Miracle-dz/UniFAR-FTUs huggingface dataset:
Random cographs: Brownian graphon limit and asymptotic degree distribution

We consider uniform random cographs (either labeled or unlabeled) of large size. Our first main result is the convergence toward a Brownian limiting object in the space of graphons. We then show that the degree of a uniform random vertex in a uniform cograph is of order n, and converges after normalization to the Lebesgue measure on [0,1] . We finally analyze the vertex connectivity (i.e., the minimal number of vertices whose removal disconnects the graph) of random connected cographs, and show that this statistics converges in distribution without renormalization. Unlike for the graphon limit and for the degree of a random vertex, the limiting distribution of the vertex connectivity is different in the labeled and unlabeled settings. Our proofs rely on the classical encoding of cographs via cotrees. We then use mainly combinatorial arguments, including the symbolic method and singularity analysis.
Astro2020 Project White Paper: The Cosmic Accelerometer

Author(s): Eikenberry, Stephen S; Gonzalez, Anthony; Darling, Jeremy; Liske, Jochen; Slepian, Zachary; Mueller, Guido; Conklin, John; Fulda, Paul; Oliveira, Claudia Mendes de; Bentz, Misty; Jeram, Sarik; Dong, Chenxing; Townsend, Amanda; Nakazono, Lilianne Mariko Izuti; Quimby, Robert; Welsh, William; Harrington, Joseph; Law, Nicholas | Abstract: We propose an experiment, the Cosmic Accelerometer, designed to yield velocity precision of $\leq 1$ cm/s with measurement stability over years to decades. The first-phase Cosmic Accelerometer, which is at the scale of the Astro2020 Small programs, will be ideal for precision radial velocity measurements of terrestrial exoplanets in the Habitable Zone of Sun-like stars. At the same time, this experiment will serve as the technical pathfinder and facility core for a second-phase larger facility at the Medium scale, which can provide a significant detection of cosmological redshift drift on a 6-year timescale. This larger facility will naturally provide further detection/study of Earth twin planet systems as part of its external calibration process. This experiment is fundamentally enabled by a novel low-cost telescope technology called PolyOculus, which harnesses recent advances in commercial off the shelf equipment (telescopes, CCD cameras, and control computers) combined with a novel optical architecture to produce telescope collecting areas equivalent to standard telescopes with large mirror diameters. Combining a PolyOculus array with an actively-stabilized high-precision radial velocity spectrograph provides a unique facility with novel calibration features to achieve the performance requirements for the Cosmic Accelerometer.
Charged current 
b→cτν¯τ
 anomalies in a general 
W′
 boson scenario

The very recent experimental information obtained from Belle experiment, along with the one accumulated by the BABAR and LHCb experiments have shown the existence of anomalies in the ratios $R(D)$ and $R(D^{*})$ associated with the charged current transition $b \to c \tau \bar{\nu}_\tau$. Although the Belle measurements are in agreement with the SM predictions, the new experimental world averages still exhibit a tension. In addition, the $D^\ast$ longitudinal polarization $F_L(D^\ast)$ related with the channel $B \to D^\ast \tau \bar{\nu}_\tau$ observed by the Belle and the ratio $R(J/\psi)$ measured by the LHCb also show discrepancies with their corresponding SM estimations. In this work, we present a model-independent study based on the most general effective Lagrangian that yields to a tree-level effective contribution to the transition $b \to c \tau \bar{\nu}_\tau$ induced by a general $W^\prime$ gauge boson. Instead of considering any specific new physics (NP) realization, we performed an analysis by considering all the different chiral charges to the charm-bottom and $\tau$-$\nu_{\tau}$ interaction terms with a charged $W^\prime$ boson that explains the anomalies. We present a phenomenological study of parameter space allowed by the new experimental $b \to c \tau \bar{\nu}_\tau$ data and with the mono-tau signature $pp \to \tau_h X + \rm{MET}$ at the LHC. For comparison, we include some of the $W^\prime$ boson NP realizations that have already been studied in the literature.
A Total Error Framework for Digital Traces of Humans

The interactions and activities of hundreds of millions of people worldwide are recorded as digital traces every single day. When pulled together, these data offer increasingly comprehensive pictures of both individuals and groups interacting on different platforms, but they also allow inferences about broader target populations beyond those platforms, representing an enormous potential for the Social Sciences. Notwithstanding the many advantages of digital traces, recent studies have begun to discuss the errors that can occur when digital traces are used to learn about humans and social phenomena. Incidentally, many similar errors also affect survey estimates, which survey designers have been addressing using error conceptualization frameworks such as the Total Survey Error Framework. In this work, and leveraging the systematic approach of the Total Survey Error Framework, we propose a conceptual framework to diagnose, understand and avoid errors that may occur in studies that are based on digital traces of humans.
Numerical Methods for Simulating Star Formation

We review the numerical techniques for ideal and non-ideal magneto-hydrodynamics (MHD) used in the context of star formation simulations. We outline the specific challenges offered by modelling star forming environments, which are dominated by supersonic turbulence in a radiative, self-gravitating fluid. These conditions are rather unique in physics and engineering and pose particularly severe restrictions on the robustness and accuracy of numerical codes. One striking aspect is the formation of collapsing fluid elements leading to the formation of singularities that represent point-like objects, namely the proto-stars. Although a few studies have attempted to resolve the formation of the first and second Larson's cores, resolution limitations force us to use sink particle techniques, with sub-grid models to compute the accretion rates of mass, momentum and energy, as well as their ejection rate due to radiation and jets from the proto-stars. We discuss the most popular discretisation techniques used in the community, namely smoothed particle hydrodynamics, finite difference and finite volume methods, stressing the importance to maintain a divergence free magnetic field. We discuss how to estimate the truncation error of a given numerical scheme, and its importance in setting the magnitude of the numerical diffusion. This can have a strong impact on the outcome of these simulations, where viscosity and resistivity are implemented at the grid scale. We then present various numerical techniques to model non-ideal MHD effects (Ohmic and ambipolar diffusion, and Hall effect). These physical ingredients are posing strong challenges in term of resolution and time stepping. For the latter, several strategies are discussed to overcome the limitations due to ridiculously small time steps. An important aspect of star formation simulations is the radiation field. We discuss the current state-of-the-art, with a variety of techniques offering pros and cons in different conditions. Finally, we present more advanced strategies to mitigate the adverse effect of finite numerical resolution, which are very popular in the context of supersonic, self-gravitating fluids, namely adaptive mesh refinement, moving meshes, SPH and high-order methods. Advances in these directions are likely to trigger immense progresses in the future of our field.
Astro2020 Project White Paper: PolyOculus -- Low-cost Spectroscopy for the Community.

As astronomy moves into the era of large-scale time-domain surveys, we are seeing a flood of new transient and variable sources which will reach biblical proportions with the advent of LSST. A key strategic challenge for astronomy in this era is the lack of suitable spectroscopic followup facilities. In response to this need, we have developed the PolyOculus approach for producing large-area-equivalent telescopes by using fiber optics to link modules of multiple semi-autonomous, small, inexpensive, commercial-off-the-shelf telescopes. Crucially, this scalable design has construction costs which are $>10x$ lower than equivalent traditional large-area telescopes. In addition, PolyOculus is inherently highly automated and well-suited for remote operations. Development of this technology will enable the expansion of major research efforts in the LSST era to a host of smaller universities and colleges, including primarily-undergraduate institutions, for budgets consistent with their educational expenditures on similar facilities. We propose to develop and deploy a 1.6-m prototype demonstrator at the Mt. Laguna Observatory in California, followed by a full-scale 5-meter-class PolyOculus facility for linkage to existing and upcoming time-domain surveys.
Attitude Control of a Novel Tailsitter: Swiveling Biplane-Quadrotor

This paper proposes a solution to the attitude tracking problem for a novel quadrotor tailsitter unmanned aerial vehicle called swiveling biplane quadrotor. The proposed vehicle design addresses the lack of yaw control authority in conventional biplane quadrotor tailsitters by proposing a new design wherein two wings with two attached propellers are joined together with a rod through a swivel mechanism. The yaw torque is generated by relative rotation of the thrust vector of each wing. The unique design of this configuration having two rigid bodies interconnected through a rod with zero torsional rigidity makes the vehicle underactuated in the attitude configuration manifold. An output tracking problem is posed which results in a single equivalent rigid body attitude tracking problem with second-order moment dynamics. The proposed controller is uniformly valid for all attitudes and is based on dynamic feedback linearization in a geometric control framework. Almost-global asymptotic stability of the desired equilibrium of the tracking error dynamics is shown. The efficacy of the controller is shown with numerical simulation and flight tests.
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling

A sound event detection (SED) method typically takes as an input a sequence of audio frames and predicts the activities of sound events in each frame. In real-life recordings, the sound events exhibit some temporal structure: for instance, a "car horn" will likely be followed by a "car passing by". While this temporal structure is widely exploited in sequence prediction tasks (e.g., in machine translation), where language models (LM) are exploited, it is not satisfactorily modeled in SED. In this work we propose a method which allows a recurrent neural network (RNN) to learn an LM for the SED task. The method conditions the input of the RNN with the activities of classes at the previous time step. We evaluate our method using F1 score and error rate (ER) over three different and publicly available datasets; the TUT-SED Synthetic 2016 and the TUT Sound Events 2016 and 2017 datasets. The obtained results show an increase of 6% and 3% at the F1 (higher is better) and a decrease of 3% and 2% at ER (lower is better) for the TUT Sound Events 2016 and 2017 datasets, respectively, when using our method. On the contrary, with our method there is a decrease of 10% at F1 score and an increase of 11% at ER for the TUT-SED Synthetic 2016 dataset.
Orientation control and nonlinear trajectory tracking of colloidal particles using microfluidics

Suspensions of anisotropic Brownian particles are commonly encountered in a wide array of applications such as drug delivery and manufacturing of fiber-reinforced composites. Technological applications and fundamental studies of small anisotropic particles critically require precise control of particle orientation over defined trajectories and paths. In this work, we demonstrate robust control over the two-dimensional (2D) center-of-mass position and orientation of anisotropic Brownian particles using only fluid flow. We implement a path-following model predictive control scheme to manipulate colloidal particles over defined trajectories in position space, where the speed of movement along the path is a degree of freedom in the controller design. We further explore how the external flow field affects the orientation dynamics of anisotropic particles in steady and transient extensional flow using a combination of experiments and analytical modeling. Overall, this technique offers new avenues for fundamental studies of anisotropic colloidal particles using only fluid flow, without the need for external electric or optical fields.
Trojans in the Solar Neighborhood

About 20% of stars in the solar vicinity are in the Hercules stream, a bundle of stars that move together with a velocity distinct from the Sun. Its origin is still uncertain. Here, we explore the possibility that Hercules is made of trojans, stars captured at L4, one of the Lagrangian points of the stellar bar. Using GALAKOS–a high-resolution N-body simulation of the Galactic disk–we follow the motions of stars in the corotating frame of the bar and confirm previous studies on Hercules being formed by stars in corotation resonance with the bar. Unlike previous work, we demonstrate that the retrograde nature of trojan orbits causes the asymmetry in the radial velocity distribution, typical of Hercules in the solar vicinity. We show that trojans remain at capture for only a finite amount of time, before escaping L4 without being captured again. We anticipate that in the kinematic plane the Hercules stream will depopulate along the bar’s major axis and be visible at azimuthal angles behind the solar vicinity with a peak toward L4. This test can exclude the outer Lindblad resonance origin of the Hercules stream and be validated by Gaia DR3 and DR4.
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.
Parallel Algorithms for Butterfly Computations

Butterflies are the smallest non-trivial subgraph in bipartite graphs, and therefore having efficient computations for analyzing them is crucial to improving the quality of certain applications on bipartite graphs. In this paper, we design a framework called ParButterfly that contains new parallel algorithms for the following problems on processing butterflies: global counting, per-vertex counting, per-edge counting, tip decomposition (vertex peeling), and wing decomposition (edge peeling). The main component of these algorithms is aggregating wedges incident on subsets of vertices, and our framework supports different methods for wedge aggregation, including sorting, hashing, histogramming, and batching. In addition, ParButterfly supports different ways of ranking the vertices to speed up counting, including side ordering, approximate and exact degree ordering, and approximate and exact complement coreness ordering. For counting, ParButterfly also supports both exact computation as well as approximate computation via graph sparsification. We prove strong theoretical guarantees on the work and span of the algorithms in ParButterfly. We perform a comprehensive evaluation of all of the algorithms in ParButterfly on a collection of real-world bipartite graphs using a 48-core machine. Our counting algorithms obtain significant parallel speedup, outperforming the fastest sequential algorithms by up to 13.6x with a self-relative speedup of up to 38.5x. Compared to general subgraph counting solutions, we are orders of magnitude faster. Our peeling algorithms achieve self-relative speedups of up to 10.7x and outperform the fastest sequential baseline by up to several orders of magnitude.
Impact of DCSB and dynamical diquark correlations on proton GPDs

We calculate the leading-twist, helicity-independent generalized parton distributions (GPDs) of the proton, at finite skewness, in the Nambu–Jona-Lasinio (NJL) model of quantum chromodynamics (QCD). The NJL model reproduces low-energy characteristics of QCD, including dynamical chiral symmetry breaking (DCSB). The proton bound-state amplitude is solved for using the Faddeev equation in a quark-diquark approximation, including both dynamical scalar and axial vector diquarks. GPDs are calculated using a dressed nonlocal correlator, consistent with DCSB, which is obtained by solving a Bethe-Salpeter equation. The model and approximations used observe Lorentz covariance, and as a consequence the GPDs obey polynomiality sum rules. The electromagnetic and gravitational form factors are obtained from the GPDs. We find a D term of −1.08 when the nonlocal correlator is properly dressed, and 0.85 when the bare correlator is used instead, suggesting that within this framework proton stability requires the constituent quarks to be dressed consistently with DCSB. We also find that the anomalous gravitomagnetic moment vanishes, as required by Poincare symmetry.
Q-type asteroids: Possibility of non-fresh weathered surfaces

Itokawa particles, which are samples recovered from the S-complex asteroid 25143 Itokawa by the Hayabusa spacecraft, demonstrate that S-complex asteroids are parent bodies of ordinary chondrite meteorites. Furthermore, they clarify that the space-weathering age of the Itokawa surface is of the order of several thousand years. Traditionally, Q-type asteroids have been considered fresh-surfaced. However, as the space-weathering timescale is approximately three orders of magnitude lesser than the conventionally considered age, the previously proposed formation mechanisms of Q-type asteroids cannot sufficiently explain the surface refreshening. In this study, we propose a new hypothesis on the surface state of Q-type asteroids: Q-type asteroids have a non-fresh weathered surface with a paucity of fine particles. For verifying this hypothesis, laboratory experiments on the space weathering of ordinary chondrites are performed. Based on the results of these experiments, we found that large (more than $100\, \mu \mathrm{m}$) ordinary chondritic particles with space weathering exhibit spectra consistent with Q-type asteroids.
Learning High-Level Planning Symbols from Intrinsically Motivated Experience

In symbolic planning systems, the knowledge on the domain is commonly provided by an expert. Recently, an automatic abstraction procedure has been proposed in the literature to create a Planning Domain Definition Language (PDDL) representation, which is the most widely used input format for most off-the-shelf automated planners, starting from `options', a data structure used to represent actions within the hierarchical reinforcement learning framework. We propose an architecture that potentially removes the need for human intervention. In particular, the architecture first acquires options in a fully autonomous fashion on the basis of open-ended learning, then builds a PDDL domain based on symbols and operators that can be used to accomplish user-defined goals through a standard PDDL planner. We start from an implementation of the above mentioned procedure tested on a set of benchmark domains in which a humanoid robot can change the state of some objects through direct interaction with the environment. We then investigate some critical aspects of the information abstraction process that have been observed, and propose an extension that mitigates such criticalities, in particular by analysing the type of classifiers that allow a suitable grounding of symbols.
Composing Diverse Policies for Temporally Extended Tasks

Robot control policies for temporally extended and sequenced tasks are often characterized by discontinuous switches between different local dynamics. These change-points are often exploited in hierarchical motion planning to build approximate models and to facilitate the design of local, region-specific controllers. However, it becomes combinatorially challenging to implement such a pipeline for complex temporally extended tasks, especially when the sub-controllers work on different information streams, time scales and action spaces. In this letter, we introduce a method that can automatically compose diverse policies comprising motion planning trajectories, dynamic motion primitives and neural network controllers. We introduce a global goal scoring estimator that uses local, per-motion primitive dynamics models and corresponding activation state-space sets to sequence diverse policies in a locally optimal fashion. We use expert demonstrations to convert what is typically viewed as a gradient-based learning process into a planning process without explicitly specifying pre- and post-conditions. We first illustrate the proposed framework using an MDP benchmark to showcase robustness to action and model dynamics mismatch, and then with a particularly complex physical gear assembly task, solved on a PR2 robot. We show that the proposed approach successfully discovers the optimal sequence of controllers and solves both tasks efficiently.