Miracle-dz/UniFAR-FTUs huggingface dataset:
The mid-2016 flaring activity of the flat spectrum radio quasar PKS 2023-07

Context. Flat spectrum radio quasars (FSRQs) can suffer strong absorption above E = 25∕(1 + z) GeV, due to gamma–gamma interaction if the emitting region is at sub-parsec scale from the super-massive black hole (SMBH). Aims. Gamma-ray flares from these astrophysical sources can be used to investigate the location of the high-energy emission region and the physics of the radiating processes. Methods. We present an episode of remarkable gamma-ray flaring activity from FSRQ PKS 2023-07 during April 2016, as detected by both the AGILE and Fermi satellites. An intensive multiwavelength campaign, triggered by Swift, covered the entire duration of the flaring activity, including the peak gamma-ray activity. Results. We report the results of multiwavelength observations of the blazar. We found that during the peak emission, the most energetic photon had an energy of 44 GeV, putting strong constraints on the opacity of the gamma-ray dissipation region. The overall spectral energy distribution (SED) is interpreted in terms of leptonic models for blazar jets, with the emission site located beyond the broad line region (BLR).
A Modern C++ Point of View of Programming in Image Processing

C++ is a multi-paradigm language that enables the programmer to set up efficient image processing algorithms easily. This language strength comes from many aspects. C++ is high-level, so this enables developing powerful abstractions and mixing different programming styles to ease the development. At the same time, C++ is low-level and can fully take advantage of the hardware to deliver the best performance. It is also very portable and highly compatible which allows algorithms to be called from high-level, fast-prototyping languages such as Python or Matlab. One fundamental aspects where C++ shines is generic programming. Generic programming makes it possible to develop and reuse bricks of software on objects (images) of different natures (types) without performance loss. Nevertheless, conciliating genericity, efficiency, and simplicity at the same time is not trivial. Modern C++ (post-2011) has brought new features that made it simpler and more powerful. In this paper, we focus on some C++20 aspects of generic programming: ranges, views, and concepts, and see how they extend to images to ease the development of generic image algorithms while lowering the computation time.
Randezvous: Making Randomization Effective on MCUs

Internet-of-Things devices such as autonomous vehicular sensors, medical devices, and industrial cyber-physical systems commonly rely on small, resource-constrained microcontrollers (MCUs). MCU software is typically written in C and is prone to memory safety vulnerabilities that are exploitable by remote attackers to launch code reuse attacks and code/control data leakage attacks. We present Randezvous, a highly performant diversification-based mitigation to such attacks and their brute force variants on ARM MCUs. Atop code/data layout randomization and an efficient execute-only code approach, Randezvous creates decoy pointers to camouflage control data in memory; code pointers in the stack are then protected by a diversified shadow stack, local-to-global variable promotion, and return address nullification. Moreover, Randezvous adds a novel delayed reboot mechanism to slow down persistent attacks and mitigates control data spraying attacks via global guards. We demonstrate Randezvous’s security by statistically modeling leakage-equipped brute force attacks under Randezvous, crafting a proof-of-concept exploit that shows Randezvous’s efficacy, and studying a real-world CVE. Our evaluation of Randezvous shows low overhead on three benchmark suites and two applications.
Effect of distillery industry discharge waste water and dye in aqueous solution treated by chemical modi�ed Penicillium biomass on Arachis hypogaea (groundnut) growth parameters and anti oxidants pro�le

In this present study, phytotoxicity of high strength distillery industry discharge waste water and methyl orange dye in aqueous solution that treated by chemically modi�ed fungal biomass Penicillium on Arachis hypogaea was investigated. Biomass of Penicillium strain was subjected to a simple eco-friendly acid treatment which brought about highly stable, structurally modi�ed fungal biomass (CMFBM). CMFBM thus obtained was evaluated for wastewater treatment e�cacy of high strength distillery industry discharge wastewater by determination of various pollutants level. Results of this study revealed CMFBM treatment exhibited notable removal of pollutants Batch adsorption studies were carried out to demonstrate the adsorption e�cacy of methyl orange dye in an aqueous solution. Studies reveal that CMFCB exhibited high adsorption e�cacy and the rate of adsorption was affected by dosage and time Phytotoxicity studies reveals that the CMFBM treated industry waste water discharge and dye in aqueous solution was not shown any phytotoxic effect on groundnut. No impact on seedlings emergence, growth parameters like shoot length, new branches emerged, total foliage density, total chlorophyll content and enzymatic anti oxidants pro�le clearly demonstrates the non target effect of CMFBM treated industry discharged waste water and methyl orange dye in aqueous solution. The present investigation would propose the possible utilization of CMFBM in waste water treatment process and irrigation.
Quantum computation, quantum state engineering, and quantum phase transitions driven by dissipation

We investigate the computational power of creating steady-states of quantum dissipative systems whose evolution is governed by time-independent and local couplings to a memoryless environment. We show that such a model allows for efficient universal quantum computation with the result of the computation encoded in the steady state. Due to the purely dissipative nature of the process, this way of doing quantum computation exhibits some inherent robustness and defies some of the DiVincenzo criteria for quantum computation. We show that there is a natural class of problems that can be solved with such a model - the preparation of ground states of frustration free quantum Hamiltonians. This allows for robust and efficient creation of exotic states that exhibit features like topological quantum order and the creation of PEPS and it proves the existence of novel dissipative phase transitions. In particular the latter can in principle be verified experimentally with present day technology such as with optical lattices.
Optimized cavity-mediated dispersive two-qubit gates between spin qubits

The recent realization of a coherent interface between a single electron in a silicon quantum dot and a single photon trapped in a superconducting cavity opens the way for implementing photon-mediated two-qubit entangling gates. In order to couple a spin to the cavity electric field some type of spin-charge hybridization is needed, which impacts spin control and coherence. In this work we propose a cavity-mediated two-qubit gate and calculate cavity-mediated entangling gate fidelities in the dispersive regime, accounting for errors due to the spin-charge hybridization, as well as photon- and phonon-induced decays. By optimizing the degree of spin-charge hybridization, we show that two-qubit gates mediated by cavity photons are capable of reaching fidelities exceeding 90% in present-day device architectures. High iSWAP gate fidelities are achievable even in the presence of charge noise at the level of $2\,\mu\text{eV}$.
Local-spin-density functional for multideterminant density functional theory

(Dated: February 6, 2008)Based on exact limits and quantum Monte Carlo simulations, we obtain, at any density and spinpolarization, an accurate estimate for the energy of a modiﬁed homogeneous electron gas where elec-trons repel each other only with a long-range coulombic tail. This allows us to construct an analyticlocal-spin-density exchange-correlation functional appropriate to new, multideterminantal versionsof the density functional theory, where quantum chemistry and approximate exchange-correlationfunctionals are combined to optimally describe both long- and short-range electron correlations.I. INTRODUCTION
Extremely Narrow, Sharp-Peaked Resonances at the Edge of the Continuum

The resonant interaction between propagating waves in the cavity is encountered in the broad range of systems in nature and technology. It is a classical problem in wave mechanics and such topic became extremely popular during the recent years in the field of micro-, nano photonics and plasmonics, and specifically, with the Fano resonances in such optical systems. Primarily, it can act as a reflection filter and a polarizer for incident light beams due to its guided mode resonance effect [1].
Constructing the Milky Way Stellar Halo in the Galactic Center by Direct Orbit Integration

The halo stars on highly-radial orbits should inevitably pass the center regions of the Milky Way. Under the assumption that the stellar halo is in “dynamical equilibrium” and is axisymmetric, we integrate the orbits of ∼10,000 halo K giants at 5 ≤ r ≤ 50 kpc cross-matched from LAMOST DR5 and Gaia DR3. By carefully considering the selection function, we construct the stellar halo distribution at the entire regions of r ≤ 50 kpc. We find that a double-broken power-law function well describes the stellar halo’s density distribution with shallower slopes in the inner regions and the two breaks at r = 10 kpc and r = 25 kpc, respectively. The stellar halo becomes flatter from outer to inner regions but has q ∼ 0.5 at r ≲ 5 kpc. The stellar halo becomes isotropic with a slight prograde rotation in the inner 5 kpc, and reaches velocity dispersions of ∼250 km s−1. We get a weak negative metallicity gradient of −0.005 dex kpc−1 at 5 ≤ r ≤ 50 kpc, while there is an excess of relative metal-rich stars with [Fe/H] > −1 in the inner 10 kpc. The halo interlopers at r ≤ 5 kpc from integration of our sample has a mass of ∼1.2 × 108 M ⊙ (∼4.7×107 M ⊙ at [Fe/H] < 1.5), which can explain 50–100% of the metal-poor stars with [Fe/H] < −1.5 directly observed in the Galactic central regions.
Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria

Despite their remarkable performance, deep neural networks remain unadopted in clinical practice, which is considered to be partially due to their lack of explainability. In this work, we apply explainable attribution methods to a pre-trained deep neural network for abnormality classification in 12-lead electrocardiography to open this "black box" and understand the relationship between model prediction and learned features. We classify data from two public databases (CPSC 2018, PTB-XL) and the attribution methods assign a "relevance score" to each sample of the classified signals. This allows analyzing what the network learned during training, for which we propose quantitative methods: average relevance scores over a) classes, b) leads, and c) average beats. The analyses of relevance scores for atrial fibrillation and left bundle branch block compared to healthy controls show that their mean values a) increase with higher classification probability and correspond to false classifications when around zero, and b) correspond to clinical recommendations regarding which lead to consider. Furthermore, c) visible P-waves and concordant T-waves result in clearly negative relevance scores in atrial fibrillation and left bundle branch block classification, respectively. Results are similar across both databases despite differences in study population and hardware. In summary, our analysis suggests that the DNN learned features similar to cardiology textbook knowledge.
A Fast Solution Method for Large-Scale Unit Commitment Based on Lagrangian Relaxation and Dynamic Programming

The unit commitment problem (UC) is crucial for the operation and market mechanism of power systems. With the development of modern electricity, the scale of power systems is expanding, and solving the UC problem is also becoming more and more difficult. To this end, this article proposes a new fast solution method based on Lagrangian relaxation and dynamic programming. Firstly, the UC solution is estimated to be an initial trial UC solution by a fast method based on Lagrangian relaxation. This initial trial UC solution fully considers the system-wide constraints. Secondly, a dynamic programming module is introduced to adjust the trial UC solution to make it satisfy the unit-wise constraints. Thirdly, a method for constructing a feasible UC solution is proposed based on the adjusted trial UC solution. Specifically, a feasibility-testing model and an updating strategy for the trial UC solution are established in this part. Numerical tests are implemented on IEEE 24-bus, IEEE 118-bus, Polish 2383-bus, and French 6468-bus systems, which verify the effectiveness and efficiency of the proposed method.
Optimizing Fiducial Marker Placement for Improved Visual Localization

Adding fiducial markers to a scene is a well-known strategy for making visual localization algorithms more robust. Traditionally, these marker locations are selected by humans who are familiar with visual localization techniques. This letter explores the problem of automatic marker placement within a scene. Specifically, given a predetermined set of markers and a scene model, we compute optimized marker positions within the scene that can improve accuracy in visual localization. Our main contribution is a novel framework for modeling camera localizability that incorporates both natural scene features and artificial fiducial markers added to the scene. We present optimized marker placement (OMP), a greedy algorithm that is based on the camera localizability framework. We have also designed a simulation framework for testing marker placement algorithms on 3D models and images generated from synthetic scenes. We have evaluated OMP within this testbed and demonstrate an improvement in the localization rate by up to 20 percent on four different scenes.
Partially-Observable Security Games for Automating Attack-Defense Analysis

Network systems often contain vulnerabilities that remain unfixed in a network for various reasons, such as the lack of a patch or knowledge to fix them. With the presence of such residual vulnerabilities, the network administrator should properly react to the malicious activities or proactively prevent them, by applying suitable countermeasures that minimize the likelihood of an attack by the attacker. In this paper, we propose a stochastic game-theoretic approach for analyzing network security and synthesizing defense strategies to protect a network. To support analysis under partial observation, where some of the attacker’s activities are unobservable or undetectable by the defender, we construct a one-sided partially observable security game and transform it into a perfect game for further analysis. We prove that this transformation is sound for a sub-class of security games and a subset of properties specified in the logic rPATL. We implement a prototype that fully automates our approach and evaluate it by conducting experiments on a real-life network.
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images

Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.
Dead-zone compensation via passivity-based control for a class of mechanical systems

This manuscript introduces a passivity-based control methodology for fully-actuated mechanical systems with symmetric or asymmetric dead-zones. To this end, we find a smooth approximation of the inverse of the function that describes such a nonlinearity. Then, we propose an energy and damping injection approach - based on the PI-PBC technique - that compensates for the dead-zone. Moreover, we provide an analysis of the performance of the proposed controller near the equilibrium. We conclude this paper by experimentally validating the results on a two degrees-of-freedom planar manipulator.
A unconditionally energy dissipative, adaptive IMEX BDF2 scheme and its error estimates for Cahn-Hilliard equation on generalized SAV approach

An adaptive implicit-explicit (IMEX) BDF2 scheme is investigated on generalized SAV approach for the Cahn-Hilliard equation by combining with Fourier spectral method in space. It is proved that the modiﬁed energy dissipation law is unconditionally preserved at discrete levels. Under a mild ratio restriction, i.e., A1 : 0 < r k := τ k /τ k − 1 < r max ≈ 4 . 8645, we establish a rigorous error estimate in H 1 -norm and achieve optimal second-order accuracy in time. The proof involves the tools of discrete orthogonal convolution (DOC) kernels and inequality zoom. It is worth noting that the presented adaptive time-step scheme only requires solving one linear system with constant coeﬃcients at each time step. In our analysis, the ﬁrst-consistent BDF1 for the ﬁrst step does not bring the order reduction in H 1 -norm. The H 1 bound of numerical solution under periodic boundary conditions can be derived without any restriction (such as zero mean of the initial data). Finally, numerical examples are provided to verify our theoretical analysis and the algorithm eﬃciency.
Fast integrators with sensitivity propagation for use in CasADi

Efficient integrators with sensitivity propagation are an essential ingredient for the numerical solution of optimal control problems. This paper gives an overview on the acados integrators, their Python interface and presents a workflow that allows using them with their sensitivities within a nonlinear programming (NLP) solver interfaced by CashDi. The implementation is discussed, demonstrated and provided as open-source software. The computation times of the proposed integrator and its sensitivity computation are compared to the native CasADi collocation integrator, CVODES and IDhS on different examples. A speedup of one order of magnitude for simulation and of up to three orders of magnitude for the forward sensitivity propagation is shown for an airborne wind energy system model.
A Consistent Estimator for Confounding Strength

Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding. Confounding strength measures this mismatch, but estimating it requires itself additional assumptions. A common assumption is the independence of causal mechanisms, which relies on concentration phenomena in high dimensions. While high dimensions enable the estimation of confounding strength, they also necessitate adapted estimators. In this paper, we derive the asymptotic behavior of the confounding strength estimator by Janzing and Sch\"olkopf (2018) and show that it is generally not consistent. We then use tools from random matrix theory to derive an adapted, consistent estimator.
Tunable Chiral Bound States in a Dimer Chain of Coupled Resonators

We study an excitation hopping on a one-dimensional (1D) dimer chain of coupled resonators with the alternate on-site photon energies, which interacts with a two-level emitter (TLE) by a coupling point or two adjacent coupling points. In the single-excitation subspace, this system not only possesses two energy bands with propagating states, but also possesses photonic bound states. The number of bound states depends on the coupling forms between the TLE and the dimer chain. It is found that when the TLE is locally coupled to one resonator of the dimer chain, the bound-state that has mirror reflection symmetry. When the TLE is nonlocally coupled to two adjacent resonators, three bound states with preferred direction arise due to the mirror symmetry breaking. By using chirality to measure the asymmetry, it is found that the chirality of these bound states can be tuned by changing the energy differences of single photon in the adjacent resonators, the coupling strengths and the transition energy of the TLE.
Higher-order quantum bright solitons in Bose-Einstein condensates show truly quantum emergent behavior

When an interaction quench by a factor of four is applied to an attractive Bose-Einstein condensate, a higher-order quantum bright soliton exhibiting robust oscillations is predicted in the semiclassical limit by the Gross-Pitaevskii equation. Combining matrix-product state simulations of the Bose-Hubbard Hamiltonian with analytical treatment via the Lieb-Liniger model and the eigenstate thermalization hypothesis, we show these oscillations are absent. Instead, one obtains a large stationary soliton core with a small thermal cloud, a smoking-gun signal for non-semiclassical behavior on macroscopic scales and therefore a fully quantum emergent phenomenon.
Spin-polarized droplets in the unitary Fermi gas

We demonstrate the existence of a new type of spatially localized excitations in the unitary Fermi gas: spin polarized droplets with a peculiar internal structure involving the abrupt change of the pairing phase at the surface of the droplet. It resembles the structure of the Josephson-$\pi$ junction occurring when a slice of a ferromagnet is sandwiched between two superconductors. The stability of the impurity is enhanced by the mutual interplay between the polarization effects and the pairing field, resulting in an exceptionally long-lived state. The prospects for its realization in experiment are discussed.
Chaos in multiplanetary extrasolar systems

Here we present an initial look at the dynamics and stability of 178 multiplanetary systems which are already confirmed and listed in the NASA Exoplanet Archive. To distinguish between the chaotic and regular nature of a system, the value of the MEGNO indicator for each system was determined. Almost three-quarters of them could be labelled as long-term stable. Only 45 studied systems show chaotic behaviour. We consequently investigated the effects of the number of planets and their parameters on the system stability. A comparison of results obtained using the MEGNO indicator and machine-learning algorithm SPOCK suggests that the SPOCK could be used as an effective tool for reviewing the stability of multiplanetary systems. A similar study was already published by Laskar and Petit in 2017. We compared their analysis based on the AMD criterion with our results. The possible discrepancies are discussed.