Miracle-dz/UniFAR-FTUs huggingface dataset:
A Recurrence for the Surface Area of the ($n, k$)-Star Graph

We present a simple recurrence for the surface area of the ($n, k$) -star graph, $0 < k < n$, i.e., the number of nodes at a certain distance from the identity node in the graph, an important parameter for interconnection networks in parallel computing. The family of the ($n, k$) -star graphs includes several popular interconnection networks such as the star graph and the alternating group network. Previously, a surface area recurrence has been obtained for a special case, e.g., when $k=n-1$, in the family of ($n, k$) -star. Our recurrence gives one single recurrence for all graphs in the family, thus completely solving the surface area of ($n, k$) -star for all $0 < k < n$. Compared to explicit surface area formulas previously obtained through complicated and involved combinatorial analysis and generating function approach, our derivation is more elementary and our recurrence gives a way to compute the surface area of the ($n, k$))-star efficiently.
A Review on Energy Efficient Cooperative Routing Algorithm for Wireless Sensor Networks

Wireless communications have experienced speedy and technical development in this modern era. Significant improvements are required in terms of communication speed link, energy level, size of the device, network lifetime and applications for different generations of wireless devices. Due to the advancement in technical development, the researchers have started to build WNArchitectures the conventional method of data communication using peer-to-peer basis with a centralized (BS) controller. In Wireless Sensor Networks (WSNs), the conventional method of data communication in the network is instituted and this allows the nodes to forward data through other nodes in the communication network and create new communication paths that involve several wireless hops in the communication network. Recently, there has been an upraise in cooperative communication. This promising methodology plays an important role in relieving wireless channel fading, as well as, recuperating steadfastness in networks which permit nodes that allow cooperation among them. The nodes share data in cooperative communication by exploiting the communication in their broadcasting nature. Simulations were run for various adjustments in order to approve the Cooperative Power and Energy-efficient routing protocol (COPE) [14] convention's implementation alongside the proposed Minimum Power Least Cost Routing (MPLCR) Algorithm conventions.
Hierarchical Services of Convolutional Neural Networks via Probabilistic Selective Encryption

Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selective encryption (SE) algorithm to protect CNN models from unauthorized access, with a unique feature of providing hierarchical services to users. Our algorithm first selects important model parameters via the proposed Probabilistic Selection Strategy (PSS). It then encrypts the most important parameters with the designed encryption method called Distribution Preserving Random Mask (DPRM), so as to maximize the performance degradation by encrypting only a very small portion of model parameters. We also design a set of access permissions, using which different amount of most important model parameters can be decrypted. Hence, different levels of model performance can be naturally provided for users. Experimental results demonstrate that the proposed scheme could effectively protect the classification model VGG19 by merely encrypting <inline-formula><tex-math notation="LaTeX">$8\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>8</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq1-3136601.gif"/></alternatives></inline-formula> parameters of convolutional layers. We also implement the proposed model protection scheme in the denoising model DnCNN, showcasing the hierarchical denoising services.
Canard cycles in the presence of slow dynamics with singularities

We study the cyclicity of limit periodic sets that occur in families of vector fields of slow–fast type. The limit periodic sets are formed by a fast orbit and a curve of singularities containing a unique turning point. At this turning point a stability change takes place: on one side of the turning point the dynamics point strongly towards the curve of singularities; on the other side the dynamics point away from the curve of singularities. The presence of periodic orbits in a perturbation is related to the presence of canard orbits passing near this turning point, i.e. orbits that stay close to the curve of singularities despite the exponentially strong repulsion near this curve. All existing results deal with a non-zero slow movement, permitting a good estimate of the cyclicity by considering the slow-divergence integral along the curve of singularities. In this paper we study what happens when the slow dynamics exhibit singularities. In particular, our study includes the cyclicity of the slow–fast two-saddle cycle, formed by a regular saddle connection (the fast part) and a part of the curve of singularities (the slow part). We see that the relevant information is no longer merely contained in the slow-divergence integral.
Angular diffraction of an optical vortex induced by the Gouy phase

We investigate the diffraction of an optical vortex, when half of the beam is blocked by a knife edge. The Gouy phase is shown to influence the diffraction pattern strongly, in particular leading to the emergence of distorted and deformed diffraction fringes in the azimuthal direction. In analogy with single-slit diffraction, this phenomenon is analyzed in terms of angular diffraction within the azimuthal degree of freedom, by considering the uncertainty principle for the angular position and the angular momentum. Numerical simulations based on the fast-Fourier algorithm and the angular momentum expansion fit well with the experimental results.
Computer-Intensive Methods in Auditing: Bootstrap Difference and Ratio Estimation

Computer-intensive methods are a recent development in the theory of statistics with potential applicability in audit and accounting sampling. Whereas traditional sampling approaches can require complex analytics and questionable distributional assumptions, computer-intensive methods generate sampling distributions using simple, though intensive, computer computations. Generating sampling distributions empirically provides increased versatility, ease of use, and the potential for increased efficiency. However, because computer-intensive methods are sensitive to the representativeness of sample evidence, it becomes an empirical question as to whether they outperform other approaches. This study provides initial evidence on the performance of computer-intensive methods by applying one of them, the bootstrap, to difference and ratio estimation. Tests based on the Neter and Loebbecke [1975] populations reveal the potential for increased efficiency/reliability across a range of sample size and error rate conditions. However, these advantages require an appropriate choice between two versions of the bootstrap. A decision rule for making this choice is hypothesized. While these findings cannot be generalized easily to other populations, they suggest that computer-intensive methods warrant further investigation.
New formulas for tropospheric scatter path loss

The predicted performance of tropospheric scatter transmission is investigated in terms of spectrum slope, refractive index variance, and scale of turbulence. The common volume integral is evaluated to yield a useful expression for path loss in terms of these parameters. Formulas for the coupling loss in the asymptotic case of very wide and very narrow antenna beams are derived. Approximate formulas for intermediate size antenna beams are deduced and compared with more exact computer calculations. Our results suggest that previous path loss prediction methods do not place enough emphasis on turbulent scatter and that a model with spectrum slope 11/3 should be used. The resulting model provides an initial step toward the development of a simpler and more realistic alternative to the NBS model, and also refines a related approach by Gjessing.
Ultra-high electron beam power and energy densities using a plasma-filled rod-pinch diode

The plasma-filled rod-pinch diode is a new technique to concentrate an intense electron beam to high power and energy density. Current from a pulsed power generator (typically ∼MV, MA, 100 ns pulse duration) flows through the injected plasma, which short-circuits the diode for 10–70 ns, then the impedance increases and a large fraction of the ∼MeV electron-beam energy is deposited at the tip of a 1 mm diameter, tapered rod anode, producing a small (sub-mm diameter), intense x-ray source. The current and voltage parameters imply 20–150 μm effective anode-cathode gaps at the time of maximum radiation, much smaller gaps than can be used between metal electrodes without premature shorting. Interferometric diagnostics indicate that the current initially sweeps up plasma in a snowplow-like manner, convecting current toward the rod tip. The density distribution is more diffuse at the time of beam formation with a low-density region near the rod surface where gap formation could occur. Particle simulations of the.
Deep learning aided SCL decoding of polar codes with shifted-pruning

Recently, a generalized successive cancellation list (SCL) decoder implemented with shifted-pruning (SP) scheme, namely the SCL-SP-ω decoder, is presented for polar codes, which is able to shift the pruning window at most ω times during each SCL re-decoding attempt to prevent the correct path from being eliminated. The candidate positions for applying the SP scheme are selected by a shifting metric based on the probability that the elimination occurs. However, the number of exponential/logarithm operations involved in the SCL-SP-ω decoder grows linearly with the number of information bits and list size, which leads to high computational complexity. In this paper, we present a detailed analysis of the SCL-SP-ω decoder in terms of the decoding performance and complexity, which unveils that the choice of the shifting metric is essential for improving the decoding performance and reducing the re-decoding attempts simultaneously. Then, we introduce a simplified metric derived from the path metric (PM) domain, and a custom-tailored deep learning (DL) network is further designed to enhance the efficiency of the proposed simplified metric. The proposed metrics are both free of transcendental functions and hence, are more hardware-friendly than the existing metrics. Simulation results show that the proposed DL-aided metric provides the best error correction performance as comparison with the state of the art.
Stability of trapped electrons in SiO 2

Thermally stimulated current and capacitance voltage methods are used to investigate the thermal stability of trapped electrons associated with radiation-induced trapped positive charge in metal-oxide-semiconductor capacitors. The density of deeply trapped electrons in radiation-hardened 45 nm oxides exceeds that of shallow electrons by a factor of {approximately}3 after radiation exposure, and by up to a factor of 10 or more during biased annealing. Shallow electron traps anneal faster than deep traps, and seem to be at least qualitatively consistent with the model of Lelis et al. Deeper traps maybe part of a fundamentally distinct dipole complex, and/or have shifted energy levels that inhibit charge exchange with the Si.
Three Level Charge Pumping On Dielectric Hafnium Oxide Gate

A major reliability concern in modern high-k field effect transistors (FETs) resembles the defect density distribution within the hafnium oxide layer as well as its interaction with the interfacial oxide layer. For a deeper understanding of the distribution of charged traps both energetically as well as spatially, it is essential to upgrade from a two level charge pumping scheme to a three level scheme. Through variation in pulse width and amplitude of a subsequent second level pulse, defect energy and location can be extracted inside the gate stack, which is important to understand the overall reliability impact of these traps onto the device properties.
Coupled wave theory for thick hologram gratings

A coupled wave analysis is given of the Bragg diffraction of light by thick hologram gratings, which is analogous to Phariseau's treatment of acoustic gratings and to the “dynamical” theory of X-ray diffraction. The theory remains valid for large diffraction efficiencies where the incident wave is strongly depleted. It is applied to transmission holograms and to reflection holograms. Spatial modulations of both the refractive index and the absorption constant are allowed for. The effects of loss in the grating and of slanted fringes are also considered. Algebraic formulas and their numerical evaluations are given for the diffraction efficiencies and the angular and wavelength sensitivities of the various hologram types.
Annulus Wall Boundary Layers in Turbomachines.

Abstract : The paper reviews a substantial body of fundamental and applied research work on the subject of annulus wall boundary layers in turbomachines. A product of this work is a method for predicting, with reasonable accuracy, the full three-dimensional boundary layer that develops through a single blade row. The limitations of the method are discussed in relation to the experimental information available to the authors. In Part 2 the fundamental work is subjected to scrutiny in order to extract a boundary layer calculation method suitable for estimating the effects of annulus wall boundary layers on the performance of turbomachines. It is shown that a great many assumptions and simplifications are necessary at the present time but that reasonably satisfactory estimates are nevertheless possible. A computer program for the estimation of annulus blockage is listed.
Fast Space-Time Adaptive Processing for Clutter Suppression in Airborne Radar with Coprime Arrays under Gain and Phase Errors

The clutter suppression problem with space-time adaptive processing (STAP) for coprime arrays in presence of gain and phase (GP) errors is considered. More precisely, the proposed algorithm firstly formulates the virtual space-time signal model for the signal observed by the coprime arrays with GP errors. Then, the clutter spectrum and array GP errors estimation are formulated as a joint optimization problem based on the clutter sparsity and a fast method is derived to solve the resultant optimization problem based on the alternating direction method of multipliers (ADMM). Finally, the virtual covariance matrix can be estimated using the clutter spectrum and GP errors estimates and the conventional STAP filters can be directly applied. Simulation examples are provided to demonstrate its effectiveness and show low complexity than existing sparsity-based STAP algorithms for coprime arrays.
What Makes Rumor Rebuttals Viral on Social Media?

This paper investigates the relationship between the characteristics of online rumor rebuttals and their virality on social media. Virality was conceptualized in terms of the volume of Likes (affective evaluation), Comments (message deliberation), and Shares (viral reach) attracted by rumor rebuttals on Facebook. The dataset included 479 online rumor rebuttal posts. Qualitative content analysis was employed to identify characteristics of the rebuttals while quantitative methods were used to examine how these characteristics predicted their virality. Rebuttal virality was found to be positively predicted by message posters' credibility (#Likes, #Comments, and #Shares), justification of the rebuttal (#Likes and #Comments), call to action (#Comments and #Shares), and the presence of images (#Comments). In contrast, rebuttal virality was negatively predicted by the presence of debunking statements (#Comments) and URLs (#Likes, #Comments).
Improving the Detection of Small Oriented Objects in Aerial Images

Small oriented objects that represent tiny pixel-area in large-scale aerial images are difficult to detect due to their size and orientation. Existing oriented aerial detectors have shown promising results but are mainly focused on orientation modeling with less regard to the size of the objects. In this work, we proposed a method to accurately detect small oriented objects in aerial images by enhancing the classification and regression tasks of the oriented object detection model. We designed the Attention-Points Network consisting of two losses: Guided-Attention Loss (GALoss) and Box-Points Loss (BPLoss). GALoss uses an instance segmentation mask as ground-truth to learn the attention features needed to improve the detection of small objects. These attention features are then used to predict box points for BPLoss, which determines the points' position relative to the target oriented bounding box. Experimental results show the effectiveness of our Attention-Points Network on a standard oriented aerial dataset with small object instances (DOTA-v1.S) and on a maritime-related dataset (HRSC2016). The code is publicly available11https://github.com/chandlerbing65nm/APDetection.
DOA estimation of unknown frequency for vector hydrophone array based on baffle

When the rectangular air cavity sonar baffle is applied to the direction estimation of vector hydrophone uniform linear array, the performance of the direction estimation algorithm based on the reflection coefficient of the baffle is degraded due to the unknown reflection coefficient, In order to reduce the influence of the reflection coefficient of the baffle on the azimuth estimation results, the training set and the test set are obtained under the condition of considering the vibration and sound structure coupling of the steel plate layer of the rectangular air cavity baffle, Through the training and testing of the network, it is proved that the machine learning algorithm can solve the azimuth estimation problem of the vector hydrophone array based on the baffle when the frequency of the incoming wave is unknown, and obtain better performance than the algorithm based on the reflection coefficient of the baffle.
Conceptual Study of Moderately Coupled Plasmas and Experimental Comparison of Laboratory X-ray Sources

In this thesis the fundamental concepts of moderately coupled plasmas, for which 2{approx_lt}ln{Lambda}{sub b}{approx_lt}10, are, for the first time, presented. This investigation is motivated because neither the conventional Fokker-Planck approximation [for weakly coupled plasmas (ln{Lambda}{sub b}{approx_lt}10)] nor the theory of dielectric response with correlations for strongly coupled plasmas (ln{Lambda}{sub b}{approx_lt}1) has satisfactorily addressed this regime. Specifically, herein the standard Fokker-Planck operator for Coulomb collisions has been modified to include hitherto neglected terms that are directly associated with large-angle scattering. In addition a reduced electron-ion collision operator has been calculated that, for the first time, manifests 1/ln{Lambda}{sub b} corrections. Precise calculations of some relaxation rates and crude calculations of electron transport coefficients have been made. As one of major applications of the modified Fokker-Planck equation, the stopping powers and {rho}R have been calculated for charged fusion products ({alpha}`s, {sup 3}H, {sup 3}He) and hot electrons interacting with plasmas relevant to inertial confinement fusion. In the second major topic of this thesis, advances made in the area of laboratory x-ray sources are presented. First, and most importantly, through the use a Cockcroft-Walton linear accelerator, a charged particle induced x-ray emission (PIXE) source has been developed. Intense line x radiation (including K-, L-, M-, and N-lines) with wavelengths from 0.5 {angstrom} to 111 {angstrom} have been successfully produced. Second, a new high intensity electron-beam x-ray generator has also been developed, and it has been used with advantage in the soft x-ray region ( < 3 keV). Finally, a direct comparisons of both sources (PIXE and electron-beam x-ray sources) to a commercially available radioactive {alpha} fluorescent x-ray source has been made.
Impact of Transparency and Disclosure on Financial Distress: Considering Inventory as Moderator

The firm's financial distress is an unwanted situation that needs to be overcome timely, otherwise, it leads to business failure. This study examines the influence of transparency and disclosure (TD) on financial distress (FD) using inventory as a moderator. This study used secondary data from CMIE Prowess database for the five years from 2016 to 2020 of BSE100 listed firms. This study has used the panel data analysis (PDA) method for data analysis. The result of this study states that transparency and disclosure (TD) have a negative significance on financial distress under the influence of inventory in health firms in India. The findings provide key implications for managers, and policymakers to entertain the inventory and T &D on a serious node to improve the firm's FD in the health industry. Investors should also consider inventory and T &D as important segments for the firm's FD to have informed decisions. This study contributes substantially to the existing literature to provide insights on T &D, inventory, and firm FD. There exists no such study, hence it provides novel evidence on the association of T&D with firm's FD of Indian health firms considering inventory as moderator.
Distributed Banach-Picard Iteration: Application to Distributed Parameter Estimation and PCA

We recently proposed an algorithmic framework, distributed Banach-Picard iteration (DBPI), allowing a set of agents linked by a communication network to find a fixed point of a map that: (a) is the average of individual maps held by said agents; (b) is locally contractive (LC). Given such a map, DBPI yields a distributed algorithm provably inheriting the local linear convergence (LLC) of the standard Banach-Picard iteration for the centralized (average) map. Here, we instantiate DBPI in two classical problems, which amounts to proving that the conditions guaranteeing the LLC of DBPI hold. First, taking Sanger's algorithm for principal component analysis (PCA), we show that it corresponds to iterating an LC map that can be written as the average of local maps held by agents with private data subsets. Applying DBPI then recovers a previous distributed PCA algorithm, which lacked a convergence proof, thus closing that gap. In the second instantiation, we show that a variant of the expectation-maximization (EM) algorithm for parameter estimation from noisy, faulty measurements in sensor networks can be written as iterating an LC map that is the average of local maps. Consequently, the DBPI framework yields a distributed algorithm automatically inheriting the LLC guarantee of its centralized counterpart. Verifying the LC condition for EM is nontrivial (as the underlying operator depends on random samples) and a contribution in itself, possibly of independent interest. Finally, we illustrate experimentally the linear convergence of the proposed distributed EM algorithm, contrasting with the sub-linear rate of the previous version.