Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Location of SMES for Improving Power system Voltage Stability Using PSO algorithm

Superconducting Magnetic Energy Storage (SMES) system is an equipment that can help to improve the voltage stability of power system. Location of SMES in multi-node power network plays a significant role for the stability improvement level. In this paper, Particle Swarm Optimization(PSO) of the SMES location based on the quantitative voltage stability(L-index). In PSO, voltage stability index is used as the fitness function. The proposed algorithm is tested on IEEE 14-bus system. and best results are obtained.
A comprehensive assessment of statin discontinuation among patients who concurrently initiate statins and CYP3A4-inhibitor drugs; a multistate transition model

Aims: To describe the 1-year direct and indirect transition probabilities to premature discontinuation of statin therapy after concurrently initiating statins and CYP3A4-inhibitor drugs. Methods and Results: A retrospective new-user cohort study design was used to identify (N=160828) patients who concurrently initiated CYP3A4-inhibitors (diltiazem, ketoconazole, clarithromycin, others) and CYP3A4-metabolized statins (statin DDI exposed, n = 104774) vs. other statins (unexposed, n = 56054) from the MarketScan Commercial claims database (2012 to 2017). These groups were matched (2:1) through propensity score-matching techniques. We applied a multistate transition model to compare the 1-year transition probabilities involving four distinct states (start, adverse drug events [ADEs], discontinuation of CYP3A4-inhibitor drugs, and discontinuation of statin therapy) between those exposed to statin DDIs, vs. unexposed. Statistically significant differences were assessed by comparing the 95% confidence intervals (CIs) of probabilities. Patients exposed to statin DDIs, vs. unexposed, were significantly less likely to discontinue statin therapy (71.4 [95% CI: 71.1, 71.6] vs. 73.3 [95% CI: 72.9, 73.6]) but more likely to experience an ADE (3.4 [95% CI: 3.3, 3.5] vs. 3.2 [95% CI: 3.1, 3.3]) and discontinue with CYP3A4-inhibitor therapy (21.0 [95% CI: 20.8, 21.3] vs. 19.5 [95% CI: 19.2, 19.8]) directly after concurrently starting stains and CYP3A. Subsequent to experiencing an ADE, those exposed to statin DDIs were still less likely to discontinue statin therapy but were significantly more likely to discontinue CYP3A4-inhibitor therapy. Conclusion: While statin DDI exposure was associated with higher likelihood of ADEs, this did not increase the risk of premature statin discontinuation among patients exposed to statin DDIs, versus unexposed.
Corporate Lifecycle and CEO Compensation Structure

In this study, we argue that firms would need to adjust CEO compensation incentives at different corporate lifecycle stages. Specifically, we examine a research question of whether the CEOs’ compensation structures are influenced by different corporate lifecycle stages. Using the data of the US listed non-financial firms from 2006 to 2014, we find that firms at the young and growth stages prefer to adopt more incentives of equity and pay-for-performance compensation arrangement. In addition, firms at mature and decline stages are more likely to provide cashor debt-based compensation (pension and deferred compensation) to stabilize their business and operation. Our research is the first to link corporate lifecycle theory from financial decisions to CEOs compensation structure. It also explains why firms transit among different lifecycle stages from the perspectives of CEO compensation incentives.
Towards dynamic adaptation in broadcasting with hybrid rateless codes

There are many situations, such as in training and education, in which there is a frequent need to distribute large files to many clients, e.g., operating system boot images or raw data files. To carry out such distribution efficiently, we use wireless broadcast and a hybrid coding technique that combines forward coding using weak LT Codes with a feedback phase at the end, which allows concluding the process faster with lower computing cost than traditional LT codes. However, for the sake of scalability, in the feedback phase a scheduler bounds the maximum number of clients that can communicate feedback in each given cycle and schedules them. Moreover, using a shorter or longer feedback phase also impacts on the number of simultaneous clients in feedback mode, resulting in more or less impact of the scheduler and more or less effectiveness of the feedback itself. In this short paper we briefly describe a recently developed prototype and we summarize some preliminary results confirming the advantage of using scheduled feedback. Moreover, we discuss the interplay between duration of the feedback phase and clients scheduling as a line for future research in scheduling-coding co-design.
D ISCUSSION P APER S ERIES – N O . 1 Can Cryptocurrencies Preserve Privacy and Comply with Regulations?

Cryptocurrencies oﬀer an alternative to traditional methods of electronic value exchange, promising anonymous, cash-like electronic transfers, but in practice they fall short for several key reasons. We consider the false choice between total surveillance, as represented by banking as currently implemented by institutions, and impenetrable lawlessness, as represented by privacy-enhancing cryp-tocurrencies as currently deployed. We identify a range of alternatives between those two extremes, and we consider two potential compromise approaches that oﬀer both the auditability required for regulators and the anonymity required for users.
UNIVERSITÀ DEGLI STUDI DI PADOVA Dipartimento di Fisica e Astronomia “ Galileo Galilei ” Corso di Laurea in Fisica Tesi di Laurea The Tolman-Oppenheimer-Volkoff equations in unified models of dark matter and dark energy

In this thesis we will work on scalar field models for dark matter and dark energy. In particular, we will study unified haloes by means of a static and spherically symmetric metric which leads to the anisotropic Tolman-Oppenheimer-Volkoff (TOV) equation. In this framework, we will develop a procedure which allows us to compute an unified density profile starting from a generic cold dark matter one, or, equivalently, from its rotation velocity. In particular, we will focus our attention on finding a suitable Lagrangian of a given type, which provides us with a microscopical description of the problem.
ORGANIZATIONAL MINDFULNESS AND CORPORATE RESILIENCE OF HOSPITALITY FIRMS IN RIVERS STATE , NIGERIA

This study is an empirical inquiry into the adoption and practice of metaphysical components such as organizational mindfulness in management and its relationship with social realities such as organizational resilience in Nigerian businesses, specifically as it applies to Hospitality Firms in Rivers state, Nigeria. As a cross sectional survey, data for the study was generated using structured questionnaire from 39 respondents across 8 sampled registered hospitality firms. A total of three hypotheses were postulated with analysis revealing significant correlations between organizational mindfulness and the measures of organizational resilience; Affective commitment, organizational involvement and Customer Satisfaction; implying that the adoption and practice of organizational mindfulness is a prerequisite for effectively attending to and addressing work-related issues and thus enhancing organizational resilience. In conclusion, the findings of this study lend credence to its support for the adoption and practice of organizational mindfulness in achieving a more sensitized and supportive work environment and thereby a more desirable expression of work attitudes from employees. It was thereafter recommended that organizations should on a consistent basis, assess their dispositions and the applicability of their systems and technologies within their various contexts. The applicability of such features determines their effectiveness and also impacts on their resilience.
Approximation Algorithms For The Euclidean Dispersion Problems

In this article, we consider the Euclidean dispersion problems. Let $P=\{p_{1}, p_{2}, \ldots, p_{n}\}$ be a set of $n$ points in $\mathbb{R}^2$. For each point $p \in P$ and $S \subseteq P$, we define $cost_{\gamma}(p,S)$ as the sum of Euclidean distance from $p$ to the nearest $\gamma $ point in $S \setminus \{p\}$. We define $cost_{\gamma}(S)=\min_{p \in S}\{cost_{\gamma}(p,S)\}$ for $S \subseteq P$. In the $\gamma$-dispersion problem, a set $P$ of $n$ points in $\mathbb{R}^2$ and a positive integer $k \in [\gamma+1,n]$ are given. The objective is to find a subset $S\subseteq P$ of size $k$ such that $cost_{\gamma}(S)$ is maximized. We consider both $2$-dispersion and $1$-dispersion problem in $\mathbb{R}^2$. Along with these, we also consider $2$-dispersion problem when points are placed on a line. In this paper, we propose a simple polynomial time $(2\sqrt 3 + \epsilon )$-factor approximation algorithm for the $2$-dispersion problem, for any $\epsilon>0$, which is an improvement over the best known approximation factor $4\sqrt3$ [Amano, K. and Nakano, S. I. , An approximation algorithm for the $2$-dispersion problem, IEICE Transactions on Information and Systems, Vol. 103(3), pp. 506-508, 2020]. Next, we develop a common framework for designing an approximation algorithm for the Euclidean dispersion problem. With this common framework, we improve the approximation factor to $2\sqrt 3$ for the $2$-dispersion problem in $\mathbb{R}^2$. Using the same framework, we propose a polynomial time algorithm, which returns an optimal solution for the $2$-dispersion problem when points are placed on a line. Moreover, to show the effectiveness of the framework, we also propose a $2$-factor approximation algorithm for the $1$-dispersion problem in $\mathbb{R}^2$.
Diamond channel-cut crystals for high-heat-load beam-multiplexing narrow-band X-ray monochromators.

Next-generation high-brilliance X-ray photon sources call for new X-ray optics. Here we demonstrate the possibility of using monolithic diamond channel-cut crystals as high-heat-load beam-multiplexing narrow-band mechanically stable X-ray monochromators with high-power X-ray beams at cutting-edge high-repetition-rate X-ray free-electron laser (XFEL) facilities. The diamond channel-cut crystals fabricated and characterized in these studies are designed as two-bounce Bragg reflection monochromators directing 14.4 or 12.4 keV X-rays within a 15 meV bandwidth to 57Fe or 45Sc nuclear resonant scattering experiments, respectively. The crystal design allows out-of-band X-rays transmitted with minimal losses to alternative simultaneous experiments. Only ≲2% of the incident ∼100 W X-ray beam is absorbed in the 50 µm-thick first diamond crystal reflector, ensuring that the monochromator crystal is highly stable. Other X-ray optics applications of diamond channel-cut crystals are anticipated.
MCALF: Multi-Component Atmospheric Line Fitting

Determining accurate velocity measurements from observations of the Sun is of vital importance to solar physicists who are studying the wave dynamics in the solar atmosphere. Weak chromospheric absorption lines, due to dynamic events in the solar atmosphere, often consist of multiple spectral components. Isolating these components allows for the velocity field of the dynamic and quiescent regimes to be studied independently. However, isolating such components is particularly challenging due to the wide variety of spectral shapes present in the same dataset. MCALF provides a novel method and infrastructure to determine Doppler velocities in a large dataset. Each spectrum is fitted with a model adapted to its specific spectral shape.
A Note on High-Dimensional Confidence Regions

Abstract: Recent advances in statistics introduced versions of the central limit theorem for high-dimensional vectors, allowing for the construction of confidence regions for high-dimensional parameters. In this note, s-sparsely convex high-dimensional confidence regions are compared with respect to their volume. Specific confidence regions which are based on `p-balls are found to have exponentially smaller volume than the corresponding hypercube. The theoretical results are validated by a comprehensive simulation study.
Essential Metadata for 3D BRAIN Microscopy

Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments’ images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for 3D microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting Essential Metadata for 3D BRAIN Microscopy includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage
Airborne Urban Microcells with Grasping End Effectors: A Game Changer for 6G Networks?

Airborne (or flying) base stations (ABSs) embedded on drones or unmanned aerial vehicles (UAVs) can be deemed as a central element of envisioned 6G cellular networks where significant levels of cell densification with mmWave/Terahertz communications will be part of the ecosystem. Nonetheless, one of the key challenges facing the deployment of ABSs is the inherent limited available energy of the drone, which limits the hovering time for serving ground users to the order of minutes. This impediment deteriorate the performance of a UAV-assisted cellular network and hinders wide adoption and use of the technology. In this paper, we propose robotic airborne base stations (RABSs) with grasping capabilities to increase the serving time of ground users by multiple orders of magnitude compared to the nominal hovering based operation. More specifically, to perform the grasping task, the RABS is equipped with a versatile, albeit, general purpose gripper manipulator. Depending on the type of the gripper RABS can provide service in the range of hours, compared to minutes of hovering based ABSs. In theory it is possible that grasping can be energy neutral, hence service time is bounded by the communication components energy consumption. To illustrate the case, energy consumption comparison between hovering and grasping is performed in order to reveal the significant benefits of the proposed approach. Finally, overarching challenges, design considerations for RABS, and future avenues of research are outlined to realize the full potential of the proposed robotic aerial base stations.
Reentrant pion superfluidity and cosmic trajectories within a PNJL model

In this work, we self-consistently explore the possibility of charged pion superfluidity and cosmic trajectories in early Universe under the framework of Polyakov-Nambu–Jona-Lasinio model. By taking the badly constrained lepton flavor asymmetries le and lμ as free parameters, the upper boundaries of pion superfluidity phase are consistently found to be around the pseudocritical temperature at zero chemical potentials. So the results greatly support the choice of T = 0.16 GeV as the upper boundary of pion superfluidity in the previous lattice QCD study. Take le + lμ = −0.2 as an example, we demonstrate the features of pion condensation and the associated cosmic trajectories with the evolution of early Universe. While the trajectory of electric chemical potential reacts strongly at both the lower and upper boundaries of reentrant pion superfluidity, the trajectories of other chemical potentials only respond strongly at the upper boundary.
Generalizable Person Re-identification with Relevance-aware Mixture of Experts

Domain generalizable (DG) person re-identification (ReID) is a challenging problem because we cannot access any unseen target domain data during training. Almost all the existing DG ReID methods follow the same pipeline where they use a hybrid dataset from multiple source domains for training, and then directly apply the trained model to the unseen target domains for testing. These methods often neglect individual source domains’ discriminative characteristics and their relevances w.r. t. the unseen target domains, though both of which can be leveraged to help the model’s generalization. To handle the above two issues, we propose a novel method called the relevance-aware mixture of experts (RaMoE), using an effective voting-based mixture mechanism to dynamically leverage source domains’ diverse characteristics to improve the model’s generalization. Specifically, we propose a decorrelation loss to make the source domain networks (experts) keep the diversity and discriminability of individual domains’ characteristics. Besides, we design a voting network to adaptively integrate all the experts’ features into the more generalizable aggregated features with domain relevance. Considering the target domains’ invisibility during training, we propose a novel learning-to-learn algorithm combined with our relation alignment loss to update the voting network. Extensive experiments demonstrate that our proposed RaMoE outperforms the state-of-the-art methods.
Nonperturbative waveguide quantum electrodynamics

Understanding physics of quantum emitters strongly interacting with quantized electromagnetic continuum is one of the primary goals in the emergent field of waveguide quantum electrodynamics (QED). When the light-matter coupling strength is comparable to or even exceeds energies of elementary excitations, conventional approaches based on perturbative treatment of light-matter interactions, two-level description of matter excitations, and photon-number truncation are no longer sufficient. Here we study in and out of equilibrium properties of waveguide QED in such nonperturbative regimes by developing a comprehensive and rigorous theoretical approach using an asymptotic decoupling unitary transformation. In the transformed frame, factorization between light and matter becomes exact in the strong-coupling limit and an accurate effective model can be derived for arbitrary interaction strengths. We uncover several surprising features ranging from symmetry-protected many-body bound states in the continuum to strong renormalization of the effective mass and potential; the latter may explain recent experiments demonstrating cavity-induced changes in chemical reactivity as well as enhancements of ferromagnetism or superconductivity. We demonstrate these results by applying our general formalism to a model of coupled cavity arrays, which is relevant to experiments in superconducting qubits interacting with microwave resonators or atoms coupled to photonic crystals. We examine the relation between our results and delocalization-localization transition in the spin-boson model; notably, we point out that one can find a quantum phase transition akin to the superradiant transition in multi-emitter waveguide QED systems. Besides waveguide resonators, we discuss possible applications of our framework to other systems relevant to quantum optics, condensed matter physics, and quantum chemistry.
Detecting subparsec supermassive binary black holes: Long-term monitoring perspective

Here we consider the perspective to detect sub-pc super-massive binary black-hole (SMBBH) systems using long-term photometric and spectroscopic monitoring campaigns of active galactic nuclei. This work explores the nature of long-term spectral variability caused by the dynamical effects of SMBBH systems. We describe in great detail a model of SMBBH system which considers that both black holes have their accretion disc and additional line emitting region(s). We simulate the HV spectral band (continuum+broad HV line) for different mass ratios of components and different total masses of the SMBBH systems (106 − 108M⊙). We analyze the set of continuum and broad line light curves for several full orbits of SMBBHs with different parameters, to test the possibility to extract the periodicity of the system. We consider different levels of the signal-to-noise ratio, which is added to the simulated spectra. Our analysis showed that the continuum and broad line profiles emitted from an SMBBH system are strongly dependent, not only on the mass ratio of the components but also on the total mass of the system. We found that the mean broad line profile and its rms could indicate the presence of an SMBBH. However, some effects caused by the dynamics of a binary system could be hidden due to a low signal-to-noise ratio. Finally, we can conclude that the long-term AGN monitoring campaigns could be beneficial for the detection of SMBBH candidates.
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction

Click-through rate (CTR) prediction is one of the most central tasks in online advertising systems. Recent deep learning-based models that exploit feature embedding and high-order data nonlinearity have shown dramatic successes in CTR prediction. However, these models work poorly on cold-start ads with new IDs, whose embeddings are not well learned yet. In this paper, we propose Graph Meta Embedding (GME) models that can rapidly learn how to generate desirable initial embeddings for new ad IDs based on graph neural networks and meta learning. Previous works address this problem from the new ad itself, but ignore possibly useful information contained in existing old ads. In contrast, GMEs simultaneously consider two information sources: the new ad and existing old ads. For the new ad, GMEs exploit its associated attributes. For existing old ads, GMEs first build a graph to connect them with new ads, and then adaptively distill useful information. We propose three specific GMEs from different perspectives to explore what kind of information to use and how to distill information. In particular, GME-P uses Pre-trained neighbor ID embeddings, GME-G uses Generated neighbor ID embeddings and GME-A uses neighbor Attributes. Experimental results on three real-world datasets show that GMEs can significantly improve the prediction performance in both cold-start (i.e., no training data is available) and warm-up (i.e., a small number of training samples are collected) scenarios over five major deep learning-based CTR prediction models. GMEs can be applied to conversion rate (CVR) prediction as well.
QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion

Knowledge graph embedding has been an active research topic for knowledge base completion (KGC), with progressive improvement from the initial TransE, TransH, RotatE et al to the current state-of-the-art QuatE. However, QuatE ignores the multi-faceted nature of the entity and the complexity of the relation, only using rigorous operation on quaternion space to capture the interaction between entitiy pair and relation, leaving opportunities for better knowledge representation which will finally help KGC. In this paper, we propose a novel model, QuatDE, with a dynamic mapping strategy to explicitly capture the variety of relational patterns and separate different semantic information of the entity, using transition vectors to adjust the point position of the entity embedding vectors in the quaternion space via Hamilton product, enhancing the feature interaction capability between elements of the triplet. Experiment results show QuatDE achieves state-of-the-art performance on three well-established knowledge graph completion benchmarks. In particular, the MR evaluation has relatively increased by 26% on WN18 and 15% on WN18RR, which proves the generalization of QuatDE.