Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA

Abstract Extending rank-based inference to a multivariate setting such as multiple-output regression or MANOVA with unspecified d-dimensional error density has remained an open problem for more than half a century. None of the many solutions proposed so far is enjoying the combination of distribution-freeness and efficiency that makes rank-based inference a successful tool in the univariate setting. A concept of center-outward multivariate ranks and signs based on measure transportation ideas has been introduced recently. Center-outward ranks and signs are not only distribution-free but achieve in dimension d > 1 the (essential) maximal ancillarity property of traditional univariate ranks. In the present case, we show that fully distribution-free testing procedures based on center-outward ranks can achieve parametric efficiency. We establish the Hájek representation and asymptotic normality results required in the construction of such tests in multiple-output regression and MANOVA models. Simulations and an empirical study demonstrate the excellent performance of the proposed procedures.
A dark clue to seesaw and leptogenesis in singlet doublet scenario with (non)standard cosmology

We present an appealing alternative scenario of leptogenesis assisted by dark sector which leads to the baryon asymmetry of the Universe satisfying all theoretical and experimental constraints. We adopt the singlet doublet fermionic dark matter framework with the inclusion of copies of a singlet scalar field within this dark sector. A small Majorana mass term for the singlet fermion, in addition to the typical Dirac term, provide the more favourable dark matter of pseudo-Dirac type, capable of escaping direct search. Such a construction also offers a pivotal role in the radiative generation of active neutrino masses. In the presence of a (non)standard thermal history of the Universe, we perform the detailed dark matter phenomenology adopting the suitable benchmark scenarios, consistent with direct detection and neutrino oscillations data. Besides, we have demonstrated that the singlet scalars can go through CP-violating out of equilibrium decay, producing an ample amount of lepton asymmetry. Such an asymmetry then gets converted into the observed baryon asymmetry of the Universe through the non-perturbative sphaleron processes owing to the presence of the alternative cosmological background considered here. Unconventional thermal history of the Universe can thus aspire to lend a critical role both in the context of dark matter as well as in realizing baryogenesis.
Communication-Efficient Federated Learning via Optimal Client Sampling

Federated learning is a private and efficient framework for learning models in settings where data is distributed across many clients. Due to interactive nature of the training process, frequent communication of large amounts of information is required between the clients and the central server which aggregates local models. We propose a novel, simple and efficient way of updating the central model in communication-constrained settings by determining the optimal client sampling policy. In particular, modeling the progression of clients’ weights by an Ornstein-Uhlenbeck process allows us to derive the optimal sampling strategy for selecting a subset of clients with significant weight updates. The central server then collects local models from only the selected clients and subsequently aggregates them. We propose two client sampling strategies and test them on two federated learning benchmark tests, namely, a classification task on EMNIST and a realistic language modeling task using the Stackoverflow dataset. The results show that the proposed framework provides significant reduction in communication while maintaining competitive or achieving superior performance compared to baseline. Our methods introduce a new line of communication strategies orthogonal to the existing user-local methods such as quantization or sparsification, thus complementing rather than aiming to replace them.
An Analysis of the Roles of Experience in Information System Continuance

The notion of information systems (IS) continuance has recently emerged as one of the most important research issues in the field of IS. A great deal of research has been conducted thus far on the basis of theories adapted from various disciplines including consumer behaviors and social psychology, in addition to theories regarding information technology (IT) acceptance. This previous body of knowledge provides a robust research framework that can already account for the determination of IS continuance; however, this research points to other, thus-far-unelucidated determinant factors such as habit, which were not included in traditional IT acceptance frameworks, and also re-emphasizes the importance of emotion-related constructs such as satisfaction in addition to conscious intention with rational beliefs such as usefulness. Experiences should also be considered one of the most important factors determining the characteristics of information system (IS) continuance and the features distinct from those determining IS acceptance, because more experienced users may have more opportunities for IS use, which would allow them more frequent use than would be available to less experienced or non-experienced users. Interestingly, experience has dual features that may contradictorily influence IS use. On one hand, attitudes predicated on direct experience have been shown to predict behavior better than attitudes from indirect experience or without experience; as more information is available, direct experience may render IS use a more salient behavior, and may also make IS use more accessible via memory. Therefore, experience may serve to intensify the relationship between IS use and conscious intention with evaluations. On the other hand, experience may culminate in the formation of habits: greater experience may also imply more frequent performance of the behavior, which may lead to the formation of habits. Hence, like experience, users’ activation of an IS may be more dependent on habit-that is, unconscious automatic use without deliberation regarding the IS--and less dependent on conscious intentions. Furthermore, experiences can provide basic information necessary for satisfaction with the use of a specific IS, thus spurring the formation of both conscious intentions and unconscious habits. Whereas IT adoption
Functionality‐Driven Musculature Retargeting

We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation‐ready, so we can physically simulate muscle‐actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi‐planar X‐ray images and medical examination.
Conditions for Convergence of Dynamic Regressor Extension and Mixing Parameter Estimators Using LTI Filters

In this article, we study the conditions for convergence of the recently introduced dynamic regressor extension and mixing (DREM) parameter estimator when the extended regressor is generated using linear time-invariant filters. In particular, we are interested in relating these conditions with the ones required for convergence of the classical gradient (or least squares), namely the well-known <italic>persistent excitation</italic> (PE) requirement on the original regressor vector, <inline-formula><tex-math notation="LaTeX">$\phi (t) \in \mathbb {R}^q$</tex-math></inline-formula>, with <inline-formula><tex-math notation="LaTeX">$q \in \mathbb {N}$</tex-math></inline-formula> the number of unknown parameters. Moreover, we study the case when only <italic>interval excitation</italic> (IE) is available, under which DREM, concurrent, and composite learning schemes ensure global convergence, being the convergence for DREM in a <italic>finite time</italic>. Regarding PE, we prove, under some mild technical assumptions, that if <inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> is PE, then the scalar regressor of DREM, <inline-formula><tex-math notation="LaTeX">$\Delta _N \in \mathbb {R}$</tex-math></inline-formula>, is also PE ensuring exponential convergence. Concerning IE, we prove that if <inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> is IE, then <inline-formula><tex-math notation="LaTeX">$\Delta _N$</tex-math></inline-formula> is also IE. All these results are established in the <italic>almost sure</italic> sense, namely proving that the set of filter parameters for which the claims do not hold is of zero measure. The main technical tool used in our proof is inspired by a study of Luenberger observers for nonautonomous nonlinear systems recently reported in the literature.
Confirming new white dwarf-ultracool dwarf binary candidates

We present the results of a study to discover prospective new white dwarf-L dwarf binaries as identified by their near-infrared excesses in the UKIDSS catalogue. We obtained optical spectra to validate the white dwarf nature for 22 of the candidate primary stars, confirming ten as white dwarfs and determining their effective temperatures and gravities. For all 10 white dwarfs, we determined that the near-infrared excess was indeed indicative of a cool companion. Six of these are suggestive of late M dwarf companions, and three are candidate L dwarf companions, with one straddling the M−L boundary. We also present near-infrared spectra of eight additional candidate white dwarf-ultracool dwarf binaries, where the white dwarf primary had been previously confirmed. These spectra indicate one candidate at the M−L boundary, three potential L dwarf companions, and one suspected M dwarf, which showed photometric variability on a ∼6 h period, suggesting the system may be close. Radial velocity follow-up is required to confirm whether these systems are close, or widely separated.
Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance

Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) Under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model and (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.
Analogical Concept Memory for Architectures Implementing the Common Model of Cognition

Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable concept acquisition from examples obtained interactively. We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We frame the problem of concept learning as embedded within the larger context of interactive task learning (ITL) and embodied language processing (ELP). We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts that are useful not only in recognition of a concept in the environment but also in action selection. Our approach has been instantiated in an implemented cognitive system AILEEN and evaluated on a simulated robotic domain.
Data mining for safety transportation by means of using Internet survey

Internet survey may be one of the effective means to collect big data from the real world. Collected data may realize meaningful analysis of targeted field. Intelligent Transportation (hereinafter: ITS) is one of smart city applications which bring us safety driving as well as comfortable driving by mitigation of the traffic congestion. This study proposes an example of vehicle-infrastructure cooperative function which would be incorporate into vehicle safety system for smart city application. Driver's state adaptive driving support safety function may be one of key functions that can bring road traffic safety in combination with road infrastructure interactively. Consequently this study clarified root cause of traffic accidents by analysing data of experiences on traffic incidents. Data was collected by two methods, one was collected through direct interview, and the other was collected through Internet. From the analysis, haste, distraction were major factor of a driver's psychosomatic states just before traffic incidents. As an alternative characteristic of driver's cognitive distraction, this study acquired physiological information, which were movements of eyes and head, and heart rate. A method of detecting driver's cognitive distraction was established by using pattern recognition, which were AdaBoost and Error-Correcting Output Coding (ECOC). This study proposes driver's psychosomatic adaptive driving support safety function in combination with ITS service for smart city applications.
Far-from-equilibrium search for the QCD critical point

Initial conditions for relativistic heavy-ion collisions may be far from equilibrium (i.e. there are large initial contributions from the shear stress tensor and bulk pressure) but it is expected that on very short time scales the dynamics converge to a universal attractor that defines hydrodynamic behavior. Thus far, studies of this nature have only considered an idealized situation at LHC energies (high temperatures $T$ and vanishing baryon chemical potential $\mu_B=0$) but, in this work, we investigate for the first time how far-from-equilibrium effects may influence experimentally driven searches for the Quantum Chromodynamic critical point at RHIC. We find that the path to the critical point is heavily influenced by far from equilibrium initial conditions where viscous effects lead to dramatically different $\left\{T,\mu_B\right\}$ trajectories through the QCD phase diagram. We compare hydrodynamic equations of motion with shear and bulk coupled together at finite $\mu_B$ for both DNMR and phenomenological Israel-Stewart equations of motion and discuss their influence on potential attractors at finite $\mu_B$ and their corresponding $\left\{T,\mu_B\right\}$ trajectories.
Unsupervised deep learning for super-resolution reconstruction of turbulence

Abstract Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows have used supervised learning, which requires paired data for training. This limitation hinders more practical applications of super-resolution reconstruction. Therefore, we present an unsupervised learning model that adopts a cycle-consistent generative adversarial network (CycleGAN) that can be trained with unpaired turbulence data for super-resolution reconstruction. Our model is validated using three examples: (i) recovering the original flow field from filtered data using direct numerical simulation (DNS) of homogeneous isotropic turbulence; (ii) reconstructing full-resolution fields using partially measured data from the DNS of turbulent channel flows; and (iii) generating a DNS-resolution flow field from large-eddy simulation (LES) data for turbulent channel flows. In examples (i) and (ii), for which paired data are available for supervised learning, our unsupervised model demonstrates qualitatively and quantitatively similar performance as that of the best supervised learning model. More importantly, in example (iii), where supervised learning is impossible, our model successfully reconstructs the high-resolution flow field of statistical DNS quality from the LES data. Furthermore, we find that the present model has almost universal applicability to all values of Reynolds numbers within the tested range. This demonstrates that unsupervised learning of turbulence data is indeed possible, opening a new door for the wide application of super-resolution reconstruction of turbulent fields.
How Does ENSO Impact the Solar Radiation Forecast in South America? The Self-affinity Analysis Approach.

The major challenge we face today in the energy sector is to meet the growing demand for electricity with less impact on the environment. South America is an important player in the renewable energy resource. Brazil accelerated the growth of photovoltaic installed capacity in 2018. From April of 2017 to April of 2018, the capacity increased $1351.5\%$. It is expected to reach the value of $2.4\ GW$ until the end of the year. The new Chilean regulation request that 20\% of the total electricity production in 2025 must come from renewable energy sources. The aim of this paper is to establish time series behavior changes between El Ni\~no Southern Oscillation and the solar radiation resource in South America. The results can be used to validate forecasts of energy production for new solar plants. The method used to verify the behavior of the time series was the Detrended Fluctuation Analysis. Solar radiation data were collected in twenty-five cities distributed inside the Brazilian solar belt, plus six cities in Chile, covering the continent from east to west, in a region with high potential of solar photovoltaic generation. The results shows the impact of El Ni\~no Southern Oscillation on the climatic behavior of the evaluated data. It is a factor that may lead to the wrong forecast of the long term potential solar power generation for the region.
Floral colour diversity in plant communities, bee colour space and a null model

Evolutionary biologists have long hypothesized that the diversity of flower colours we see is in part a strategy to promote memorization by pollinators, pollinator constancy, and therefore, a directed and efficient pollen transfer between plants. However, this hypothesis has never been tested against a biologically realistic null model, nor were colours assessed in the way pollinators see them. Our intent here is to fill these gaps. Throughout one year, we sampled floral species compositions at five ecologically distinct sites near Berlin, Germany. Bee–subjective colours were quantified for all 168 species. A model of colour vision was used to predict how similar the colours of sympatric and simultaneously blooming flowers were for bees. We then compared flower colour differences in the real habitats with those of random plant communities. We did not find pronounced deviations from chance when we considered common plants. When we examined rare plants, however, we found significant divergence in two of the five plant communities. At one site, similarly coloured species were found to be more frequent than expected, and at the other two locations, flower colours were indistinguishable from a random distribution. These results fit theoretical considerations that rare plants are under stronger selective pressure to secure pollination than common plants. Our study illustrates the power of linking such distinct biological traditions as community ecology and the neuroethology of bee vision.
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge

Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. It is more challenging than its single-label counterpart. On one hand, the unconstrained number of labels assigned to each image makes the model more easily overfit to those seen classes. On the other hand, there is a large semantic gap between seen and unseen classes in the existing multi-label classification datasets. To address these difficult issues, this paper introduces a novel multi-label zero-shot classification framework by learning to transfer from external knowledge. We observe that ImageNet is commonly used to pretrain the feature extractor and has a large and fine-grained label space. This motivates us to exploit it as external knowledge to bridge the seen and unseen classes and promote generalization. Specifically, we construct a knowledge graph including not only classes from the target dataset but also those from ImageNet. Since ImageNet labels are not available in the target dataset, we propose a novel PosVAE module to infer their initial states in the extended knowledge graph. Then we design a relational graph convolutional network (RGCN) to propagate information among classes and achieve knowledge transfer. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed approach.
Artifact-less coded aperture imaging in the x-ray band with multiple different random patterns

Abstract. The coded aperture imaging technique is a useful method of x-ray imaging in observational astrophysics. However, the presence of imaging noise or so-called artifacts in a decoded image is a drawback of this method. We propose a coded aperture imaging method using multiple different random patterns for significantly reducing the image artifacts. This aperture mask contains multiple different patterns each of which generates a different artifact distribution in its decoded image. By summing all decoded images of the different patterns, the artifact distributions are cancelled out, and we obtain a remarkably accurate image. We demonstrate this concept with imaging experiments of a monochromatic 16-keV hard x-ray beam at the synchrotron photon facility SPring-8, using the combination of a complementary metal-oxide-semiconductor image sensor and an aperture mask that has four different random patterns composed of holes with a diameter of 27 μm and a separation of 39 μm. The entire imaging system is installed in a 25-cm-long compact size and achieves an angular resolution of <30 arc sec (full-width at half-maximum). In addition, we show by Monte Carlo simulation that the artifacts can be reduced more effectively if the number of different patterns increases to 8 or 16.
Quantum correlations in dipolar droplets: Time-dependent Hartree-Fock-Bogoliubov theory

We investigate the effects of quantum correlations on dipolar quantum droplets. To this end,we derive self-consistent time-dependent Hartree-Fock-Bogoliubov equations that fairly describe the dynamics of the order parameter, the normal, and anomalous quantum correlations of the droplet. We analyze the density profiles, the critical number of particles, the condensate depletion, and the pair correlation function. Our predictions are compared with very recent experimental and Quantum Monte-Carlo simulations results and excellent agreement is found.