to a Korringa process, 1/ T 1 ∝ T , below ∼ 2 K, which evidences a rare two-ion Kondo scenario: the system goes into a dense Kondo coherent state at 2.0 and 0.8 K for the two diﬀerent Ga sites.
The d-separation criterion in Categorical Probability

The d-separation criterion detects the compatibility of a joint probability distribution with a directed acyclic graph through certain conditional independences. In this work, we study this problem in the context of categorical probability theory by introducing a categorical definition of causal models, a categorical notion of d-separation, and proving an abstract version of the d-separation criterion. This approach has two main benefits. First, categorical d-separation is a very intuitive criterion based on topological connectedness. Second, our results apply both to measure-theoretic probability (with standard Borel spaces) and beyond probability theory, including to deterministic and possibilistic networks. It therefore provides a clean proof of the equivalence of local and global Markov properties with causal compatibility for continuous and mixed random variables as well as deterministic and possibilistic variables.
Physical Characterization of 2015 JD1: A Possibly Inhomogeneous Near-Earth Asteroid

The surfaces of airless bodies such as asteroids are exposed to many phenomena that can alter their physical properties. Bennu, the target of the OSIRIS-REx mission, has demonstrated how complex the surface of a small body can be. In 2019 November, the potentially hazardous asteroid 2015 JD1 experienced a close approach of 0.033 1 au from the Earth. We present results of the physical characterization of 2015 JD1 based on ground-based radar, spectroscopy, and photometric observations acquired during 2019 November. Radar polarimetry measurements from the Arecibo Observatory indicate a morphologically complex surface. The delay-Doppler images reveal a contact binary asteroid with an estimated visible extent of ∼150 m. Our observations suggest that 2015 JD1 is an E-type asteroid with a surface composition similar to aubrites, a class of differentiated enstatite meteorites. The dynamical properties of 2015 JD1 suggest that it came from the ν 6 resonance with Jupiter, and spectral comparison with major E-type bodies suggests that it may have been derived from a parental body similar to the progenitor of the E-type (64) Angelina. Significantly, we find rotational spectral variation across the surface of 2015 JD1 from the red to blue spectral slope. Our compositional analysis suggests that the spectral slope variation could be due to the lack of iron and sulfides in one area of the surface of 2015 JD1 and/or differences in grain sizes.
Applied Trajectory Design for close-proximity operations of Asteroid CubeSat Mission

In this paper, a practical approach to the trajectory design for asteroid exploration missions with CubeSats is presented. When applied trajectories are sought, operative concerns and uncertainties affecting the spacecraft dynamics must be considered during the design process. Otherwise, trajectories that are possible on paper might become unfeasible when real-world constraints are considered. The risk of such eventualities leads to the urge of extending the trajectory design focus on the uncertainties affecting the dynamics and on the operative constraints derived by ground operations. This is especially true when targeting highly perturbed environments such as small bodies with low-cost solutions as CubeSats, whose capabilities in deep-space are still unknown. The case study presented is the Milani CubeSat which will be launched in 2024 with Hera in the frame of the AIDA mission.
Certified Adversarial Robustness via Anisotropic Randomized Smoothing

Randomized smoothing has achieved great suc-cess for certiﬁed robustness against adversarial perturbations. Given any arbitrary classiﬁer, randomized smoothing can guarantee the classiﬁer’s prediction over the perturbed input with provable robustness bound by injecting noise into the classiﬁer. However, all of the existing methods rely on ﬁxed i.i. d. probability distribution to generate noise for all dimensions of the data (e.g., all the pixels in an image), which ignores the heterogeneity of inputs and data dimensions. Thus, existing randomized smoothing methods cannot provide optimal protection for all the inputs. To address this limitation, we propose a novel anisotropic randomized smoothing method which ensures provable robustness guarantee based on pixel-wise noise distributions. Also, we design a novel CNN-based noise generator to efﬁciently ﬁne-tune the pixel-wise noise distributions for all the pixels in each input. Experimental results demonstrate that our method signiﬁcantly outperforms the state-of-the-art randomized smoothing methods.