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In this work, we present conception and study of gallium nitride (GaN) nanostructures on a gallium arsenide (GaAs) substrate with (111)A orientation. The nanostructures were designed by GaN droplet epitaxy and studied in-situ by X-ray photoelectron spectroscopy and ex-situ by atomic force microscopy, scanning electron microscopy and transmission electron microscopy. These studies were coupled with kinetic Monte Carlo simulations to precisely understand the phenomena occurring during the nitridation and to find the optimum conditions for complete nitridation of gallium droplets. The HRTEM observation showed a cubic (zinc blende) crystal structure of the GaN nanodots for a nitridation at 300{\deg}C. Ramping the temperature from 100{\deg}C to 350{\deg}C during droplet nitridation enabled to obtain a very high density (>1011cm-2) of GaN nanodots with the zinc blende crystallinity.

Compressed representations of 3D shapes that are compact, accurate, and can be processed efficiently directly in compressed form, are extremely useful for digital media applications. Recent approaches in this space focus on learned implicit or parametric representations. While implicits are well suited for tasks such as in-out queries, they lack natural 2D parameterization, complicating tasks such as texture or normal mapping. Conversely, parametric representations support the latter tasks but are ill-suited for occupancy queries. We propose a novel learned alternative to these approaches, based on intersections of localized explicit, or height-field, surfaces. Since explicits can be trivially expressed both implicitly and parametrically, NESI directly supports a wider range of processing operations than implicit alternatives, including occupancy queries and parametric access. We represent input shapes using a collection of differently oriented height-field bounded half-spaces combined using volumetric Boolean intersections. We first tightly bound each input using a pair of oppositely oriented height-fields, forming a Double Height-Field (DHF) Hull. We refine this hull by intersecting it with additional localized height-fields (HFs) that capture surface regions in its interior. We minimize the number of HFs necessary to accurately capture each input and compactly encode both the DHF hull and the local HFs as neural functions defined over subdomains of R^2. This reduced dimensionality encoding delivers high-quality compact approximations. Given similar parameter count, or storage capacity, NESI significantly reduces approximation error compared to the state of the art, especially at lower parameter counts.

The angular distributions of elastic scattering of $^{7}$Li, as well as the lithium-induced reaction of one-nucleon transfers $^{10}$B($^{7}$Li,$^{6}$Li)$^{11}$B were measured at $E_\text{lab}$ = 58 MeV. Experiment was done using U-400 accelerator beam of the FLNR JINR, Dubna. Angular distribution for reaction $^{10}$B($^{7}$Li,$^{6}$Li)$^{11}$B with excitation of the 3.56 MeV state ($^{6}$Li*) is presented for the first time. The DWBA analysis of the differential cross section of the $^{10}$B($^{7}$Li,$^{6}$Li)$^{11}$B ground state (g.s.) transition and excited ($J^\pi$ = 0$^+$, $T$ = 1, $E$ = 3.56 MeV) state of $^{6}$Li transition was performed. The optical model potentials were obtained by fitting of measured elastic scattering data and evaluating parameters for the output reaction channels. Phenomenological approach based on solving an approximate equation for the reaction form factor was used to determine its radial dependence and empirical values of asymptotic normalization coefficient (ANC). Obtained values of ANCs for the $^{6}$Li$_\text{g.s.}$ and $^{6}$Li*(3.56 MeV) states are in agreement with the literature ones. Comparison of the radial dependences of form factors shows that the wave function of the $^{6}$Li nucleus in excited ($J^\pi$ = 0$^+$, $T$ = 1, $E$ = 3.56 MeV) state has increased spatial dimension compared to the ground state. This result is an argument in favor of a halo existence in $^{6}$Li*(3.56 MeV) state, while the question of a halo in $^{6}$Li$_\text{g.s.}$ still leaves open., Comment: The article has undergone several updates: the title has been changed, Sergey Dmitriev has been added as an author, and acknowledgments to the research team have been added. Expansions and clarifications have been added to all chapters of the article. Typos have been corrected. The original results remain unchanged

Physics-Informed Neural Networks (PINNs) have revolutionized solving differential equations by integrating physical laws into neural network training. This paper explores PINNs for open-loop optimal control problems (OCPs) with incomplete information, such as unknown initial conditions and sparse boundary data. We derive optimality conditions from the Lagrangian multipliers and use a neural network to predict the state, adjoint, and control variables. In contrast with previous methods, our approach integrates these elements into a single neural network and addresses scenarios with consistently limited data. Specifically, we address the study of partially unknown equations identifying underlying parameters online by searching for the optimal solution. Numerical examples show the effectiveness of the proposed method even in scenarios characterized by a considerable lack of information.

Nowadays, cybersecurity has grown into a more significant and difficult scientific issue. The recog-nition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guarantees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an an-swer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryp-tion standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this re-search is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to com-pare the outcomes of the techniques evaluated in this research. The assessment focused on pro-cessing time and speed parameters, examining visual encoding and decoding using Java as the pri-mary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryp-tion and decryption, being over 50% faster than some other algorithms. However, the Twofish algo-rithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

Data clustering involves identifying latent similarities within a dataset and organizing them into clusters or groups. The outcomes of various clustering algorithms differ as they are susceptible to the intrinsic characteristics of the original dataset, including noise and dimensionality. The effectiveness of such clustering procedures directly impacts the homogeneity of clusters, underscoring the significance of evaluating algorithmic outcomes. Consequently, the assessment of clustering quality presents a significant and complex endeavor. A pivotal aspect affecting clustering validation is the cluster validity metric, which aids in determining the optimal number of clusters. The main goal of this study is to comprehensively review and explain the mathematical operation of internal and external cluster validity indices, but not all, to categorize these indices and to brainstorm suggestions for future advancement of clustering validation research. In addition, we review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms, such as the evolutionary clustering algorithm star (ECA*). Finally, we suggest a classification framework for examining the functionality of both internal and external clustering validation measures regarding their ideal values, user-friendliness, responsiveness to input data, and appropriateness across various fields. This classification aids researchers in selecting the appropriate clustering validation measure to suit their specific requirements.

We present an approach for the optimization of irrigation in a Richards' equation framework. We introduce a proper cost functional, aimed at minimizing the amount of water provided by irrigation, at the same time maximizing the root water uptake, which is modeled by a sink term in the continuity equation. The control is acting on the boundary of the dynamics and due to the nature of the mathematical problem we use a State-Dependent Riccati approach which provides suboptimal control in feedback form, applied to the system of ODEs resulting from the Richards' equation semidiscretization in space. The problem is tested with existing hydraulic parameters, also considering proper root water uptake functions. The numerical simulations also consider the presence of noise in the model to further validate the use of a feedback control approach.

This paper introduces an innovative entanglement-based protocol that accomplishes multiparty quantum private comparison leveraging maximally entangled GHZ3 triplets. The primary motivation is the design of a protocol that can be executed by contemporary quantum computers. This is made possible because the protocol uses only GHZ3 triplets, irrespective of the number of millionaires. While more complex multi-particle entangled states are possible, they are challenging to produce with existing quantum apparatus, leading to extended preparation time and complexity, particularly in scenarios involving numerous participants. By relying on GHZ3 states, which are the easiest to produce after Bell states, we avoid these drawbacks, and take a step towards the practical implementation of the protocol. An important quantitative characteristic of this protocol is that the required quantum resources are linear both in the number of millionaires and the volume of information to be compared. A notable aspect of the protocol is its suitability for both parallel and sequential execution. Although the execution of the quantum part of the protocol is envisioned to take place completely in parallel, it is also possible to be implemented sequentially. So, if the quantum resources do not suffice for the execution of the protocol in one go, it is possible to partition the millionaires into smaller groups and process these groups sequentially. Notably, our protocol involves two third parties; Trent is now accompanied by Sophia. This dual setup allows simultaneous processing of all n millionaires' fortunes. Implementation-wise, uniformity is ensured as all millionaires use similar private quantum circuits composed of Hadamard and CNOT gates. Lastly, the protocol is information-theoretically secure, preventing outside parties from learning about fortunes or inside players from knowing each other's secret numbers.

The environments where individuals live can present diverse navigation challenges, resulting in varying navigation abilities and strategies. Inspired by differing urban layouts and the Dual Solutions Paradigm test used for human navigators, we developed a simulated navigation environment to train deep reinforcement learning agents in a shortcut usage task. We modulated the frequency of exposure to a shortcut and navigation cue, leading to the development of artificial agents with differing abilities. We examined the encoded representations in artificial neural networks driving these agents, revealing intricate dynamics in representation learning, and correlated them with shortcut use preferences. Furthermore, we demonstrated methods to analyze representations across a population of nodes, which proved effective in finding patterns in what would otherwise be noisy single-node data. These techniques may also have broader applications in studying neural activity. From our observations in representation learning dynamics, we propose insights for human navigation learning, emphasizing the importance of navigation challenges in developing strong landmark knowledge over repeated exposures to landmarks alone.

The market for domestic robots made to perform household chores is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labor, in contrast to industrial robots, which are frequently criticized for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in several minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviors. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to optimize their actions to maximize rewards. However, the goal of Deep Reinforcement Learning is to address more complicated, continuous action-state spaces in real-world settings by combining RL with Neural Networks. The efficacy of DeepRL can be further augmented through interactive feedback, in which a trainer offers real-time guidance to expedite the robot's learning process. Nevertheless, the current methods have drawbacks, namely the transient application of guidance that results in repeated learning under identical conditions. Therefore, we present a novel method to preserve and reuse information and advice via Deep Interactive Reinforcement Learning, which utilizes a persistent rule-based system. This method not only expedites the training process but also lessens the number of repetitions that instructors will have to carry out. This study has the potential to advance the development of household robots and improve their effectiveness and efficiency as learners.

In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. We propose a radically new, to the best of our knowledge, algorithm that uses quantum ``entanglement'' (think of it as a special connection) to help news aggregators sniff out bad actors, whether they be other news sources or even fact-checkers trying to spread misinformation. This algorithm doesn't rely on quantum signatures, it just uses basic quantum technology we already have, in particular, special pairs of particles called ``EPR pairs'' that are much easier to create than other options. More complex entangled states are like juggling too many balls - they're hard to make and slow things down, especially when many players are involved. For instance, bigger, more complex states like ``GHZ states'' work for small groups, but they become messy with larger numbers. So, we stick with Bell states, the simplest form of entanglement, which are easy to generate no matter how many players are in the game. This means our algorithm is faster to set up, works for any number of participants, and is more practical for real-world use. Bonus points: it finishes in a fixed number of steps, regardless of how many players are involved, making it even more scalable. This new approach may lead to a powerful and efficient way to fight misinformation in the digital age, using the weird and wonderful world of quantum mechanics.

End-to-end multi-task dialogue systems are usually designed with separate modules for the dialogue pipeline. Among these, the policy module is essential for deciding what to do in response to user input. This policy is trained by reinforcement learning algorithms by taking advantage of an environment in which an agent receives feedback in the form of a reward signal. The current dialogue systems, however, only provide meagre and simplistic rewards. Investigating intrinsic motivation reinforcement learning algorithms is the goal of this study. Through this, the agent can quickly accelerate training and improve its capacity to judge the quality of its actions by teaching it an internal incentive system. In particular, we adapt techniques for random network distillation and curiosity-driven reinforcement learning to measure the frequency of state visits and encourage exploration by using semantic similarity between utterances. Experimental results on MultiWOZ, a heterogeneous dataset, show that intrinsic motivation-based debate systems outperform policies that depend on extrinsic incentives. By adopting random network distillation, for example, which is trained using semantic similarity between user-system dialogues, an astounding average success rate of 73% is achieved. This is a significant improvement over the baseline Proximal Policy Optimization (PPO), which has an average success rate of 60%. In addition, performance indicators such as booking rates and completion rates show a 10% rise over the baseline. Furthermore, these intrinsic incentive models help improve the system's policy's resilience in an increasing amount of domains. This implies that they could be useful in scaling up to settings that cover a wider range of domains., Comment: 6 pages, 1 figure, 18th IEEE International Conference on Semantic Computing

Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for researchers developing new methods and practitioners confronted with the choice of the most suitable method. The term simulation usually refers to parametric simulation, that is, computer experiments using artificial data made up of pseudo-random numbers. Plasmode simulation, that is, computer experiments using the combination of resampling feature data from a real-life dataset and generating the target variable with a known user-selected outcome-generating model (OGM), is an alternative that is often claimed to produce more realistic data. We compare parametric and Plasmode simulation for the example of estimating the mean squared error (MSE) of the least squares estimator (LSE) in linear regression. If the true underlying data-generating process (DGP) and the OGM were known, parametric simulation would obviously be the best choice in terms of estimating the MSE well. However, in reality, both are usually unknown, so researchers have to make assumptions: in Plasmode simulation for the OGM, in parametric simulation for both DGP and OGM. Most likely, these assumptions do not exactly reflect the truth. Here, we aim to find out how assumptions deviating from the true DGP and the true OGM affect the performance of parametric and Plasmode simulations in the context of MSE estimation for the LSE and in which situations which simulation type is preferable. Our results suggest that the preferable simulation method depends on many factors, including the number of features, and on how and to what extent the assumptions of a parametric simulation differ from the true DGP. Also, the resampling strategy used for Plasmode influences the results. In particular, subsampling with a small sampling proportion can be recommended.