Miracle-dz/UniFAR-FTUs huggingface dataset:
Baryon acoustic oscillations in the projected cross-correlation function between the eBOSS DR16 quasars and photometric galaxies from the DESI Legacy Imaging Surveys

We search for the baryon acoustic oscillations in the projected cross-correlation function binned into transverse comoving radius between the SDSS-IV DR16 eBOSS quasars and a dense photometric sample of galaxies selected from the DESI Legacy Imaging Surveys. We estimate the density of the photometric sample of galaxies in this redshift range to be about 2900 deg−2, which is deeper than the official DESI emission line galaxy selection, and the density of the spectroscopic sample is about 20 deg−2. In order to mitigate the systematics related to the use of different imaging surveys close to the detection limit, we use a neural network approach that accounts for complex dependences between the imaging attributes and the observed galaxy density. We find that we are limited by the depth of the imaging surveys that affects the density and purity of the photometric sample and its overlap in redshift with the quasar sample, which thus affects the performance of the method. When cross-correlating the photometric galaxies with quasars in the range 0.6 ≤ z ≤ 1.2, the cross-correlation function can provide better constraints on the comoving angular distance DM (6 per cent precision) compared to the constraint on the spherically averaged distance DV (9 per cent precision) obtained from the autocorrelation. Although not yet competitive, this technique will benefit from the arrival of deeper photometric data from upcoming surveys that will enable it to go beyond the current limitations we have identified in this work.
The Sloan Digital Sky Survey Reverberation Mapping Project: the XMM-Newton X-Ray Source Catalog and Multiband Counterparts

The XMM-RM project was designed to provide X-ray coverage of the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) field. Forty-one XMM-Newton exposures, placed surrounding the Chandra AEGIS field, were taken, covering an area of 6.13 deg2 and reaching a nominal exposure depth of ∼15 ks. We present an X-ray catalog of 3553 sources detected in these data, using a PSF-fitting algorithm and a sample selection threshold that produces a ∼5% fraction of spurious sources. In addition to the PSF-fitting likelihood, we calculate a second source reliability measure based on Poisson theory using source and background counts within an aperture. Using the Poissonian likelihood, we select a subsample with a high purity and find that it has number count profiles similar to previous X-ray surveys. The Bayesian method “NWAY” was employed to identify counterparts of the X-ray sources from the optical Legacy and the IR unWISE catalogs, using a two-dimensional unWISE magnitude–color prior created from optical/IR counterparts of Chandra X-ray sources. A significant number of the optical/IR counterparts correspond to sources with low detection likelihoods, proving the value of retaining the low-likelihood detections in the catalog. A total of 932 of the XMM-RM sources are covered by SDSS spectroscopic observations, where 89% of them are classified as active galactic nuclei, and 71% of these active galactic nuclei are in the SDSS-RM quasar catalog. Among the SDSS-RM quasars, 80% are detectable at the depth of the XMM observations.
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection

We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action associated with a randomly generated contextual covariate. This setting finds essential applications such as personalized recommendation, online advertisement, and personalized medicine. However, it is very challenging as we need to balance exploration and exploitation. We propose doubly growing epochs and estimating the parameter using the best subset selection method, which is easy to implement in practice. This approach achieves $ \tilde{\mathcal{O}}(s\sqrt{T})$ regret with high probability, which is nearly independent in the ``ambient'' regression model dimension $d$. We further attain a sharper $\tilde{\mathcal{O}}(\sqrt{sT})$ regret by using the \textsc{SupLinUCB} framework and match the minimax lower bound of low-dimensional linear stochastic bandit problems. Finally, we conduct extensive numerical experiments to demonstrate the applicability and robustness of our algorithms empirically.
Accurate TLS Fingerprinting using Destination Context and Knowledge Bases

Network fingerprinting is used to identify applications, provide insight into network traffic, and detect malicious activity. With the broad adoption of TLS, traditional fingerprinting techniques that rely on clear-text data are no longer viable. TLS-specific techniques have been introduced that create a fingerprint string from carefully selected data features in the client_hello to facilitate process identification before data is exchanged. Unfortunately, this approach fails in practice because hundreds of processes can map to the same fingerprint string. We solve this problem by presenting a TLS fingerprinting system that makes use of the destination address, port, and server name in addition to a carefully constructed fingerprint string. The destination context is used to disambiguate the set of processes that match a fingerprint string by applying a weighted naive Bayes classifier, resulting in far greater performance.
X-ray Flux and Spectral Variability of Blazar H 2356-309

We present the results of timing and spectral analysis of the blazar H 2356-309 using XMM-Newton observations. This blazar is observed during 13 June 2005–24 December 2013 in total nine observations. Five of the observations show moderate flux variability with amplitude 1.7–2.2%. We search for the intra-day variability timescales in these five light curves, but did not find in any of them. The fractional variability amplitude is generally lower in the soft bands than in the hard bands, which is attributed to the energy dependent synchrotron emission. Using the hardness ratio analysis, we search for the X-ray spectral variability along with flux variability in this source. However, we did not find any significant spectral variability on intra-day timescales. We also investigate the X-ray spectral curvature of blazar H 2356-309 and found that six of our observations are well described by the log parabolic model with α = 1.99–2.15 and β = 0.03–0.18. Three of our observations are well described by power law model. The break energy of the X-ray spectra varies between 1.97–2.31 keV. We investigate the correlation between various parameters that are derived from log parabolic model and their implications are discussed.
FedDistill: Making Bayesian Model Ensemble Applicable to Federated Learning

Federated learning aims to collaboratively train a strong global model by accessing users' locally trained models but not their own data. A crucial step is therefore to aggregate local models into a global model, which has been shown challenging when users have non-i.i. d. data. In this paper, we propose a novel aggregation algorithm named FedBE, which takes a Bayesian inference perspective by sampling higher-quality global models and combining them via Bayesian model Ensemble, leading to much robust aggregation. We show that an effective model distribution can be constructed by simply fitting a Gaussian or Dirichlet distribution to the local models. Our empirical studies validate FedBE's superior performance, especially when users' data are not i.i. d. and when the neural networks go deeper. Moreover, FedBE is compatible with recent efforts in regularizing users' model training, making it an easily applicable module: you only need to replace the aggregation method but leave other parts of your federated learning algorithm intact.
Kinematic signatures of nuclear discs and bar-driven secular evolution in nearby galaxies of the MUSE TIMER project

The central regions of disc galaxies hold clues to the processes that dominate their formation and evolution. To exploit this, the TIMER project has obtained high signal-to-noise and spatial resolution integral-field spectroscopy data of the inner few kpc of 21 nearby massive barred galaxies, which allows studies of the stellar kinematics in their central regions with unprecedented spatial resolution. We confirm theoretical predictions of the effects of bars on stellar kinematics and identify box/peanuts through kinematic signatures in mildly and moderately inclined galaxies, finding a lower limit to the fraction of massive barred galaxies with box/peanuts at ∼62%. Further, we provide kinematic evidence of the connection between barlenses, box/peanuts, and bars. We establish the presence of nuclear discs in 19 galaxies and show that their kinematics are characterised by near-circular orbits with low pressure support and that they are fully consistent with the bar-driven secular evolution picture for their formation. In fact, we show that these nuclear discs have, in the region where they dominate, larger rotational support than the underlying main galaxy disc. In addition, we define a kinematic radius for the nuclear discs and show that it relates to bar radius, ellipticity and strength, and bar-to-total ratio. Comparing our results with photometric studies of galaxy bulges, we find that careful, state-of-the-art galaxy image decompositions are generally able to discern nuclear discs from classical bulges if the images employed have high enough physical spatial resolution. In fact, we show that nuclear discs are typically identified in such image decompositions as photometric bulges with (near-)exponential profiles. However, we find that the presence of composite bulges (galaxies hosting both a classical bulge and a nuclear disc) can often be unnoticed in studies based on photometry alone and suggest a more stringent threshold to the Sérsic index to identify galaxies with pure classical bulges.
Bose-Einstein Condensation of Quantum Hard-Spheres as a Deposition Phase Transition and New Relations Between Bosonic and Fermionic Pressures.

We investigate the phase transition of Bose-Einstein particles with the hard-core repulsion in the grand canonical ensemble within the Van der Waals approximation. It is shown that the pressure of non-relativistic Bose-Einstein particles is mathematically equivalent to the pressure of simplified version of the statistical multifragmentation model of nuclei with the vanishing surface tension coefficient and the Fisher exponent $\tau_F = \frac{5}{2}$, which for such parameters has the 1-st order phase transition. The found similarity of these equations of state allows us to show that within the present approach the high density phase of Bose-Einstein particles is a classical macro-cluster with vanishing entropy at any temperature which, similarly to the classical hard spheres, is a kind of solid state. To show this we establish new relations which allow us to identically represent the pressure of Fermi-Dirac particles in terms of pressures of Bose-Einstein particles of two sorts.
Predicting the Refractive Index of Tissue Models Using Light Scattering Spectroscopy

In this work, we report the application of Raman microspectroscopy for analysis of the refractive index of a range of tissue phantoms. Using both a custom-developed setup with visible laser source and a commercial microspectrometer with near infrared laser, we measured the Raman spectra of gelatin hydrogels at various concentrations. By building a calibration curve from measured refractometry data and Raman scattering intensity for different vibrational modes of the hydrogel, we were able to predict the refractive indices of the gels from their Raman spectra. This work highlights the importance of a correlative approach through Brillouin–Raman microspectroscopy for the mechano–chemical analysis of biologically relevant samples.
On the analogy between black holes and bathtub vortices

Analogical thinking is a valuable tool in theoretical physics, since it allows us to take the understanding we have developed in one system and apply it to another. In this thesis, we study the analogy between two seemingly unlikely systems: rotating black holes, elusive cosmic entities that push our theoretical understanding of modern physics to its limits, and bathtub vortices, an occurrence so common that they can be observed on a day-to-day basis in almost any household. Despite the clear difference between these two systems, we argue that lessons from each can be used to learn something about the other. We investigate the equivalence between surface wave propagation in shallow water and the propagation of a massless scalar field on an effective spacetime, focussing in particular on the rotating black hole geometry sourced by a rotating draining vortex flow. Using this analogy, we verify for the first time that three effects predicted to occur around rotating black holes also occur in a laboratory experiment. These are superradiance, an energy enhancement process whereby waves extract rotational energy from the system, quasi-normal ringing, describing the relaxation of the system toward equilibrium, and the backreaction, which mediates the exchange of energy between fluctuations and the background they experience.
Baryon loading of blazar jets independent of accretion rate, not so their luminosity

The most extreme active galactic nuclei (AGN) are the radio active ones whose relativistic jet propagates close to our line of sight. These objects were first classified according to their emission line features into flat-spectrum radio quasars (FSRQs) and BL Lacertae objects (BL Lacs). More recently, observations revealed a trend between these objects known as the \emph{blazar sequence}, along with an anti-correlation between the observed power and the frequency of the synchrotron peak. In the present work, we propose a fairly simple idea that could account for the whole blazar population: all jets are launched with similar energy per baryon, independently of their power. In the case of FSRQs, the most powerful jets, manage to accelerate to high bulk Lorentz factors, as observed in the radio. As a result, they have a rather modest magnetization in the emission region, resulting in magnetic reconnection injecting a steep particle energy distribution and, consequently, steep emission spectra in the $\gamma$-rays. For the weaker jets, namely BL Lacs, the opposite holds true; i.e., the jet does not achieve a very high bulk Lorentz factor, leading to more magnetic energy available for non-thermal particle acceleration, and harder emission spectra at frequencies $\gtrsim$ GeV. In this scenario, we recover all observable properties of blazars with our simulations, including the \emph{blazar sequence} for models with mild baryon loading ($60 \lesssim \mu \lesssim 80$). This interpretation of the blazar population, therefore, tightly constrains the energy per baryon of blazar jets regardless of their luminosity.
Model extraction from counterfactual explanations

Post-hoc explanation techniques refer to a posteriori methods that can be used to explain how black-box machine learning models produce their outcomes. Among post-hoc explanation techniques, counterfactual explanations are becoming one of the most popular methods to achieve this objective. In particular, in addition to highlighting the most important features used by the black-box model, they provide users with actionable explanations in the form of data instances that would have received a different outcome. Nonetheless, by doing so, they also leak non-trivial information about the model itself, which raises privacy issues. In this work, we demonstrate how an adversary can leverage the information provided by counterfactual explanations to build high-fidelity and high-accuracy model extraction attacks. More precisely, our attack enables the adversary to build a faithful copy of a target model by accessing its counterfactual explanations. The empirical evaluation of the proposed attack on black-box models trained on real-world datasets demonstrates that they can achieve high-fidelity and high-accuracy extraction even under low query budgets.
Defending the P-value

Attacks on the P-value are nothing new, but the recent attacks are increasingly more serious. They come from more mainstream sources, with widening targets such as a call to retire the significance testing altogether. While well meaning, I believe these attacks are nevertheless misdirected: Blaming the P-value for the naturally tentative trial-and-error process of scientific discoveries, and presuming that banning the P-value would make the process cleaner and less error-prone. However tentative, the skeptical scientists still have to form unambiguous opinions, proximately to move forward in their investigations and ultimately to present results to the wider community. With obvious reasons, they constantly need to balance between the false-positive and false-negative errors. How would banning the P-value or significance tests help in this balancing act? It seems trite to say that this balance will always depend on the relative costs or the trade-off between the errors. These costs are highly context specific, varying by area of applications or by stage of investigation. A calibrated but tunable knob, such as that given by the P-value, is needed for controlling this balance. This paper presents detailed arguments in support of the P-value.
AnyDB: An Architecture-less DBMS for Any Workload

In this paper, we propose a radical new approach for scale-out distributed DBMSs. Instead of hard-baking an architectural model, such as a shared-nothing architecture, into the distributed DBMS design, we aim for a new class of so-called architecture-less DBMSs. The main idea is that an architecture-less DBMS can mimic any architecture on a per-query basis on-the-fly without any additional overhead for reconfiguration. Our initial results show that our architecture-less DBMS AnyDB can provide significant speed-ups across varying workloads compared to a traditional DBMS implementing a static architecture.
On-demand generation of dark soliton trains in Bose-Einstein condensates

Matter-wave interference mechanisms in one-dimensional Bose-Einstein condensates that allow for the controlled generation of dark soliton trains upon choosing suitable box-type initial configurations are described. First, the direct scattering problem for the defocusing nonlinear Schr\"odinger equation with nonzero boundary conditions and general box-type initial configurations is discussed, and expressions for the discrete spectrum corresponding to the dark soliton excitations generated by the dynamics are obtained. It is found that the size of the initial box directly affects the number, size and velocity of the solitons, while the initial phase determines the parity of the solutions. The analytical results obtained for the untrapped system are compared to those of numerical simulations of the Gross-Pitaevskii equation, both in the absence and in the presence of a harmonic trap. The numerical results bear out the analytical results with excellent agreement.
Entanglement entropy of excited states in the quantum Lifshitz model

In this work we calculate the entanglement entropy of certain excited states of the quantum Lifshitz model (QLM). The QLM is a 2 + 1-dimensional bosonic quantum field theory with an anisotropic scaling symmetry between space and time that belongs to the universality class of the quantum dimer model and its generalizations. The states we consider are constructed by exciting the eigenmodes of the Laplace–Beltrami operator on the spatial manifold of the model. We perform a replica calculation and find that, whenever a simple assumption is satisfied, the bipartite entanglement entropy of any such excited state can be evaluated analytically. We show that the assumption is satisfied for all excited states on the rectangle and for almost all excited states on the sphere and provide explicit examples in both geometries. We find that the excited state entanglement entropy obeys an area law and is related to the entanglement entropy of the ground state by two universal constants. We observe a logarithmic dependence on the excitation number when all excitations are put onto the same eigenmode.
Consistent detection and optimal localization of all detectable change points in piecewise stationary arbitrarily sparse network-sequences

We consider the offline change point detection and localization problem in the context of piecewise stationary networks, where the observable is a finite sequence of networks. We develop algorithms involving some suitably modified CUSUM statistics based on adaptively trimmed adjacency matrices of the observed networks for both detection and localization of single or multiple change points present in the input data. We provide rigorous theoretical analysis and finite sample estimates evaluating the performance of the proposed methods when the input (finite sequence of networks) is generated from an inhomogeneous random graph model, where the change points are characterized by the change in the mean adjacency matrix. We show that the proposed algorithms can detect (resp. localize) all change points, where the change in the expected adjacency matrix is above the minimax detectability (resp. localizability) threshold, consistently without any a priori assumption about (a) a lower bound for the sparsity of the underlying networks, (b) an upper bound for the number of change points, and (c) a lower bound for the separation between successive change points, provided either the minimum separation between successive pairs of change points or the average degree of the underlying networks goes to infinity arbitrarily slowly. We also prove that the above condition is necessary to have consistency.
Zero-Bias Deep Learning for Accurate Identification of Internet-of-Things (IoT) Devices

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework for IoT device identification using physical-layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from automatic dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to the accurate identification of IoT devices in a variety of IoT applications and services.