Miracle-dz/UniFAR-FTUs huggingface dataset:
ON COISOTROPIC DEFORMATIONS OF HOLOMORPHIC SUBMANIFOLDS

We describe the differential graded Lie algebras governing Poisson deformations of a holomorphic Poisson manifold and coisotropic embedded deformations of a coisotropic holomorphic submanifold. In both cases, under some mild additional assumption, we show that the infinitesimal first order deformations induced by the anchor map are unobstructed. Applications include the analog of Kodaira stability theorem for coisotropic deformation and a generalization of McLean-Voisin's theorem about the local moduli space of lagrangian submanifold. Finally it is shown that our construction is homotopy equivalent to the homotopy Lie algebroid, in the cases where this is defined.
Wideband High-Gain Circularly Polarized Substrate Integrated Cavity Antenna Array for Millimeter-Wave Applications

A wideband high-gain circularly polarized (CP) substrate integrated cavity (SIC) antenna array is presented for millimeter-wave applications. The proposed antenna element is simply composed of a slot-coupled high-order-mode resonant square SIC and a pair of obliquely placed strip-shaped parasitic patches on the top surface of the SIC. The parasitic patches are used to adjust the aperture electromagnetic field distribution and phase of the two orthogonal resonant modes of TM211 and TM121 in the SIC to achieve a high-gain CP radiation and meanwhile introduce an additional cavity-backed slot-dipole CP radiation to extend axial ratio (AR) bandwidth of the proposed antenna element. By combining the above two high-gain CP radiation mechanisms, a wideband high-gain CP SIC antenna element is achieved. The simulated results of the proposed antenna element show an overlaid bandwidth of 20.36% from 26.20 to 32.14 GHz, while considering the −10 dB reflection coefficient, 3 dB AR, 3 dB gain bandwidths properties, and a maximum right-hand CP gain of 10.53 dBi at 29.40 GHz. On this basis, a wideband high-gain 8 $\times $ 8 CP array fed by a full-corporate substrate integrated waveguide (SIW) network is designed and fabricated. The measured results show that the proposed array has an overlaid bandwidth of 21.72% from 25.85 to 32.15 GHz and a measured gain of up to 26.10 dBic at 29.00 GHz.
Validation of the Sonographic Measurement of Lateral Parapharyngeal Wall Thickness in Childhood Obstructive Sleep Apnea

Background Lateral parapharyngeal wall (LPW) thickness is a potentially useful anatomical marker of childhood obstructive sleep apnea (OSA). Measuring LPW thickness by ultrasonography (USG) is technically feasible but its use in children has not been validated. Therefore, this study aimed to assess the intra- and inter-operator reliability of the sonographic measurements of LPW thickness in children and to assess its validity against magnetic resonance imaging (MRI) measurements. Methods Prepubertal children aged 6–11 years suspected to suffer from OSA were recruited. Repeated measurements of LPW thickness by USG were conducted to evaluate the intra- and inter-operator reliability, examined by intraclass correlation coefficient (ICC). LPW thickness was measured as the distance between the internal carotid artery and the echogenic surface of the pharynx in an oblique coronal plane by USG. LPW thickness was measured by MRI at the retropalatal level. The agreement between the LPW thickness measured by USG and MRI was assessed by ICC and Bland-Altman plot. Results Thirty-four children (mean age: 8.66 ± 1.61, 26 male) were recruited. The intra- and inter-operator reliability of the LPW thickness by USG was good (ICC = 0.84 and 0.82, respectively). The agreement between the USG-measured and MRI-measured LPW thickness was moderate (ICC = 0.72). he Bland-Altman plot demonstrated a mean difference of 0.061 cm and a 95% limit of agreement from 0.91 to 1.12 cm. Conclusion In this study, we demonstrated that ultrasonography is a valid and reliable method to assess LPW thickness in children. This study was supported by the Direct Grant for Research from the Research Committee of the Chinese University of Hong Kong (Project no. 2020.073).
Stones From Other Hills Can Polish the Jade: Exploiting Wireless-Powered Cooperative Jamming for Boosting Wireless Information Surveillance

This paper studies information surveillance over wireless-powered suspicious multiuser communications, where multiple suspicious transmitters (STs) first harvest wireless energy from the suspicious power beacon (PB) in phase I and then communicate with the suspicious destination (SD) in phase II over mutually orthogonal channels, and there is a legitimate monitor (M) aiming to overhear the suspicious signals of the STs based on wireless-powered cooperative jamming. Specifically, the jammers first harvest energy from M in phase I and then interfere with the SD in phase II. Considering the fairness issue, M aims to maximize the minimum eavesdropping success probability of these suspicious signals, by jointly optimizing its transmit power in phase I and the jammers’ power allocations in phase II. To solve the problem, first we strictly prove that M should exhaust its maximum power for the energy transfer, even the additional energy can be harvested by the STs to enhance their transmit power and rate. Then, the general successive convex approximation (SCA) technique and one low-complexity solution are respectively proposed to optimize the jammers’ power allocations. Further, the closed-form jamming power allocations are derived in the high signal-to-noise ratio range to reveal some interesting insights. The joint deployments of M and the jammers are also investigated to enhance the eavesdropping performance. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.
Some asymptotic results for the transient distribution of the Halfin–Whitt diffusion process

We consider the Halfin–Whitt diffusion process Xd(t), which is used, for example, as an approximation to the m-server M/M/m queue. We use recently obtained integral representations for the transient density p(x,t) of this diffusion process, and obtain various asymptotic results for the density. The asymptotic limit assumes that a drift parameter β in the model is large, and the state variable x and the initial condition x0 (with Xd(0) = x0 > 0) are also large. We obtain some alternate representations for the density, which involve sums and/or contour integrals, and expand these using a combination of the saddle point method, Laplace method and singularity analysis. The results give some insight into how steady state is achieved, and how if x0 > 0 the probability mass migrates from Xd(t) > 0 to the range Xd(t) < 0, which is where it concentrates as t → ∞, in the limit we consider. We also discuss an alternate approach to the asymptotics, based on geometrical optics and singular perturbation techniques.
Phase Reduction of Stochastic Biochemical Oscillators

A common method for analyzing the effects of molecular noise in chemical reaction networks is to approximate the underlying chemical master equation by a Fokker-Planck equation, and to study the statistics of the associated chemical Langevin equation. This so-called diffusion approximation involves performing a perturbation expansion with respect to a small dimensionless parameter $\epsilon =\Omega^{-1}$, where $\Omega$ characterizes the system size. For example, $\Omega$ could be the mean number of proteins produced by a gene regulatory network. In the deterministic limit $\Omega\rightarrow \infty$, the chemical reaction network evolves according to a system of ordinary differential equations based on classical mass action kinetics. In this paper we develop a phase reduction method for chemical reaction networks that support a stable limit cycle in the deterministic limit. We present a variational principle for the phase reduction, yielding an exact analytic expression for the resulting phase dynamics. We demonstrate that this decomposition is accurate over timescales that are exponential in the system size $\Omega$. This contrasts with the phase equation obtained under the diffusion approximation, which is only accurate up to times $O(\Omega)$. In particular, we show that for a constant $C$, the probability that the system leaves an $O(\zeta)$ neighborhood of the limit cycle before time $T$ scales as $T\exp\big(-C\Omega b\zeta^2 \big)$, where $b$ is the rate of attraction to the limit cycle. We illustrate our analysis using the example of a chemical Brusselator.
Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as they provide the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. Building Blocks for IoT Analytics is devoted to the presentation of the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the roles of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with main building blocks for IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these building blocks in the scope of pragmatic contexts. Technical topics discussed in the book include:
Efficient and Privacy-Preserving Spatial-Feature-Based Reverse kNN Query

Reverse k nearest neighbor (RkNN) query has been widely applied in the targeted push of information. Many schemes for the RkNN query on encrypted data have been proposed for coordinating the emerging trend of outsourcing data to the cloud. However, none of them supports the spatial data with many features, a prevalent data type in location-based services, e.g., each user in online dating apps usually has a spatial location and many personality trait features. Meanwhile, incorporating features with the spatial data endows the spatial-feature-based RkNN query to provide more precise services than the spatial-based RkNN query. Therefore, as a steppingstone, we propose an efficient and privacy-preserving spatial-feature-based RkNN scheme in this work for the first time. Specifically, we first design a modified intersection and union R tree (MIUR-tree) to index the spatial and feature data. Then, we introduce an MIUR-tree based RkNN query algorithm in the filter and refinement framework to efficiently process RkNN queries. After that, based on a symmetric homomorphic encryption (SHE) scheme, we design a private filter protocol and a private refinement protocol, and leverage them to propose our RkNN query scheme. Rigorous security analysis demonstrates that our scheme is privacy-preserving, and extensive experiments indicate that our scheme is computationally efficient.
Addendum to `Bosonic formulas for $(k,l)$-admissible partitions'

In our earlier paper we made a combinatorial study of ( k, l )-admissible partitions. This object appeared already in the work of M. Primc as a label of a basis of level k -integrable modules over b sl l . We clarify the relation between these two works. As a byproduct we obtain an explicit parameterization of the aﬃne Weyl group of b sl l by a simple combinatorial set.
The Cauchy problem for the homogeneous Monge–Ampère equation, III. Lifespan

We prove several results on the lifespan, regularity, and uniqueness of solutions of the Cauchy problem for the homogeneous complex and real Monge-Ampere equations (HCMA/HRMA) under various a priori regularity conditions. We use methods of characteristics in both the real and complex settings to bound the lifespan of solutions with prescribed regularity. In the complex domain, we characterize the C^3 lifespan of the HCMA in terms of analytic continuation of Hamiltonian mechanics and intersection of complex time characteristics. We use a conservation law type argument to prove uniqueness of solutions of the Cauchy problem for the HCMA. We then prove that the Cauchy problem is ill-posed in C^3, in the sense that there exists a dense set of C^3 Cauchy data for which there exists no C^3 solution even for a short time. In the real domain we show that the HRMA is equivalent to a Hamilton--Jacobi equation, and use the equivalence to prove that any differentiable weak solution is smooth, so that the differentiable lifespan equals the convex lifespan determined in our previous articles. We further show that the only obstruction to C^1 solvability is the invertibility of the associated Moser maps. Thus, a smooth solution of the Cauchy problem for HRMA exists for a positive but generally finite time and cannot be continued even as a weak C^1 solution afterwards. Finally, we introduce the notion of a "leafwise subsolution" for the HCMA that generalizes that of a solution, and many of our aforementioned results are proved for this more general object.
Sensory Characteristics and Antimicrobial Activities of Soft Candy with Essential Basil (Ocimum Sanctum L.) Against Candida Albicans and Streptococcus Mutans

Soft candy is a type of confectionery product that has a chewy texture. Chewing candy can stimulate the activity of salivary secretion, thus it supports the release of active compounds in the mouth. Essential oil contains of active compounds that are able to control bacteria growth that may cause bad breath. The purpose of this study was to observe the antimicrobial activity of soft candy with the addition of basil essential oil (0.2%, 0.4%, 0.6%, and 0.8%) against Streptococcus mutans and Candida albicans and to determine the most acceptable candy by consumers. Parameters of the research observations included antimicrobial activity with solid diffusion and dilution methods, descriptive sensory test with RATA test and acceptability test (aroma, taste, mouthfeel, aftertaste, and overall), and effectiveness test. The results showed that the addition of 0.4% basil essential oil in soft candy performed the highest effectiveness value (0.71). The effectiveness value was obtained from the results of the inhibition zone (disc) test, and the results of the acceptability test. Therefore, from the research, it can be concluded that soft candy with basil essential oil has a great potential to be utilized as antimicrobial agent to overcome mouth problems.
High-Sensitivity Sensor Loaded With Coupled Complementary Spiral Resonators for Simultaneous Permittivity and Thickness Measurement of Solid Dielectric Sheet

High-precision measurement of permittivity and thickness enables advanced quality control of microwave substrates. In this article, a subwavelength planar microwave sensor is proposed, which is composed of a single-microstrip transmission line with a pair of coupled circular complementary spiral resonators (CSRs). Thus, two robust band-stop notches in the transmission response are developed, which can be utilized for simultaneous thickness and permittivity measurements of a dielectric sheet. The mutual capacitance and inductance, which quantify the coupling effect between the two resonators, are added to the equivalent lumped circuit model. They enhance the frequency shifts, thus improving sensitivity. The results of full-wave simulations and measurements on five different types of samples verify the concept’s effectiveness.
Machine Learning Algorithms and Frameworks in Ransomware Detection

Ransomware has been one of the biggest cyber threats against consumers in recent years. It can leverage various attack vectors while it also evolves in terms of finding more innovative ways to invade different cyber security systems. There have been many efforts to detect ransomware within the workforce and academia leveraging machine learning algorithms, which has shown promising results. Accordingly, there is a considerably large body of literature addressing various solutions on how ransomware threats can be detected and mitigated. Such large and rapidly growing scientific and technical materials start to make it difficult in knowing the actual ML algorithm(s) being used. Hence, the aim of this paper is to give insight about ransomware detection frameworks and those ML algorithms which are typically being used to extract ever-evolving characteristics of ransomware. In addition, this study will provide the cyber security community with a detailed analysis of those frameworks. This will be augmented with information such as datasets being used along with the challenges that each framework may be faced with in detecting a wide variety of ransomware accurately. To summarize, this paper delivers a comparative study which can be used by peers as a reference for future work in ransomware detection.
Analysis of the Influence of Boundary Condition on Simulation Accuracy of Solidification Thermokinetics Model

For a sample steel casting of a cylindrical shape that was cast in a metallic cylindrical mould, the analysis of influence and importance of the main boundary condition on the simulation accuracy of the temperature field in the system of casting-mould-environment was performed. For this system, the influence of the boundary condition on the frame of the mould, on the bottom of the mould, on the top of the casting, and on the casting-mould interface was analysed. As a comparing quantity for accuracy, the time-dependent temperature field (by means of isotherms) of the casting and of the mould and the total solidification time were selected. When the simulation considers the boundary conditions of one of these boundaries of the system, the heat transfer coefficient on the remaining three boundaries of the system is equal zero. The resultant heat flow along the axis is zero always. The conclusions can be used for fine-tuning of solidification models as well as for other applications; e.g. analysis and solution of inverse heat transfer problems.
New structure for orthogonal quantum group invariants

Based on the orthogonal Labastida-Mari{\~n}o-Ooguri-Vafa conjecture made by L. Chen & Q. Chen [5], we derive an infinite product formula for Chern-Simons partition functions, which generalizes the Liu-Peng's [19] recent results to the orthogonal case. Symmetry property of this new infinite product structure is also discussed.
Bethe vectors of GL(3)-invariant integrable models

We study GL(3)-invariant integrable models solvable by the nested algebraic Bethe ansatz. Different formulas are given for the Bethe vectors and the actions of the generators of the Yangian 𝒴(gl3) on the Bethe vectors are considered. These actions are relevant for the calculation of correlation functions and form factors of local operators of the underlying quantum models.
Integrable semi-discretization of the massive Thirring system in laboratory coordinates

Several integrable semi-discretizations are known in the literature for the massive Thirring system in characteristic coordinates. We present for the first time an integrable semi-discretization of the massive Thirring system in laboratory coordinates. Our approach relies on the relation between the continuous massive Thirring system and the Ablowitz–Ladik lattice. In particular, we derive the Lax pair for the integrable semi-discretization of the massive Thirring system by combining together the time evolution of the massive Thirring system in laboratory coordinates and the Bäcklund transformation for solutions to the Ablowitz–Ladik lattice.
The Role of Oxidation Pattern and Water Content in the Spatial Arrangement and Dynamics of Oxidized Graphene-Based Aqueous Dispersions

In this work, we employ fully atomistic molecular dynamics simulations to elucidate the effects of the oxidation pattern and of the water content on the organization of graphene sheets in aqueous dispersions and on the dynamic properties of the different moieties at neutral pH conditions. Analysis of the results reveals the role of the oxidation motif (peripherally or fully oxidized flakes) in the tendency of the flakes to self-assemble and in the control of key structural characteristics, such as the interlayer distance between the sheets and the average size and the distribution of the formed aggregates. In certain cases, the results are compared to a pertinent experimental system, validating further the relevant computational models. Examination of the diffusional motion of the oxidized flakes shows that different degrees of spatial restriction are imposed upon the decrease in the water content and elucidates the conditions under which a motional arrest of the flakes takes place. At constant water content, the structural differences between the formed aggregates appear to additionally impart distinct diffusional characteristics of a water molecule. A detailed examination of the counterion dynamics describes their interaction with the oxidized flakes and their dependence on the water content and on the oxidation pattern, offering new insight into the expected electrical properties of the dispersions. The detailed information provided by this work will be particularly useful in applications such as molecular sieving, nanofiltration, and in cases where conductive membranes based on oxidized forms of graphene are used.
Convolution-Bidirectional Temporal Convolutional Network for Protein Secondary Structure Prediction

As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem. Howerover, ordinary temporal convolutional networks can not deal well protein secondary structure prediction because of their one-way analysis. Therefore, we propose an integrated deep learning model called Convolutional-Bidirectional Temporal Convolutional Network. for 3-state and 8-state protein secondary structure predictions based on a convolutional neural network and bidirectional temporal convolutional networks. Because the model combines the advantages of the convolutional neural network and bidirectional temporal convolution network, it can not only capture the local correlation in the amino acid sequence but also analyse the long-distance interaction in the amino acid sequence. Therefore, this model can effectively improve the accuracy of protein secondary structure predictions. The experimental results show that the combination of convolutional neural network and bidirectional temporal convolutional network is effective for predicting protein secondary structure.