Miracle-dz/UniFAR-FTUs huggingface dataset:
Multielemental single–atom-thick A layers in nanolaminated V2(Sn, A) C (A = Fe, Co, Ni, Mn) for tailoring magnetic properties

Significance Mn+1AXn phases are a family of inherently nanolaminated ternary compounds with hexagonal crystal structure (space group P63/mmc, 194). Here, M is vanadium element, and A is Fe, Co, Ni, Mn, or their binary/ternary/quaternary mixtures. Due to the elemental flexibility at A site, 15 nanolaminated V2(AxSn1-x)C MAX phases are synthesized, including 1 high-entropy MAX phase that all Fe, Co, Ni, Mn, and Sn elements simultaneously occupied A site. Tailoring of individual single–atom-thick layers in nanolaminated MAX phases offers atomic-level control of material properties, such as their distinct magnetic behaviors. The alloying in 2-dimensional A layer of MAX phases provides a unique route to design their crystal structure and to discover unexploited properties, which would develop promising functional materials for microelectronic device. Tailoring of individual single–atom-thick layers in nanolaminated materials offers atomic-level control over material properties. Nonetheless, multielement alloying in individual atomic layers in nanolaminates is largely unexplored. Here, we report 15 inherently nanolaminated V2(AxSn1-x)C (A = Fe, Co, Ni, Mn, and combinations thereof, with x ∼ 1/3) MAX phases synthesized by an alloy-guided reaction. The simultaneous occupancy of the 4 magnetic elements and Sn in the individual single–atom-thick A layers constitutes high-entropy MAX phase in which multielemental alloying exclusively occurs in the 2-dimensional (2D) A layers. V2(AxSn1-x)C exhibit distinct ferromagnetic behavior that can be compositionally tailored from the multielement A-layer alloying. Density functional theory and phase diagram calculations are performed to understand the structure stability of these MAX phases. This 2D multielemental alloying approach provides a structural design route to discover nanolaminated materials and expand their chemical and physical properties. In fact, the magnetic behavior of these multielemental MAX phases shows strong dependency on the combination of various elements.
Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning

Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge. In this paper, we present a deep learning scheme that acquires a prior model of robot approaching behavior in simulation and applies it to real-world interaction with a physical robot approaching groups of humans. The scheme, which we refer to as Staged Social Behavior Learning (SSBL), considers different stages of learning in social scenarios. We learn robot approaching behaviors towards small groups in simulation and evaluate the performance of the model using objective and subjective measures in a perceptual study and a HRI user study with human participants. Results show that our model generates more socially appropriate behavior compared to a state-of-the-art model.
VISON: An Ontology-Based Approach for Software Visualization Tool Discoverability

Although many tools have been presented in the research literature of software visualization, there is little evidence of their adoption. To choose a suitable visualization tool, practitioners need to analyze various characteristics of tools such as their supported software concerns and level of maturity. Indeed, some tools can be prototypes for which the lifespan is expected to be short, whereas others can be fairly mature products that are maintained for a longer time. Although such characteristics are often described in papers, we conjecture that practitioners willing to adopt software visualizations require additional support to discover suitable visualization tools. In this paper, we elaborate on our efforts to provide such support. To this end, we systematically analyzed research papers in the literature of software visualization and curated a catalog of 70 available tools that employ various visualization techniques to support the analysis of multiple software concerns. We further encapsulate these characteristics in an ontology. VISON, our software visualization ontology, captures these semantics as concepts and relationships. We report on early results of usage scenarios that demonstrate how the ontology can support (i) developers to find suitable tools for particular development concerns, and (ii) researchers who propose new software visualization tools to identify a baseline tool for a controlled experiment.
An introduction to quantum measurements with a historical motivation

We provide an introduction to the theory of quantum measurements that is centered on the pivotal role played by John von Neumann's model. This introduction is accessible to students and researchers from outside the field of foundations of quantum mechanics and presented within a historical context. We first explain the origins and the meaning of the measurement problem in quantum theory, and why it is not present in classical physics. We perform a chronological review of the quantization of action and explain how this led to successive restrictions on what could be measured in atomic phenomena, until the consolidation of the orthodox interpretation of quantum mechanics. The clear separation between quantum system and classical apparatus that causes these restrictions is subverted in von Neumann's paradigmatic model of quantum measurements, a subject whose concepts we explain, while also providing the mathematical tools necessary to apply it to new problems. We show how this model was important in discussing the interpretations of quantum mechanics and how it is still relevant in modern applications. In particular, we explain in detail how it can be used to describe weak measurements and the surprising results they entail. We also discuss the limitations of von Neumann's model of measurements, and explain how they can be overcome with POVMs and Kraus operators. We provide the mathematical tools necessary to work with these generalized measurements and to derive master equations from them. Finally, we demonstrate how these can be applied in research problems by calculating the Quantum Zeno Effect.
Citations in Software Engineering -- Paper-related, Journal-related, and Author-related Factors

Many factors could affect the number of citations to a paper. Citations have an important role in research policy and in measuring the excellence of research and researchers. This work is the first study in software engineering (SE) to assess multiple factors affecting the number of citations to SE papers. We use (a) negative binomial regression and (b) quantile regression to study arithmetic mean and median expected citations of a paper. Our dataset includes all the 25,113 papers which have been published in a set of 16 main SE journals, between 1970 and 2018. Our results indicate that publication venue, author team's past citations, paper length, the number of references, and the recency of references are the most influential factors on the number of citations to SE papers. From our empirical findings, we present several implications and advice to researchers for getting higher citations on their papers, which are in addition to the obvious case of conducting high-quality technical research, e.g. (1) Aim for high-profile venues, (2) Build a high-quality author team with highly cited past papers, and (3) Aim for high-quality work that has comprehensive content (thus longer paper length and reference list).
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels

Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be known. Often, systems such as optical digitizing scanners are used for accurate localization with a stylus. However, the approach is time-consuming as each electrode needs to be scanned manually and the scanning systems are expensive. We propose using an RGBD camera to directly track electrodes in the images using deep learning methods. Studying and evaluating deep learning methods requires large amounts of labeled data. To overcome the time-consuming data annotation, we generate a large number of ground-truth labels using a robotic setup. We demonstrate that deep learning-based electrode detection is feasible with a mean absolute error of 5.69 +- 6.1mm and that our annotation scheme provides a useful environment for studying deep learning methods for electrode detection.
Goodness‐of‐fit testing in high dimensional generalized linear models

We propose a family of tests to assess the goodness of fit of a high dimensional generalized linear model. Our framework is flexible and may be used to construct an omnibus test or directed against testing specific non‐linearities and interaction effects, or for testing the significance of groups of variables. The methodology is based on extracting left‐over signal in the residuals from an initial fit of a generalized linear model. This can be achieved by predicting this signal from the residuals by using modern powerful regression or machine learning methods such as random forests or boosted trees. Under the null hypothesis that the generalized linear model is correct, no signal is left in the residuals and our test statistic has a Gaussian limiting distribution, translating to asymptotic control of type I error. Under a local alternative, we establish a guarantee on the power of the test. We illustrate the effectiveness of the methodology on simulated and real data examples by testing goodness of fit in logistic regression models. Software implementing the methodology is available in the R package GRPtests.
GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions

Despite the remarkable success of generative adversarial networks, their performance seems less impressive for diverse training sets, requiring learning of discontinuous mapping functions. Though multi-mode prior or multi-generator models have been proposed to alleviate this problem, such approaches may fail depending on the empirically chosen initial mode components. In contrast to such bottom-up approaches, we present GAN-Tree, which follows a hierarchical divisive strategy to address such discontinuous multi-modal data. Devoid of any assumption on the number of modes, GAN-Tree utilizes a novel mode-splitting algorithm to effectively split the parent mode to semantically cohesive children modes, facilitating unsupervised clustering. Further, it also enables incremental addition of new data modes to an already trained GAN-Tree, by updating only a single branch of the tree structure. As compared to prior approaches, the proposed framework offers a higher degree of flexibility in choosing a large variety of mutually exclusive and exhaustive tree nodes called GAN-Set. Extensive experiments on synthetic and natural image datasets including ImageNet demonstrate the superiority of GAN-Tree against the prior state-of-the-art.
Naut Your Everyday Jellyfish Model: Exploring How Tentacles and Oral Arms Impact Locomotion

Jellyfish are majestic, energy-efficient, and one of the oldest species that inhabit the oceans. It is perhaps the second item, their efficiency, that has captivated scientists for decades into investigating their locomotive behavior. Yet, no one has specifically explored the role that their tentacles and oral arms may have on their potential swimming performance. We perform comparative in silico experiments to study how tentacle/oral arm number, length, placement, and density affect forward swimming speeds, cost of transport, and fluid mixing. An open source implementation of the immersed boundary method was used (IB2d) to solve the fully coupled fluid–structure interaction problem of an idealized flexible jellyfish bell with poroelastic tentacles/oral arms in a viscous, incompressible fluid. Overall tentacles/oral arms inhibit forward swimming speeds, by appearing to suppress vortex formation. Nonlinear relationships between length and fluid scale (Reynolds Number) as well as tentacle/oral arm number, density, and placement are observed, illustrating that small changes in morphology could result in significant decreases in swimming speeds, in some cases by upwards of 80–90% between cases with or without tentacles/oral arms.
Efficient Data Representation by Selecting Prototypes with Importance Weights

Prototypical examples that best summarize and compactly represent an underlying complex data distribution, communicate meaningful insights to humans in domains where simple explanations are hard to extract. In this paper, we present algorithms with strong theoretical guarantees to mine these data sets and select prototypes, a.k. a. representatives that optimally describes them. Our work notably generalizes the recent work by Kim et al. (2016) where in addition to selecting prototypes, we also associate non-negative weights which are indicative of their importance. This extension provides a single coherent framework under which both prototypes and criticisms (i.e. outliers) can be found. Furthermore, our framework works for any symmetric positive definite kernel thus addressing one of the key open questions laid out in Kim et al. (2016). By establishing that our objective function enjoys a key property of that of weak submodularity, we present a fast ProtoDash algorithm and also derive approximation guarantees for the same. We demonstrate the efficacy of our method on diverse domains such as retail, digit recognition (MNIST) and on publicly available 40 health questionnaires obtained from the Center for Disease Control (CDC) website maintained by the US Dept. of Health. We validate the results quantitatively as well as qualitatively based on expert feedback and recently published scientific studies on public health, thus showcasing the power of our technique in providing actionability (for retail), utility (for MNIST), and insight (on CDC datasets), which arguably are the hallmarks of an effective interpretable machine learning method.
Definition of local realism weaker than Bell compatible with quantum predictions for entangled photon pairs

A definition of local realism is proposed that assumes the reality of the vacuum fields. It is weaker than Bell definition of local hidden variables theories. A model is presented for the polarization correlation experiment of two maximally entangled photons created by spontaneous parametric down conversion, that reproduces the quantum predictions. In the model entanglement appears as a correlation between a signal field and a vacuum field.
A Flaring AGN in a ULIRG Candidate in Stripe 82

We report the discovery of a mid-infrared variable AGN that is hosted by an ultraluminous infrared galaxy (ULIRG) in the Sloan Stripe 82 field. WISE J030654.88+010833.6 is a red, extended galaxy, which we estimate to be at a photometric redshift of 0.28 ≤ z ≤ 0.31, based on its optical and near-infrared spectral energy distribution (SED). The factor of two variability over 8 yr seen in the Wide-field Infrared Survey Explorer (WISE) 3.4 and 4.6 μm wavelength channels is not clearly correlated with optical variability in archival data. Based on our estimation of the physical parameters of the host galaxy, J030654.88+010833.6 is possibly a composite AGN/starburst ULIRG in a phase where high star formation ∼70 M⊙ yr−1 is occurring. Our estimate of the black hole mass to stellar mass ratio also appears to be consistent with that of broad line AGN in the local universe. The long-term variability of J030654.88+010833.6 as seen in the WISE W1 and W2 light curves is likely due to variations in the accretion rate, with the energy being reprocessed by dust in the vicinity of the AGN.
Some worst-case datasets of deterministic first-order methods for solving binary logistic regression

We present in this paper some worst-case datasets of deterministic first-order methods for solving large-scale binary logistic regression problems. Under the assumption that the number of algorithm iterations is much smaller than the problem dimension, with our worst-case datasets it requires at least $\mathcal{O}(1/\sqrt{\varepsilon})$ first-order oracle inquiries to compute an $\varepsilon$-approximate solution. From traditional iteration complexity analysis point of view, the binary logistic regression loss functions with our worst-case datasets are new worst-case function instances among the class of smooth convex optimization problems.
Classical capacity of generalized Pauli channels

Finding the classical capacity of a quantum channel is not easy, yet we are able to analytically calculate this capacity for new channels. We analyze the bounds of the Holevo capacity and classical capacity for the generalized Pauli channels. In particular, by generalizing earlier results for the Weyl channels, we obtain the lower and upper bounds of the Holevo capacity and show that, if these bounds coincide, the Holevo capacity is weakly additive. We also prove the weak additivity of the lower bound. Two new examples of the generalized Pauli channels with known classical capacity are presented. Finally, we relate the change rate of the classical capacity to the P-divisibility of Pauli channels.
A Frequency and Molecular Typing Study of Methicillin-Resistant Staphylococcus aureus Isolates in Teaching Hospitals in Shahrekord, SouthWestern Iran

Methicillin-resistant Staphylococcus aureus (MRSA) remains a significant public health problem and treatment challenge. Objectives: This study was conducted to determine the frequency, molecular types, and drug resistance of S. aureus isolated from nasal carriers in two teaching hospitals (Hajar and Kashani) in Shahrekord, southwestern Iran. Methods: In this cross-sectional study, 262 nasal specimens were obtained from healthcare staff. The disk-diffusion method was used to detect MRSA. Nine antibiotic disks were used to determine the antibiotic susceptibility pattern. Staphylococcal cassette chromosome mec (SCCmec) types were identified by the multiplex polymerase chain reaction (PCR). The data analysis was performed using Fisher’s exact test with SPSS software. Results: Forty-eight (18.8%) specimens were identified as S. aureus, of which 30 (11.45%) specimens were methicillin resistant. The nasal colonization rate of the MRSA isolates was not associated with age or gender (P > 0.05). The highest resistance (33%) recorded was to rifampin, and all the isolates were susceptible to quinupristin-dalfopristin, vancomycin, and linezolid. The SCCmec results showed that 16.7%, 6.7%, 20%, and 56.6% of MRSA isolates were types I, II, III, and IV, respectively. Conclusions: Nasal isolates of MRSA were prevalent among hospital staff. The highest level of resistance was to rifampin, and all the isolates were susceptible to quinupristin-dalfopristin, vancomycin, and linezolid. SCCmec type 4 was the most frequent MRSA isolate. © 2016, Ahvaz Jundishapur University of Medical Sciences.
Identifying and characterizing ZMap scans: a cryptanalytic approach

Network scanning tools play a major role in Internet security. They are used by both network security researchers and malicious actors to identify vulnerable machines exposed on the Internet. ZMap is one of the most common probing tools for high-speed Internet-wide scanning. We present novel identification methods based on the IPv4 iteration process of ZMap. These methods can be used to identify ZMap scans with a small number of addresses extracted from the scan. We conduct an experimental evaluation of these detection methods on synthetic, network telescope, and backbone traffic. We manage to identify 28.5% of the ZMap scans in real-world traffic. We then perform an in-depth characterization of these scans regarding, for example, targeted prefix and probing speed.
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction

One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow improving the energy efficiency of Artificial Intelligence by orders of magnitude with regards to its implementation on computers and graphics cards. In particular, ST-MRAM could be ideal for implementing Binarized Neural Networks (BNNs), a type of deep neural networks discovered in 2016, which can achieve state-of-the-art performance with a highly reduced memory footprint with regards to conventional artificial intelligence approaches. The challenge of ST-MRAM, however, is that it is prone to write errors and usually requires the use of error correction. In this work, we show that these bit errors can be tolerated by BNNs to an outstanding level, based on examples of image recognition tasks (MNIST, CIFAR-10 and ImageNet): bit error rates of ST-MRAM up to 0.1% have little impact on recognition accuracy. The requirements for ST-MRAM are therefore considerably relaxed for BNNs with regards to traditional applications. By consequence, we show that for BNNs, ST-MRAMs can be programmed with weak (low-energy) programming conditions, without error correcting codes. We show that this result can allow the use of low energy and low area ST-MRAM cells, and show that the energy savings at the system level can reach a factor two.
Correlations between the Variation of the Ionizing Continuum and Broad Absorption Lines in Individual Quasars

We discover the significant (significance level of >99%) correlations between the fractional variation of the ionizing continuum and that of the C IV and/or Si IV BALs in each of 21 BAL quasars that have at least five-epoch observations from the Sloan Digital Sky Survey-I/II/III. This result reveals that the fluctuation of the ionizing continuum is the driver of most of these BAL variations. Among them, 17 show negative correlations and the other 4 positive correlations, which agrees with the prediction of photoionization models that absorption line variability response to ionization changes is not monotonic. 8 quasars out of 21 examples have been observed at least 30 times on rest-frame timescales as short as a few days, which reveals that changes in the incident ionizing continuum can cause BAL variability even in such a short period of time. In addition, we find that most of the 21 quasars show larger variation amplitude in Si IV than C IV, which reveals the ubiquity of saturation in these BALs (at least for C IV BALs).
Quantum compiling on locally adjusted circuits of designated architecture

We propose a method for compiling arbitrary $n$-qubit gates via circuits of fixed architecture supplemented with a sufficient number of adjustable single-qubit operations. The circuits are tested against efficiency requirements and then a method for identifying the parameters of the single-qubit operations is applied. The latter extends quantum control techniques developed by G. Harel and V. Akulin $\left[\right. $Phys.Rev.Lett. 82, 1 (1999)$\left. \right]$ and stays computationally tractable for several qubits. We present the method of compiling using a series of $2^n$ $n$-qubit quantum Fourier transform circuits and test it for $n\leq 4$ reporting a low count of two-qubit gates.