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Given a graph $G=(V,E)$, a function $f:V\to \{0,1,2\}$ is said to be a \emph{Roman Dominating function} if for every $v\in V$ with $f(v)=0$, there exists a vertex $u\in N(v)$ such that $f(u)=2$. A Roman Dominating function $f$ is said to be an \emph{Independent Roman Dominating function} (or IRDF), if $V_1\cup V_2$ forms an independent set, where $V_i=\{v\in V~\vert~f(v)=i\}$, for $i\in \{0,1,2\}$. The total weight of $f$ is equal to $\sum_{v\in V} f(v)$, and is denoted as $w(f)$. The \emph{Independent Roman Domination Number} of $G$, denoted by $i_R(G)$, is defined as min$\{w(f)~\vert~f$ is an IRDF of $G\}$. For a given graph $G$, the problem of computing $i_R(G)$ is defined as the \emph{Minimum Independent Roman Domination problem}. The problem is already known to be NP-hard for bipartite graphs. In this paper, we further study the algorithmic complexity of the problem. In this paper, we propose a polynomial-time algorithm to solve the Minimum Independent Roman Domination problem for distance-hereditary graphs, split graphs, and $P_4$-sparse graphs.

Consider a graph $G = (V, E)$ and a function $f: V \rightarrow \{0, 1, 2\}$. A vertex $u$ with $f(u)=0$ is defined as \emph{undefended} by $f$ if it lacks adjacency to any vertex with a positive $f$-value. The function $f$ is said to be a \emph{Weak Roman Dominating function} (WRD function) if, for every vertex $u$ with $f(u) = 0$, there exists a neighbour $v$ of $u$ with $f(v) > 0$ and a new function $f': V \rightarrow \{0, 1, 2\}$ defined in the following way: $f'(u) = 1$, $f'(v) = f(v) - 1$, and $f'(w) = f(w)$, for all vertices $w$ in $V\setminus\{u,v\}$; so that no vertices are undefended by $f'$. The total weight of $f$ is equal to $\sum_{v\in V} f(v)$, and is denoted as $w(f)$. The \emph{Weak Roman Domination Number} denoted by $\gamma_r(G)$, represents $min\{w(f)~\vert~f$ is a WRD function of $G\}$. For a given graph $G$, the problem of finding a WRD function of weight $\gamma_r(G)$ is defined as the \emph{Minimum Weak Roman domination problem}. The problem is already known to be NP-hard for bipartite and chordal graphs. In this paper, we further study the algorithmic complexity of the problem. We prove the NP-hardness of the problem for star convex bipartite graphs and comb convex bipartite graphs, which are subclasses of bipartite graphs. In addition, we show that for the bounded degree star convex bipartite graphs, the problem is efficiently solvable. We also prove the NP-hardness of the problem for split graphs, a subclass of chordal graphs. On the positive side, we give polynomial-time algorithms to solve the problem for $P_4$-sparse graphs. Further, we have presented some approximation results.

To investigate the transport properties in random alloys, it is important to model the alloy disorder using supercells. Though traditional methods like Virtual Crystal Approximation (VCA) are computationally efficient, the local disorder in the system is not accurately captured as artificial translational symmetry is imposed on the system. However, in the case of supercells, the error introduced by self-image interaction between the impurities is reduced and translational symmetry is explicitly imposed over larger length scales. In this work, we have investigated the Effective Phonon Dispersion (EPD) and transport properties, from first principle calculations using supercells in AlxGa1-xN alloy systems. Using our in-house developed code, the EPD of AlGaN is obtained and the individual modes are identified. Next, we discuss our in-house developed method to calculate low-field transport properties in supercells. First to validate our methods we have solved the Boltzmann Transport Equation using Rode method to compare the phonon limited mobility in the 4 atom GaN primitive cell and 12 atom GaN supercell. Using the same technique, we have investigated the low field transport in random AlxGa1-xN alloy systems. Our calculations show that along with alloy scattering, electron-phonon scattering may also play an important role at room temperature and high-temperature device operation. This technique opens up the path for calculating phonon-limited transport properties in random alloy systems.

In this work, the Effective Phonon Dispersion of $\beta$- (Al$_x$Ga$_{1-x}$)$_2$O$_3$ alloy is investigated using the Phonon unfolding formalism. To capture the true randomness of the system, supercells are designed using the technique of special quasirandom structues. The phonon unfolding technique is then used to obtain the effective phonon dispersion (EPD) for aluminium fractions of 18.75 \% and 37.5 \% with respect to gallium atoms. The impact of disorder on the high frequency and DC dielectric constant is also investigated. The unfolding procedure was also applied to the dipole moment where the projection probability was used to obtain the IR spectra of the disordered system. The calculated properties gives us further insights to calculate the transport properties in these alloy semiconductors while capturing the true randomness of the system.

The lack of p-type doping has impeded the development of vertical gallium oxide (Ga2O3) devices. Current blocking layers (CBL) using implanted deep acceptors has been used to demonstrate vertical devices. This paper presents the first demonstration of in situ Mg-doped beta-Ga2O3 CBLs grown using metalorganic chemical vapor deposition. Device structures were designed with in-situ Mg doped layers with varied targeted Mg doping concentrations, which were calibrated by quantitative secondary ion mass spectroscopy (SIMS). The effectiveness of the CBL is characterized using temperature dependent current-voltage measurements using n-Mg-doped-n structures, providing crucial insight into the underlying mechanisms. To further validate the experimental results, a TCAD simulation is performed and the electrically active effective doping is found to be dependent on the Mg-doping density, offering a new perspective on the optimization of CBL performance. Breakdown measurements show a 3.4 MV/cm field strength. This study represents a significant step forward in the development of Ga2O3-based devices and paves the way for future advancements in this exciting field.

Epilepsy or the occurrence of epileptic seizures, is one of the world's most well-known neurological disorders affecting millions of people. Seizures mostly occur due to non-coordinated electrical discharges in the human brain and may cause damage, including collapse and loss of consciousness. If the onset of a seizure can be forecast then the subject can be placed into a safe environment or position so that self-injury as a result of a collapse can be minimised. However there are no definitive methods to predict seizures in an everyday, uncontrolled environment. Previous studies have shown that pet dogs have the ability to detect the onset of an epileptic seizure by scenting the characteristic volatile organic compounds exuded through the skin by a subject prior a seizure occurring and there are cases where assistance dogs, trained to scent the onset of a seizure, can signal this to their owner/trainer. In this work we identify how we can automatically detect the signalling behaviours of trained assistance dogs and use this to alert their owner. Using data from an accelerometer worn on the collar of a dog we describe how we gathered movement data from 11 trained dogs for a total of 107 days as they exhibited signalling behaviour on command. We present the machine learning techniques used to accurately detect signalling from routine dog behaviour. This work is a step towards automatic alerting of the likely onset of an epileptic seizure from the signalling behaviour of a trained assistance dog., Comment: 8 pages, 5 tables, 6 figures

Insider threats is the most concerned cybersecurity problem which is poorly addressed by widely used security solutions. Despite the fact that there have been several scientific publications in this area, but from our innovative study classification and structural taxonomy proposals, we argue to provide the more information about insider threats and defense measures used to counter them. While adopting the current grounded theory method for a thorough literature evaluation, our categorization's goal is to organize knowledge in insider threat research. Along with an analysis of major recent studies on detecting insider threats, the major goal of the study is to develop a classification of current types of insiders, levels of access, motivations behind it, insider profiling, security properties, and methods they use to attack. This includes use of machine learning algorithm, behavior analysis, methods of detection and evaluation. Moreover, actual incidents related to insider attacks have also been analyzed.

In this article, the electronic band structure $\beta-(Al_xGa_{1-x})_2O_3$ alloy system is calculated with $\beta-Ga_2O_3$ as the bulk crystal. The technique of band unfolding is implemented to obtain the effective bandstructure \textit{(EBS)} for aluminium fractions varying between 12.5\% and 62.5\% with respect to the gallium atoms. A 160 atom supercell is used to model the disordered system that is generated using the technique of special quasirandom structures which mimics the site correlation of a truly random alloy and reduces the configurational space that arises due to the vast enumeration of alloy occupation sites. The impact of the disorder is then evaluated on the electron effective mass and bandgap which is calculated under the generalized gradient approximation \textit{(GGA)}. The EBS of disordered systems gives an insight into the effect of the loss of translational symmetry on the band topology which manifests as band broadening and can be used to evaluate disorder induced scattering rates and electron lifetimes. This technique of band unfolding can be further extended to alloy phonon dispersion and subsequently phonon lifetimes can also be evaluated from the band broadening.

Nonlinear optical (NLO) response in terms of reverse saturable absorption (RSA) has been exploited extensively for optical limiting. Here, we experimentally demonstrate that flower-like hybrid nanostructures of NiCo2O4 and Multiwall Carbon Nanotubes (NCO@MWCNT) exhibit a strong non-linearity in their absorption, specifically an excited state absorption (ESA) induced RSA, when exposed to nanosecond laser pulses. We obtain strong nonlinear absorption coefficient (\b{eta}) and nonlinear refractive index (n2) in hybrid NCO@MWCNT, the values that are 2-times and 2-orders of magnitude higher, respectively compared to NCO or MWCNT alone. This offers straightforward application in optical limiting with optical limiting (FOL) and optical onset (FON) threshold ca. 2-10 times lower than benchmark NLO materials, e.g., graphene, family of 2-D transition metal dichalcogenides (TMDC) materials and recently established NCO. Notably, for femtosecond pumping, NLO response of NCO@MWCNT is dominated by saturable absorption (SA) with a week contribution from two photon absorption (TPA), arising respectively from MWCNT and NCO. A remarkable sign reversal along with a larger amplitude is obtained for n2 thanks to the charge transfer from MWCNT to NCO., Comment: 25 pages, 4 figures, 2 tables

We propose a fusion algorithm for haze removal that combines color information from an RGB image and edge information extracted from its corresponding NIR image using Haar wavelets. The proposed algorithm is based on the key observation that NIR edge features are more prominent in the hazy regions of the image than the RGB edge features in those same regions. To combine the color and edge information, we introduce a haze-weight map which proportionately distributes the color and edge information during the fusion process. Because NIR images are, intrinsically, nearly haze-free, our work makes no assumptions like existing works that rely on a scattering model and essentially designing a depth-independent method. This helps in minimizing artifacts and gives a more realistic sense to the restored haze-free image. Extensive experiments show that the proposed algorithm is both qualitatively and quantitatively better on several key metrics when compared to existing state-of-the-art methods., Comment: Accepted in 25th International Conference on Pattern Recognition (ICPR 2020)