Miracle-dz/UniFAR-FTUs huggingface dataset:
The chemical evolution of galaxy clusters: Dissecting the iron mass budget of the intracluster medium

Aims. We study the chemical evolution of galaxy clusters by measuring the iron mass in the ICM after dissecting the abundance profiles into different components. Methods. We used Chandra archival observations of 186 morphologically regular clusters in the redshift range of [0.04, 1.07]. For each cluster, we computed the azimuthally averaged iron abundance and gas density profiles. In particular, our aim is to identify a central peak in the iron distribution, which is associated with the central galaxy, and an approximately constant plateau reaching the largest observed radii, which is possibly associated with early enrichment that occurred before or shortly after achieving virialization within the cluster. We were able to firmly identify two components in the iron distribution in a significant fraction of the sample simply by relying on the fit of the iron abundance profile. From the abundance and ICM density profiles, we computed the iron mass included in the iron peak and iron plateau, and the gas mass-weighted iron abundance of the ICM out to an extraction radius of 0.4r500 and to r500 by extending the abundance profile as a constant. Results. We find that the iron plateau shows no evolution with redshift. On the other hand, we find a marginal (< 2σ c.l.) decrease with redshift in the iron mass included in the iron peak rescaled by the gas mass. We measure that the fraction of iron peak mass is typically a few percent (∼1%) of the total iron mass within r500. Therefore, since the total iron mass budget is dominated by the plateau, we find consistently that the global gas mass-weighted iron abundance does not evolve significantly across our sample. We were also able to reproduce past claims of evolution in the global iron abundance, which turn out to be due to the use of cluster samples with different selection methods combined with the use of emission-weighted, instead of gas mass-weighted, abundance values. Finally, while the intrinsic scatter in the iron plateau mass is consistent with zero, the iron peak mass exhibits a large scatter, in line with the fact that the peak is produced after the virialization of the halo and depends on the formation history of the hosting cool core and the strength of the associated feedback processes. Conclusions. We conclude that only a spatially resolved approach can resolve the issue of iron abundance evolution in the ICM, reconciling the contradictory results obtained in the last ten years. Evolutionary effects below z ∼ 1 are marginally measurable with present-day data, while at z > 1 the constraints are severely limited by poor knowledge of the high-z cluster population. The path towards a full and comprehensive chemical history of the ICM requires the application of high angular resolution X-ray bolometers and a dramatic increase in the number of faint, extended X-ray sources.
Millimeter-wave in-package: tackling the system-in-package interconnection paradigm.

This work describes the architecture and implementation of a high-data-rate, energy-efficient millimeter-wave (mm-wave) waveguide solution for integration inside an Integrated Circuit (IC) package. The complete waveguide solution (together with couplers) has been designed to fit into the tight dimensional requirements of a wafer bonded 3D package and provides an extremely wide bandwidth in the mm-wave spectrum. To achieve this, we investigate the use of new high-permittivity materials. To be able to fulfill the data-rate demands of modern System-in-Packages (SiPs), Monolithic Microwave Integrated Circuits(MMICs) and integrated high performance analog ICs together with Digital Signal Processing (DSP), our solution is inherently scalable and adaptable in several ways. The work is novel and also discusses the various opportunities and challenges associated with the solution.
Assessing the Security of OPC UA Deployments

To address the increasing security demands of industrial deployments, OPC UA is one of the first industrial protocols explicitly designed with security in mind. However, deploying it securely requires a thorough configuration of a wide range of options. Thus, assessing the security of OPC UA deployments and their configuration is necessary to ensure secure operation, most importantly confidentiality and integrity of industrial processes. In this work, we present extensions to the popular Metasploit Framework to ease network-based security assessments of OPC UA deployments. To this end, we discuss methods to discover OPC UA servers, test their authentication, obtain their configuration, and check for vulnerabilities. Ultimately, our work enables operators to verify the (security) configuration of their systems and identify potential attack vectors.
Effect of open minimally invasive parathyroidectomy in the management of primary hyperparathyroidism

Primary hyperparathyroidism (PHPT) in India, unlike in the Western world, is largely asymptomatic disease. In Indian series, females were commonly affected (1.7: 1, F:M). 71.5% of the cases were less than 40 yrs of age whereas patients from developed nations are diagnosed in the fifth and sixth decades. The mean duration of symptoms was from months which indicate the delay in diagnosis. Surgical approach has ranged from bilateral conventional neck exploration and unilateral neck exploration to focused parathyroidectomy. Focused parathyroidectomy was reported in 38% of cases from one of the centers.
Properties of Streamer Wave Events Observed during the STEREO Era

Transverse waves are sometimes observed in solar helmet streamers, typically after the passage of a coronal mass ejection (CME). The CME-driven shock wave moves the streamer sideways, and a decaying oscillation of the streamer is observed after the CME passage. Previous works generally reported observations of streamer oscillations taken from a single vantage point (typically the Solar and Heliospheric Observatory (SOHO) spacecraft). We conduct a data survey searching for streamer wave events observed by the COR2 coronagraphs on board the STEREO spacecraft. For the first time, we report observations of streamer wave events from multiple vantage points by using the COR2 instrument on both STEREO A and B, as well as the SOHO/LASCO C2+C3 coronagraphs. We investigate the properties of streamer waves by comparing the different events and performing a statistical analysis. Common observational features give us additional insight on the physical nature of streamer wave events. The most important conclusion is that there appears to be no relation between the speed of the CME and the phase speed of the resulting streamer wave, indicating that the streamer wave speed is determined by the physical properties of the streamer rather than the properties of the CME. This result makes streamer wave events excellent candidates for coronal seismology studies. From a comparison between the measured phase speeds and the phase speeds calculated from the measured periods and wavelengths, we could determine that the speed of the postshock solar wind flow in our streamers is around 300 km s−1.
Financial Time Series Representation Learning

This paper addresses the difficulty of forecasting multiple financial time series (TS) conjointly using deep neural networks (DNN). We investigate whether DNN-based models could forecast these TS more efficiently by learning their representation directly. To this end, we make use of the dynamic factor graph (DFG) from that we enhance by proposing a novel variable-length attention-based mechanism to render it memory-augmented. Using this mechanism, we propose an unsupervised DNN architecture for multivariate TS forecasting that allows to learn and take advantage of the relationships between these TS. We test our model on two datasets covering 19 years of investment funds activities. Our experimental results show that our proposed approach outperforms significantly typical DNN-based and statistical models at forecasting their 21-day price trajectory.
A Flexible Job Shop Scheduling Representation of the Autonomous In-Space Assembly Task Assignment Problem

As in-space exploration increases, autonomous systems will play a vital role in building the necessary facilities to support exploration. To this end, an autonomous system must be able to assign tasks in a scheme that efficiently completes all of the jobs in the desired project. This research proposes a flexible job shop problem (FJSP) representation to characterize an autonomous assembly project and then proposes both a mixed integer programming (MIP) solution formulation and a reinforcement learning (RL) solution formulation. The MIP formulation encodes all of the constraints and interjob dynamics a priori and was able to solve for the optimal solution to minimize the makespan. The RL formulation did not converge to an optimal solution but did successfully learn implicitly interjob dynamics through interaction with the reward function. Future work will include developing a solution formulation that utilizes the strengths of both proposed solution methods to handle scaling in size and complexity.
A new scheme for neutrino mass generation and relevant new physics

We propose a new scheme for neutrino mass generation through a modification of the standard Higgs mechanism implemented by introducing an unconventional tiny vacuum breaking to the charged Higgs field. In this modified Higgs mechanism, with identical particle spectrum as the standard electroweak model, the lepton-Higgs Yukawa coupling Lagrangian would carry a modified lepton mass matrix. Combining the latest data of particle experiments with this modified lepton mass matrix enable us to locate the value of the parameter parametrizing the extra perturbative Higgs vacuum breaking and produce masses for neutrinos of three generations. We show that our results satisfy the constraints from current physical experiments and cosmological observations. In the new Higgs mechanism now we propose, the Higgs vacua are completely broken, such that, the very minuteness of the extra Higgs vacuum breaking explains the origin of the very minuteness of neutrino masses, while the relative greatness of the standard Higgs vacuum breaking is recognized as the origin of the relative greatness of charged lepton masses. This proposal can bring rich new physics to Higgs-relevant problems in particle physics, besides injecting new inspiration to the solution of massive neutrino problems.
A Hybrid-Order Distributed SGD Method for Non-Convex Optimization to Balance Communication Overhead, Computational Complexity, and Convergence Rate

In this paper, we propose a method of distributed stochastic gradient descent (SGD), with low communication load and computational complexity, and still fast convergence. To reduce the communication load, at each iteration of the algorithm, the worker nodes calculate and communicate some scalers, that are the directional derivatives of the sample functions in some \emph{pre-shared directions}. However, to maintain accuracy, after every specific number of iterations, they communicate the vectors of stochastic gradients. To reduce the computational complexity in each iteration, the worker nodes approximate the directional derivatives with zeroth-order stochastic gradient estimation, by performing just two function evaluations rather than computing a first-order gradient vector. The proposed method highly improves the convergence rate of the zeroth-order methods, guaranteeing order-wise faster convergence. Moreover, compared to the famous communication-efficient methods of model averaging (that perform local model updates and periodic communication of the gradients to synchronize the local models), we prove that for the general class of non-convex stochastic problems and with reasonable choice of parameters, the proposed method guarantees the same orders of communication load and convergence rate, while having order-wise less computational complexity. Experimental results on various learning problems in neural networks applications demonstrate the effectiveness of the proposed approach compared to various state-of-the-art distributed SGD methods.
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning

The information bottleneck (IB) principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models. However, multiple competing objectives have been proposed based on this principle, and the information-theoretic quantities in these objectives are difficult to compute for large deep neural networks. This, in turn, limits their use as a training objective. In this work, we review these quantities, compare and unify previously proposed objectives and relate them to surrogate objectives more friendly to optimization. We find that these surrogate objectives allow us to apply the information bottleneck to modern neural network architectures. We demonstrate our insights on Permutation-MNIST, MNIST and CIFAR10.
Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective

Blind people have limited access to information about their surroundings, which is important for ensuring one's safety, managing social interactions, and identifying approaching pedestrians. With advances in computer vision, wearable cameras can provide equitable access to such information. However, the always-on nature of these assistive technologies poses privacy concerns for parties that may get recorded. We explore this tension from both perspectives, those of sighted passersby and blind users, taking into account camera visibility, in-person versus remote experience, and extracted visual information. We conduct two studies: an online survey with MTurkers (N=206) and an in-person experience study between pairs of blind (N=10) and sighted (N=40) participants, where blind participants wear a working prototype for pedestrian detection and pass by sighted participants. Our results suggest that both of the perspectives of users and bystanders and the several factors mentioned above need to be carefully considered to mitigate potential social tensions.
Text-based Technological Signatures and Similarities: How to create them and what to do with them

This paper describes a new approach to measure technological similarity between patents by leveraging their textual description. Using embedding techniques from natural language processing, we represent their description as a high dimensional numerical vector, thus capturing their technological signature. Deploying an almost near linear-scaling approximate nearest neighbor matching techniques, we are able to compute technological similarity scores for all existing patents. This enables us to represent the whole patent universe as a technological network. We validate both technological signature and similarity in various ways, and demonstrate their usefulness to create patent quality indicators, measure knowledge flows, and map technological change.
Finding dark matter faster with explicit profile likelihoods

Liquid xenon time-projection chambers are the world's most sensitive detectors for a wide range of dark matter candidates. We show that the statistical analysis of their data can be improved by replacing detector response Monte Carlo simulations with an equivalent deterministic calculation. This allows the use of high-dimensional undiscretized models, yielding up to $\sim\! 2$ times better discrimination of the dominant backgrounds. In turn, this could significantly extend the physics reach of upcoming experiments such as XENONnT and LZ, and bring forward a potential $5 \sigma$ dark matter discovery by over a year.
Full-waveform inversion by source extension: Why it works

An extremely simple single-trace transmission example shows how an extended source formulation of full waveform inversion can produce an optimization problem without spurious local minima ("cycle skipping"). The data consist of a single trace recorded at a given distance from a point source. The velocity or slowness is presumed homogeneous, and the target source wavelet is presumed quasi-impulsive or focused at zero time lag. The source is extended by permitting energy to spread in time, and the spread is controlled by adding a weighted mean square of the extended source wavelet to the data misfit, to produce the extended inversion objective. The objective function and its gradient can be computed explicitly, and it is easily seen that all local minimizers must be within a wavelength of the correct slowness. The derivation shows several important features of all similar extended source algorithms. For example, nested optimization, with the source estimation in the inner optimization (variable projection method), is essential. The choice of the weight operator, controlling the extended source degrees of freedom, is critical: the choice presented here is a differential operator, and that property is crucial for production of an objective immune from cycle-skipping.
0.7 Å Resolution Electron Tomography Enabled by Deep‐Learning‐Aided Information Recovery

The 3D determination of a nanomaterial's atomic structure is crucial for understanding their physical, chemical, and electronic properties. Electron tomography, as an important 3D imaging method, offers a powerful method to probe the 3D structure of materials from nanoscale to atomic scale. However, the grand challenge—the missing‐wedge‐induced information loss and artifacts—has greatly hindered them from obtaining 3D atomic structures with high contrast, high precision, and high fidelity. Herein, for the first time, by combining atomic electron tomography with an artificially intelligent “deepfake” neural network, this work demonstrates that the resolution of 3D imaging can be improved down to 0.71 Å, which is a record high resolution achieved by electron tomography. It is also shown that the lost information in reconstructed tomograms can be effectively recovered by only acquiring data from −50 to +50 ° (44% reduction of dosage compared with −90 to +90 ° full tilt series). In contrast to conventional methods, the deep‐learning model shows outstanding performance for both macroscopic objects and atomic features solving the long‐standing dosage and missing‐wedge problems in electron tomography. This work provides important guidance for the application of machine learning methods to tomographic imaging atomic‐scale features in nanomaterials.
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment

Unsupervised distribution alignment has many applications in deep learning, including domain adaptation and unsupervised image-to-image translation. Most prior work on unsupervised distribution alignment relies either on minimizing simple non-parametric statistical distances such as maximum mean discrepancy, or on adversarial alignment. However, the former fails to capture the structure of complex real-world distributions, while the latter is difficult to train and does not provide any universal convergence guarantees or automatic quantitative validation procedures. In this paper we propose a new distribution alignment method based on a log-likelihood ratio statistic and normalizing flows. We show that, under certain assumptions, this combination yields a deep neural likelihood-based minimization objective that attains a known lower bound upon convergence. We experimentally verify that minimizing the resulting objective results in domain alignment that preserves the local structure of input domains.
Boolean learning under noise-perturbations in hardware neural networks

Abstract A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems have recently implemented some or all of these operations, yet the focus was placed on addressing technological challenges. Fundamental questions regarding learning in hardware neural networks remain largely unexplored. Noise in particular is unavoidable in such architectures, and here we experimentally and theoretically investigate its interaction with a learning algorithm using an opto-electronic recurrent neural network. We find that noise strongly modifies the system’s path during convergence, and surprisingly fully decorrelates the final readout weight matrices. This highlights the importance of understanding architecture, noise and learning algorithm as interacting players, and therefore identifies the need for mathematical tools for noisy, analogue system optimization.
A Hybrid Quantum-Classical Approach to Mitigating Measurement Errors in Quantum Algorithms

When noisy intermediate scalable quantum (NISQ) devices are applied in information processing, all of the stages through preparation, manipulation, and measurement of multipartite qubit states contain various types of noise that are generally hard to be verified in practice. In this article, we present a scheme to deal with unknown quantum noise and show that it can be used to mitigate errors in measurement readout with NISQ devices. Quantum detector tomography that identifies a type of noise in a measurement can be circumvented. The scheme applies single-qubit operations only, that are with relatively higher precision than measurement readout or two-qubit gates. A classical post-processing is then performed with measurement outcomes. The scheme is implemented in quantum algorithms with NISQ devices: the Bernstein-Vazirani algorithm and a quantum amplitude estimation algorithm in <inline-formula><tex-math notation="LaTeX">$\mathrm{IBMQ\_yorktown}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi> IBMQ </mml:mi><mml:mo>_</mml:mo><mml:mi> yorktown </mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="bae-ieq1-3009664.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathrm{IBMQ\_essex}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi> IBMQ </mml:mi><mml:mo>_</mml:mo><mml:mi> essex </mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="bae-ieq2-3009664.gif"/></alternatives></inline-formula>. The enhancement in the statistics of the measurement outcomes is presented for both of the algorithms with NISQ devices.
Substrate Induced Optical Anisotropy in Monolayer MoS2

In-plane optical anisotropy has been detected from monolayer MoS$_2$ grown on a-plane (11-20) sapphire substrate in the ultraviolet-visible wavelength range. Based on the measured optical anisotropy, the energy differences between the optical transitions polarized along the ordinary and extraordinary directions of the underlying sapphire substrate have been determined. The results corroborate comprehensively with the dielectric environment induced modification on the electronic band structure and exciton binding energy of monolayer MoS$_2$ predicted recently by first principle calculations. The output of this study proposes the symmetry as a new degree of freedom for dielectric engineering of the two-dimensional materials.
The MESAS Project: ALMA Observations of the F-type Stars γ Lep, γ Vir A, and γ Vir B

The spectrum of stars in the submillimeter to centimeter wavelength range remains poorly constrained due to a lack of data for most spectral types. An accurate characterization of stellar emission in this regime is needed to test stellar atmosphere models, and is also essential for revealing emission associated with unresolved circumstellar debris. We present Atacama Large Millimeter/submillimeter Array observations of the three nearby, main-sequence, debris-poor, F-type stars γ Lep, γ Vir A, and γ Vir B at 0.87 and 1.29 mm. We use these data to constrain semiempirical atmospheric models. We discuss the atmospheric structure of these stars, explore potential short-term variability, and the potential impact on debris disk studies. These results are part of an ongoing campaign to obtain long wavelength observations of debris-poor stars, entitled Measuring the Emission of Stellar Atmospheres at Submillimeter/millimeter wavelengths.