Search

We introduce the notion of isoclinism between central extensions in the category of algebras with bracket. We provide several equivalent conditions under which algebras with bracket are isoclinic. We also study the connection between isoclinism and the Schur multiplier of algebras with bracket. It is shown that for finite-dimensional central extensions of algebras with bracket with same dimension, the notion of isoclinism and isomorphism are equivalent. Furthermore, we indicate that all stem covers of an algebra with bracket are isoclinic.

Recent advancements in nonreciprocal thermal emitters challenge the conventional Kirchhoff's law, which states that emissivity and absorptivity should be equal for a given direction, frequency, and polarization. These emitters can break Kirchhoff's law and enable unprecedented thermal photon control capabilities. However, current studies mainly focus on increasing the magnitude of the contrast between emissivity and absorptivity, with little attention paid to how the sign or bandwidth of the contrast may be controlled. In this work, we show such control ability can be achieved by coupling resonances that can provide opposite contrasts between emissivity and absorptivity.

We study the generation of narrowband terahertz (THz) pulses by stimulated Raman scattering and molecular modulation in hydrogen-filled hybrid hollow-core fibers. Using a judicious combination of materials and transverse structures, this waveguide design enables simultaneous confinement of optical and THz signals with reasonably low attenuation, as well as high nonlinear overlap. The THz pulses are then generated as the second Stokes band of a ns-long near-infrared pump pulse, aided by Raman coherence waves excited in the gaseous core by the beat-note created by the pump and its first Stokes band. Optimization of the fiber characteristics facilitates phase matching between the corresponding transitions and coherence waves while avoiding coherent gain suppression, resulting in optical-to-THz conversion efficiencies up to 60%, as confirmed by rigorous numerical modelling under ideal conditions. When the current optical material constraints are considered, however, the attainable efficiencies relax to 0.2%, a still competitive value compared to other systems. The approach is in principle power and energy scalable, as well as tunable in the 1 - 10 THz range without any spectral gaps, thereby opening new pathways to the development of fiber-based THz sources complementary to other mature technologies such as quantum cascade lasers., Comment: 11 pages, 7 figures

The nonequilibrium dynamics of a periodically driven extended XY model, in the presence of linear time dependent magnetic filed, is investigated using the notion of dynamical quantum phase transitions (DQPTs). Along the similar lines to the equilibrium phase transition, the main purpose of this work is to search the fundamental concepts such as scaling and universality at the ramped quench DQPTs. We have shown that the critical points of the model, where the gap closing occurs, can be moved by tuning the driven frequency and consequently the presence/absence of DQPTs can be flexibly controlled by adjusting the driven frequency. %Taking advantage of this property, We have uncovered that, for a ramp across the single quantum critical point, the critical mode at which DQPTs occur is classified into three regions: the Kibble-Zurek (KZ) region, where the critical mode scales linearly with the square root of the sweep velocity, pre-saturated (PS) region, and the saturated (S) region where the critical mode makes a plateau versus the sweep velocity. While for a ramp that crosses two critical points, the critical modes disclose just KZ and PS regions. On the basis of numerical simulations, we find that the dynamical free energy scales linerly with time, as approaches to DQPT time, with the exponent $\nu=1\pm 0.01$ for all sweep velocities and driven frequencies., Comment: 11 pages

We study how time-dependent energy fluctuations impact the dynamical quantum phase transitions (DQPTs) following a noisy ramped quench of the transverse magnetic field in a quantum Ising chain. By numerically solving the stochastic Schr\"odinger equation of the mode-decoupled fermionic Hamiltonian of the problem, we identify two generic scenarios: Depending on the amplitude of the noise and the rate of the ramp, the expected periodic sequence of noiseless DQPTs may either be uniformly shifted in time or else replaced by a disarray of closely spaced DQPTs. Guided by an exact noise master equation, we trace the phenomenon to the interplay between noise-induced excitations which accumulate during the quench and the near-adiabatic dynamics of the massive modes of the system. Our analysis generalizes to any 1D fermionic two-band model subject to a noisy quench., Comment: 11 pages, 4 figures (including Supplemental Material)

Relying on dependency packages accelerates software development, but it also increases the exposure to security vulnerabilities that may be present in dependencies. While developers have full control over which dependency packages (and which version) they use, they have no control over the dependencies of their dependencies. Such transitive dependencies, which often amount to a greater number than direct dependencies, can become infected with vulnerabilities and put software projects at risk. To mitigate this risk, Practitioners need to select dependencies that respond quickly to vulnerabilities to prevent the propagation of vulnerable code to their project. To identify such dependencies, we analyze more than 450 vulnerabilities in the npm ecosystem to understand why dependent packages remain vulnerable. We identify over 200,000 npm packages that are infected through their dependencies and use 9 features to build a prediction model that identifies packages that quickly adopt the vulnerability fix and prevent further propagation of vulnerabilities. We also study the relationship between these features and the response speed of vulnerable packages. We complement our work with a practitioner survey to understand the applicability of our findings. Developers can incorporate our findings into their dependency management practices to mitigate the impact of vulnerabilities from their dependency supply chain.

Lung cancer is highly lethal, emphasizing the critical need for early detection. However, identifying lung nodules poses significant challenges for radiologists, who rely heavily on their expertise for accurate diagnosis. To address this issue, computer-aided diagnosis (CAD) systems based on machine learning techniques have emerged to assist doctors in identifying lung nodules from computed tomography (CT) scans. Unfortunately, existing networks in this domain often suffer from computational complexity, leading to high rates of false negatives and false positives, limiting their effectiveness. To address these challenges, we present an innovative model that harnesses the strengths of both convolutional neural networks and vision transformers. Inspired by object detection in videos, we treat each 3D CT image as a video, individual slices as frames, and lung nodules as objects, enabling a time-series application. The primary objective of our work is to overcome hardware limitations during model training, allowing for efficient processing of 2D data while utilizing inter-slice information for accurate identification based on 3D image context. We validated the proposed network by applying a 10-fold cross-validation technique to the publicly available Lung Nodule Analysis 2016 dataset. Our proposed architecture achieves an average sensitivity criterion of 97.84% and a competition performance metrics (CPM) of 96.0% with few parameters. Comparative analysis with state-of-the-art advancements in lung nodule identification demonstrates the significant accuracy achieved by our proposed model.

We generalize a recently introduced formulation of relativistic spinful and vortical fluid to relativistic magnetohydrodynamics (MHD). We refer to it as the "Spinful-Vortical MHD" (SVMHD). The aim is to scrutinize the interplay between the vorticity, magnetic field, and spin, which is treated as a quantum object, in contrast to other formulations of spin hydrodynamics. To this purpose, we first perform a standard entropy current analysis up to first-order gradient expansion, $\mathcal{O}\left(\partial\right)$ as well as $\mathcal{O}\left(\hbar\partial\right)$, where $\hbar$ is the Planck constant. In contrast to alternative formulations of spin MHD, in the absence of vorticity, the zeroth-order energy-momentum tensor includes an additional magneto-vorticity mixed term and reduces, as expected, to the energy-momentum tensor of MHD. We show that in the first-order of gradient expansion, $36$ dissipative transport coefficients appear. They satisfy certain constraints that guarantee the positive definiteness of the entropy production rate. We then modify the formulation of SVMHD by replacing the magnetic part of the thermal vorticity tensor with its electric part. Carrying out the same analysis as in the standard formulation, we show that in this case, the first-order constitutive relations consist of $11$ nondissipative Hall-like coefficients, apart from $25$ dissipative coefficients. This difference arises from different behavior of the electric and magnetic part of the thermal vorticity under time-reversal transformation., Comment: 13 pages

The recently reported Bilinear Magnetoeletric Resistance (BMR) in novel materials with rich spin textures, such as bismuth selenide (Bi$_2$Se$_3$) and tungsten ditelluride (WTe$_2$), opens new possibilities for probing the spin textures via magneto-transport measurements. By its nature, the BMR effect is directly linked to the crystal symmetry of the materials and its spin texture. Therefore, understanding the crystallographic dependency of the effect is crucial. Here we report the observation of crystallographic-dependent BMR in thin WTe$_2$ layers and explore how it is linked to its spin textures. The linear response measured in first harmonic signals and the BMR measured in second harmonic signals are both studied under a wide range of magnitudes and directions of magnetic field, applied current and at different temperatures. We discover a three-fold symmetry contribution of the BMR when current is applied along the a-axis of the WTe$_2$ thin layer at 10 K, which is absent for when current is applied along the b-axis.

Using the Density Functional Theory (DFT) calculations, we determined the electronic and magnetic properties of a T-phase VS$_2$ bilayer as a function of tensile and compressive strain. First, we determine the ground state structural parameters and then the band structure, magnetic anisotropy, exchange parameters, and Curie temperature. Variation of these parameters with the strain is carefully analyzed and described. The easy-plane anisotropy, which is rather small in the absence of strain, becomes remarkably enhanced by tensile strain and reduced almost to zero by compressive strain. We also show that the exchange parameters and the Curie temperature are remarkably reduced for the compressive strains below roughly -4$\%$., Comment: 9 Pages, 7 figures, 3 Tables

Clustering of urban traffic patterns is an essential task in many different areas of traffic management and planning. In this paper, two significant applications in the clustering of urban traffic patterns are described. The first application estimates the missing speed values using the speed of road segments with similar traffic patterns to colorify map tiles. The second one is the estimation of essential road segments for generating addresses for a local point on the map, using the similarity patterns of different road segments. The speed time series extracts the traffic pattern in different road segments. In this paper, we proposed the time series clustering algorithm based on K-Means and Dynamic Time Warping. The case study of our proposed algorithm is based on the Snapp application's driver speed time series data. The results of the two applications illustrate that the proposed method can extract similar urban traffic patterns.

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings.