Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraints on decreases in η Carinae's mass-loss from 3D hydrodynamic simulations of its binary colliding winds

Recent work suggests that the mass-loss rate of the primary star Eta-A in the massive colliding wind binary Eta Carinae dropped by a factor of 2-3 between 1999 and 2010. We present result from large- (+/- 1545 au) and small- (+/- 155 au) domain, 3D smoothed particle hydrodynamics (SPH) simulations of Eta Car's colliding winds for three Eta-A mass-loss rates ( (dot-M(sub Eta-A) = 2.4, 4.8 and 8.5 10(exp 4) M(solar)/ yr), investigating the effects on the dynamics of the binary wind-wind collision (WWC). These simulations include orbital motion, optically thin radiative cooling and radiative forces. We find that dot-M Eta-A greatly affects the time-dependent hydrodynamics at all spatial scales investigated. The simulations also show that the post-shock wind of the companion star Eta-B switches from the adiabatic to the radiative-cooling regime during periastron passage (Phi approx.= 0.985-1.02). This switchover starts later and ends earlier the lower the value of dot-M Eta-A and is caused by the encroachment of the wind of Eta-A into the acceleration zone of Eta-B's wind, plus radiative inhibition of Eta-B's wind by Eta-A. The SPH simulations together with 1D radiative transfer models of Eta-A's spectra reveal that a factor of 2 or more drop in dot-M EtaA should lead to substantial changes in numerous multiwavelength observables. Recent observations are not fully consistent with the model predictions, indicating that any drop in dot- M Eta-A was likely by a factor of approx. < 2 and occurred after 2004. We speculate that most of the recent observed changes in Eta Car are due to a small increase in the WWC opening angle that produces significant effects because our line of sight to the system lies close to the dense walls of the WWC zone. A modest decrease in dot-M Eta-A may be responsible, but changes in the wind/stellar parameter of Eta-B, while less likely, cannot yet be fully ruled out. We suggest observations during Eta-Car's next periastron in 2014 to further test for decreases in dot-M Eta-A. If dot-M Eta-A is declining and continues to do so, the 2014 X-ray minimum should be even shorter than that of 2009.
Study of the Impact of the Airflow and Filling Ratio on the Thermal Performances of a Two-Phase Immersion Cooling Prototype

Evacuating high heat fluxes for new generation microprocessors is a challenge in electronics cooling applications. We conducted a thermal performance study on a novel two-phase thermosyphon prototype to cool high-power microprocessors in a 4U form factor. The prototype was mounted on a heater assembly for the thermal performance tests. A heat spreader was attached on top of the square 2.54 cm by 2.54 cm heater, with the top side of the heat spreader being enhanced with a 500 micron thick multi-scale electroplated porous copper coating. The localized boiling and condensation occurred inside the prototype, using the Novec™ 7000 liquid from the 3M Corporation. The prototype was air-cooled (using a single fan), and different air flows were tested, from 102 m3/h to 237 m3/h. The effect of the filling ratio was studied between 9% and 70%. At a filling ratio of 9 % and an airflow of 237 m3/h, the maximum power achieved was (525 ± 7) W (a heat flux of 81± 1.2 W/cm2), the heater wall temperature was 75°C, and no dry out was observed. At an airflow of 102 m3/h at the same power level, the heater wall temperature was less than 83°C, so the prototype could be used even with low airflow rates if the fan power consumption needs to be reduced in specific electronics cooling applications. The total thermal resistance included the contributions of the heater body, its thermal interface material layer, the evaporator (boilerplate with porous coating), the condenser, and the air-cooled heat exchanger. At the highest tested airflow rate, the minimum achieved heater to ambient air thermal resistance was (0.096 ± 0.0018)°C/W for a filling ratio of 9%. The dominant thermal resistance was from the heater to the liquid, representing between 50% and 70 % of the total resistance in all the tested scenarios at different filling ratios.
New switched adaptive controller for the industrial tanks

Industrial environments are full of phenomena that represent enormous challenges to control engineers. Sometimes, a simple process such as the liquid level control in an arrangement of cylindrical tanks imposes several constraints that require continuous human supervision. Most of the traditional control techniques in the industry are linear, although the industrial plant is nonlinear. This fact, associated with disturbances, uncertainties in the parameters, and coupling, among others, imposes constraints on the traditional controllers, which, sometimes, allows them to show good response in a limited operation point. Thus, this work presents a robust nonlinear controller for industrial cylindrical tanks. It does not have constraints related to operation points and is robust to several industrial phenomena. To verify the performance of the proposed controller, we applied it for the quadruple tank process. The work beyond the proposed controller presents results of stability analysis and simulations.
Verbal and Non-Verbal Conflict Resolution Strategies for Service Robots

When service robots will be employed in private and public spaces, conflicts in human-robot interaction (HRI) might arise. To gain priority and continue its tasks, the robot would benefit from conflict resolution strategies (CRS) that are acceptable and effective. Previous studies have mainly investigated verbal or text-based CRS. As verbal interaction might not suit every application context or robot type, movement-based CRS were investigated. First, four possible implementations were pre-tested in an online study (N = 101). Then two CRS varying in modality (verbal vs. motoric) and assertiveness (submissive vs. dominant) were tested for acceptance and compliance in a lab study (N = 31) and compared for three robot types (humanoid, zoomorphic, and mechanoid). The verbal appeal was the most effective strategy to achieve user compliance. The motoric dominant strategy (moving back and forth) was perceived as most assertive and least polite if applied by the mechanoid cleaning robot but was not more effective than the verbal strategy. These studies provide insights into the influence of robot type on the acceptability and effectiveness of robot conflict resolution behavior depending on the modality.
The magnetic properties of the star Kepler-78

Kepler-78 is host to a transiting 8.5-h orbit super-Earth. In this paper, the rotation and magnetic properties of the planet host star are studied. We first revisit the Kepler photometric data for a detailed description of the rotation properties of Kepler-78, showing that the star seems to undergo a cycle in the spot pattern of similar to 1300 d duration. We then use spectropolarimetric observations with Canada-France-Hawaii Telescope (CFHT)/ESPaDOnS to measure the circular polarization in the line profile of the star during its rotation cycle, as well as spectroscopic proxies of the chromospheric activity. The average field has a strength of 16 G. The magnetic topology is characterized by a poloidal and a toroidal component, encompassing 60 per cent and 40 per cent of the magnetic energy, respectively. Differential rotation is detected with an estimated rate of 0.105 +/- 0.039 rad d(-1). Activity tracers vary with the rotation cycle of the star; there is no hint that a residual activity level is related to the planetary orbit at the precision of our data. The description of the star magnetic field's characteristics then may serve as input for models of interactions between the star and its close-by planet, e.g. Ohmic dissipation and unipolar induction.
Critical and noncritical long-range entanglement in Klein-Gordon fields

We investigate the entanglement between two spatially separated intervals in the vacuum state of a free one-dimensional Klein-Gordon field by means of explicit computations in the continuum limit of the linear harmonic chain. We demonstrate that the entanglement, which we quantify by the logarithmic negativity, is finite with no further need for renormalization. We find that in the critical regime, the quantum correlations are scale invariant as they depend only on the ratio of distance to length. They decay much faster than the classical correlations as in the critical limit long-range entanglement decays exponentially for separations larger than the size of the blocks, while classical correlations follow a power-law decay. With decreasing distance of the blocks, the entanglement diverges as a power law in the distance. The noncritical regime manifests richer behavior, as the entanglement depends both on the size of the blocks and on their separation. In correspondence with the von Neumann entropy also long-range entanglement distinguishes critical from noncritical systems.
Hybrid Attention-based Transformer for Long-range Document Classification

Transformer with the self-attention mechanism, which allows fully-connected contextual encoding over input tokens, has achieved outstanding performances in various NLP tasks, but it suffers from quadratic complexity with the input sequence length. Long-range contexts are often tackled by Transformer in chunks using a sliding window to avoid GPU memory overflow. However, how to achieve considerable performance on downstream tasks under the premise of modeling sequences as long as possible with limited GPU resources is still a problem to be explored. To address this issue, we propose a new framework using hybrid attention-based Transformer to capture long-range contextual features. More specifically, we make a combination of three types of attention, i.e. sliding window local attention, clustering-based long-range attention and specific global attention. Experiments comparing the performance of our model with mainstream efficient improved Transformers are conducted on document classification task in public datasets IMDB and CAIL-Long. Experimental results demonstrate that the proposed approach can outperform the state-of-the-art models on the adopted datasets, which verifies the effectiveness of our model on long-range document classification task.
The dynamics and scaling laws of planetary dynamos driven by inertial waves

Great progress has been made in the numerical simulation of planetary dynamos, though these numerical experiments still operate in a regime very far from the planets. For example, it seems unlikely that viscous forces are at all significant in planetary interiors, yet some of the simulations display a significant dependence on viscosity, and indeed in some of the simulations the dynamo mechanism is itself viscously driven, taking the form of helical Ekman pumping within columnar convection rolls. Given the similarity of the external magnetic fields observed in the terrestrial planets and gas giants, and the extremely small value of the Ekman number in all such cases, it seems natural to suppose that the underlying dynamo mechanism in these planets is simple, robust, independent of viscosity and insensitive to mechanical boundary conditions. A key step to identifying this mechanism is to determine the source of helicity in planetary cores, which itself should be robust, independent of viscosity and insensitive to boundary conditions. In this paper, we explore the possibility that the helicity in the core of the Earth arises from the spontaneous emission of inertial waves, driven by the equatorial heat flux in the outer core. We also ask if a similar mechanism might operate in other planets, and perhaps act to supplement the helicity driven by Ekman pumping in the (viscous) numerical simulations. We demonstrate that such waves do indeed produce the required helicity distribution outside the tangent cylinder. Moreover, we show that these waves inevitably propagate along the axis of the columnar vortices, and indeed they are the very mechanism by which the columnar vortices form in the first place and the means by which the columns subsequently evolve. We also calculate the emf induced by such axially propagating inertial waves and show that, in principle, this emf is sufficient to support a self-sustaining dynamo of the α2 type. Finally, we derive the scaling laws for this kind of inertial-wave dynamo. We compare these predictions with the (imperfect) simulations, and also with what little we know about the Earth's core. The numerical experiments fall into two categories; the slowly rotating simulations which cannot sustain inertial waves at the small scales and the rapidly rotating (planet-like) ones which can. Our scaling laws are consistent with the latter class of simulations, and also with what we know about the Earth. © The Authors 2014. Published by Oxford University Press on behalf of The Royal Astronomical Society.
Learning Knowledge Graph Embedding with Batch Circle Loss

Knowledge Graph Embedding (KGE) is the process to learn low-dimension representations for entities and relations in knowledge graphs. It is a critical component in Knowledge Graph (KG) for link prediction and knowledge discovery. Many works focus on designing proper score function for KGE, while the study of loss function has attracted relatively less attention. In this paper, we focus on improving the loss function when learning KGE. Specifically, we find that the frequently used margin-based loss in KGE models seeks to maximize the gap between the true facts score fp and the false facts score fn and only cares about the relative order of scores. Since its optimization objective is fp - fn = m, increasing fp is equivalent to decreasing fn. Its optimization objective creates an ambiguous convergence status which impairs the separability of positive and negative facts in embedding space. Inspired by the circle loss that offers a more flexible optimization manner with definite convergence targets and is widely used in computer vision tasks, we further extend it into the KGE model with the presented Batch Circle Loss (BCL). BCL allows multiple positives to be considered per anchor (h, r) (or (r, t)) in addition to multiple negatives (as opposed to a single positive sample as used before in KGE models). By comparing with other approaches, the obtained KGE models using our proposed loss function and training method shows superior performance.
Conversion Time-Power Tradeoff in Capacitance-to-Digital Converters with Dual-Mode Logic

In this paper, the tradeoff between conversion time and power in nW-power capacitance-to-digital converters (CDCs) is explored. The CDC in this work leverages the delay-power flexibility of dual-mode logic, is based on swappable oscillators and operates at nW power and low voltage down to 0.3 V without requiring any additional circuitry, reference or voltage regulation. Its self-calibration compensates PVT variations and mismatch at any point of the chip lifecycle, eliminating the need for trimming at testing time. Testchip demonstration of the CDC in 180nm shows that its power consumption can be dynamically adjusted from 1.37 nW down to 418 pW at a conversion time down to hundreds of ms. This makes the CDC suitable for harvested systems with very limited tight power budget and fluctuating voltage.
Rephasing processes and quantum memory for light: reversibility issues and how to fix them

Time reversibility has been absent from some recently proposed quantum memory protocols such as the atomic frequency comb (AFC) scheme. Focusing on AFC memory, we show that quantum efficiency and fidelity are reduced dramatically, as a consequence of non-reversibility, when the spectral width of the incoming signal approaches the memory bandwidth. Non-reversibility is revealed through spectral dispersion, giving rise to phase mismatching. We propose a modified AFC scheme that restores reversibility. This way, signals can be retrieved with excellent efficiency over the entire memory bandwidth. This study could be extended to other quantum memory rephasing schemes in inhomogeneously broadened absorbing media.
Moving mesh cosmology: tracing cosmological gas accretion

We investigate the nature of gas accretion onto haloes and galaxies at z=2 using cosmological hydrodynamic simulations run with the moving mesh code AREPO. Implementing a Monte Carlo tracer particle scheme to determine the origin and thermodynamic history of accreting gas, we make quantitative comparisons to an otherwise identical simulation run with the smoothed particle hydrodynamics (SPH) code GADGET-3. Contrasting these two numerical approaches, we find significant physical differences in the thermodynamic history of accreted gas in haloes above 10^10.5 solar masses. In agreement with previous work, GADGET simulations show a cold fraction near unity for galaxies forming in massive haloes, implying that only a small percentage of accreted gas heats to an appreciable fraction of the virial temperature during accretion. The same galaxies in AREPO show a much lower cold fraction, <20% in haloes above 10^11 solar masses. This results from a hot gas accretion rate which, at this same halo mass, is an order of magnitude larger than with GADGET, while the cold accretion rate is also lower. These discrepancies increase for more massive systems, and we explain both as due to numerical inaccuracies in the standard formulation of SPH. We also observe that the relatively sharp transition from cold to hot mode dominated accretion, at a halo mass of ~10^11, is a consequence of comparing past gas temperatures to a constant threshold value independent of virial temperature. Examining the spatial distribution of accreting gas, we find that gas filaments in GADGET tend to remain collimated and flow coherently to small radii, or artificially fragment and form a large number of purely numerical "blobs". Similar gas streams in AREPO show increased heating and disruption at 0.25-0.5 virial radii and contribute to the hot gas accretion rate in a manner distinct from classical cooling flows.
Preserving Data Manifold Structure in Latent Space for Exploration through Network Geodesics

While variational autoencoders have been successful in several tasks, the use of conventional priors are limited in their ability to encode the underlying structure of input data. We introduce an Encoded Prior Sliced Wasserstein AutoEncoder wherein an additional prior-encoder network learns a geometry and topology preserving embedding of any data manifold, thus improving the structure of latent space. The autoencoder and prior-encoder networks are iteratively trained using the Sliced Wasserstein distance, which facilitates the learning of nonstandard complex priors. We then introduce a graph-based algorithm to explore the learned manifold by traversing latent space through network-geodesics that lie along the manifold and hence are more realistic compared to conventional Euclidean interpolation. Specifically, we identify network-geodesics by maximizing the density of samples along the path while minimizing total energy. We use the 3D-spiral data to show that the prior encodes the geometry underlying the data unlike conventional autoencoders, and to demonstrate the exploration of the embedded data manifold through the network algorithm. We apply our framework to artificial as well as image datasets to demonstrate the advantages of learning improved latent structure, outlier generation, and geodesic interpolation.
Mitigating Judgmental Fallacies with Social Robot Advisors*

The role of social robots as advisors for decision making is investigated. It has been consistently shown that when asked to rank options, people often make fallacious judgements. Furthermore, such fallacies can be sensitive to presentation mode. We study whether having social robot advisors presenting options can mitigate and reduce the fallacy rates of participants. For this purpose we explored a novel presentation mode of options with conjunction judgmental fallacy, namely, choosing among different rank-orders, as opposed to rank the options themselves. We first show that the mere presentation mode has a significant mitigating effect on the fallacy rates. We then further show that when social robot advisors present the rank-orders, the fallacy rates of participants significantly decrease even further. Moreover, participants perceive the fallacious robot as more likeable and intelligent, but assign the non-fallacious robot to trustworthy roles, such as jury and analyst. These results suggest that social robot advisors may be used to influence and mitigate human fallacious judgmental decision making.
Multiple Kernel Learning for Modeling Resting State EEG Connectomes using Structural Connectivity of the Brain

An active area of research in cognitive science is characterizing the relationship between brain structure and the observed functional activations. Recent graph diffusion models have had great success in mapping whole-brain, resting-state dynamics measured using functional Magnetic Resonance Imaging (fMRI) to the brain structure derived using diffusion and T1 brain imaging. Here we test the application of one such graph diffusion method called the Multiple Kernel Learning (MKL) model. MKL model, formulated as a reaction-diffusion system using Wilson-Cowan equations, combines multiple diffusion kernels at different scales to predict functional connectome (FC) arising from a fixed structural connectome (SC). Our simulation results demonstrate that the MKL model successfully mapped the relationship between SC and FC from five different Electroen-cephalogram (EEG) bands (delta, theta, alpha, beta, and gamma). We used simultaneously acquired EEG-fMRI and NODDI dataset of 17 participants. The correlation between predicted FC and ground truth FC was higher for EEG bands than for fMRI data. The prediction accuracy peaked for the alpha band, and the highest frequency band, gamma had the lowest prediction accuracy. To the best of our knowledge, this is the first such end-to-end application of multiple kernel graph diffusion framework for modeling EEG data. One of the important features of MKL model is its ability to incorporate structural connectivity features into the generative model that predicts the EEG functional connectivity.
Questioning Wizard of Oz: Effects of Revealing the Wizard behind the Robot

Wizard of Oz, a very commonly employed technique in human-robot interaction, faces the criticism of being deceptive as the humans interacting with the robot are told, if at all, only at the end of their interaction that there was in fact a human behind the robot. What if the robot reveals the wizard behind itself very early in the interaction? We built a deep wizard of Oz setup to allow for a robot to play together with a human against a computer AI in the context of Connect 4 game. This cooperative game interaction against a common opponent is then followed by a conversation between the human and the robot. We conducted an exploratory user study with 29 adults with three conditions where the robot reveals the wizard, lies about the wizard, and does not say anything, respectively. We also split the data based on how the participants perceive the robot in terms of autonomy. Using different metrics, we evaluate how the users interact with and perceive the robot in both the experimental and perceived conditions. We find that while there is indeed a significant difference in the participants willingness to follow robots suggestions between the experimental conditions as well as in the effort they put to prove themselves as humans (reverse Turing test), there isn’t any significant difference in their robot perception. Additionally, how humans perceive whether the robot is tele-operated or autonomous seems to be indifferent to the robot revealing its identity, i.e., the pre-conceived notions may be uninfluenced even if the robot explicitly states otherwise. Lastly, interestingly in the perception based conditions, absence of statistical significance may suggest that, in certain contexts, wizard of oz may not require hiding the wizard after all.