Miracle-dz/UniFAR-FTUs huggingface dataset:
Searches for Population III pair-instability supernovae: Predictions for ULTIMATE-Subaru and WFIRST

ULTIMATE-Subaru (Ultra-wide Laser Tomographic Imager and MOS with AO for Transcendent Exploration on Subaru) and WFIRST (Wide Field Infra-Red Survey Telescope) are the next generation of near-infrared instruments that have a large field-of-view. They allow us to conduct deep and wide transient surveys in the near-infrared. Such a near-infrared transient survey enables us to find very distant supernovae that are redshifted to the near-infrared wavelengths. We have performed mock transient surveys with ULTIMATE-Subaru and WFIRST to investigate their ability to discover Population III pair-instability supernovae. We found that a five-year 1 deg2K-band transient survey with a point-source limiting magnitude of 26.5 mag with ULTIMATE-Subaru may find about two Population III pair-instability supernovae beyond the redshift of 6. A five-year 10 deg2 survey with WFIRST reaching 26.5 mag in the F184 band may find about seven Population III pair-instability supernovae beyond the redshift of 6. We also find that the expected numbers of the Population III pair-instability supernova detections increase by about a factor of 2 if the near-infrared transient surveys are performed towards clusters of galaxies. Other supernovae, such as Population II pair-instability supernovae, would also be detected in the same survey. This study demonstrates that these future wide-field near-infrared instruments allow us to investigate the explosions of first-generation supernovae by performing deep and wide near-infrared transient surveys.
TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos

Recent advancements in cabled ocean observatories have increased the quality and prevalence of underwater videos; this data enables the extraction of high-level biologically relevant information such as species’ behaviours. Despite this increase in capability, most modern methods for the automatic interpretation of underwater videos focus only on the detection and counting organisms. We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. TempNet also presents temporal attention during spatial encoding as well as Wavelet Down-Sampling pre-processing to improve model accuracy. Although our system is designed for applications to diverse fish behaviours (i.e, is generic), we demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events. We compare the proposed approach with a state-of-the-art end-to-end video detection method (ReMotENet) and a hybrid method previously offered exclusively for the detection of sablefish’s startle events in videos from an existing dataset. Results show that our novel method comfortably outperforms the comparison baselines in multiple metrics, reaching a per-clip accuracy and precision of 80% and 0.81, respectively. This represents a relative improvement of 31% in accuracy and 27% in precision over the compared methods using this dataset. Our computational pipeline is also highly efficient, as it can process each 4-second video clip in only 38ms. Furthermore, since it does not employ features specific to sablefish startle events, our system can be easily extended to other behaviours in future works.
Reliable mixed passive and 
 ℋ∞ filtering for semi‐Markov jump systems with randomly occurring uncertainties and sensor failures

This paper investigates the reliable mixed passive and ℋ∞ filtering problem for uncertain semi‐Markov jump delayed systems subject to sensor failures. The parameter uncertainties are randomly occurring with two stochastic variables, which are mutually independent and satisfy certain probabilistic distributions on the interval [0,1]. The objective is to focus on the design of a reliable filter ensuring the mixed passivity and ℋ∞ performance level of the resulting filtering error system in the presence of sensor failures. By using an improved reciprocally convex approach combined with a novel integral inequality, sufficient conditions of mixed passivity and ℋ∞ performance analysis for the considered systems are derived. Based on these, a desired reliable filter design method is developed, and the parameters of the filter are readily calculated by employing a simple expression of the desired filter. Three numerical examples are presented to demonstrate the effectiveness and the reduced conservatism of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.
Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection

Approximately 1.2% of the world's population has impaired voice production. As a result, automatic dysphonic voice detection has attracted considerable academic and clinical interest. However, existing methods for automated voice assessment often fail to generalize outside the training conditions or to other related applications. In this paper, we propose a deep learning framework for generating acoustic feature embeddings sensitive to vocal quality and robust across different corpora. A contrastive loss is combined with a classification loss to train our deep learning model jointly. Data warping methods are used on input voice samples to improve the robustness of our method. Empirical results demonstrate that our method not only achieves high in-corpus and cross-corpus classification accuracy but also generates good embeddings sensitive to voice quality and robust across different corpora. We also compare our results against three baseline methods on clean and three variations of deteriorated in-corpus and cross-corpus datasets and demonstrate that the proposed model consistently outperforms the baseline methods.
α-Rank-Collections: Analyzing Expected Strategic Behavior with Uncertain Utilities

Game theory largely rests on the availability of cardinal utility functions. In contrast, only ordinal preferences are elicited in fields such as matching under preferences. The literature focuses on mechanisms with simple dominant strategies. However, many real-world applications do not have dominant strategies, so intensities between preferences matter when participants determine their strategies. Even though precise information about cardinal utilities is unavailable, some data about the likelihood of utility functions is typically accessible. We propose to use Bayesian games to formalize uncertainty about decision-makers utilities by viewing them as a collection of normal-form games where uncertainty about types persist in all game stages. Instead of searching for the Bayes-Nash equilibrium, we consider the question of how uncertainty in utilities is reflected in uncertainty of strategic play. We introduce $\alpha$-Rank-collections as a solution concept that extends $\alpha$-Rank, a new solution concept for normal-form games, to Bayesian games. This allows us to analyze the strategic play in, for example, (non-strategyproof) matching markets, for which we do not have appropriate solution concepts so far. $\alpha$-Rank-collections characterize a range of strategy-profiles emerging from replicator dynamics of the game rather than equilibrium point. We prove that $\alpha$-Rank-collections are invariant to positive affine transformations, and that they are efficient to approximate. An instance of the Boston mechanism is used to illustrate the new solution concept.
Exploring through Random Curiosity with General Value Functions

Efficient exploration in reinforcement learning is a challenging problem commonly addressed through intrinsic rewards. Recent prominent approaches are based on state novelty or variants of artificial curiosity. However, directly applying them to partially observable environments can be ineffective and lead to premature dissipation of intrinsic rewards. Here we propose random curiosity with general value functions (RC-GVF), a novel intrinsic reward function that draws upon connections between these distinct approaches. Instead of using only the current observation's novelty or a curiosity bonus for failing to predict precise environment dynamics, RC-GVF derives intrinsic rewards through predicting temporally extended general value functions. We demonstrate that this improves exploration in a hard-exploration diabolical lock problem. Furthermore, RC-GVF significantly outperforms previous methods in the absence of ground-truth episodic counts in the partially observable MiniGrid environments. Panoramic observations on MiniGrid further boost RC-GVF's performance such that it is competitive to baselines exploiting privileged information in form of episodic counts.
New Parallaxes for the Upper Scorpius OB Association

Upper Scorpius is a subgroup of the nearest OB association, Scorpius–Centaurus. Its young age makes it an important association to study star and planet formation. We present parallaxes to 52 low-mass stars in Upper Scorpius, 28 of which have full kinematics. We measure ages of the individual stars by combining our measured parallaxes with pre-main-sequence evolutionary tracks. We find a significant difference in the ages of stars with and without circumstellar disks. The stars without disks have a mean age of 4.9 ± 0.8 Myr and those with disks have an older mean age of 8.2 ± 0.9 Myr. This somewhat counterintuitive result suggests that evolutionary effects in young stars can dominate their apparent ages. We also attempt to use the 28 stars with full kinematics (i.e., proper motion, radial velocity (RV), and parallax) to trace the stars back in time to their original birthplace to obtain a trackback age. As expected, given the large measurement uncertainties on available RV measurements, we find that measurement uncertainties alone cause the group to diverge after a few Myr.
Data-driven Basis for Reconstructing the Contrast in Born Inverse Scattering: Picard Criterion, Regularity, Regularization and Stability

We consider the inverse medium scattering of reconstructing the medium contrast using Born data, including the full aperture, limited-aperture, and multi-frequency data. We propose data-driven basis functions for these inverse problems based on the generalized prolate spheroidal wave functions and related eigenfunctions. Such data-driven eigenfunctions are eigenfunctions of a Fourier integral operator; they remarkably extend analytically to the whole space, are doubly orthogonal, and are complete in the class of band-limited functions. We first establish a Picard criterion for reconstructing the contrast using the data-driven basis, where the reconstruction formula can also be understood from the viewpoint of data processing and analytic extrapolation. Another salient feature associated with the generalized prolate spheroidal wave functions is that the data-driven basis for a disk is also a basis for a Sturm-Liouville differential operator. With the help of Sturm-Liouville theory, we estimate the $L^2$ approximation error for a spectral cutoff approximation of $H^s$ functions. This yields a spectral cutoff regularization strategy for noisy data and an explicit stability estimate for contrast in $H^s$ ($0<s<1/2$) in the full aperture case. In the limited-aperture and multi-frequency cases, we also obtain spectral cutoff regularization strategies for noisy data and stability estimates for a class of contrast.
On k-ranks of topological spaces

In this paper, the concepts of $K$-subset systems and $k$-well-filtered spaces are introduced, which provide another uniform approach to $d$-spaces, $s$-well-filtered spaces (i.e., $\mathcal{U}_{S}$-admissibility) and well-filtered spaces. We prove that the $k$-well-filtered reflection of any $T_{0}$ space exists. Meanwhile, we propose the definition of $k$-rank, which is an ordinal that measures how many steps from a $T_{0}$ space to a $k$-well-filtered space. Moreover, we derive that for any ordinal $\alpha$, there exists a $T_{0}$ space whose $k$-rank equals to $\alpha$. One immediate corollary is that for any ordinal $\alpha$, there exists a $T_{0}$ space whose $d$-rank (respectively, $wf$-rank) equals to $\alpha$.
Estimating more camera poses for ego-centric videos is essential for VQ3D

Visual queries 3D localization (VQ3D) is a task in the Ego4D Episodic Memory Benchmark. Given an egocentric video, the goal is to answer queries of the form"Where did I last see object X? ", where the query object X is specified as a static image, and the answer should be a 3D displacement vector pointing to object X. However, current techniques use naive ways to estimate the camera poses of video frames, resulting in a low query with pose (QwP) ratio, thus a poor overall success rate. We design a new pipeline for the challenging egocentric video camera pose estimation problem in our work. Moreover, we revisit the current VQ3D framework and optimize it in terms of performance and efficiency. As a result, we get the top-1 overall success rate of 25.8% on VQ3D leaderboard, which is two times better than the 8.7% reported by the baseline.
Gemini/GMOS Spectroscopy of Globular Clusters in the Merger Remnant Galaxy M85

M85 is a peculiar S0 galaxy in Virgo and a well-known merger remnant. We present the first spectroscopic study of globular clusters (GCs) in M85. We obtain spectra for 21 GC candidates and the nucleus of M85 using the Gemini Multi-Object Spectrograph on the Gemini North 8.1 m telescope. From their radial velocities, 20 of the GCs are found to be members of M85. We find a strong rotation signal of the M85 GC system with a rotation amplitude of 235 km s−1. The rotation axis of the GC system has a position angle of about 161°, which is 51. °5 larger than that of the stellar light. The rotation-corrected radial velocity dispersion of the GC system is estimated to be km s−1. The rotation parameter of the GC system is derived to be , which is one of the largest among known early-type galaxies. The ages and metallicities of the GCs, which show the same trend as the results based on Lick indices, are derived from full spectrum fitting (ULySS). About half of the GCs are an intermediate-age population whose mean age is ∼3.7 ± 1.9 Gyr, having a mean [Fe/H] value of −0.26. The other half are old and metal-poor. These results suggest that M85 experienced a wet merging event about 4 Gyr ago, forming a significant population of star clusters. The strong rotational feature of the GC system can be explained by an off-center major merging.
Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes

We present a efficient multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs. Unlike previous representations, where the global illumination of large scenes is simplified as multiple environment maps, we propose a compact representation called Texture-based Lighting (TBL). It consists of 3D mesh and HDR textures, and efficiently models direct and infinite-bounce indirect lighting of the entire large scene. Based on TBL, we further propose a hybrid lighting representation with precomputed irradiance, which significantly improves the efficiency and alleviates the rendering noise in the material optimization. To physically disentangle the ambiguity between materials, we propose a three-stage material optimization strategy based on the priors of semantic segmentation and room segmentation. Extensive experiments show that the proposed method outperforms the state-of-the-art quantitatively and qualitatively, and enables physically-reasonable mixed-reality applications such as material editing, editable novel view synthesis and relighting. The project page is at https://lzleejean.github. io/TexIR.
GAMMT: Generative Ambiguity Modeling Using Multiple Transformers

We introduce a novel model called GAMMT (Generative Ambiguity Models using Multiple Transformers) for sequential data that is based on sets of probabilities. Unlike conventional models, our approach acknowledges that the data generation process of a sequence is not deterministic, but rather ambiguous and influenced by a set of probabilities. To capture this ambiguity, GAMMT employs multiple parallel transformers that are linked by a selection mechanism, allowing for the approximation of ambiguous probabilities. The generative nature of our approach also enables multiple representations of input tokens and sequences. While our models have not yet undergone experimental validation, we believe that our model has great potential to achieve high quality and diversity in modeling sequences with uncertain data generation processes.
Numerical calculations of the B 1 g Raman spectrum of the two-dimensional Heisenberg model

The ${B}_{1g}$ Raman spectrum of the two-dimensional $S=1/2$ Heisenberg model is discussed within Loudon-Fleury theory at both zero and finite temperature. The exact $T=0$ spectrum for lattices with up to $6\ifmmode\times\else\texttimes\fi{}6$ sites is computed using Lancz\"os exact diagonalization. A quantum Monte Carlo (QMC) method is used to calculate the corresponding imaginary-time correlation function and its first two derivatives for lattices with up to $16\ifmmode\times\else\texttimes\fi{}16$ spins. The imaginary-time data are continued to real frequency using the maximum-entropy method, as well as a fit based on spin-wave theory. The numerical results are compared with spin-wave calculations for finite lattices. There is a surprisingly large change in the exact spectrum going from $4\ifmmode\times\else\texttimes\fi{}4$ to $6\ifmmode\times\else\texttimes\fi{}6$ sites. In the former case there is a single dominant two-magnon peak at $\ensuremath{\omega}/J\ensuremath{\approx}3.0$, whereas in the latter case there are two approximately equal-sized peaks at $\ensuremath{\omega}/J\ensuremath{\approx}2.7$ and $3.9$. This is in good qualitative agreement with the spin-wave calculations including two-magnon processes on the same lattices. The spin-wave results for larger lattices show how additional peaks emerge with increasing lattice size, and eventually develop into the well known two-magnon profile peaked at $\ensuremath{\omega}/J\ensuremath{\approx}3.2$ and with weight extending up to $\ensuremath{\omega}/J\ensuremath{\approx}4.6$. Both the Lancz\"os and the QMC results indicate that the actual two-magnon profile is broader than the narrow peak obtained in spin-wave theory, but the positions of the maxima agree to within a few percent. The higher-order contributions present in the numerical results are merged with the two-magnon profile and extend up to frequencies $\ensuremath{\omega}/J\ensuremath{\approx}7$. The first three frequency cumulants of the spectrum are in excellent agreement with results previously obtained from a series expansion around the Ising limit. Typical experimental ${B}_{1g}$ spectra for ${\mathrm{La}}_{2}{\mathrm{CuO}}_{4}$ are only slightly broader than what we obtain here. The exchange constant extracted from the peak position is $J\ensuremath{\approx}1400 \mathrm{K}$, in good agreement with values obtained from neutron scattering and NMR experiments. We discuss the implications of our present results for more sophisticated theories of Raman scattering suggested recently.
Exotic quantum statistics from a many-body theory of Majorana fermions

Starting with a simple counting argument, we construct a statistical and thermodynamic model of free Majorana fermions. Originally defined as a fermion identical to its own antiparticle state, Majorana particles often appear in the contemporary many-body literature as non-Abelian zero energy modes in topological superconductors. We deviate from the usual anyonic description and instead consider a gas of non-interacting, spin-1/2 Majorana fermions as Ettore Majorana first envisioned them. The existence of a fermionic ground state in such a system is heavily implied by standard quantum mechanical arguments and current theoretical insights from cold dark matter physics. This allows us to build a quantum statistical theory of the Majorana system in the low temperature, low density limit without the need to account for strong fluctuations in the particle number. A combinatorical analysis of the many-body Majorana ensemble leads to a configurational entropy which deviates from the fermionic result with an increasing number of available microstates. A Majorana distribution function is then derived which shows signatures of a sharply-defined Fermi surface at finite temperatures. The thermodynamics of such a system is shown to be nearly identical to that of a free Fermi gas, except now distinguished by a two-fold ground state degeneracy and, subsequently, a residual entropy at zero temperature. Experimental realization of the Majorana thermodynamics is then discussed in the context of superconductors, topological matter, and dense neutrino gases from supernovae emissions and relics from the Big Bang.
Solving Relaxations of MAP-MRF Problems: Combinatorial in-Face Frank-Wolfe Directions

We consider the problem of solving LP relaxations of MAP-MRF inference problems, and in particular the method proposed recently in [16], [35]. As a key computational subroutine, it uses a variant of the Frank-Wolfe (FW) method to minimize a smooth convex function over a combinatorial polytope. We propose an efficient implementation of this subroutine based on in-face Frank-Wolfe directions, introduced in [4] in a different context. More generally, we define an abstract data structure for a combinatorial subproblem that enables in-face FW directions, and describe its specialization for tree-structured MAP-MRF inference subproblems. Experimental results indicate that the resulting method is the current state-of-art LP solver for some classes of problems. Our code is available at pub.ist. ac.at/~vnk/papers/IN-FACE-FW.html.
Learning Group Importance using the Differentiable Hypergeometric Distribution

Partitioning a set of elements into subsets of a priori unknown sizes is essential in many applications. These subset sizes are rarely explicitly learned - be it the cluster sizes in clustering applications or the number of shared versus independent generative latent factors in weakly-supervised learning. Probability distributions over correct combinations of subset sizes are non-differentiable due to hard constraints, which prohibit gradient-based optimization. In this work, we propose the differentiable hypergeometric distribution. The hypergeometric distribution models the probability of different group sizes based on their relative importance. We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering. In both applications, we outperform previous approaches, which rely on suboptimal heuristics to model the unknown size of groups.