Miracle-dz/UniFAR-FTUs huggingface dataset:
A Radio-selected Population of Dark, Long Gamma-Ray Bursts: Comparison to the Long Gamma-Ray Burst Population and Implications for Host Dust Distributions

We present centimeter-band and millimeter-band afterglow observations of five long-duration γ-ray bursts (GRBs; GRB 130131A, 130420B, 130609A, 131229A, 140713A) with dust-obscured optical afterglow emission, known as “dark” GRBs. We detect the radio afterglow of two of the dark GRBs (GRB 130131A and 140713A), along with a tentative detection of a third (GRB 131229A) with the Karl G. Jansky Very Large Array (VLA). Supplemented by three additional VLA-detected dark GRBs from the literature, we present uniform modeling of their broadband afterglows. We derive high line-of-sight dust extinctions of A V,GRB ≈ 2.2– ≳ 10.6 mag. Additionally, we model the host galaxies of the six bursts in our sample, and derive host galaxy dust extinctions of A V,Host ≈ 0.3–4.7 mag. Across all tested γ-ray (fluence and duration) and afterglow properties (energy scales, geometries, and circumburst densities), we find dark GRBs to be representative of more typical unobscured long GRBs, except in fluence, for which observational biases and inconsistent classification may influence the dark GRB distribution. Additionally, we find that A V,GRB is not related to a uniform distribution of dust throughout the host, nor to the extremely local environment of the burst, indicating that a larger-scale patchy dust distribution is the cause of the high line-of-sight extinction. Since radio observations are invaluable to revealing heavily dust-obscured GRBs, we make predictions for the detection of radio emission from host star formation with the next-generation VLA.
Distilling Governing Laws and Source Input for Dynamical Systems from Videos

Distilling interpretable physical laws from videos has led to expanded interest in the computer vision community recently thanks to the advances in deep learning, but still remains a great challenge. This paper introduces an end-to-end unsupervised deep learning framework to uncover the explicit governing equations of dynamics presented by moving object(s), based on recorded videos. Instead in the pixel (spatial) coordinate system of image space, the physical law is modeled in a regressed underlying physical coordinate system where the physical states follow potential explicit governing equations. A numerical integrator-based sparse regression module is designed and serves as a physical constraint to the autoencoder and coordinate system regression, and, in the meanwhile, uncover the parsimonious closed-form governing equations from the learned physical states. Experiments on simulated dynamical scenes show that the proposed method is able to distill closed-form governing equations and simultaneously identify unknown excitation input for several dynamical systems recorded by videos, which fills in the gap in literature where no existing methods are available and applicable for solving this type of problem.
Understanding the Moments of Tabulation Hashing via Chaoses

Simple tabulation hashing dates back to Zobrist in 1970 and is defined as follows: Each key is viewed as $c$ characters from some alphabet $\Sigma$, we have $c$ fully random hash functions $h_0, \ldots, h_{c - 1} \colon \Sigma \to \{0, \ldots, 2^l - 1\}$, and a key $x = (x_0, \ldots, x_{c - 1})$ is hashed to $h(x) = h_0(x_0) \oplus \ldots \oplus h_{c - 1}(x_{c - 1})$ where $\oplus$ is the bitwise XOR operation. The previous results on tabulation hashing by P{\v a}tra{\c s}cu and Thorup~[J.ACM'11] and by Aamand et al.~[STOC'20] focused on proving Chernoff-style tail bounds on hash-based sums, e.g., the number keys hashing to a given value, for simple tabulation hashing, but their bounds do not cover the entire tail. Chaoses are random variables of the form $\sum a_{i_0, \ldots, i_{c - 1}} X_{i_0} \cdot \ldots \cdot X_{i_{c - 1}}$ where $X_i$ are independent random variables. Chaoses are a well-studied concept from probability theory, and tight analysis has been proven in several instances, e.g., when the independent random variables are standard Gaussian variables and when the independent random variables have logarithmically convex tails. We notice that hash-based sums of simple tabulation hashing can be seen as a sum of chaoses that are not independent. This motivates us to use techniques from the theory of chaoses to analyze hash-based sums of simple tabulation hashing. In this paper, we obtain bounds for all the moments of hash-based sums for simple tabulation hashing which are tight up to constants depending only on $c$. In contrast with the previous attempts, our approach will mostly be analytical and does not employ intricate combinatorial arguments. The improved analysis of simple tabulation hashing allows us to obtain bounds for the moments of hash-based sums for the mixed tabulation hashing introduced by Dahlgaard et al.~[FOCS'15].
Offset-Free Strategy by Double-Layered Linear Model Predictive Control

In the real applications, the model predictive control (MPC) technology is separated into two layers, that is, a layer of conventional dynamic controller, based on which is an added layer of steady-state target calculation. In the literature, conditions for offset-free linear model predictive control are given for combined estimator (for both the artificial disturbance and system state), steady-state target calculation, and dynamic controller. Usually, the offset-free property of the double-laye red MPC is obtained under the assumption that the system is asymptotically stable. This paper considers the dynamic stability property of the double-layered MPC.
Hybrid Finite Difference Schemes for Elliptic Interface Problems with Discontinuous and High-Contrast Variable Coefficients

For elliptic interface problems with discontinuous coeﬃcients, the maximum accuracy order for compact 9-point ﬁnite diﬀerence scheme in irregular points is three [7]. The discontinuous coeﬃcients usually have abrupt jumps across the interface curve in the porous medium of realistic problems, causing the pollution eﬀect of numerical methods. So, to obtain a reasonable numerical solution of the above problem, the higher order scheme and its eﬀective implementation are necessary. In this paper, we propose an eﬃcient and ﬂexible way to achieve the implementation of a hybrid (9-point scheme with sixth order accuracy for interior regular points and 13-point scheme with ﬁfth order accuracy for interior irregular points) ﬁnite diﬀerence scheme in uniform meshes for the elliptic interface problems with discontinuous and high-contrast piecewise smooth coeﬃcients in a rectangle Ω. We also derive the 6-point and 4-point ﬁnite diﬀerence schemes in uniform meshes with sixth order accuracy for the side points and corner points of various mixed boundary conditions (Dirichlet, Neumann and Robin) of elliptic equations in a rectangle. Our numerical experiments conﬁrm the ﬂexibility and the sixth order accuracy in l 2 and l ∞ norms of the proposed hybrid scheme.
Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter

Warning users about misinformation on social media is not a simple usability task. Soft moderation has to balance between debunking falsehoods and avoiding moderation bias while preserving the social media consumption flow. Platforms thus employ minimally distinguishable warning tags with generic text under a suspected misinformation content. This approach resulted in an unfavorable outcome where the warnings"backfired"and users believed the misinformation more, not less. In response, we developed enhancements to the misinformation warnings where users are advised on the context of the information hazard and exposed to standard warning iconography. We ran an A/B evaluation with the Twitter's original warning tags in a 337 participant usability study. The majority of the participants preferred the enhancements as a nudge toward recognizing and avoiding misinformation. The enhanced warning tags were most favored by the politically left-leaning and to a lesser degree moderate participants, but they also appealed to roughly a third of the right-leaning participants. The education level was the only demographic factor shaping participants' preferences. We use our findings to propose user-tailored improvements in the soft moderation of misinformation on social media.
Transition to chaos in extended systems and their quantum impurity models

Chaos sets a fundamental limit to quantum-information processing schemes. We study the onset of chaos in spatially extended quantum many-body systems that are relevant to quantum optical devices. We consider an extended version of the Tavis–Cummings model on a finite chain. By studying level-spacing statistics, adjacent gap ratios, and spectral form factors, we observe the transition from integrability to chaos as the hopping between the Tavis–Cummings sites is increased above a finite value. The results are obtained by means of exact numerical diagonalization which becomes notoriously hard for extended lattice geometries. In an attempt to circumvent these difficulties, we identify a minimal single-site quantum impurity model that successfully captures the spectral properties of the lattice model. This approach is intended to be adaptable to other lattice models with large local Hilbert spaces.
The robustness of slow contraction and the shape of the scalar field potential

We use numerical relativity simulations to explore the conditions for a canonical scalar field ϕ minimally coupled to Einstein gravity to generate an extended phase of slow contraction that robustly smooths the universe for a wide range of initial conditions and then sets the conditions for a graceful exit stage. We show that to achieve robustness it suffices that the potential V(ϕ) is negative and M Pl|V, ϕ /V|≳5 during the smoothing phase. We also show that, to exit slow contraction, the potential must have a minimum. Beyond the minimum, we find no constraint on the uphill slope including the possibility of ending on a positive potential plateau or a local minimum with V min > 0. Our study establishes ultralocality, i.e., all spatial gradients quickly becoming negligible, for a wide range of potentials as a key both to robust smoothing and to graceful exit.
Freeze-in Dark Matter and ∆N eﬀ via Light Dirac Neutrino Portal

We propose a scenario where dark matter (DM) and dark radiation ( ∆N eﬀ ) can be generated non-thermally due to the presence of a light Dirac neutrino portal between the standard model (SM) and dark sector particles. The SM is minimally extended by three right handed neutrinos ( ν R ), a Dirac fermion DM candidate ( ψ ) and a complex scalar ( φ ), transforming non-trivially under an unbroken Z 4 symmetry while being singlets under the SM gauge group. While DM and ν R couplings are considered to be tiny in order to be in the non-thermal or freeze-in regime, φ can be produced either thermally or non-thermally depending upon the strength of its Higgs portal coupling. We consider both these possibilities and ﬁnd out the resulting DM abundance and ∆N eﬀ via freeze-in mechanism to constrain the model parameters in the light of Planck 2018 data. We ﬁnd that the scenario where φ remains out of equilibrium throughout or after a certain epoch allows more parameter space consistent with DM phenomenology and ∆N eﬀ in view of Planck 2018 data. The next generation experiments like CMB-S4, SPT-3G etc. will have the required sensitivities to probe a major portion of the entire model parameter space, oﬀering a promising way of probing such non-thermal DM scenario which typical direct detection experiments are not much sensitive to.
Influencing Factors on the Strength of Electromagnetically Produced Form-Fit Joints using Knurled Surfaces

Joining by electromagnetic forming is a non-contact assembling method that is especially suitable for connections in aluminum space frame structures. By reason of increased joint strength along with lower charging energies, form-fit connections are favored over interference-fit connections for this joining process. In contrast to conventional form-fit concepts, in which the inner joining partner has grooves or pockets, the use of knurled surfaces offers several advantages like easier machinability or the resistance against combined axial and torsional loadings. The objective of this paper is to identify the influencing geometry and process parameters on the joint strength of tubular joints using mandrels with knurled surfaces, with tube and mandrel being made of the same aluminum alloy AA6060-T6. For that reason, experimental studies were conducted: In addition to pull-out tests to determine the axial strength of joints, first computed tomographic images and, afterwards, micrographs of joined components were produced to analyze the contact zone between tube and mandrel and the deformation behavior of the inner joining partner by non-destructive and destructive means. Based on the detailed knowledge of the influencing variables, guidelines for joint and process design are derived.
CTM - A Model for Large-Scale Multi-View Tweet Topic Classification

Automatically associating social media posts with topics is an important prerequisite for effective search and recommendation on many social media platforms. However, topic classification of such posts is quite challenging because of (a) a large topic space (b) short text with weak topical cues, and (c) multiple topic associations per post. In contrast to most prior work which only focuses on post-classification into a small number of topics (10-20), we consider the task of large-scale topic classification in the context of Twitter where the topic space is 10 times larger with potentially multiple topic associations per Tweet. We address the challenges above and propose a novel neural model, that (a) supports a large topic space of 300 topics (b) takes a holistic approach to tweet content modeling – leveraging multi-modal content, author context, and deeper semantic cues in the Tweet. Our method offers an effective way to classify Tweets into topics at scale by yielding superior performance to other approaches (a relative lift of \mathbf{20}\% in median average precision score) and has been successfully deployed in production at Twitter.
Universal Optimality and Robust Utility Bounds for Metric Differential Privacy

We study the privacy-utility trade-off in the context of metric differential privacy. Ghosh et al. introduced the idea of universal optimality to characterise the “best” mechanism for a certain query that simultaneously satisfies (a fixed) $\mathcal{E-}$ differential privacy constraint whilst at the same time providing better utility compared to any other s-differentially private mechanism for the same query. They showed that the Geometric mechanism is universally optimal for the class of counting queries. On the other hand, Brenner and Nissim showed that outside the space of counting queries, and for the Bayes risk loss function, no such universally optimal mechanisms exist. Except for universal optimality of the Laplace mechanism, there have been no generalisations of these universally optimal results to other classes of differentially-private mechanisms. In this paper we use metric differential privacy and quantitative information flow as the fundamental principle for studying universal optimality. Metric differential privacy is a generali-sation of both standard (i.e., central) differential privacy and local differential privacy, and it is increasingly being used in various application domains, for instance in location privacy and in privacy preserving machine learning. As do Ghosh et al. and Brenner and Nissim, we measure utility in terms of loss functions, and we interpret the notion of a privacy mechanism as an information-theoretic channel satisfying constraints defined by ε-differcntlal privacy and a metric meaningful to the underlying state space. Using this framework we are able to clarify Nissim and Brenner's negative results by (a) that in fact all privacy types contain optimal mechanisms relative to certain kinds of non-trivial loss functions, and (b) extending and generalising their negative results beyond Bayes risk specifically to a wide class of non-trivial loss functions. Our exploration suggests that universally optimal mechanisms are indeed rare within privacy types. We therefore propose weaker universal benchmarks of utility called privacy type ca-pacities. We show that such capacities always exist and can be computed using a convex optimisation algorithm. We illustrate these ideas on a selection of examples with several different underlying metrics.
New Tsallis agegraphic dark energy

The proposed model is a study of the nature of dark energy through nonextensive Tsallis entropy. The method is based on the Karolyhazy relation which is a combined idea from quantum physics and general relativity. Dark energy is the energy density of quantum fluctuations in spacetime. This is the key idea behind proposing agegraphic dark energy (ADE) models here. The parameter [Formula: see text] is used to measure the quantitative distinction from the standard scenario. To look at the cosmological implications of the hypothesized dark energy model, as well as the expansion of the universe filled with zero pressure matter and the resulting dark energy alternatives, the role of IR cutoff is played by age of the universe. The dynamic behavior of dark energy density parameter is carried out. The expressions for the equation of state parameter and deceleration parameter are obtained. The analysis is performed by taking into account a no flow as well as a flow of energy among the dark matter and dark energy sectors of the universe.
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning

Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks, due to variance in lesion characteristics imparted by different scanners and acquisition parameters. In this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance. In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training. Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by outperforming other FL methods significantly. Furthermore, the segmentation performance of FL incorporating our proposed aggregation mechanism can exceed centralised training with all the raw data. The extensive evaluation also indicated the superiority of our method when estimating brain volume differences estimation after lesion inpainting.
A Riccati-Lyapunov Approach to Nonfeedback Capacity of MIMO Gaussian Channels Driven by Stable and Uns table Noise

We show that the nonfeedback capacity of multiple-input multiple-output (MIMO) additive Gaussian noise (AGN) channels, when the noise is nonstationary and unstable, is characterized by an asymptotic optimization problem–the per unit time limit of the characterization of a finite block or transmission without feedback information (FTwFI) capacity, that involves two generalized matrix difference Riccati equations (DREs) of filtering theory, and a matrix difference Lyapunov equation of stability theory, of Gaussian systems. Further, we identify conditions and prove, that the characterization of nonfeedback capacity is the uniform asymptotic per unit time limit, over all initial distributions. The asymptotic characterization of capacity involves two generalized matrix algebraic Riccati equations (AREs) and a matrix algebraic Lyapunov equation. We also present an example to illustrate that our characterization of capacity produces a known closed-form expression of the water-filling solution of capacity (for power levels above a minimum power).
A Survey of Deep Learning Models for Structural Code Understanding

In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights. The number of approaches and applications in code understanding is growing, with deep learning techniques being used in many of them to better capture the information in code data. In this survey, we present a comprehensive overview of the structures formed from code data. We categorize the models for understanding code in recent years into two groups: sequence-based and graph-based models, further make a summary and comparison of them. We also introduce metrics, datasets and the downstream tasks. Finally, we make some suggestions for future research in structural code understanding field.
Exotic Quantum Difference Equations and Integral Solutions

Hunter Dinkins: One of the fundamental objects in the K -theoretic enumerative geometry of Nakajima quiver varieties is known as the the capping operator. It is uniquely determined as the fundamental solution to a system of q -di ﬀ erence equations. Such di ﬀ erence equations involve shifts of two sets of variables, the variables arising as equivariant parameters for a torus that acts on the variety and an additional set of variables known as Kähler parameters. The di ﬀ erence equations in the former variables were identiﬁed with the qKZ equations in [28]. The di ﬀ erence equations in the latter variables were identiﬁed representation theoretically in [30] using an analog of the quantum dynamical Weyl group. Once this representation theoretic description is known, there is an obvious generalization of these equations, which we refer to as exotic quantum di ﬀ erence equations. They depend on a choice of alcove in a certain hyperplane arrangement in Pic( X ) ⊗ R , with the usual di ﬀ erence equations corresponding to the alcove containing small anti-ample line bundles. As our main result, we relate the fundamental solution of these equations back to quasimap counts using the so-called vertex with descendants. The particular descendants of interest are given in terms of K -theoretic stable envelopes. In the case of the Hilbert scheme of points in C 2 , we write our exotic quantum di ﬀ erence equations explicitly using the algebra U (cid:126) ( (cid:98)(cid:98) gl 1 ). We use the results of [6] to obtain formulas for the K -theoretic stable envelopes of arbitrary slope. Using this, we are able to write explicit formulas for the solutions of the exotic di ﬀ erence equations. These formulas can be written as contour integrals. As a partially conjectural application of our results, we apply the saddlepoint approximation to these integrals to diagonalize the Bethe subalgebras of U (cid:126) ( (cid:98)(cid:98) gl 1 ) for arbitrary slope. nonsingular conditions, relative conditions, descendant insertions, quantum di ﬀ erence equations, and localization of virtual classes.
Saliency map using features derived from spiking neural networks of primate visual cortex

We propose a framework inspired by biological vision systems to produce saliency maps of digital images. Well-known computational models for receptive fields of areas in the visual cortex that are specialized for color and orientation perception are used. To model the connectivity between these areas we use the CARLsim library which is a spiking neural network(SNN) simulator. The spikes generated by CARLsim, then serve as extracted features and input to our saliency detection algorithm. This new method of saliency detection is described and applied to benchmark images.