Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Fuel an AGN: Mapping Circumnuclear Gas in NGC 6240 with ALMA

Dynamical black hole mass measurements in some gas-rich galaxy mergers indicate that they are overmassive relative to their host galaxy properties. Overmassive black holes in these systems present a conflict with the standard progression of galaxy merger–quasar evolution; an alternative explanation is that a nuclear concentration of molecular gas driven inward by the merger is affecting these dynamical black hole mass estimates. We test for the presence of such gas near the two black holes in NGC 6240 using long-baseline ALMA Band 6 observations (beam size 0.″06 × 0.″03 or 30 pc × 15 pc). We find (4.2–9.8) × 107 M☉ and (1.2–7.7) × 108 M☉ of molecular gas within the resolution limit of the original black hole mass measurements for the north and south black holes, respectively. In the south nucleus, this measurement implies that 6%–89% of the original black hole mass measurement actually comes from molecular gas, resolving the tension in the original black hole scaling relations. For the north, only 5%–11% is coming from molecular gas, suggesting the north black hole is actually overmassive. Our analysis provides the first measurement of significant molecular gas masses contaminating dynamical black hole mass measurements. These high central molecular gas densities further present a challenge to theoretical black hole accretion prescriptions, which often assume accretion proceeds rapidly through the central 10 pc.
Scalable Deep Neural Networks via Low-Rank Matrix Factorization

Compressing deep neural networks (DNNs) is important for real-world applications operating on resource-constrained devices. However, it is difficult to change the model size once the training is completed, which needs re-training to configure models suitable for different devices. In this paper, we propose a novel method that enables DNNs to flexibly change their size after training. We factorize the weight matrices of the DNNs via singular value decomposition (SVD) and change their ranks according to the target size. In contrast with existing methods, we introduce simple criteria that characterize the importance of each basis and layer, which enables to effectively compress the error and complexity of models as little as possible. In experiments on multiple image-classification tasks, our method exhibits favorable performance compared with other methods.
Temporal interference of relativistic bosons.

In quantum mechanics, time is introduced as a non-measurable quantity, as there is no possibility to build a hermitian operator canonically conjugated to the Hamiltonian. We cannot have, therefore, the time operator, which means that the temporal structure of the evolution of quantum systems is ill-defined. We present an extension to the model, in which the time evolution is based on the projection postulate rather than the unitary operator. This approach is in agreement with all other aspects of quantum mechanics and allows to discuss time as an observable. Using this framework we present a description of the temporal double-slit experiment in which a~single particle interferes with itself from a different instant of time. Such behaviour has already been observed experimentally but lacked a consistent theoretical explanation.
The Six Fronts of the Generative Adversarial Networks

Generative Adversarial Networks fostered a newfound interest in generative models, resulting in a swelling wave of new works that new-coming researchers may find formidable to surf. In this paper, we intend to help those researchers, by splitting that incoming wave into six "fronts": Architectural Contributions, Conditional Techniques, Normalization and Constraint Contributions, Loss Functions, Image-to-image Translations, and Validation Metrics. The division in fronts organizes literature into approachable blocks, ultimately communicating to the reader how the area is evolving. Previous surveys in the area, which this works also tabulates, focus on a few of those fronts, leaving a gap that we propose to fill with a more integrated, comprehensive overview. Here, instead of an exhaustive survey, we opt for a straightforward review: our target is to be an entry point to this vast literature, and also to be able to update experienced researchers to the newest techniques.
Vectorizing SpMV by Exploiting Dynamic Regular Patterns

Modern optimizing compilers can exploit memory access and computation patterns to generate vectorized codes. However, such patterns in irregular programs such as SpMV are unknown until runtime due to the input dependence. Thus, either compiler’s static optimization or profile-guided optimization cannot represent the patterns for any common input, which leads to suboptimal vectorization. To address the above drawback, we propose DynVec, a framework to automatically exploit regular patterns buried deeply inside SpMV programs and apply corresponding optimizations for better vectorization. Due to the ability to represent instruction features and identify regular patterns with effective feature extraction and data re-arranging methods, DynVec can generate highly efficient vectorized codes by replacing gather/scatter/reduction operations with optimized operation groups. We evaluate DynVec on optimizing SpMV with representative sparse matrix datasets. The experiment results show that DynVec achieves significant speedup compared to the state-of-the-art SpMV implementations across a range of platforms.
Measurement of the 
Bc−
 meson production fraction and asymmetry in 7 and 13 TeV 
pp
 collisions

The production fraction of the Bc-meson with respect to the sum of B- A nd B0 mesons is measured in both 7 and 13 TeV center-of-mass (c.m.) energy pp collisions produced by the Large Hadron Collider (LHC), using the LHCb detector. The rate, approximately 3.7 per mille, does not change with energy, but shows a transverse momentum dependence. The Bc-Bc+ production asymmetry is also measured and is consistent with zero within the determined statistical and systematic uncertainties of a few percent.
Compelling bounds on equilibration times—the issue with Fermi’s golden rule

Putting a general, physically relevant upper bound on equilibration times in closed quantum systems is a recently much pursued endeavor. In 2017 Phys. Rev. X 7 031027 García-Pintos et al suggest such a bound. We point out that the general assumptions which allow for an actual estimation of this bound are violated in cases in which Fermi’s golden rule and related open quantum system theories apply. To probe the range of applicability of Fermi’s golden rule for systems of the type addressed in the above work, we numerically solve the corresponding Schrödinger equation for some finite spin systems comprising up to 25 spins. These calculations shed light on the breakdown of standard quantum master equations in the ‘superweak’ coupling limit, which occurs for finite sized baths.
Improving Sequence-To-Sequence Speech Recognition Training with On-The-Fly Data Augmentation

Sequence-to-Sequence (S2S) models recently started to show state-of-the-art performance for automatic speech recognition (ASR). With these large and deep models overfitting remains the largest problem, outweighing performance improvements that can be obtained from better architectures. One solution to the overfitting problem is increasing the amount of available training data and the variety exhibited by the training data with the help of data augmentation. In this paper we examine the influence of three data augmentation methods on the performance of two S2S model architectures. One of the data augmentation method comes from literature, while two other methods are our own development – a time perturbation in the frequency domain and sub-sequence sampling. Our experiments on Switchboard and Fisher data show state-of-the-art performance for S2S models that are trained solely on the speech training data and do not use additional text data.
Analyzing China's Consumer Price Index Comparatively with that of United States

This paper provides a thorough analysis on the dynamic structures and predictability of China's Consumer Price Index (CPI-CN), with a comparison to those of the United States. Despite the differences in the two leading economies, both series can be well modeled by a class of Seasonal Autoregressive Integrated Moving Average Model with Covariates (S-ARIMAX). The CPI-CN series possess regular patterns of dynamics with stable annual cycles and strong Spring Festival effects, with fitting and forecasting errors largely comparable to their US counterparts. Finally, for the CPI-CN, the diffusion index (DI) approach offers improved predictions than the S-ARIMAX models.
The Featureless Transmission Spectra of Two Super-puff Planets

The Kepler mission revealed a class of planets known as “super-puffs,” with masses only a few times larger than Earth’s but radii larger than Neptune, giving them very low mean densities. All three of the known planets orbiting the young solar-type star Kepler 51 are super-puffs. The Kepler 51 system thereby provides an opportunity for a comparative study of the structures and atmospheres of this mysterious class of planets, which may provide clues about their formation and evolution. We observed two transits each of Kepler 51b and 51d with the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope. Combining new WFC3 transit times with reanalyzed Kepler data and updated stellar parameters, we confirmed that all three planets have densities lower than 0.1 g cm−3. We measured the WFC3 transmission spectra to be featureless between 1.15 and 1.63 μm, ruling out any variations greater than 0.6 scale heights (assuming a H/He-dominated atmosphere), thus showing no significant water absorption features. We interpreted the flat spectra as the result of a high-altitude aerosol layer (pressure <3 mbar) on each planet. Adding this new result to the collection of flat spectra that have been observed for other sub-Neptune planets, we find support for one of the two hypotheses introduced by Crossfield & Kreidberg, that planets with cooler equilibrium temperatures have more high-altitude aerosols. We strongly disfavor their other hypothesis that the H/He mass fraction drives the appearance of large-amplitude transmission features.
Accretion Kinematics in the T Tauri Binary TWA 3A: Evidence for Preferential Accretion onto the TWA 3A Primary

We present time-series, high-resolution optical spectroscopy of the eccentric T Tauri binary TWA 3A. Our analysis focuses on variability in the strength and structure of the accretion-tracing emission lines Hα and He i 5876 Å. We find emission line strengths to display the same orbital phase–dependent behavior found with time-series photometry, namely, bursts of accretion near periastron passages. Such bursts are in good agreement with numerical simulations of young eccentric binaries. During accretion bursts, the emission of He i 5876 Å consistently traces the velocity of the primary star. After removing a model for the system’s chromospheric emission, we find the primary star typically emits ∼70% of the He i accretion flux. We interpret this result as evidence for circumbinary accretion streams that preferentially feed the TWA 3A primary. This finding is in contrast to most numerical simulations, which predict the secondary should be the dominant accretor in a binary system. Our results may be consistent with a model in which the precession of an eccentric circumbinary disk gap alternates between preferentially supplying mass to the primary and secondary.
Microsoft Word-Liu_Final_Arvix.docx

The constraints induced by prohibited zones (PZs) were traditionally formulated as multiple disjoint regions. It was difficult to solve the optimal power flow (OPF) problems subject to the disjoint constraints. This paper proposes a new formulation for the OPF problem with PZs. The proposed formulation significantly expedites the algorithm implementation. The effectiveness of the new approach is verified by different methods including traditional optimization methods, PSO and particle swarm optimization with adaptive parameter control which is conducted on the IEEE 30-bus test system. Keywords— Disjoint feasible regions, modified particle swarm optimization, optimal power flow, prohibited zones. NOTATIONS For convenience, the main notations to be used are listed below: : , ik ik G B Elements of the bus admittance matrix : i V Voltage magnitude at bus i : i f Objective function of generator i : i m Number of feasible zones of generator i : b n Number of total buses : g n Number of generators : di P Real power load at bus i : gi P Real power generation at bus i : di Q Reactive power load at bus i : gi Q Reactive power generation at bus i : i  The phase angle at bus i
OSSOS. XVIII. Constraining Migration Models with the 2:1 Resonance Using the Outer Solar System Origins Survey

Resonant dynamics plays a significant role in the past evolution and current state of our outer solar system. The population ratios and spatial distribution of Neptune’s resonant populations are direct clues to understanding the history of our planetary system. The orbital structure of the objects in Neptune’s 2:1 mean-motion resonance (“twotinos”) has the potential to be a tracer of planetary migration processes. Different migration processes produce distinct architectures, recognizable by well-characterized surveys. However, previous characterized surveys only discovered a few twotinos, making it impossible to model the intrinsic twotino population. With a well-designed cadence and nearly 100% tracking success, the Outer Solar System Origins Survey (OSSOS) discovered 838 trans-Neptunian objects, of which 34 are securely twotinos with well-constrained libration angles and amplitudes. We use the OSSOS twotinos and the survey characterization parameters via the OSSOS survey simulator to inspect the intrinsic population and orbital distributions of twotinos. The estimated twotino population, with Hr < 8.66 (diameter ∼100 km) at 95% confidence, is consistent with the previous low-precision estimate. We also constrain the width of the inclination distribution to a relatively narrow value of and find that the eccentricity distribution is consistent with a Gaussian centered on ec = 0.275 with a width ew = 0.06. We find a single-slope exponential luminosity function with α = 0.6 for the twotinos. Finally, for the first time, we meaningfully constrain the fraction of symmetric twotinos and the ratio of the leading asymmetric islands; both fractions are in the range of 0.2–0.6. These measurements rule out certain theoretical models of Neptune’s migration history.
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots

Conventional chatbots focus on two-party response generation, which simplifies the real dialogue scene. In this paper, we strive toward a novel task of Response Generation on Multi-Party Chatbot (RGMPC), where the generated responses heavily rely on the interlocutors’ roles (e.g., speaker and addressee) and their utterances. Unfortunately, complex interactions among the interlocutors’ roles make it challenging to precisely capture conversational contexts and interlocutors’ information. Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC. Specifically, we employ interactive representations to capture dialogue contexts for different interlocutors. Moreover, we leverage an addressee memory to enhance contextual interlocutor information for the target addressee. Finally, we construct a corpus for RGMPC based on an existing open-access dataset. Automatic and manual evaluations demonstrate that the ICRED remarkably outperforms strong baselines.
Decentralized Parallel Algorithm for Training Generative Adversarial Nets

Generative Adversarial Networks (GANs) are powerful class of generative models in the deep learning community. Current practice on large-scale GAN training \cite{brock2018large} utilizes large models and distributed large-batch training strategies, and is implemented on deep learning frameworks (e.g., TensorFlow, PyTorch, etc.) designed in a centralized manner. In the centralized network topology, every worker needs to communicate with the central node. However, when the network bandwidth is low or network latency is high, the performance would be significantly degraded. Despite recent progress on decentralized algorithms for training deep neural networks, it remains unclear whether it is possible to train GANs in a decentralized manner. In this paper, we design a decentralized algorithm for solving a class of non-convex non-concave min-max problem with provable guarantee. Experimental results on GANs demonstrate the effectiveness of the proposed algorithm.
Conformal mass in EGB theory

In this paper, we show that the physical information given by conserved charges for asymptotically AdS spacetimes in Einstein-Gauss-Bonnet AdS gravity is encoded in the electric part of the Weyl tensor. This result generalizes the conformal mass definition by Ashtekar-Magnon-Das (AMD) to a gravity theory with a Gauss-Bonnet term. This proof makes use of the Noether charges obtained from an action renormalized by the addition of counterterms which depend on the extrinsic curvature (Kounterterms). If the asymptotic fall-off behaviour of the Weyl tensor is same as the one considered in the AMD method, then the Kounterterm charges and the AMD charges agree in any dimension. dileep@hri.res.in gkofinas@aegean.gr olivera.miskovic@ucv.cl rodrigo.olea@unab.cl
Real-time Bidding campaigns optimization using attribute selection

Real-Time Bidding is nowadays one of the most promising systems in the online advertising ecosystem. In the presented study, the performance of RTB campaigns is improved by optimising the parameters of the users' profiles and the publishers' websites. Most studies about optimising RTB campaigns are focused on the bidding strategy. In contrast, the objective of our research consists of optimising RTB campaigns by finding out configurations that maximise both the number of impressions and their average profitability. The experiments demonstrate that, when the number of required visits by advertisers is low, it is easy to find configurations with high average profitability, but as the required number of visits increases, the average profitability tends to go down. Additionally, configuration optimisation has been combined with other interesting strategies to increase, even more, the campaigns' profitability. Along with parameter configuration the study considers the following complementary strategies to increase profitability: i) selecting multiple configurations with a small number of visits instead of a unique configuration with a large number, ii) discarding visits according to the thresholds of cost and profitability, iii) analysing a reduced space of the dataset and extrapolating the solution, and iv) increasing the search space by including solutions below the required number of visits. The developed campaign optimisation methodology could be offered by RTB platforms to advertisers to make their campaigns more profitable.
Autonomous UAV Landing System Based on Visual Navigation

In this paper, we present an autonomous unmanned aerial vehicle (UAV) landing system based on visual navigation. We design the landmark as a topological pattern in order to enable the UAV to distinguish the landmark from the environment easily. In addition, a dynamic thresholding method is developed for image binarization to improve detection efficiency. The relative distance in the horizontal plane is calculated according to effective image information, and the relative height is obtained using a linear interpolation method. The landing experiments are performed on a static and a moving platform, respectively. The experimental results illustrate that our proposed landing system performs robustly and accurately.
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation

The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal. We present Cascaded Variational Inference (CAVIN) Planner, a model-based method that hierarchically generates plans by sampling from latent spaces. To facilitate planning over long time horizons, our method learns latent representations that decouple the prediction of high-level effects from the generation of low-level motions through cascaded variational inference. This enables us to model dynamics at two different levels of temporal resolutions for hierarchical planning. We evaluate our approach in three multi-step robotic manipulation tasks in cluttered tabletop environments given high-dimensional observations. Empirical results demonstrate that the proposed method outperforms state-of-the-art model-based methods by strategically interacting with multiple objects.
Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs’ local computation and training data. Despite its advantages such as preserving data privacy, FL still has challenges of heterogeneity across UEs’ data and physical resources. To address these challenges, we first propose FEDL, a FL algorithm which can handle heterogeneous UE data without further assumptions except strongly convex and smooth loss functions. We provide a convergence rate characterizing the trade-off between local computation rounds of each UE to update its local model and global communication rounds to update the FL global model. We then employ FEDL in wireless networks as a resource allocation optimization problem that captures the trade-off between FEDL convergence wall clock time and energy consumption of UEs with heterogeneous computing and power resources. Even though the wireless resource allocation problem of FEDL is non-convex, we exploit this problem’s structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights into problem design. Finally, we empirically evaluate the convergence of FEDL with PyTorch experiments, and provide extensive numerical results for the wireless resource allocation sub-problems. Experimental results show that FEDL outperforms the vanilla FedAvg algorithm in terms of convergence rate and test accuracy in various settings.