Miracle-dz/UniFAR-FTUs huggingface dataset:
Frustrated magnetic interactions in a cyclacene crystal

We study the emergence of magnetism and its interplay with structural properties in a two dimensional molecular crystal of cyclacenes, using density functional theory (DFT). Isolated cyclacenes with an even number of fused benzenes host two unpaired electrons in two topological protected zero modes, at the top and bottom carbon rings that form the molecule. We show that, in the gas phase, electron repulsion promotes an open-shell singlet with strong intramolecular antiferromagnetic exchange. We consider a closed packing triangular lattice crystal phase and we find a strong dependence of the band structure and magnetic interactions on the rotation angle of the cyclacenes with respect to the crystal lattice vectors. The orientational ground state maximizes the intermolecular hybridization, yet local moments survive. Intermolecular exchange is computed to be antiferromagnetic, and DFT predicts a broken symmetry $120^\circ$ spin phase reflecting the frustration of the intermolecular spin coupling. Thus, the cyclacene crystal realizes a bilayer of two antiferromagnetically coupled S = 1/2 triangular lattices. Our results provide a bottom-up route towards carbon based strongly correlated platforms in two dimensions.
Numerical solution of a matrix integral equation arising in Markov Modulated Lévy processes

Markov-modulated L\'evy processes lead to matrix integral equations of the kind $ A_0 + A_1X+A_2 X^2+A_3(X)=0$ where $A_0$, $A_1$, $A_2$ are given matrix coefficients, while $A_3(X)$ is a nonlinear function, expressed in terms of integrals involving the exponential of the matrix $X$ itself. In this paper we propose some numerical methods for the solution of this class of matrix equations, perform a theoretical convergence analysis and show the effectiveness of the new methods by means of a wide numerical experimentation.
Robust Explainability: A tutorial on gradient-based attribution methods for deep neural networks

The rise in deep neural networks (DNNs) has led to increased interest in explaining their predictions. While many methods for this exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning (DL) research; however, it has been hardly talked about in explainability until very recently.
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study

Generative adversarial networks (GANs) nowadays are capable of producing images of incredible realism. Two concerns raised are whether the state-of-the-art GAN’s learned distribution still suffers from mode collapse and what to do if so. Existing diversity tests of samples from GANs are usually conducted qualitatively on a small scale and/or depend on the access to original training data as well as the trained model parameters. This article explores GAN intra-mode collapse and calibrates that in a novel black-box setting: access to neither training data nor the trained model parameters is assumed. The new setting is practically demanded yet rarely explored and significantly more challenging. As a first stab, we devise a set of statistical tools based on sampling that can visualize, quantify, and rectify intra-mode collapse. We demonstrate the effectiveness of our proposed diagnosis and calibration techniques, via extensive simulations and experiments, on unconditional GAN image generation (e.g., face and vehicle). Our study reveals that the intra-mode collapse is still a prevailing problem in state-of-the-art GANs and the mode collapse is diagnosable and calibratable in black-box settings. Our codes are available at https://github.com/VITA-Group/BlackBoxGANCollapse.
Photofragmentation of large dimers into singly charged ions via the knockout mechanism

We study the fragmentation of very large dimers into two singly charged ions caused by absorption of a photon. In this process a photo electron emitted from one atom of the dimer has certain chances to hit the other atom knocking out one of its electrons. This results in the production of two singly charged ions and the consequent Coulomb explosion of the residual doubly charged system. We develop a theory of this process and apply it to calculate the fragmentation of He2, Li He and Li He dimers. Our results for the helium dimer are in good agreement with available experimental data. For the Li-He dimers (where experimental data are still absent) our results predict that this fragmentation mechanism becomes much more efficient than in the case of He2. We also show that, provided the recoil effects are of minor importance, the so called reflection approximation is very accurate also for large dimers.
Low Complexity Hybrid Precoding Designs for Multiuser mmWave/THz Ultra Massive MIMO Systems

Millimeter-wave and terahertz technologies have been attracting attention from the wireless research community since they can offer large underutilized bandwidths which can enable the support of ultra-high-speed connections in future wireless communication systems. While the high signal attenuation occurring at these frequencies requires the adoption of very large (or the so-called ultra-massive) antenna arrays, in order to accomplish low complexity and low power consumption, hybrid analog/digital designs must be adopted. In this paper we present a hybrid design algorithm suitable for both mmWave and THz multiuser multiple-input multiple-output (MIMO) systems, which comprises separate computation steps for the digital precoder, analog precoder and multiuser interference mitigation. The design can also incorporate different analog architectures such as phase shifters, switches and inverters, antenna selection and so on. Furthermore, it is also applicable for different structures, namely fully-connected structures, arrays of subarrays (AoSA) and dynamic arrays of subarrays (DAoSA), making it suitable for the support of ultra-massive MIMO (UM-MIMO) in severely hardware constrained THz systems. We will show that, by using the proposed approach, it is possible to achieve good trade-offs between spectral efficiency and simplified implementation, even as the number of users and data streams increases.
Long-wavelength fluctuations and dimensionality crossover in confined liquids

The phase behavior of liquids confined in a slit geometry does not reveal a crossover from a threeto a two-dimensional behavior as the gap size decreases. Indeed, the prototypical two-dimensional hexatic phase only occurs in liquids confined to a monolayer. Here, we demonstrate that the dimensionality crossover is apparent in the lateral size dependence of the relaxation dynamics of confined liquids, developing a Debye model for the density of vibrational states of confined systems and performing extensive numerical simulations. In confined systems, Mermin-Wagner fluctuations enhance the amplitude of vibrational motion or Debye-Waller factor by a quantity scaling as the inverse gap width and proportional to the logarithm of the aspect ratio, as a clear signature of a two-dimensional behaviour. As the temperature or lateral system size increases, the crossover to a size-independent relaxation dynamics occurs when structural relaxation takes place before the vibrational modes with the longest wavelength develop.
HW2VEC: a Graph Learning Tool for Automating Hardware Security

The time-to-market pressure and continuous growing complexity of hardware designs have promoted the globalization of the Integrated Circuit (IC) supply chain. However, such globalization also poses various security threats in each phase of the IC supply chain. Although the advancements of Machine Learning (ML) have pushed the frontier of hardware security, most conventional ML-based methods can only achieve the desired performance by manually finding a robust feature representation for circuits that are non-Euclidean data. As a result, modeling these circuits using graph learning to improve design flows has attracted research attention in the Electronic Design Automation (EDA) field. However, due to the lack of supporting tools, only a few existing works apply graph learning to resolve hardware security issues. To attract more attention, we propose HW2VEC, an open-source graph learning tool that lowers the threshold for newcomers to research hardware security applications with graphs. HW2VEC provides an automated pipeline for extracting a graph representation from a hardware design in various abstraction levels (register transfer level or gate-level netlist). Besides, HW2VEC users can automatically transform the non-Euclidean hardware designs into Euclidean graph embeddings for solving their problems. In this paper, we demonstrate that HW2VEC can achieve state-of-the-art performance on two hardware security-related tasks: Hardware Trojan Detection and Intellectual Property Piracy Detection. We provide the time profiling results for the graph extraction and the learning pipelines in HW2VEC.
Optimal control of epidemic spreading in the presence of social heterogeneity

The spread of COVID-19 has been thwarted in most countries through non-pharmaceutical interventions. In particular, the most effective measures in this direction have been the stay-at-home and closure strategies of businesses and schools. However, population-wide lockdowns are far from being optimal, carrying heavy economic consequences. Therefore, there is nowadays a strong interest in designing more efficient restrictions. In this work, starting from a recent kinetic-type model which takes into account the heterogeneity described by the social contact of individuals, we analyse the effects of introducing an optimal control strategy into the system, to limit selectively the mean number of contacts and reduce consequently the number of infected cases. Thanks to a data-driven approach, we show that this new mathematical model permits us to assess the effects of the social limitations. Finally, using the model introduced here and starting from the available data, we show the effectiveness of the proposed selective measures to dampen the epidemic trends. This article is part of the theme issue ‘Kinetic exchange models of societies and economies’.
Forward Modeling of Solar Coronal Magnetic-field Measurements Based on a Magnetic-field-induced Transition in Fe x

It was recently proposed that the intensity ratios of several extreme ultraviolet spectral lines from Fe x ions can be used to measure the solar coronal magnetic field based on magnetic-field-induced transition (MIT) theory. To verify the suitability of this method, we performed forward modeling with a three-dimensional radiation magnetohydrodynamic model of a solar active region. Intensities of several spectral lines from Fe x were synthesized from the model. Based on MIT theory, the intensity ratios of the MIT line Fe x 257 Å to several other Fe x lines were used to derive magnetic-field strengths, which were then compared with the field strengths in the model. We also developed a new method to simultaneously estimate the coronal density and temperature from the Fe x 174/175 and 184/345 Å line ratios. Using these estimates, we demonstrated that the MIT technique can provide reasonably accurate measurements of the coronal magnetic field in both on-disk and off-limb solar observations. Our investigation suggests that a spectrometer that can simultaneously observe the Fe x 174, 175, 184, 257, and 345 Å lines and allow an accurate radiometric calibration for these lines is highly desired to achieve reliable measurements of the coronal magnetic field. We have also evaluated the impact of the uncertainty in the Fe x 3p4 3d 4D5/2 and 4D7/2 energy difference on the magnetic-field measurements.
Amoeba: Automated Molecular Excitation Bayesian Line-fitting Algorithm

The hyperfine transitions of the ground-rotational state of the hydroxyl radical (OH) have emerged as a versatile tracer of the diffuse molecular interstellar medium. We present a novel automated Gaussian decomposition algorithm designed specifically for the analysis of the paired on-source and off-source optical depth and emission spectra of these OH transitions. In contrast to existing automated Gaussian decomposition algorithms, Amoeba (Automated Molecular Excitation Bayesian line-fitting Algorithm) employs a Bayesian approach to model selection, fitting all four optical-depth and four emission spectra simultaneously. Amoeba assumes that a given spectral feature can be described by a single centroid velocity and full width at half maximum, with peak values in the individual optical-depth and emission spectra then described uniquely by the column density in each of the four levels of the ground-rotational state, thus naturally including the real physical constraints on these parameters. Additionally, the Bayesian approach includes informed priors on individual parameters that the user can modify to suit different data sets. Here we describe Amoeba and establish its validity and reliability in identifying and fitting synthetic spectra with known (but hidden) parameters, finding that the code performs very well in a series of practical tests. Amoeba’s core algorithm could be adapted to the analysis of other species with multiple transitions interconnecting shared levels (e.g., the 700 MHz lines of the first excited rotational state of CH). Users are encouraged to adapt and modify Amoeba to suit their own use cases.
In Defense of Fluid Democracy

Fluid democracy is a voting paradigm that allows voters to choose between directly voting and transitively delegating their votes to other voters. While fluid democracy has been viewed as a system that can combine the best aspects of direct and representative democracy, it can also result in situations where few voters amass a large amount of influence. To analyze the impact of this shortcoming, we consider what has been called an epistemic setting, where voters decide on a binary issue for which there is a ground truth. Previous work has shown that under certain assumptions on the delegation mechanism, the concentration of power is so severe that fluid democracy is less likely to identify the ground truth than direct voting. We examine different, arguably more realistic, classes of mechanisms, and prove they behave well by ensuring that (with high probability) there is a limit on concentration of power. Our proofs demonstrate that delegations can be treated as stochastic processes and that they can be compared to well-known processes from the literature -- such as preferential attachment and multi-types branching process -- that are sufficiently bounded for our purposes. Our results suggest that the concerns raised about fluid democracy can be overcome, thereby bolstering the case for this emerging paradigm.
Performance vs Programming Effort between Rust and C on Multicore Architectures: Case Study in N-Body

Historically, Fortran and C have been the default programming languages in High-Performance Computing (HPC). In both, programmers have primitives and functions available that allow manipulating system memory and interacting directly with the underlying hardware, resulting in efficient code in both response times and resource use. On the other hand, it is a real challenge to generate code that is maintainable and scalable over time in these types of languages. In 2010, Rust emerged as a new programming language designed for concurrent and secure applications, which adopts features of procedural, object-oriented and functional languages. Among its design principles, Rust is aimed at matching C in terms of efficiency, but with increased code security and productivity. This paper presents a comparative study between C and Rust in terms of performance and programming effort, selecting as a case study the simulation of N computational bodies (N-Body), a popular problem in the HPC community. Based on the experimental work, it was possible to establish that Rust is a language that reduces programming effort while maintaining acceptable performance levels, meaning that it is a possible alternative to C for HPC.
Multi-messenger emission from the parsec-scale jet of the flat-spectrum radio quasar PKS 1502+106 coincident with high-energy neutrino IceCube-190730A

On July 30th, 2019 IceCube detected a high-energy astrophysical muon neutrino candidate, IC-190730A with a 67% probability of astrophysical origin. The flat spectrum radio quasar (FSRQ) PKS 1502 +106 is in the error circle of the neutrino. Motivated by this observation, we study PKS 1502+106 as a possible source of IC-190730A. PKS 1502+106 was in a quiet state in terms of UV/optical/X-ray/γ-ray flux at the time of the neutrino alert, we therefore model the expected neutrino emission from the source during its average long-term state, and investigate whether the emission of IC-190730A as a result of the quiet long-term emission of PKS 1502+106 is plausible. We analyse UV/optical and X-ray data and collect additional observations from the literature to construct the multi-wavelength spectral energy distribution of PKS 1502+106. We perform leptohadronic modelling of the multi-wavelength emission of the source and determine the most plausible emission scenarios and the maximum expected accompanying neutrino flux. A model in which the multi-wavelength emission of PKS 1502+106 originates beyond the broad-line region and inside the dust torus is most consistent with the observations. In this scenario, PKS 1502+106 can have produced up to of order one muon neutrino with energy exceeding 100 TeV in the lifetime of IceCube. An appealing feature of this model is that the required proton luminosity is consistent with the average required proton luminosity if blazars power the observed ultra-high-energy-cosmic-ray flux and well below the source's Eddington luminosity. If such a model is ubiquitous among FSRQs, additional neutrinos can be expected from other bright sources with energy ≳ 10 PeV.
ScaleHLS: Scalable High-Level Synthesis through MLIR

High-level Synthesis (HLS) has been widely adopted as it significantly improves the hardware design productivity and enables efficient design space exploration (DSE). HLS tools can be used to deliver solutions for many different kinds of design problems, which are often better solved with different levels of abstraction. While existing HLS tools are built using compiler infrastructures largely based on a single-level abstraction (e.g., LLVM), we propose ScaleHLS 1 , a next-generation HLS compilation flow, on top of a multi-level compiler infrastructure called MLIR, for the first time. By using an intermediate representation (IR) that can be better tuned to particular algorithms at different representation levels, we are able to build this new HLS tool that is more scalable and customizable towards various applications coming with intrinsic structural or functional hierarchies. ScaleHLS is able to represent and optimize HLS designs at multiple levels of abstraction and provides an HLS-dedicated transform and analysis library to solve the optimization problems at the suitable representation levels. On top of the library, we also build an automated DSE engine to explore the multi-dimensional design space efficiently. In addition, we develop an HLS C front-end and a C/C++ emission back-end to translate HLS designs into/from MLIR for enabling the end-to-end ScaleHLS flow. Experimental results show that, comparing to the baseline designs only optimized by Xilinx Vivado HLS, ScaleHLS improves the performances with amazing quality-of-results – up to 768.1 × better on computation kernel level programs and up to 3825.0 × better on neural network models
Federated Action Recognition on Heterogeneous Embedded Devices

Federated learning allows a large number of devices to jointly learn a model without sharing data. In this work, we enable clients with limited computing power to perform action recognition, a computationally heavy task. We first perform model compression at the central server through knowledge distillation on a large dataset. This allows the model to learn complex features and serves as an initialization for model fine-tuning. The fine-tuning is required because the limited data present in smaller datasets is not adequate for action recognition models to learn complex spatio-temporal features. Because the clients present are often heterogeneous in their computing resources, we use an asynchronous federated optimization and we further show a convergence bound. We compare our approach to two baseline approaches: fine-tuning at the central server (no clients) and fine-tuning using (heterogeneous) clients using synchronous federated averaging. We empirically show on a testbed of heterogeneous embedded devices that we can perform action recognition with comparable accuracy to the two baselines above, while our asynchronous learning strategy reduces the training time by 40%, relative to synchronous learning.
Morphology-Controlled Promoting Activity of Nanostructured MnO2 for Methanol and Ethanol Electrooxidation on Pt/C

Herein, an evident microstructure effect of MnO2 in the electrooxidation of methanol and ethanol on Pt/MnO2/C electrocatalyst composite is reported. In this context, urchin-like MnO2 composed of identical nanorods and microcubes of MnO2 were selectively synthesized via reduction of KMnO4 by HCl and forced hydrolysis of MnCl2 by urea, respectively. The physicochemical studies showed smaller crystallite size (∼11 nm) and higher BET surface area (∼61 m2 g–1) of MnO2-nanorods as compared to the MnO2-microcubes with crystallite size of ∼15 nm and BET surface area of ∼26 m2 g–1. The HRTEM analysis was performed to evaluate the inherent morphology effect of MnO2 on the size and dispersion of Pt crystallites in Pt/MnO2/C composite, and the results showed miniaturization and higher dispersion of Pt crystallites in the presence of MnO2-nanorods. The cyclic voltammetry studies revealed higher current response and lower overpotential by the MnO2-nanorod-modified Pt/C as compared to the MnO2-microcube-modified Pt/C an.
Fair Allocation with Interval Scheduling Constraints

We study a fair resource scheduling problem, where a set of interval jobs are to be allocated to heterogeneous machines controlled by agents. Each job is associated with release time, deadline, and processing time such that it can be processed if its complete processing period is between its release time and deadline. The machines gain possibly different utilities by processing different jobs, and all jobs assigned to the same machine should be processed without overlap. We consider two widely studied solution concepts, namely, maximin share fairness and envy-freeness. For both criteria, we discuss the extent to which fair allocations exist and present constant approximation algorithms for various settings.
Imaging Sources in the Third Realization of the International Celestial Reference Frame

The third iteration of the International Celestial Reference Frame (ICRF3) is made up of 4536 quasars observed at S/X bands using very-long-baseline interferometry. These sources are high-redshift quasars, typically between 1 < z < 2, that are believed to host active galactic nuclei at their centers. The position of compact radio sources can be determined better than sources with large amounts of extended radio structure. Here we report information on a series of 20 observations from 2017 January through December 2017 that were designed for precise astrometry and to monitor the structure of sources included in the ICRF3. We targeted 3627 sources over the one year campaign and found the median flux density of 2697 detected sources at S band is 0.13 Jy, and the median flux density of 3209 sources detected at X band is 0.09 Jy. We find that 70% of detected sources in our campaign are considered compact at X band and ideal for use in the ICRF and 89% of the 2615 sources detected at both frequencies have a flat spectral index, α > 0.5