Miracle-dz/UniFAR-FTUs huggingface dataset:
The analogy of K-correction in the topic of gamma-ray bursts

Abstract It is well-known that there are two types of gamma-ray bursts (GRBs): short/hard and long/soft ones, respectively. The long GRBs are coupled to supernovae, but the short ones are associated with the so called macronovae (also known as kilonovae), which can serve as the sources of gravitational waves as well. The kilonovae can arise from the merging of two neutron-stars. The neutron stars can be substituded by more massive black holes as well. Hence, the topic of gamma-ray bursts (mainly the topic of short ones) and the topic of massive binaries, are strongly connected. In this contribution, the redshifts of GRBs are studied. The surprising result - namely that the apparently fainter GRBs can be in average at smaller distances - is discussed again. In essence, the results of Mészáros et al. (2011) are studied again using newer samples of GRBs. The former result is confirmed by the newer data.
Curvature-induced Polarization and Spectral Index Behavior for PKS 1502+106

A comprehensive study of multifrequency correlations can shed light on the nature of variation for blazars. In this work, we collect the long-term radio, optical, and γ-ray light curves of PKS 1502+106. After performing the localized cross-correlation function analysis, we find that correlations between radio and γ-ray or V band are beyond the 3σ significance level. The lag of the γ-ray relative to 15 GHz is days, translating to a distance of parsec (pc) between them. Within uncertainties, the locations of the γ-ray and optical emitting regions are roughly the same, and are away from the jet base within 1.2 pc. The derived magnetic field in optical and γ-ray emitting regions is about 0.36 G. The logarithm of γ-ray flux is significantly linearly correlated with that of V-band fluxes, which can be explained by the synchrotron self-Compton process, the external Compton processes, or the combination of them. We find a significant linear correlation in the plot of (polarization degree) versus at V band, and use the empirical relation Π ∼ sinn θ′ (θ′ is the observing angle in the comoving frame blob) to explain it. The behaviors of color index (generally redder when brighter at the active state) and γ-ray spectral index (softer when brighter) could be well explained by the twisted jet model. These findings suggest that the curvature effect (mainly due to the change of the viewing angle) is dominant in the variation phenomena of fluxes, spectral indices, and polarization degrees for PKS 1502+106.
Gait Graph Optimization: Generate Variable Gaits from One Base Gait for Lower-limb Rehabilitation Exoskeleton Robots

The most concentrated application of lower-limb rehabilitation exoskeleton (LLE) robot is that it can help paraplegics "re-walk". However, "walking" in daily life is more than just walking on flat ground with fixed gait. This paper focuses on variable gaits generation for LLE robot to adapt complex walking environment. Different from traditional gaits generator for biped robot, the generated gaits for LLEs should be comfortable to patients. Inspired by the pose graph optimization algorithm in SLAM, we propose a graph-based gait generation algorithm called gait graph optimization (GGO) to generate variable, functional and comfortable gaits from one base gait collected from healthy individuals to adapt the walking environment. Variants of walking problem, e.g., stride adjustment, obstacle avoidance, and stair ascent and descent, help verify the proposed approach in simulation and experimentation. We open source our implementation.
Load balancing under $d$-thinning

In the classical balls-and-bins model, $m$ balls are allocated into $n$ bins one by one uniformly at random. In this note, we consider the $d$-thinning variant of this model, in which the process is regulated in an on-line fashion as follows. For each ball, after a random bin has been selected, an overseer may decide, based on all previous history, whether to accept this bin or not. However, one of every $d$ consecutive suggested bins must be accepted. The maximum load of this setting is the number of balls in the most loaded bin. We show that after $\Theta(n)$ balls have been allocated, the least maximum load achievable with high probability is $(d+o(1))\sqrt[d]{\frac{d\log n}{\log\log n}}$. This should be compared with the related $d$-choice setting, in which the optimal maximum load achievable with high probability is $\frac{\log\log n}{\log d}+O(1)$.
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU

Signatory is a library for calculating signature and logsignature transforms and related functionality. The focus is on making this functionality available for use in machine learning, and as such includes features such as GPU support and backpropagation. To our knowledge it is the first publically available GPU-capable library for these operations. It also implements several new algorithmic improvements, and provides several new features not available in previous libraries. The library operates as a Python wrapper around C++, and is compatible with the PyTorch ecosystem. It may be installed directly via \texttt{pip}. Source code, documentation, examples, benchmarks and tests may be found at \texttt{\url{this https URL}}. The license is Apache-2.0.
Towards Intelligent Robotic Process Automation for BPMers

Robotic Process Automation (RPA) is a fast-emerging automation technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI), and allows organizations to automate high volume routines. RPA tools are able to capture the execution of such routines previously performed by a human users on the interface of a computer system, and then emulate their enactment in place of the user by means of a software robot. Nowadays, in the BPM domain, only simple, predictable business processes involving routine work can be automated by RPA tools in situations where there is no room for interpretation, while more sophisticated work is still left to human experts. In this paper, starting from an in-depth experimentation of the RPA tools available on the market, we provide a classification framework to categorize them on the basis of some key dimensions. Then, based on this analysis, we derive four research challenges and discuss prospective approaches necessary to inject intelligence into current RPA technology, in order to achieve more widespread adoption of RPA in the BPM domain.
PI-GAN: Learning Pose Independent representations for multiple profile face synthesis

Generating a pose-invariant representation capable of synthesizing multiple face pose views from a single pose is still a difficult problem. The solution is demanded in various areas like multimedia security, computer vision, robotics, etc. Generative adversarial networks (GANs) have encoder-decoder structures possessing the capability to learn pose-independent representation incorporated with discriminator network for realistic face synthesis. We present PIGAN, a cyclic shared encoder-decoder framework, in an attempt to solve the problem. As compared to traditional GAN, it consists of secondary encoder-decoder framework sharing weights from the primary structure and reconstructs the face with the original pose. The primary framework focuses on creating disentangle representation, and secondary framework aims to restore the original face. We use CFP high-resolution, realistic dataset to check the performance.
Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans

In this paper, we address the problem of forecasting agent trajectories in unknown environments, conditioned on their past motion and scene structure. Trajectory forecasting is a challenging problem due to the large variation in scene structure, and the multi-modal nature of the distribution of future trajectories. Unlike prior approaches that directly learn one-to-many mappings from observed context, to multiple future trajectories, we propose to condition trajectory forecasts on \textit{plans} sampled from a grid based policy learned using maximum entropy inverse reinforcement learning policy (MaxEnt IRL). We reformulate MaxEnt IRL to allow the policy to jointly infer plausible agent goals and paths to those goals on a coarse 2-D grid defined over an unknown scene. We propose an attention based trajectory generator that generates continuous valued future trajectories conditioned on state sequences sampled from the MaxEnt policy. Quantitative and qualitative evaluation on the publicly available Stanford drone dataset (SDD) shows that our model generates trajectories that are (1) diverse, representing the multi-modal predictive distribution, and (2) precise, conforming to the underlying scene structure over long prediction horizons, achieving state of the art results on the TrajNet benchmark split of SDD.
Optimization of mean-field spin glasses

Mean-field spin glasses are families of random energy functions (Hamiltonians) on high-dimensional product spaces. In this paper we consider the case of Ising mixed $p$-spin models, namely Hamiltonians $H_N:\Sigma_N\to {\mathbb R}$ on the Hamming hypercube $\Sigma_N = \{\pm 1\}^N$, which are defined by the property that $\{H_N({\boldsymbol \sigma})\}_{{\boldsymbol \sigma}\in \Sigma_N}$ is a centered Gaussian process with covariance ${\mathbb E}\{H_N({\boldsymbol \sigma}_1)H_N({\boldsymbol \sigma}_2)\}$ depending only on the scalar product $\langle {\boldsymbol \sigma}_1,{\boldsymbol \sigma}_2\rangle$. The asymptotic value of the optimum $\max_{{\boldsymbol \sigma}\in \Sigma_N}H_N({\boldsymbol \sigma})$ was characterized in terms of a variational principle known as the Parisi formula, first proved by Talagrand and, in a more general setting, by Panchenko. The structure of superlevel sets is extremely rich and has been studied by a number of authors. Here we ask whether a near optimal configuration ${\boldsymbol \sigma}$ can be computed in polynomial time. We develop a message passing algorithm whose complexity per-iteration is of the same order as the complexity of evaluating the gradient of $H_N$, and characterize the typical energy value it achieves. When the $p$-spin model $H_N$ satisfies a certain no-overlap gap assumption, for any $\varepsilon>0$, the algorithm outputs ${\boldsymbol \sigma}\in\Sigma_N$ such that $H_N({\boldsymbol \sigma})\ge (1-\varepsilon)\max_{{\boldsymbol \sigma}'} H_N({\boldsymbol \sigma}')$, with high probability. The number of iterations is bounded in $N$ and depends uniquely on $\varepsilon$. More generally, regardless of whether the no-overlap gap assumption holds, the energy achieved is given by an extended variational principle, which generalizes the Parisi formula.
Convolutional Neural Network-based Topology Optimization (CNN-TO) By Estimating Sensitivity of Compliance from Material Distribution

This paper proposes a new topology optimization method that applies a convolutional neural network (CNN), which is one deep learning technique for topology optimization problems. Using this method, we acquire a structure with a little higher performance that could not be obtained by the previous topology optimization method. In particular, in this paper, we solve a topology optimization problem aimed at maximizing stiffness with a mass constraint, which is a common type of topology optimization. In this paper, we first formulate the conventional topology optimization by the solid isotropic material with penalization method. Next, we formulate the topology optimization using CNN. Finally, we show the effectiveness of the proposed topology optimization method by solving a verification example, namely a topology optimization problem aimed at maximizing stiffness. In this research, as a result of solving the verification example for a small design area of 16x32 element, we obtain the solution different from the previous topology optimization method. This result suggests that stiffness information of structure can be extracted and analyzed for structural design by analyzing the density distribution using CNN like an image. This suggests that CNN technology can be utilized in the structural design and topology optimization.
Relation Between Loudness and Masking

A functional relationship between the loudness of a sound and the degree to which it masks single frequency tones, that is, the masking audiogram of the sound, is developed. A loudness masking function is determined experimentally. From this loudness masking relationship the loudness of a sound can be computed by simply integrating the area under the masking audiogram plotted on a special chart. Comparisons of computed and observed loudness levels are shown for a number of sounds and serve to illustrate the precision to be expected from the method. Finally, the results of a large number of masking tests are given in the form of masking contours, which enable one to predict the masking audiogram of a sound from measurements of its intensity spectrum.
Mathematics and Mathematica for Economists

Part I: Introduction to Mathematica. Part II: A Review of Calculus. Part III: Vectors. Part IV: Matrices. Part V: Systems of Linear Equations. Part VI: Eigenvalues and Eigenvectors. Part VII: Real Quadratic Forms. Part VIII: Multivariable Differential Calculus. Part IX: Taylor Series and Implicit Functions. Part X: Concave and Quasiconcave Functions. Part XI: Optimization. Part XII: Mathematical Programming. Part XIII: Ordinary Differential Equations. Part XIV: Systems of Differential Equations. Part XV: Difference Equations.
Rayleigh–Brillouin Scattering in Compressed H2, D2, and HD. II

The Rayleigh–Brillouin spectrum of light scattered from H2, D2, and HD has been measured at densities ranging from 1/2 to 104 amagat, all at room temperature. The apparatus consisted of a 20 mW He–Ne laser, a high pressure cell for 90° scattering, a pressure scanned Fabry–Perot interferometer, and photon counting electronics. A discrepancy is noted between the observed spectra and various theoretical spectra. It is suggested that the discrepancy arises from an omission in the theories of coupling between relaxation of the rotational states and thermal conduction.
Nonlinear networks. IIb

It may be well to point out explicitly that the networks considered in this paper and in the preceding papers are definitely not arbitrary nonlinear networks. Rather, it has been the aim to define nonlinear networks which share the uniqueness properties of passive linear networks. Physical considerations suggest that such networks may be aptly termed reliable networks. The networks considered here consist of transformers with ferromagnetic cores (assumed to have negligible hysteresis) and Ohmic resistors, arbitrarily interconnected to a set of generators. It is found that as in the case of linear networks, under given electromotive forces from the generators, the currents flowing are uniquely determined after sufficient time has elapsed. Moreover, if the electromotive force is periodic, a unique, periodic current flow can exist. The proof begins by integrating the network equations with respect to time. It turns out that the integrated equations have the same form as the network equations of a network containing linear capacitors and nonlinear resistors. The differential permeability of a ferromagnetic lies between positive limits; in the integrated equations this is imaged by the condition that the differential resistance lies between positive limits. The results stated are then easily deduced from theorems developed in preceding papers for networks containing such quasi-linear resistors [l, 2]. 1 (These papers will hereinafter be referred to as I and III.) In most applications of transformers the nonlinearity of the magnetization curve is an undesirable feature; however, the operation of flux gate magnetometers, magnetic voltage regulators, frequency multipliers, and magnetic amplifiers depends essentially on the nonlinearity. Considerable space is devoted to the concepts of a transformer and of a transformer network. In §1 the transformers are restricted to be of a symmetrical type so that their properties can be determined fairly rigorously from elementary considerations. The general transformer is treated in §4 by means of the hypothesis of the magnetic network. The concept of topologically coupled networks employed there will be found quite interesting from an abstract point of view.
Probing nuclear matter with jets and γ-hadron correlations: results from PHENIX

Fully reconstructed jets and direct photon-tagged jet fragments significantly reduce energy-loss bias, the bias toward mostly measuring particles from partons which suffer little energy loss. In d+Au collisions, one accesses the physics at large x, which yields important constraints for nuclear parton distribution functions. In both d+Au and A+A collisions, coherent multiple-scattering models of energy loss can be tested. In this contribution, we present the current results from the PHENIX experiment on fully reconstructed jets and direct γ-hadron correlations. Baseline measurements of jets in p+p collisions as well as their yield and correlation modifications in d+Au and Cu+Cu will be given. From γ-hadron correlations, we present the fragmentation function in p+p and Au+Au collisions and its modification in Au+Au to zT lower than what has previously been studied. Implications of these data on our understanding of both cold and hot, dense nuclear matter created at the RHIC are discussed.
Nonlinear theory of gyroharmonic radiation from spatiotemporally modulated electron beams.

A multimode nonlinear particle simulation code is used to find the saturated efficiency for power transfer into modes of a cylindrical waveguide carrying a spatiotemporally modulated gyrating electron beam. For a TE 51 -mode fifth harmonic 94-GHz harmonic converter using a 150-kV, 6.7-A cold beam, this code predicts a conversion efficiency of 57% when a linearly tapered guide magnetic field is used, and 70% when a nonlinear taper is used. Efficiency in the linear taper case is shown to be insensitive to beam axial velocity spread, while both cases show negligible power flow into competing modes
Toward Black-Box Sampling: A Random-Direction Interior-Point Markov Chain Approach

Abstract We briefly discuss issues concerning “black-box” algorithms for estimating properties of a κ-dimensional posterior distribution with density . Typically, is not known in closed form, but can be computed at any point as the product of the likelihood function and the prior density. Black-box algorithms provide estimates to a user who must specify only minimal information about the distribution, the properties to be estimated, and the desired precision of the estimates. Ideally, reasonable performance should require no tuning of algorithm parameters. We propose a random-direction interior-point (RDIP) Markov chain approach to black-box sampling. RDIP requires only that the product of the likelihood function and the prior density can be computed at any point . We introduce an auxiliary variable Δ and consider a random variable from the interior of S, the region under the surface and above the plane δ = 0. Instead of directly sampling from , RDIP generates a Markov chain from the uniform distribution .
Bertin, lexis and the graphical representation of event histories

After first examining the issues in visualising individual event histories, a description of a new method for viewing such data is given. The technique displays an event history as a pencil-like multi-faceted object in three-dimensional space, with changes of state being represented by changes in colour, texture or height along the length of the pencil. Viewing a single history allows a detailed examination of an individual's life events, and allows the relationship between changes of state in many variables to be examined. The extension of these ideas to viewing populations of event histories is then described. The Lexis diagram provides a suitable paradigm and is used to position the pencils in space. The resulting graphical representation is closely related to the ideas of Bertin, but with some important differences.
Nitride-Based Thin-Film Light-Emitting Diodes With Photonic Quasi-Crystal Surface

In this letter, the nitride-based thin-film light-emitting diodes (TFLEDs) with eight-fold photonic quasi-crystal (PQC) surfaces are proposed and demonstrated by a combination of wafer bonding, laser lift-off, and electron-beam lithography processes. By adopting a PQC surface, the light-output power (at 350 mA) of the PQC-TFLEDs exhibits 140% output power enhancement as compared with that of TFLEDs without a PQC surface.
Search for non-pointing and delayed photons in the diphoton and missing transverse momentum final state in 8 TeV $pp$ collisions at the LHC using the ATLAS detector

A search has been performed, using the full 20.3 fb$^{-1}$ data sample of 8 TeV proton--proton collisions collected in 2012 with the ATLAS detector at the LHC, for photons originating from a displaced vertex due to the decay of a neutral long-lived particle into a photon and an invisible particle. The analysis investigates the diphoton plus missing transverse momentum final state, and is therefore most sensitive to pair-production of long-lived particles. The analysis technique exploits the capabilities of the ATLAS electromagnetic calorimeter to make precise measurements of the flight direction, as well as the time of flight, of photons. No excess is observed over the Standard Model predictions for background. Exclusion limits are set within the context of Gauge Mediated Supersymmetry Breaking models, with the lightest neutralino being the next-to-lightest supersymmetric particle and decaying into a photon and gravitino with a lifetime in the range from 250 ps to about 100 ns.
Observer-Based Dynamic Control of an Underactuated Hand

The purpose of this paper was to construct a velocity observer based on the dynamic model and realize accurate dynamic curve and force control. Curve fitting with the observer obtained precise velocity signals. Compared with PID and factored moment methods, it decreased the fitting errors a lot and achieved ideal results. Compensated with the inverse dynamic equation, the force-based impedance control with the observer could not only realize accurate force tracking, but achieve finger dynamic control by the combination of curve fitting and force tracking. Furthermore, a static grasp model was established for appropriate force distribution. The finger could grasp slippery, fragile, comparatively heavy and large objects like an egg with only base joint torque and position sensors, which illustrated that the hand could accomplish difficult tasks by using the static grasp model and dynamic control.
ON THE ZEROS OF THE EISENSTEIN SERIES FOR Γ*0(5) AND Γ*0(7)

We locate almost all of the zeros of the Eisenstein series associated with the Fricke groups of level 5 and 7 in their fundamental domains by applying and extending the method of Rankin and Swinnerton-Dyer (On the zeros of Eisenstein series. Bull. London Math. Soc. 2 (1970), 169-170). We also use the arguments of some terms of the Eisenstein series in order to improve existing error bounds.
Caracterización Estadística y Optimización de Redes Neuronales Artificiales para Pronóstico de Series de Tiempo: Pronóstico de un Solo Período

LOS PRONOSTICOS DE SERIES DE TIEMPO CONSTITUYEN UN AREA ACTIVA PARA LA APLICACION DE REDES NEURONALES ARTIFICIALES (RNAS). AUNQUE LA SELECCION DE UNA RNA PARA TAL APLICACION SE HA SIMPLIFICADO GRANDEMENTE, LA FALTA DE UN METODO ESTABLECIDO PARA ASIGNAR LOS PARAMETROS DE LAS RNAS DE UNA MANERA ADECUADA SIGUE SIENDO UN RETO. EN ESTE TRABAJO SE PROPONE UNA METODOLOGIA BASADA EN TECNICAS ESTADISTICAS Y OPTIMIZACION PARA LA SELECCION DE PARAMETROS DE UNA RNA PARA EL PRONOSTICO DE SERIES DE TIEMPO. LA METODOLOGIA PROPUESTA SE DEMUESTRA POR MEDIO DE SU APLICACION EN UN PROBLEMA REAL DE PRONOSTICO DE DEMANDA EN LA INDUSTRIA DE LAS TELECOMUNICACIONES.