Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces

The use of large arrays might be the solution to the capacity problems in wireless communications. The signal-to-noise ratio (SNR) grows linearly with the number of array elements <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> when using Massive MIMO receivers and half-duplex relays. Moreover, intelligent reflecting surfaces (IRSs) have recently attracted attention since these can relay signals to achieve an SNR that grows as <inline-formula> <tex-math notation="LaTeX">$N^{2}$ </tex-math></inline-formula>, which seems like a major benefit. In this article, we use a deterministic propagation model for a planar array of arbitrary size, to demonstrate that the mentioned SNR behaviors, and associated power scaling laws, only apply in the far-field. They cannot be used to study the regime where <inline-formula> <tex-math notation="LaTeX">$N\to \infty $ </tex-math></inline-formula>. We derive an exact channel gain expression that captures three essential near-field behaviors and use it to revisit the power scaling laws. We derive new finite asymptotic SNR limits but also conclude that these are unlikely to be approached in practice. We further prove that an IRS-aided setup cannot achieve a higher SNR than an equal-sized Massive MIMO setup, despite its faster SNR growth. We quantify analytically how much larger the IRS must be to achieve the same SNR. Finally, we show that an optimized IRS does not behave as an “anomalous” mirror but can vastly outperform that benchmark.
New Sensitivity Curves for Gravitational-Wave Experiments

Gravitational waves (GWs) from strong first-order phase transitions (SFOPTs) in the early Universe are a prime target for upcoming GW experiments. In this paper, I construct novel peak-integrated sensitivity curves (PISCs) for these experiments, which faithfully represent their projected sensitivities to the GW signal from a cosmological SFOPT by explicitly taking into account the expected shape of the signal. Designed to be a handy tool for phenomenologists and model builders, PISCs allow for a quick and systematic comparison of theoretical predictions with experimental sensitivities, as I illustrate by a large range of examples. PISCs also offer several advantages over the conventional power-law-integrated sensitivity curves (PLISCs); in particular, they directly encode information on the expected signal-to-noise ratio for the GW signal from a SFOPT. I provide semianalytical fit functions for the exact numerical PISCs of LISA, DECIGO, and BBO. In an appendix, I moreover present a detailed review of the strain noise power spectra of a large number of GW experiments. The numerical results for all PISCs, PLISCs, and strain noise power spectra presented in this paper can be downloaded from the Zenodo online repository [https://doi.org/10.5281/zenodo.3689582]. In a companion paper [1909.11356], the concept of PISCs is used to perform an in-depth study of the GW signal from the cosmological phase transition in the real-scalar-singlet extension of the standard model.
Gravitational Waves in Axion Dark Matter

Axion dark matter is interesting as it allows a natural coupling to the gravitational Chern–Simons term. In the presence of an axion background, the gravitational Chern–Simons term produces parity violating effects in the gravitational sector, in particular on the propagation of gravitational waves. Previously, it has been shown that the coherent oscillation of the axion field leads to a parametric amplification of gravitational waves with a specific frequency. In this paper, we focus on the parity violating effects of the Chern–Simons coupling and show the occurrence of gravitational birefringence. We also find deviation from the speed of light of the velocity of the gravitational waves. We give constraints on the axion-Chern–Simons coupling constant and the abundance of axion dark matter from the observation of GW170817 and GRB170817A.
On Layer Normalization in the Transformer Architecture

The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is shown to be crucial to the final performance but will slow down the optimization and bring more hyperparameter tunings. In this paper, we first study theoretically why the learning rate warm-up stage is essential and show that the location of layer normalization matters. Specifically, we prove with mean field theory that at initialization, for the original-designed Post-LN Transformer, which places the layer normalization between the residual blocks, the expected gradients of the parameters near the output layer are large. Therefore, using a large learning rate on those gradients makes the training unstable. The warm-up stage is practically helpful for avoiding this problem. On the other hand, our theory also shows that if the layer normalization is put inside the residual blocks (recently proposed as Pre-LN Transformer), the gradients are well-behaved at initialization. This motivates us to remove the warm-up stage for the training of Pre-LN Transformers. We show in our experiments that Pre-LN Transformers without the warm-up stage can reach comparable results with baselines while requiring significantly less training time and hyper-parameter tuning on a wide range of applications.
A regression-based method for detecting publication bias in multivariate meta-analysis

Publication bias occurs when the publication of research results depends not only on the quality of the research but also on its nature and direction. The consequence is that published studies may not be truly representative of all valid studies undertaken, and this bias may threaten the validity of systematic reviews and meta-analyses - on which evidence-based medicine increasingly relies. Multivariate meta-analysis has recently received increasing attention for its ability reducing potential bias and improving statistical efficiency by borrowing information across outcomes. However, detecting and accounting for publication bias are more challenging in multivariate meta-analysis setting because some studies may be completely unpublished whereas some studies may selectively report part of multiple outcomes. In this paper, we propose a score test for jointly testing publication bias for multiple outcomes, which is novel to the multivariate setting. The proposed test is a natural multivariate extension of the univariate Egger's test, and can handle the above mentioned scenarios simultaneously, It accounts for correlations among multivariate outcomes, while allowing different types of outcomes, and can borrow information across outcomes. The proposed test is shown to be more powerful than the Egger's test, Begg's test and Trim and Fill method through simulation studies. Two data analyses are given to illustrate the performance of the proposed test in practice.
Joint distribution of eigenvalues of Hecke and Casimir operators for Hilbert Maass forms

Let $F$ be a totally real number field, $\mathcal{O}_{F}$ the ring of integers, $\mathfrak a$ and $\mathfrak I$ integral ideals and let $\chi$ a character of $\mathbb{A}_F^\times/F^\times$. For each prime ideal $\mathfrak{p}$ in $\mathcal{O}_{F}$, $\mathfrak{p}\nmid \mathfrak{I}$ let $T_{\mathfrak{p}}$ be the Hecke operator acting on the space of Maass cusp forms on $L^2(\mathrm{GL}_{2}(F) \backslash \mathrm{GL}_{2}(\mathbb{A}_F))$. In this paper we investigate the distribution of joint eigenvalues of the Hecke operators $T_{\mathfrak{p}}$ and of the Casimir operators $C_{j}$ in each archimedean component of $F$, for $1\le j \le d$. Summarily, we prove that given a family of expanding compact subsets $\Omega_{t}$ of $\mathbb{R}^{d}$ as $t \rightarrow \infty$, and an interval $I_{\mathfrak{p}} \subseteq [-2,2]$, then, if $\mathfrak{p} \nmid \mathfrak{I}$ is a square in the narrow class group of $F$, there are infinitely many automorphic forms having eigenvalues of $T_{\mathfrak{p}}$ in $I_{\mathfrak{p}}$, distributed on $I_{\mathfrak{p}}$ according to a polynomial multiple of the Sato-Tate measure and having their Casimir eigenvalues in the region $\Omega_{t}$, distributed according to the Plancherel measure.
dtControl: decision tree learning algorithms for controller representation

Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely. Compared to representations using lookup tables or binary decision diagrams, decision trees are smaller and more explainable. We present dtControl, an easily extensible tool for representing memoryless controllers as decision trees. We give a comprehensive evaluation of various decision tree learning algorithms applied to 10 case studies arising out of correct-by-construction controller synthesis. These algorithms include two new techniques, one for using arbitrary linear binary classifiers in the decision tree learning, and one novel approach for determinizing controllers during the decision tree construction. In particular the latter turns out to be extremely efficient, yielding decision trees with a single-digit number of decision nodes on 5 of the case studies.
The relative Schinzel hypothesis

The Schinzel Hypothesis is a conjecture about irreducible polynomials in one variable over the integers: under some standard condition, they should assume infinitely many prime values at integers. We consider a relative version: if the polynomials are relatively prime and no prime number divides all their values at integers, then they assume relatively prime values at at least one integer. We extend the question to all integral domains and prove it for a number of them: PIDs, UFDs containing an infinite field, polynomial rings over a UFD. Applications include a new "integral" version of the Hilbert Irreducibility Theorem, for which the irreducibility conclusion is over the ring.
A Systematic Study of the Dust of Galactic Supernova Remnants. I. The Distance and the Extinction

By combining the photometric, spectroscopic, and astrometric information of the stars in the sightline of supernova remnants (SNRs), the distances to and the extinctions of 32 Galactic SNRs are investigated. The stellar atmospheric parameters are from the Sloan Digital Sky Survey−DR14/Apache Point Observatory Galaxy Evolution Experiment and Large sky Area Multi-Object fiber Spectroscopic Telescope−DR5/LEGUE spectroscopic surveys. The multiband photometry, from optical to infrared, are collected from the Gaia, APASS, Pan-STARRS1, Two Micron All Sky Survey, and Wide-field Infrared Survey Explorer surveys. With the calibrated Gaia distances of individual stars, the distances to 15 of 32 SNRs are well determined from their produced extinction and association with molecular clouds. The upper limits of distance are derived for three SNRs. The color excess ratios of 32 SNRs are calculated, and their variation with wavebands is fitted by a simple dust model. The inferred dust grain size distribution bifurcates: while the graphite grains have comparable size to the average interstellar medium dust, the silicate grains are generally larger. Along the way, the average extinction law from optical to near-infrared of the Milky Way is derived from the 1.3-million-star sample and found to agree with the CCM89 law with .
Crystalline electric field excitations in the quantum spin liquid candidate 
NaYbSe2

Very recently we revealed a large family of triangular lattice quantum spin liquid candidates named rare-earth chalcogenides, which features a high-symmetry structure without structural/charge disorders and spin impurities, and may serve as an ideal platform exploring spin liquid physics. The knowledge of crystalline electric-field (CEF) excitations is an essential step to explore the fundamental magnetism of rare-earth spin systems. Here we employed inelastic neutron scattering (INS) and Raman scattering (RS) to carry out a comprehensive CFE investigation on $NaYbSe_{2}$, a promising representative of the family. By comparison with its nonmagnetic compound $NaLuSe_{2}$, we are able to identify the CEF excitations at 15.8, 24.3 and 30.5 meV at 5K. The selected cuts of the INS spectra are well re-produced with a large anisotropy of $g$ factors ($g_{ab}:g_{c}\sim3:1$). Further, the CEF excitations are explained well by our calculations based on the point charge model. Interestingly, $NaYbSe_{2}$ exhibits an unusual CEF shift to higher energies with increasing temperatures, and the Raman mode close to the first CEF excitation shows an anomalously large softening with decreasing temperatures. The absence of the anomalies in $NaLuSe_{2}$ clearly demonstrates a CEF-phonon coupling not reported in the family. It can be understood in term of the weaker electronegativity of Se. The fact that the smallest first CEF excitation in the sub-family of $NaYbCh_{2}$ is $\sim$ 180K (Ch=O, S, Se), guarantees that the sub-family can be strictly described with an effective S=1/2 picture at sufficiently low temperatures. Interestingly the CEF-phonon coupling revealed here may present alternative possibilities to manipulate the spin systems.
A method for mapping the aliphatic hydrocarbon content of interstellar dust towards the Galactic Centre

In the interstellar medium, the cosmic elemental carbon abundance includes the total carbon in both gas and solid phases. The aim of the study was to trial a new method for measuring the amount and distribution of aliphatic carbon within interstellar dust over wide fields of view of our Galaxy. This method is based on the measurement of the 3.4-$\mu$m absorption feature from aliphatic carbonaceous matter. This can readily be achieved for single sources using infrared (IR) spectrometers. However, making such measurements over wide fields requires an imaging IR camera, equipped with narrow-band filters that are able to sample the spectrum. While this cannot produce as good a determination of the spectra, the technique can be applied to potentially tens to hundreds of sources simultaneously, over the field of view of the camera. We examined this method for a field in the centre of the Galaxy, and produced a map showing the variation of 3.4-$\mu$m optical depth across it.
How a Technical Agency Helped Scale Up a Community Health Worker Program: An Exploratory Study in Chhattisgarh State, India

Abstract —India's goal of universal health coverage underscores the need for scale in community action for health. Among the few successes in community programs is Chhattisgarh's Mitanin Program, designed and maintained at the scale of the entire state (covering almost 20 million). Evaluations of scaled-up interventions typically examine population health outcomes, placing less emphasis on how programs succeed or fail. To address this knowledge gap, we undertook a qualitative research study to explore the role of the State Health Resource Centre (SHRC), a state technical agency, in scaling up Chhattisgarh's Mitanin health worker program over a ten-year period commencing in 2002. We undertook observation, policy documentary review, in-depth interviews, and focus group discussions with policy/program developers, facilitators and trainers, community health workers, and representatives of civil society. Data analysis followed an inductive approach of qualitative data analysis and data were thematically organized in the form of folk theories including interlinked contexts, mechanisms, and outcomes reflecting the experience of the SHRC in scaling up community action for health in the state. The first folk theory links the enabling context of the formation of a new state with mechanisms of pluralistic and multistakeholder governance of the SHRC and avoidance of overt political patronage of the program, contributing to the sustainability of the program through multiple administrative and political transitions. The second folk theory elaborates how equity-focused mechanisms such as linking the program to locally important, intersectoral agendas for marginalized communities and attentiveness to career trajectories of female frontline workers created space for these workers to organize and demand livelihood rights against a broader context where the indigenous tribal minority and women are widely excluded from the social and political mainstream. These exploratory findings illustrate how the pluralistic governance structure of the SHRC, coupled with a set of unique contextual strategies, contributed to the longevity of the program and professional growth and opportunities for female community health workers, with lessons for other low- and middle-income country decision makers.
Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow

Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of complex contagion is social reinforcement that individuals require multiple exposures to information before they begin to spread it themselves. Here we show that the quoter model, a model of the social flow of written information over a network, displays features of complex contagion, including the weakness of long ties and that increased density inhibits rather than promotes information flow. Interestingly, the quoter model exhibits these features despite having no explicit social reinforcement mechanism, unlike complex contagion models. Our results highlight the need to complement contagion models with an information-theoretic view of information spreading to better understand how network properties affect information flow and what are the most necessary ingredients when modeling social behavior.
Weighted Sum-Rate Maximization for Multi-IRS Aided Cooperative Transmission

This letter investigates multiple intelligent reflecting surfaces (IRSs) aided wireless network, where the IRSs are deployed to cooperatively assist communications between a multi-antenna base station (BS) and the multiple single-antenna cell-edge users. We aim at maximizing the weighted sum rate of all the cell-edge users by jointly optimizing the BS’s transmit beamforming and IRS’s phase shifts. Especially, the beamforming is optimally obtained based on the Lagrangian method, and the phase shifts are obtained based on the Riemannian manifold conjugate gradient (RMCG) method. Numerical results show that a significant gain is improved with the aid of IRSs.
Mendelian Randomization with Incomplete Exposure Data: a Bayesian Approach

We expand Mendelian Randomization (MR) methodology to deal with randomly missing data on either the exposure or the outcome variable, and furthermore with data from nonindependent individuals (eg components of a family). Our method rests on the Bayesian MR framework proposed by Berzuini et al (2018), which we apply in a study of multiplex Multiple Sclerosis (MS) Sardinian families to characterise the role of certain plasma proteins in MS causation. The method is robust to presence of pleiotropic effects in an unknown number of instruments, and is able to incorporate inter-individual kinship information. Introduction of missing data allows us to overcome the bias introduced by the (reverse) effect of treatment (in MS cases) on level of protein. From a substantive point of view, our study results confirm recent suspicion that an increase in circulating IL12A and STAT4 protein levels does not cause an increase in MS risk, as originally believed, suggesting that these two proteins may not be suitable drug targets for MS.
Binary–binary scattering in the secular limit

Binary–binary interactions are important in a number of astrophysical contexts including dense stellar systems such as globular clusters. Although less frequent than binary–single encounters, binary–binary interactions lead to a much richer range of possibilities such as the formation of stable triple systems. Here, we focus on the regime of distant binary–binary encounters, i.e. two binaries approaching each other on an unbound orbit with a periapsis distance Q much larger than the internal binary separations. This ‘secular’ regime gives rise to changes in the orbital eccentricities and orientations, which we study using analytic considerations and numerical integrations. We show that ‘direct’ interactions between the three orbits only occur starting at a high expansion order of the Hamiltonian (hexadecupole order), and that the backreaction of the outer orbit on the inner two orbits at lower expansion orders is weak. Therefore, to good approximation, one can obtain the changes of each orbit by using previously known analytic results for binary–single interactions, and replacing the mass of the third body with the total mass of the companion binary. Nevertheless, we find some dependence of the ‘binarity’ of the companion binary, and derive explicit analytic expressions for the secular changes that are consistent with numerical integrations. In particular, the eccentricity and inclination changes of orbit 1 due to orbit 2 scale as ϵSA, 1(a2/Q)2[m3m4/(m3 + m4)2], where ϵSA, 1 is the approximate quadrupole-order change, and a2 and (m3, m4) are the companion binary orbital semimajor axis and component masses, respectively. Our results are implemented in several python scripts that are freely available.
Self-perceived hand hygiene practices among undergraduate nursing students

Abstract Limited research has investigated the hand hygiene practices of undergraduate nursing students. A descriptive self-report survey explored the predictors of self-perceived hand hygiene compliance using a convenience sample of 306 undergraduate nursing students enrolled at a southwestern Ontario university. Compliance was defined as the performance of hand hygiene at least 90% of the time in the moments both before and after direct patient contact. The self-reported compliance rate among study participants was 74.8%. Logistic regression analysis revealed that the independent predictors of hand hygiene compliance included concern about reprimand or discipline (odds ratio (OR) 4.324; 95% confidence interval (CI) 1.465–12.758); motivation to protect patients from infection (OR 2.418; 95% CI 1.001–5.838); number of clinical placements (OR 0.815; 95% CI 0.702–0.947) and role modelling by the clinical instructor (OR 2.227; 95% CI 1.009–4.915). Other independent predictors were the perceived barriers of busyness (OR 0.231; 95% CI 0.126–0.423), forgetfulness (OR 0.356; 95% CI 0.186–0.678) and perceptions of alcohol rub-related skin damage (OR 0.163; 95% CI 0.070–0.380). The findings of this study provide research-based evidence that could be used by educators to understand better hand hygiene practices among undergraduate nursing students.
DeepMutation: A Neural Mutation Tool

Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants. To address the first point, we recently proposed an approach using a Recurrent Neural Network Encoder-Decoder architecture to learn mutants from ~787k faults mined from real programs. The empirical evaluation of this approach confirmed its ability to generate mutants representative of real faults. In this paper, we address the second point, presenting DEEPMUTATION, a tool wrapping our deep learning model into a fully automated tool chain able to generate, inject, and test mutants learned from real faults. Video: https://sites.google. com/view/learning-mutation/deepmutation