Miracle-dz/UniFAR-FTUs huggingface dataset:
G4.8+6.2, a possible kilonova remnant?

Kilonova explosions typically release ∼1050–51 erg in kinetic energy, which is sufficient to constitute a kilonova remnant (KNR); however, it has not yet been confirmed. In this work, we investigate the probable association between G4.8+6.2 and the guest star of AD 1163, which is recorded by the Korean ancient astronomers. Although the evidence available is insufficient to draw a definite conclusion, it is at least theoretically self-consistent that the guest star of AD 1163 was a historical kilonova associated with G4.8+6.2, considering the possible short visible timescale of AD 1163, the relatively high Galactic latitude of G4.8+6.2, and that G4.8+6.2 is spatially coincident with the guest star of AD 1163. Further observation of G4.8+6.2 is needed to test our hypothesis. If our interpretation is correct, our results indicate that young KNRs should have a large diameter and low surface brightness, unlike other young supernova remnants.
Synthetic estimation for the complier average causal effect

We propose an improved estimator of the complier average causal effect (CACE). Researchers typically choose a presumably consistent estimator for CACE in studies with noncompliance when many other lower variance estimators may be available. We propose a synthetic estimator that combines information across all available estimators, leveraging the efficiency in lower variance estimators while maintaining low bias. Our approach minimizes an estimate of the mean squared error of all convex combinations of the candidate estimators. We derive the asymptotic distribution of the synthetic estimator and demonstrate its good performance in simulation, displaying robustness to inclusion of even high‐bias estimators.
Kinetic Simulations of Nonrelativistic Perpendicular Shocks of Young Supernova Remnants. II. Influence of Shock-surfing Acceleration on Downstream Electron Spectra

We explore electron preacceleration at high-Mach-number nonrelativistic perpendicular shocks at, e.g., young supernova remnants, which are a prerequisite of further acceleration to very high energies via diffusive shock acceleration. Using fully kinetic particle-in-cell simulations of shocks and electron dynamics in them, we investigate the influence of shock-surfing acceleration (SSA) at the shock foot on the nonthermal population of electrons downstream of the shock. The SSA is followed by further energization at the shock ramp where the Weibel instability spawns a type of second-order Fermi acceleration. The combination of these two processes leads to the formation of a nonthermal electron population, but the importance of SSA becomes smaller for larger ion-to-electron mass ratios in the simulation. We discuss the resulting electron spectra and the relevance of our results to the physics of systems with real ion-to-electron mass ratios and fully three-dimensional behavior.
Ecosystem-level stabilizing effects of biodiversity via nutrient-diversity feedbacks in multitrophic systems

Statistical averaging and asynchronous population dynamics as portfolio mechanisms are considered as the most important processes with which biodiversity contributes to ecosystem stability. However, portfolio theories usually regard biodiversity as a fixed property, but overlook the dynamics of biodiversity altered by other ecosystem components. Here, we proposed a new mechanistic food chain model with nutrient-diversity feedback to investigate how dynamics of phytoplankton species diversity determines ecosystem stability. Our model focuses on nutrient, community biomass of phytoplankton and zooplankton, and phytoplankton species richness. The model assumes diversity effects of phytoplankton on trophic interaction strength along plankton food chain: phytoplankton diversity influences nutrient uptake by phytoplankton and zooplankton grazing on phytoplankton, which subsequently affects nutrient level and community biomass of phytoplankton and zooplankton. The nutrient level in turn affects phytoplankton diversity. These processes collectively form feedbacks between phytoplankton diversity and dynamics of plankton and nutrient. More importantly, nutrient-diversity feedback introduced additional temporal variabilities in community biomass, which apparently implies a destabilizing effect of phytoplankton diversity on ecosystem. However, the variabilities made ecosystems more robust against extinction of plankton because increasing phytoplankton diversity facilitates resource consumptions when consumers prone to extinct; while, reducing diversity weakens destabilizing dynamics caused by over-growth. Our results suggest the presence of a novel stabilizing effect of biodiversity acting through nutrient-diversity feedback, being independent of portfolio mechanisms.
An Iterative Approach for Multiple Instance Learning Problems

Multiple Instance learning (MIL) algorithms are tasked with learning how to associate sets of elements with specific set-level outputs. Towards this goal, the main challenge of MIL lies in modelling the underlying structure that characterizes sets of elements. Existing methods addressing MIL problems are usually tailored to address either: a specific underlying set structure; specific prediction tasks, e.g. classification, regression; or a combination of both. Here we present an approach where a set representation is learned, iteratively, by looking at the constituent elements of each set one at a time. The iterative analysis of set elements enables our approach with the capability to update the set representation so that it reflects whether relevant elements have been detected and whether the underlying structure has been matched. These features provide our method with some model explanation capabilities. Despite its simplicity, the proposed approach not only effectively models different types of underlying set structures, but it is also capable of handling both classification and regression tasks - all this while requiring minimal modifications. An extensive empirical evaluation shows that the proposed method is able to reach and surpass the state-of-the-art.
Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds

We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are independently and identically drawn from an unknown distribution and revealed sequentially over time. Virtually all existing online algorithms were based on learning the dual optimal solutions/prices of the linear programs (LPs), and their analyses were focused on the aggregate objective value and solving the packing LP, where all coefficients in the constraint matrix and objective are nonnegative. However, two major open questions were as follows. (i) Does the set of LP optimal dual prices learned in the existing algorithms converge to those of the “offline” LP? (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative? We resolve these two questions by establishing convergence results for the dual prices under moderate regularity conditions for general LP problems. Specifically, we identify an equivalent form of the dual problem that relates the dual LP with a sample average approximation to a stochastic program. Furthermore, we propose a new type of OLP algorithm, action-history-dependent learning algorithm, which improves the previous algorithm performances by taking into account the past input data and the past decisions/actions. We derive an [Formula: see text] regret bound (under a locally strong convexity and smoothness condition) for the proposed algorithm, against the [Formula: see text] bound for typical dual-price learning algorithms, where n is the number of decision variables. Numerical experiments demonstrate the effectiveness of the proposed algorithm and the action-history-dependent design.
Lower Bound on Tensor Rank

Tensors and tensor decompositions are natural tools to analyse datasets of high dimensionality and variety, with a pillar of tensor decompositions being the Canonical Polyadic Decomposition (CPD). While the notion of CPD is closely intertwined with that of tensor rank, $R$, unlike the matrix rank, the computation of tensor rank is as NP-hard problem, with an associated computational burden on the CPD. To address this issue, we derive a lower bound on $R$ to reduce its search space, hence reducing computational costs of the CPD. This is achieved by considering a flattened CPD, which results in the bound being the maximum possible matrix rank that the flattened tensor can attain. Moreover, it is shown that for a noiseless tensor, the proposed lower bound can be attained under very mild conditions. Numerical simulations support the approach.
Asymptotic normality for random polytopes in non-Euclidean geometries

Asymptotic normality for the natural volume measure of random polytopes generated by random points distributed uniformly in a convex body in spherical or hyperbolic spaces is proved. Also the case of Hilbert geometries is treated and central limit theorems in Lutwak's dual Brunn--Minkowski theory are established. The results follow from a central limit theorem for weighted random polytopes in Euclidean spaces. In the background are Stein's method for normal approximation and geometric properties of weighted floating bodies.
Three-body unitarity versus finite-volume 
π+π+π+
 spectrum from lattice QCD

Strong three-body interactions above threshold govern the dynamics of many exotics and conventional excited mesons and baryons. Three-body finite-volume energies calculated from lattice QCD promise an ab-initio understanding of these systems. We calculate the three-$\pi^+$ spectrum unraveling the three-body dynamics that is tightly intertwined with the $S$-matrix principle of three-body unitarity and compare it with recent lattice QCD results. For this purpose, we develop a formalism for three-body systems in moving frames and apply it numerically.
Algorithmic and Economic Perspectives on Fairness

Algorithmic systems have been used to inform consequential decisions for at least a century. Recidivism prediction dates back to the 1920s. Automated credit scoring dates began in the middle of the last century, but the last decade has witnessed an acceleration in the adoption of prediction algorithms. They are deployed to screen job applicants for the recommendation of products, people, and content, as well as in medicine (diagnostics and decision aids), criminal justice, facial recognition, lending and insurance, and the allocation of public services. The prominence of algorithmic methods has led to concerns regarding their systematic unfairness in their treatment of those whose behavior they are predicting. These concerns have found their way into the popular imagination through news accounts and general interest books. Even when these algorithms are deployed in domains subject to regulation, it appears that existing regulation is poorly equipped to deal with this issue. The word 'fairness' in this context is a placeholder for three related equity concerns. First, such algorithms may systematically discriminate against individuals with a common ethnicity, religion, or gender, irrespective of whether the relevant group enjoys legal protections. The second is that these algorithms fail to treat people as individuals. Third, who gets to decide how algorithms are designed and deployed. These concerns are present when humans, unaided, make predictions.
Quantitative Assessment of Carrier Density by Cathodoluminescence. II. 
GaAs
 Nanowires

Precise control of doping in single nanowires (NWs) is essential for the development of NW-based devices. Here, we investigate a series of MBE-grown GaAs NWs with Be (p-type) and Si (n-type) doping using high-resolution cathodoluminescence (CL) mapping at low- and room-temperature. CL spectra are analyzed selectively in different regions of the NWs. Room-temperature luminescence is fitted with the generalized Planck's law and an absorption model, and the bandgap and band tail width are extracted. For Be-doped GaAs NWs, the bandgap narrowing provides a quantitative determination of the hole concentration ranging from about $1\times 10^{18}$ to $2\times 10^{19}$ cm$^{-3}$, in good agreement with the targeted doping levels. For Si-doped GaAs NWs, the electron Fermi level and the full-width at half maximum of low-temperature CL spectra are used to assess the electron concentration to approximately $3\times 10^{17}$ to $6\times 10^{17}$ cm$^{-3}$. These findings confirm the difficulty to reach highly-doped n-type GaAs NWs, may be due to doping compensation. Notably, signatures of high concentration (5-9$\times 10^{18}$ cm$^{-3}$) at the very top of NWs are unveiled.
InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd

Natural language interfaces have become a common part of modern digital life. Chatbots utilize text-based conversations to communicate with users; personal assistants on smartphones such as Google Assistant take direct speech commands from their users; and speech-controlled devices such as Amazon Echo use voice as their only input mode. In this paper, we introduce InstructableCrowd, a crowd-powered system that allows users to program their devices via conversation. The user verbally expresses a problem to the system, in which a group of crowd workers collectively respond and program relevant multi-part IF-THEN rules to help the user. The IF-THEN rules generated by InstructableCrowd connect relevant sensor combinations (e.g., location, weather, device acceleration, etc.) to useful effectors (e.g., text messages, device alarms, etc.). Our study showed that non-programmers can use the conversational interface of InstructableCrowd to create IF-THEN rules that have similar quality compared with the rules created manually. InstructableCrowd generally illustrates how users may converse with their devices, not only to trigger simple voice commands, but also to personalize their increasingly powerful and complicated devices.
Multilingual Multimodal Digital Deception Detection and Disinformation Spread across Social Platforms

Our main contribution in this work is novel results of multilingual models that go beyond typical applications of rumor or misinformation detection in English social news content to identify fine-grained classes of digital deception across multiple languages (e.g. Russian, Spanish, etc.). In addition, we present models for multimodal deception detection from images and text and discuss the limitations of image only and text only models. Finally, we elaborate on the ongoing work on measuring deceptive content (in particular disinformation) spread across social platforms.
Multimodal Attention Branch Network for Perspective-Free Sentence Generation

In this paper, we address the automatic sentence generation of fetching instructions for domestic service robots. Typical fetching commands such as "bring me the yellow toy from the upper part of the white shelf" includes referring expressions, i.e., "from the white upper part of the white shelf". To solve this task, we propose a multimodal attention branch network (Multi-ABN) which generates natural sentences in an end-to-end manner. Multi-ABN uses multiple images of the same fixed scene to generate sentences that are not tied to a particular viewpoint. This approach combines a linguistic attention branch mechanism with several attention branch mechanisms. We evaluated our approach, which outperforms the state-of-the-art method on a standard metrics. Our method also allows us to visualize the alignment between the linguistic input and the visual features.
An Empirical Investigation of Randomized Defenses against Adversarial Attacks

In recent years, Deep Neural Networks (DNNs) have had a dramatic impact on a variety of problems that were long considered very difficult, e. g. , image classification and automatic language translation to name just a few. The accuracy of modern DNNs in classification tasks is remarkable indeed. At the same time, attackers have devised powerful methods to construct specially-crafted malicious inputs (often referred to as adversarial examples) that can trick DNNs into mis-classifying them. What is worse is that despite the many defense mechanisms proposed to protect DNNs against adversarial attacks, attackers are often able to circumvent these defenses, rendering them useless. This state of affairs is extremely worrying, especially since machine learning systems get adopted at scale. In this paper, we propose a scientific evaluation methodology aimed at assessing the quality, efficacy, robustness and efficiency of randomized defenses to protect DNNs against adversarial examples. Using this methodology, we evaluate a variety of defense mechanisms. In addition, we also propose a defense mechanism we call Randomly Perturbed Ensemble Neural Networks (RPENNs). We provide a thorough and comprehensive evaluation of the considered defense mechanisms against a white-box attacker model, six different adversarial attack methods and using the ILSVRC2012 validation data set.
A taxonomy of estimator consistency on discrete estimation problems

We describe a four-level hierarchy mapping both all discrete estimation problems and all estimators on these problems, such that the hierarchy describes each estimator's consistency guarantees on each problem class. We show that no estimator is consistent for all estimation problems, but that some estimators, such as Maximum A Posteriori, are consistent for the widest possible class of discrete estimation problems. For Maximum Likelihood and Approximate Maximum Likelihood estimators we show that they do not provide consistency on as wide a class, but define a sub-class of problems characterised by their consistency. Lastly, we show that some popular estimators, specifically Strict Minimum Message Length, do not provide consistency guarantees even within the sub-class.
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust

In this work we study input gradient regularization of deep neural networks, and demonstrate that such regularization leads to generalization proofs and improved adversarial robustness. The proof of generalization does not overcome the curse of dimensionality, but it is independent of the number of layers in the networks. The adversarial robustness regularization combines adversarial training, which we show to be equivalent to Total Variation regularization, with Lipschitz regularization. We demonstrate empirically that the regularized models are more robust, and that gradient norms of images can be used for attack detection.
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression

Existing methods on visual emotion analysis mainly focus on coarse-grained emotion classification, i.e. assigning an image with a dominant discrete emotion category. However, these methods cannot well reflect the complexity and subtlety of emotions. In this paper, we study the fine-grained regression problem of visual emotions based on convolutional neural networks (CNNs). Specifically, we develop a Polarity-consistent Deep Attention Network (PDANet), a novel network architecture that integrates attention into a CNN with an emotion polarity constraint. First, we propose to incorporate both spatial and channel-wise attentions into a CNN for visual emotion regression, which jointly considers the local spatial connectivity patterns along each channel and the interdependency between different channels. Second, we design a novel regression loss, i.e. polarity-consistent regression (PCR) loss, based on the weakly supervised emotion polarity to guide the attention generation. By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance. Extensive experiments are conducted on the IAPS, NAPS, and EMOTIC datasets, and the results demonstrate that the proposed PDANet outperforms the state-of-the-art approaches by a large margin for fine-grained visual emotion regression. Our source code is released at: https://github.com/ZizhouJia/PDANet.
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony

An open challenge in supervised learning is \emph{conceptual drift}: a data point begins as classified according to one label, but over time the notion of that label changes. Beyond linear autoregressive models, transfer and meta learning address drift, but require data that is representative of disparate domains at the outset of training. To relax this requirement, we propose a memory-efficient \emph{online} universal function approximator based on compressed kernel methods. Our approach hinges upon viewing non-stationary learning as online convex optimization with dynamic comparators, for which performance is quantified by dynamic regret. Prior works control dynamic regret growth only for linear models. In contrast, we hypothesize actions belong to reproducing kernel Hilbert spaces (RKHS). We propose a functional variant of online gradient descent (OGD) operating in tandem with greedy subspace projections. Projections are necessary to surmount the fact that RKHS functions have complexity proportional to time. For this scheme, we establish sublinear dynamic regret growth in terms of both loss variation and functional path length, and that the memory of the function sequence remains moderate. Experiments demonstrate the usefulness of the proposed technique for online nonlinear regression and classification problems with non-stationary data.
Challenges in the Decentralised Web: The Mastodon Case

The Decentralised Web (DW) has recently seen a renewed momentum, with a number of DW platforms like Mastodon, PeerTube, and Hubzilla gaining increasing traction. These offer alternatives to traditional social networks like Twitter, YouTube, and Facebook, by enabling the operation of web infrastructure and services without centralised ownership or control. Although their services differ greatly, modern DW platforms mostly rely on two key innovations: first, their open source software allows anybody to setup independent servers ("instances") that people can sign-up to and use within a local community; and second, they build on top of federation protocols so that instances can mesh together, in a peer-to-peer fashion, to offer a globally integrated platform. In this paper, we present a measurement-driven exploration of these two innovations, using a popular DW microblogging platform (Mastodon) as a case study. We focus on identifying key challenges that might disrupt continuing efforts to decentralise the web, and empirically highlight a number of properties that are creating natural pressures towards re-centralisation. Finally, our measurements shed light on the behaviour of both administrators (i.e., people setting up instances) and regular users who sign-up to the platforms, also discussing a few techniques that may address some of the issues observed.