Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Origin of Stars on Bound and Unbound Orbits Causing Tidal Disruption Events

Tidal disruption events (TDEs) provide a clue to the properties of a central supermassive black hole (SMBH) and an accretion disk around it, and to the stellar density and velocity distributions in the nuclear star cluster surrounding the SMBH. Deviations of TDE light curves from the standard occurring at a parabolic encounter with the SMBH depend on whether the stellar orbit is hyperbolic or eccentric and the penetration factor (β, the tidal disruption radius to the orbital pericenter ratio). We study the orbital parameters of bound and unbound stars being tidally disrupted by comparison of direct N-body simulation data with an analytical model. Starting from the classical steady-state Fokker–Planck model of Cohn & Kulsrud, we develop an analytical model of the number density distribution of those stars as a function of orbital eccentricity (e) and β. To do so, fittings of the density and velocity distribution of the nuclear star cluster and of the energy distribution of tidally disrupted stars are required and obtained from N-body data. We confirm that most of the stars causing TDEs in a spherical nuclear star cluster originate from the full loss-cone region of phase space, derive analytical boundaries in eccentricity-β space, and find them confirmed by N-body data. Since our limiting eccentricities are much smaller than critical eccentricities for full accretion or the full escape of stellar debris, we conclude that those stars are only very marginally eccentric or hyperbolic, close to parabolic.
Traffic Characteristics of Virtual Reality over Edge-enabled Wi-Fi Networks

Virtual reality (VR) is becoming prevalent with a plethora of applications in education, healthcare, entertainment, etc. To increase the user mobility, and to reduce the energy consumption and production cost of VR head mounted displays (HMDs), wireless VR with edge-computing has been the focus of both industry and academia. However, transferring large video frames of VR applications with their stringent Quality of Service (QoS) requirements over wireless network requires innovations and optimizations across different network layers. In order to develop efficient architectures, protocols and scheduling mechanisms, the traffic characteristics of various types of VR applications are required. In this paper, we first compute the theoretical throughput requirements of an ideal VR experience as well as a popular VR HMD. We then examine the traffic characteristics of a set of VR applications using an edge-enabled Wi-Fi network. Our results reveal interesting findings that can be considered in developing new optimizations, protocols, access mechanisms and scheduling algorithms.
Improving Document-Level Sentiment Analysis with User and Product Context

Past work that improves document-level sentiment analysis by encoding user and product in- formation has been limited to considering only the text of the current review. We investigate incorporating additional review text available at the time of sentiment prediction that may prove meaningful for guiding prediction. Firstly, we incorporate all available historical review text belonging to the author of the review in question. Secondly, we investigate the inclusion of his- torical reviews associated with the current product (written by other users). We achieve this by explicitly storing representations of reviews written by the same user and about the same product and force the model to memorize all reviews for one particular user and product. Additionally, we drop the hierarchical architecture used in previous work to enable words in the text to directly attend to each other. Experiment results on IMDB, Yelp 2013 and Yelp 2014 datasets show improvement to state-of-the-art of more than 2 percentage points in the best case.
The redshift evolution of the baryonic Tully–Fisher relation in SIMBA

The baryonic Tully–Fisher relation (BTFR) is an important tool for constraining galaxy evolution models. As 21-cm H i emission studies have been largely restricted to low redshifts, the redshift evolution of the BTFR is less studied. The upcoming LADUMA survey (Looking At the Distant Universe with the MeerKAT Array) will address this. As preparation for LADUMA, we use the SIMBA hydrodynamical galaxy formation simulation from the SIMBA-hires $(25\, h^{-1}{\rm Mpc})^3$ run to generate rotational velocity measures from galaxy rotation curves (Vflat) and H i spectral line profile widths (W50 and W20) at three different redshifts (z = 0, 0.5, and 1). Using these measures, together with the dark matter velocity dispersion and halo mass, we consider the redshift evolution of the BTFR of SIMBA galaxies. We find that LADUMA will be successful in detecting weak redshift evolution of the BTFR, provided that auxiliary data are used to distinguish galaxies with discy morphologies. W20 spectral line widths give lower scatter and more pronounced redshift evolution compared to W50. We also compare these rotational velocity measures to the dark matter velocity dispersion across redshift and galaxy morphology. We find weak redshift evolution between rotational velocity and the dark matter halo mass, and provide fits for estimating a galaxy’s dark matter halo mass from H i spectral line widths. This study with SIMBA showcases the importance of upcoming, deep Square Kilometre Array pathfinder surveys such as LADUMA, and provides predictions to compare with the redshift evolution of the BTFR and galaxy dark matter content from H i rotational velocity measures.
Out-of-Task Training for Dialog State Tracking Models

Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications. In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers. This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.
The tortoise and the hare: a causality puzzle in AdS/CFT

We pose and resolve a holographic puzzle regarding an apparent violation of causality in anti-de Sitter (AdS)/conformal field theory. If a point in the bulk of AdS moves at the speed of light, the boundary subregion that encodes it may need to move superluminally to keep up. With AdS3 as our main example, we prove that the finite extent of the encoding regions prevents a paradox. We show that the length of the minimal-size encoding interval gives rise to a tortoise coordinate on AdS that measures the nonlocality of the encoding. We use this coordinate to explore circular and radial motion in the bulk before passing to the analysis of bulk null geodesics. For these null geodesics, there is always a critical encoding where the possible violation of causality is barely avoided. We show that in any other encoding, the possible violation is subcritical.
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs

Despite high accuracy of Convolutional Neural Networks (CNNs), they are vulnerable to adversarial and out-distribution examples. There are many proposed methods that tend to detect or make CNNs robust against these fooling examples. However, most such methods need access to a wide range of fooling examples to retrain the network or to tune detection parameters. Here, we propose a method to detect adversarial and out-distribution examples against a pre-trained CNN without needing to retrain the CNN or needing access to a wide variety of fooling examples. To this end, we create adversarial profiles for each class using only one adversarial attack generation technique. We then wrap a detector around the pre-trained CNN that applies the created adversarial profile to each input and uses the output to decide whether or not the input is legitimate. Our initial evaluation of this approach using MNIST dataset show that adversarial profile based detection is effective in detecting at least 92 of out-distribution examples and 59% of adversarial examples.
Towards Olfactory Information Extraction from Text: A Case Study on Detecting Smell Experiences in Novels

Environmental factors determine the smells we perceive, but societal factors factors shape the importance, sentiment and biases we give to them. Descriptions of smells in text, or as we call them ‘smell experiences’, offer a window into these factors, but they must first be identified. To the best of our knowledge, no tool exists to extract references to smell experiences from text. In this paper, we present two variations on a semi-supervised approach to identify smell experiences in English literature. The combined set of patterns from both implementations offer significantly better performance than a keyword-based baseline.
Instructional Approach in Adult Education using Mobile Devices

Mobile technologies are currently present in many instructional areas from basic levels to higher education. This work is focused on studying the impact of these technologies in lifelong learning contexts, and more exactly, concerning adult education. To this end, an approach is proposed to organize the different items involved within an instructional process with the purpose of taking advantage from the use of mobile devices. Such approach has been applied in a course addressed to people older than 55 years where the potential of mobile technologies has been assessed to promote a more customized learning adapted to the adult learner profile. KeywordsMobile devices; Instructional design; Life-long learning; Adult education
Tau pathology in early Alzheimer’s disease disrupts selective neurophysiological network dynamics

The role of aggregation of misfolded Tau protein in the pathogenesis of Alzheimer’s disease is the subject of rapid biomarker development and new therapeutic strategies to slow or prevent dementia. We tested the hypothesis that Tau pathology is associated with functional organization of widespread neurophysiological networks. We used electro-magnetoencephalography (E/MEG) in combination with [18F]AV1451 PET scanning to quantify Tau-dependent network disruption. Using a graph theoretical approach to MEG connectivity, we quantified nodal measures of functional segregation, centrality and efficiency of information transfer. We correlated these metrics against the nodes’ uptake of [18F]AV1451. There were both regional- and frequency-specific effects of Tau levels on the efficiency of information transfer and network segregation in early AD. Tau correlated with temporal regional participation coefficient (in delta, theta, beta bands); and temporal lobar eigenvector centrality (in theta, alpha, beta bands), but greater eccentricity at higher frequencies (gamma). The results support the translational development of neurophysiological “signatures” as biomarkers of Alzheimer’s disease, with potential to facilitate experimental medicines studies.
Comprehensive anisotropic linear optical properties of the Weyl semimetals TaAs and NbAs

TaAs and NbAs are two of the earliest identified Weyl semimetals that possess many intriguing optical properties, such as chirality-dependent optical excitations and giant second harmonic generation (SHG). Linear and nonlinear optics have been employed as tools to probe the Weyl physics in these crystals. Here, we extend these studies to address two important aspects: determining the complete anisotropic dielectric response and exploring if and how they can reveal essential Weyl physics. We determine the complete anisotropic dielectric functions of TaAs and NbAs by combining spectroscopic ellipsometry and density functional theory (DFT). Parameterized Lorentz oscillators are reported from 1.2--6 eV (experiment) and 0--6 eV (DFT), and good agreement is shown between them. Both linear and nonlinear optical properties have been previously reported to reveal Weyl physics. We suggest that strong optical resonances from trivial bands are the likely origin of the large optical SHG previously reported at these energies. Furthermore, by comparing the contribution of a small $k$-space centered around the Weyl cones with the total linear dielectric function, we find that these contributions are highly anisotropic and are $l25%$ of the total dielectric function $\mathrm{below}\phantom{\rule{0.16em}{0ex}}0.5\phantom{\rule{0.16em}{0ex}}\mathrm{eV}$; $\mathrm{above}\phantom{\rule{0.16em}{0ex}}1\phantom{\rule{0.16em}{0ex}}\mathrm{eV}$, these contributions are negligible. Thus, the study of Weyl physics using optical techniques requires very low energies, and even there, a careful assessment is required in distinguishing the much smaller contributions of the Weyl bands from the dominant contributions of the trivial bands and Drude response to the total dielectric function.
Cautious Bayesian Optimization for Efficient and Scalable Policy Search

Sample efficiency is one of the key factors when applying policy search to real-world problems. In recent years, Bayesian Optimization (BO) has become prominent in the field of robotics due to its sample efficiency and little prior knowledge needed. However, one drawback of BO is its poor performance on high-dimensional search spaces as it focuses on global search. In the policy search setting, local optimization is typically sufficient as initial policies are often available, e.g., via meta-learning, kinesthetic demonstrations or sim-to-real approaches. In this paper, we propose to constrain the policy search space to a sublevel-set of the Bayesian surrogate model's predictive uncertainty. This simple yet effective way of constraining the policy update enables BO to scale to high-dimensional spaces (>100) as well as reduces the risk of damaging the system. We demonstrate the effectiveness of our approach on a wide range of problems, including a motor skills task, adapting deep RL agents to new reward signals and a sim-to-real task for an inverted pendulum system.
A violation of global symmetries from replica wormholes and the fate of black hole remnants

We show that the presence of replica wormholes in the Euclidean path integral of gravity leads to a non-perturbative violation of charge conservation for any global symmetry present in the low-energy description of quantum gravity. Explicitly, we compute the scattering probability between different charged states in several two-dimensional models of quantum gravity and find a non-vanishing answer. This suggests that the set of all charged states is typically over-complete, which has drastic consequences for the fate of black hole remnants that could carry a global symmetry charge. In the holographic context, we argue that the presence of such a symmetry in the effective description of the bulk should appear on the boundary as an emergent global symmetry after ensemble averaging.
SIENA: Stochastic Multi-Expert Neural Patcher

Neural network (NN) models that are solely trained to maximize the likelihood of an observed dataset are often vulnerable to adversarial attacks. Even though several methods have been proposed to enhance NN models' adversarial robustness, they often require re-training from scratch. This leads to redundant computation, especially in the NLP domain where current state-of-the-art models, such as BERT and ROBERTA, require great time and space resources. By borrowing ideas from Software Engineering, we, therefore, first introduce the Neural Patching mechanism to improve adversarial robustness by "patching" only parts of a NN model. Then, we propose a novel neural patching algorithm, SIENA, that transforms a textual NN model into a stochastic ensemble of multi-expert predictors by upgrading and re-training its last layer only. SIENA forces adversaries to attack not only one but multiple models that are specialized in diverse sub-sets of features, labels, and instances so that the ensemble model becomes more robust to adversarial attacks. By conducting comprehensive experiments, we demonstrate that all of CNN, RNN, BERT, and ROBERTA-based textual models, once patched by SIENA, witness an absolute increase of as much as 20% in accuracy on average under 5 different white and black-box attacks, outperforming 6 defensive baselines across 4 public NLP datasets.
Lower bound on the size of a quasirandom forcing set of permutations

A set S of permutations is forcing if for any sequence $\{\Pi_i\}_{i \in \mathbb{N}}$ of permutations where the density $d(\pi,\Pi_i)$ converges to $\frac{1}{|\pi|! }$ for every permutation $\pi \in S$ , it holds that $\{\Pi_i\}_{i \in \mathbb{N}}$ is quasirandom. Graham asked whether there exists an integer k such that the set of all permutations of order k is forcing; this has been shown to be true for any $k\ge 4$ . In particular, the set of all 24 permutations of order 4 is forcing. We provide the first non-trivial lower bound on the size of a forcing set of permutations: every forcing set of permutations (with arbitrary orders) contains at least four permutations.
Interstellar Extinction and Elemental Abundances

Elements in the interstellar medium (ISM) exist in the form of gas or dust. The interstellar extinction and elemental abundances provide crucial constraints on the composition, size, and quantity of interstellar dust. Most of the extinction modeling efforts have assumed the total (gas and dust) abundances of the dust-forming elements—known as the “interstellar abundances,” “interstellar reference abundances,” or “cosmic abundances”—to be solar and the gas-phase abundances to be environmentally independent. However, it remains unclear whether the solar abundances are an appropriate representation of the interstellar abundances. Meanwhile, the gas-phase abundances are known to exhibit appreciable variations with local environments. Here we explore the viability of the abundances of B stars, the solar and protosolar abundances, and the protosolar abundances augmented by Galactic chemical enrichment (GCE) as an appropriate representation of the interstellar abundances by quantitatively examining the extinction and abundances of 10 interstellar sight lines for which both the extinction curves and the gas-phase abundances of all the major dust-forming elements (i.e., C, O, Mg, Si and Fe) have been observationally determined. Instead of assuming a specific dust model and then fitting the observed extinction curves, for each sight line we apply the model-independent Kramers–Kronig relation, which relates the wavelength-integrated extinction to the total dust volume, to place a lower limit on the dust depletion. This, together with the observationally derived gas-phase abundances, allows us to rule out the B-star, solar, and protosolar abundances as the interstellar reference standard and support the GCE-augmented protosolar abundances as a viable representation of the interstellar abundances.
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics

We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a new, large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally designed layouts and a large number of rooms populated by high-quality textured 3D models with style compatibility. From layout semantics down to texture details of individual objects, our dataset is freely available to the academic community and beyond. Currently, 3D-FRONT contains 6,813 CAD houses, where 18,968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets. The 13,151 furniture objects all come with high-quality textures. While the floorplans and layout designs (i.e., furniture arrangements) are directly sourced from professional creations, the interior designs in terms of furniture styles, color, and textures have been carefully curated based on a recommender system we develop to attain consistent styles as expert designs. Furthermore, we release Trescope, a light-weight rendering tool, to support benchmark rendering of 2D images and annotations from 3D-FRONT. We demonstrate two applications, interior scene synthesis and texture synthesis, that are especially tailored to the strengths of our new dataset.
Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks

This study presents a methodology to quantify vulnerability of cyber attacks and their impacts based on probabilistic graphical models for intelligent transportation systems under connected and autonomous vehicles framework. Cyber attack vulnerabilities from various types and their impacts are calculated for intelligent signals and cooperative adaptive cruise control (CACC) applications based on the selected performance measures. Numerical examples are given that show impact of vulnerabilities in terms of average intersection queue lengths, number of stops, average speed, and delays. At a signalized network with and without redundant systems, vulnerability can increase average queues and delays by $3\%$ and $15\%$ and $4\%$ and $17\%$, respectively. For CACC application, impact levels reach to $50\%$ delay difference on average when low amount of speed information is perturbed. When significantly different speed characteristics are inserted by an attacker, delay difference increases beyond $100\%$ of normal traffic conditions.
Whole-Genome Sequence of the Trypoxylus dichotomus Japanese rhinoceros beetle

The draft whole-genome sequence of the Japanese rhinoceros beetle, Trypoxylus dichotomus was obtained using long-read PacBio sequence technology. The final assembled genome consisted of 739 Mbp in 2,347 contigs, with 24.5× mean coverage and a G+C content of 35.99%.
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping

Grouping has been commonly used in deep metric learning for computing diverse features. To improve the performance and interpretability, we propose an improved and interpretable grouping method to be integrated flexibly with any metric learning framework. Our method is based on the attention mechanism with a learnable query for each group. The query is fully trainable and can capture group-specific information when combined with the diversity loss. An appealing property of our method is that it naturally lends itself interpretability. The attention scores between the learnable query and each spatial position can be interpreted as the importance of that position. We formally show that our proposed grouping method is invariant to spatial permutations of features. When used as a module in convolutional neural networks, our method leads to translational invariance. We conduct comprehensive experiments to evaluate our method. Our quantitative results indicate that the proposed method outperforms prior methods consistently and significantly across different datasets, evaluation metrics, base models, and loss functions. For the first time to the best of our knowledge, our interpretation results clearly demonstrate that the proposed method enables the learning of diverse and stable semantic features across groups.