Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement-mode AlGaN/GaN HEMTs fabricated by fluorine plasma treatment

The fabrication of enhancement-mode AlGaN/GaN HEMTs by fluorine plasma treatment on sapphire substrates is reported. A new method is used to fabricate devices with different fluorine plasma RF power treatments on one wafer to avoid differences between different wafers. The plasma-treated gate regions of devices treated with different fluorine plasma RF powers were separately opened by a step-and-repeat system. The properties of these devices are compared and analyzed. The devices with 150 W fluorine plasma treatment power and with 0.6 μm gate-length exhibited a threshold voltage of 0.57 V, a maximum drain current of 501 mA/mm, a maximum transconductance of 210 mS/mm, a current gain cutoff frequency of 19.4 GHz and a maximum oscillation frequency of 26 GHz. An excessive fluorine plasma treatment power of 250 W results in a small maximum drain current, which can be attributed to the implantation of fluorine plasma in the channel.
LTE-Advanced 표준 기술 동향

현재 3GPP는 IMT-Advanced 후보 기술인 LTE-Advanced에 대한 표준화 작업이 한창이다. 본 고에서는 LTEAdvanced의 핵심 요소 기술인 캐리어 집성, 하향 링크 MIMO의 확장, 상향 링크 MIMO, 다중 점 협력통신, 무선 중계기 등을 소개하고, 각 요소 기술 별 3GPP 표준 기술 동향을 살펴본다.
Derivative-Free & Order-Robust Optimisation

In this paper, we formalise order-robust optimisation as an instance of online learning minimising simple regret, and propose Vroom, a zero'th order optimisation algorithm capable of achieving vanishing regret in non-stationary environments, while recovering favorable rates under stochastic reward-generating processes. Our results are the first to target simple regret definitions in adversarial scenarios unveiling a challenge that has been rarely considered in prior work.
An event-triggered transmission scheduling strategy for remote state estimation in the presence of an eavesdropper

We consider a remote state estimation problem in the presence of an eavesdropper over packet dropping links. A smart sensor transmits its local estimates to a legitimate remote estimator, in the course of which an eavesdropper can randomly overhear the transmission. This problem has been well studied for unstable dynamical systems, but seldom for stable systems. In this paper, we target at stable and marginally stable systems and aim to design an event-triggered scheduling strategy by minimizing the expected error covariance at the remote estimator and keeping that at the eavesdropper above a user-specified lower bound. To this end, we model the evolution of the error covariance as an infinite recurrent Markov chain and develop a recurrence relation to describe the stationary distribution of the state at the eavesdropper. Monotonicity and convergence properties of the expected error covariance are further investigated and employed to solve the optimization problem. Numerical examples are provided to validate the theoretical results.
Crust of accreting neutron stars within simplified reaction network

Transiently accreting neutron stars in low-mass X-ray binaries are generally believed to be heated up by nuclear reactions in accreted matter during hydrostatic compression. Detailed modelling of these reactions is required for the correct interpretation of observations. In this paper, we construct a simplified reaction network, which can be easily implemented and depends mainly on atomic mass tables as nuclear physics input. We show that it reproduces results of the detailed network by Lau et al. very well, if one applies the same mass model. However, the composition and the heating power are shown to be sensitive to the mass table used and treatment of mass tables boundary, if one applies several of them in one simulation. In particular, the impurity parameter Qimp at density ρ = 2 × 1012 g cm−3 can differ for a factor of few, and even increase with density increase. The profile of integrated heat release shown to be well confined between results by Fantina et al. and Lau et al.
Parallel Split-Join Networks for Shared Account Cross-Domain Sequential Recommendations

Sequential recommendation is a task in which one models and uses sequential information about user behavior for recommendation purposes. We study sequential recommendation in a context in which multiple individual users share a single account (i.e., they have a shared account) and in which user behavior is available in multiple domains (i.e., recommendations are cross-domain). These two characteristics bring new challenges on top of those of the traditional sequential recommendation task. First, we need to identify the behavior associated with different users and different user roles under the same account in order to recommend the right item to the right user role at the right time. Second, we need to identify behavior in one domain that might be helpful to improve recommendations in other domains. We study shared account cross-domain sequential recommendation and propose a parallel split-join Network (Parallel Split-Join Network (PSJNet)), a parallel modeling network to address the two challenges above. We use “split” to address the challenge raised by shared accounts; PSJNet learns role-specific representations and uses a gating mechanism to filter out, from mixed user behavior, information of user roles that might be useful for another domain. In addition, “join” is used to address the challenge raised by the cross-domain setting; PSJNet learns cross-domain representations by combining the information from “split” and then transforms it to another domain. We present two variants of PSJNet: PSJNet-I and PSJNet-II. PSJNet-I is a “split-by-join” framework that splits the mixed representations to get role-specific representations and joins them to obtain cross-domain representations at each timestamp simultaneously. PSJNet-II is a “split-and-join” framework that first splits role-specific representations at each timestamp, and then the representations from all timestamps and all roles are joined to obtain cross-domain representations. We concatenate the in-domain and cross-domain representations to compute a recommendation score for each item. Both PSJNet-I and PSJNet-II can simultaneously generate recommendations for two domains where user behavior in two domains is synchronously shared at each timestamp. We use two datasets to assess the effectiveness of PSJNet. The first dataset is a simulated shared account cross-domain sequential recommendation dataset obtained by randomly merging the Amazon logs from different users in the movie and book domains. The second dataset is a real-world shared account cross-domain sequential recommendation dataset built from smart TV watching logs of a commercial organization. Our experimental results demonstrate that PSJNet outperforms state-of-the-art sequential recommendation baselines in terms of MRR and Recall.
ROBO-AO Kepler Asteroseismic Survey. II. Do Stellar Companions Inhibit Stellar Oscillations?

The Kepler Space Telescope observed over 15,000 stars for asteroseismic studies. Of these, 75% of dwarfs (and 8% of giants) were found to show anomalous behavior, such as suppressed oscillations (low amplitude) or no oscillations at all. The lack of solar-like oscillations may be a consequence of multiplicity, due to physical interactions with spectroscopic companions or due to the dilution of oscillation amplitudes from “wide” (AO detected; visual) or spectroscopic companions introducing contaminating flux. We present a search for stellar companions to 327 of the Kepler asteroseismic sample, which were expected to display solar-like oscillations. We used direct imaging with Robo-AO, which can resolve secondary sources at ∼0. ″15, and followed up detected companions with Keck AO. Directly imaged companion systems with both separations of ≤0. ″5 and amplitude dilutions >10% all have anomalous primaries, suggesting these oscillation signals are diluted by a sufficient amount of excess flux. We also used the high-resolution spectrometer ESPaDOnS at the Canada–France–Hawai’i Telescope to search for spectroscopic binaries. We find tentative evidence for a higher fraction of spectroscopic binaries with high radial velocity scatter in anomalous systems, which would be consistent with previous results suggesting that oscillations are suppressed by tidal interactions in close eclipsing binaries.
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks

We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform those experiments which, in expectation with respect to the current model, are maximally informative about the underlying causal structure. Unlike previous work, we consider the setting of continuous random variables with non-linear functional relationships, modelled with Gaussian process priors. To address the arising problem of choosing from an uncountable set of possible interventions, we propose to use Bayesian optimisation to efficiently maximise a Monte Carlo estimate of the expected information gain.
Quantifying Life Insurance Risk using Least-Squares Monte Carlo

This article presents a stochastic framework to quantify the biometric risk of an insurance portfolio in solvency regimes such as Solvency II or the Swiss Solvency Test (SST). The main difficulty in this context constitutes in the proper representation of long term risks in the profit-loss distribution over a one year horizon. This will be resolved by using least-squares Monte Carlo methods to quantify the impact of new experience on the annual re-valuation of the portfolio. Therefore our stochastic model can be seen as an example for an internal model, as allowed under Solvency II or the SST. Since our model does not rely upon nested simulations it is computationally fast and easy to implement.
Eliminating chattering phenomenon in sliding mode control of robot manipulators in the joint space using fuzzy logic

In industrial robotic manipulator, due to the presence of quite nonlinear dynamic and structural and nonstructural uncertainties, a precise model is not easily obtained. As a result, designing a model-based controller with a suitable function is a challenging issue. Sliding mode control is a robust control with numerous applications which can overcome the aforementioned uncertainties. However, this control method has several defects such as chattering in input control in implementing stage. In this article, Fuzzy sliding mode control based on TSK method for controlling manipulator position tracking is suggested. This control method not only has advantages of sliding mode but also it has no chattering control effect in implementation process. To justify the performance of proposed Fuzzy based SMC in control signal chattering, a robot manipulator with two revolute joint has been used. The simulation results reveal the desirable efficiency of Fuzzy sliding mode control.
Stock Prediction Using Convolutional Neural Network

Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. In this paper, we proposed a deep learning method based on Convolutional Neural Network to predict the stock price movement of Chinese stock market. We set the opening price, high price, low price, closing price and volume of stock deriving from the internet as input of the architecture and then run and test the program. The result has shown that it is a bit reliable to use deep learning method based on Convolutional Neural Network to predict the stockprice movement of China.
Effects of Migration Experience on Political Attitudes

The post-1990 migration wave from Eastern to Western Europe has spurred a wealth of research, especially concerning its economic and social consequences. However, very few of these studies explore the implications of migration for the values individuals have or develop. This study contributes to the body of literature by addressing the question of whether migrants to democratic countries more often display attitudes and beliefs which are said to be in favour of liberal democracies than non-migrants. In answering this question, it uses data from Candidate Countries Eurobarometer 2002 and combines perspectives derived from democratisation and socialisation theory to explain the impact of migration on political attitudes of citizens. We find that migration experience has a substantially strong effect on political attitudes, but only when these attitudes are not deeply entrenched before the actual migration experience took place.
Co-authorship and academic productivity in economics :

We explore the relationship between collaborations in writing papers and the academic productivity of economists and, particularly, we describe the magnitude and intensity of co-authorship among economists. To that end, we employ interaction maps from Complex Systems methods to study the global properties of specific networks. We use 8,253 JCR papers from ISI-WOK, published by 5,188 economists from Spanish institutions, and their co-authors, up to 8,202 researchers, from 2002 to 2014, to identify and determine the collaborative structure of economics research in Spain, with its primary communities and figures of influence. Our results indicate that centrality and productivity are correlated, particularly with respect to a local estimator of centrality (page rank), and we provide certain recommendations, such as promoting interactions among highly productive authors who have few co-authors with other researchers in their environment, or recommending that authors who may be well-positioned but minimally productive strive to improve their productivity.
The Effect of Disorder on the Phase Diagrams of Hard-Core Lattice Bosons With Cavity-Mediated Long-Range and Nearest-Neighbor Interactions

We use quantum Monte Carlo simulations with the worm algorithm to study the phase diagram of a two-dimensional Bose-Hubbard model with cavity-mediated long-range interactions and uncorrelated disorder in the hard-core limit. Our study shows the system is in a supersolid phase at weak disorder and a disordered solid phase at stronger disorder. Due to long-range interactions, a large co-exist region of density-wave and Mott insulator phases exists in both clean and disordered systems. By comparing the phase diagrams for both clean and disordered systems, we find that disorder suppresses superfluidity and narrows the co-exist region caused by first order phase transition. We compare these results with the phase diagram of the extended Bose-Hubbard model with nearest-neighbor interactions. Here, the supersolid phase does not exist even at weak disorder. We identify two kinds of glassy phases: a Bose glass phase and a disordered solid phase. The glassy phases intervene between the density-wave and superfluid phases as the Griffiths phase of the Bose-Hubbard model. The disordered solid phase intervenes between the density-wave and Bose glass phases since both have a finite structure factor.
Scalable Nearest Neighbor Search for Optimal Transport

The Optimal Transport (a.k.a. Wasserstein) distance is an increasingly popular similarity measure for rich data domains, such as images or text documents. This raises the necessity for fast nearest neighbor search with respect to this distance, a problem that poses a substantial computational bottleneck for various tasks on massive datasets. In this work, we study fast tree-based approximation algorithms for searching nearest neighbors w.r. t. the Wasserstein-1 distance. A standard tree-based technique, known as Quadtree, has been previously shown to obtain good results. We introduce a variant of this algorithm, called Flowtree, and formally prove it achieves asymptotically better accuracy. Our extensive experiments, on real-world text and image datasets, show that Flowtree improves over various baselines and existing methods in either running time or accuracy. In particular, its quality of approximation is in line with previous high-accuracy methods, while its running time is much faster.
Another Mini Solar Maximum in the Offing: A Prediction for the Amplitude of Solar Cycle 25

We examine the temporal changes in both solar polar magnetic field (PMF) at latitudes ≥45° and heliospheric magnetic field (HMF) at 1 AU during solar cycles 21–24 with emphasis on the recent activity changes after July 2015, the so called “mini solar maximum” of cycle 24. While unsigned PMF shows solar cycle modulations in cycles 21 and 22, it shows an anti‐correlation with solar cycle variation in cycle 24. In addition, the floor level of the HMF (of 4.6 nT), that is, the value that the HMF returns to at each solar minimum, is breached about 2 years prior to cycle 24 minimum, indicating a reduced HMF floor level of 3.2 ± 0.5 nT in the upcoming cycle 24 minimum. In light of the change of unsigned PMF and the availability of a revised smoothed sunspot number (SSN V2.0) after July 2015, we revisit the correlation of unsigned PMF and HMF at solar minimum. The correlation is used to estimate values of the HMF at the cycle 24 minimum and the amplitude of the upcoming cycle 25. The updated prediction is 134 ± 11 or 131 ± 11 on the revised SSN V2.0 scale assuming the cycle 24 minimum to occur in 2020 or 2021, respectively. The SSN values indicate that the cycle 25 will be stronger than cycle 24 and a little weaker than cycle 23, even if the current solar cycle minimum occurs in 2021 instead of 2020. This implies that we will witness another mini solar maximum in the upcoming cycle 25.
Chaotic cold accretion in giant elliptical galaxies heated by AGN cosmic rays

Black hole feedback plays a central role in shaping the circumgalactic medium (CGM) of elliptical galaxies. We systematically study the impact of plasma physics on the evolution of ellipticals by performing three-dimensional non-ideal magnetohydrodynamic simulations of the interactions of active galactic nucleus (AGN) jets with the CGM including magnetic fields, and cosmic rays (CRs) and their transport processes. We find that the physics of feedback operating on large galactic scales depends very sensitively on plasma physics operating on small scales. Specifically, we demonstrate that (i) in the purely hydrodynamical case, the AGN jets initially maintain the atmospheres in global thermal balance. However, local thermal instability generically leads to the formation of massive cold discs in the vicinity of the central black hole in disagreement with observations; (ii) including weak magnetic fields prevents the formation of the discs because local B-field amplification in the precipitating cold gas leads to strong magnetic breaking, which quickly extracts angular momentum from the accreting clouds. The magnetic fields transform the cold clouds into narrow filaments that do not fall ballistically; (iii) when plasma composition in the AGN jets is dominated by CRs, and CR transport is neglected, the atmospheres exhibit cooling catastrophes due to inefficient heat transfer from the AGN to CGM despite Coulomb/hadronic CR losses being present; (iv) including CR streaming and heating restores agreement with the observations, i.e. cooling catastrophes are prevented and massive cold central discs do not form. The AGN power is reduced as its energy is utilized efficiently.
Automated Methods for Checking Differential Privacy

Differential privacy is a de facto standard for statistical computations over databases that contain private data. The strength of differential privacy lies in a rigorous mathematical definition that guarantees individual privacy and yet allows for accurate statistical results. Thanks to its mathematical definition, differential privacy is also a natural target for formal analysis. A broad line of work uses logical methods for proving privacy. However, these methods are not complete, and only partially automated. A recent and complementary line of work uses statistical methods for finding privacy violations. However, the methods only provide statistical guarantees (but no proofs). We propose the first decision procedure for checking the differential privacy of a non-trivial class of probabilistic computations. Our procedure takes as input a program P parametrized by a privacy budget $\epsilon$, and either proves differential privacy for all possible values of $\epsilon$ or generates a counterexample. In addition, our procedure applies both to $\epsilon$-differential privacy and $(\epsilon,\delta)$-differential privacy. Technically, the decision procedure is based on a novel and judicious encoding of the semantics of programs in our class into a decidable fragment of the first-order theory of the reals with exponentiation. We implement our procedure and use it for (dis)proving privacy bounds for many well-known examples, including randomized response, histogram, report noisy max and sparse vector.
Constraining the scatter in the galaxy–halo connection at Milky Way masses

We develop and implement two new methods for constraining the scatter in the relationship between galaxies and dark matter haloes. These new techniques are sensitive to the scatter at low halo masses, making them complementary to previous constraints that are dependent on clustering amplitudes or rich galaxy groups, both of which are only sensitive to more massive haloes. In both of our methods, we use a galaxy group finder to locate central galaxies in the Sloan Digital Sky Survey main galaxy sample. Our first technique uses the small-scale cross-correlation of central galaxies with all lower mass galaxies. This quantity is sensitive to the satellite fraction of low-mass galaxies, which is in turn driven by the scatter between haloes and galaxies. The second technique uses the kurtosis of the distribution of line-of-sight velocities between central galaxies and neighbouring galaxies. This quantity is sensitive to the distribution of halo masses that contain the central galaxies at fixed stellar mass. Theoretical models are constructed using peak halo circular velocity, Vpeak, as our property to connect galaxies to haloes, and all comparisons between theory and observation are made after first passing the model through the group-finding algorithm. We parametrize scatter as a lognormal distribution in M* at fixed Vpeak, σ[M*|Vpeak]. The cross-correlation technique yields a constraint of σ[M*|Vpeak] = 0.27 ± 0.05 dex at a mean Vpeak of 168 km s−1, corresponding to a scatter in log M* at fixed Mh of σ[M*|Mh] = 0.38 ± 0.06 dex at $M_\text{h}=10^{11.8}\, \text{M}_\odot$. The kurtosis technique yields σ[M*|Vpeak] = 0.30 ± 0.03 at Vpeak = 209 km s−1, corresponding to σ[M*|Mh] = 0.34 ± 0.04 at $M_\text{h}=10^{12.2}\, \text{M}_\odot$. The values of σ[M*|Mh] are significantly larger than the constraints at higher masses, in agreement with the results of hydrodynamic simulations. This increase is only partly due to the scatter between Vpeak and Mh, and it represents an increase of nearly a factor of 2 relative to the values inferred from clustering and group studies at high masses.
Investment in human capital : Does tax-deductibility matter ?

To stimulate investment in training by employees, the Dutch tax system allows a deduction of direct training expenditures from taxable income. This paper investigates to what extent the resulting cost reduction encourages training investments. Two different identification strategies are used. The first strategy takes advantage of the 2001 tax reform, which implied a substantial change in marginal tax rates. The second strategy uses the progressive structure of the income tax scheme and compares groups with taxable income just above or just below kinks. These strategies exploit different sources of exogenous variation and are based on different identifying assumptions. Nevertheless, the results point in the same direction: tax incentives increase human capital investment.