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Large Language Models (LLMs) have facilitated the definition of autonomous intelligent agents. Such agents have already demonstrated their potential in solving complex tasks in different domains. And they can further increase their performance when collaborating with other agents in a multi-agent system. However, the orchestration and coordination of these agents is still challenging, especially when they need to interact with humans as part of human-agentic collaborative workflows. These kinds of workflows need to be precisely specified so that it is clear whose responsible for each task, what strategies agents can follow to complete individual tasks or how decisions will be taken when different alternatives are proposed, among others. Current business process modeling languages fall short when it comes to specifying these new mixed collaborative scenarios. In this exploratory paper, we extend a well-known process modeling language (i.e., BPMN) to enable the definition of this new type of workflow. Our extension covers both the formalization of the new metamodeling concepts required and the proposal of a BPMN-like graphical notation to facilitate the definition of these workflows. Our extension has been implemented and is available as an open-source human-agentic workflow modeling editor on GitHub.

This exploratory study highlights the significant threats of social media polarization and weaponization in Ethiopia, analyzing the Northern Ethiopia (Tigray) War (November 2020 to November 2022) as a case study. It further uncovers the lack of effective digital peacebuilding initiatives. These issues particularly impact women, who bear a disproportionate burden in the armed conflict. These repercussions extend beyond the digital sphere, affecting women's socio-economic conditions, safety, and well-being. This reality was starkly evident during the war, where women faced gender-based and sexual violence. The research findings disclose the interface between social media polarization, conflict, and gender based violence. It also reveals the marginalization of women's voice in peacebuilding initiatives. This marginalization in peacebuilding efforts can be attributed to hostile online environments, the digital divide, cultural and societal norms, as well as top-down peace initiatives. The study highlights substantial gaps in leveraging digital media for sustainable peace and empowering women's participation. The unregulated landscape of social media in Ethiopia exacerbates these problems, necessitating heightened demands for accountability, especially from major social media platforms. The study recommends enhanced moderation and ethical considerations in algorithmic design gains traction, underlining the urgency for transparent and responsible social media frameworks. It is also recommended that digital peacebuilding initiatives should adopt a gender-sensitive and inclusive approach to address these complexities effectively and sustainably.

This study is a modification of the McKendrick equation into a growth model with fractional order to predict the abalone length growth. We have shown that the model is a special case of Taylor's series after it was analysed using Adomian decomposition method and Caputo fractional derivative. By simulating the series with some fractional orders, the results indicate that the greater the fractional order of the model, the series values generated are greater as well. Moreover, the series that is close to the real data is the one with a fractional order of $0.5$. Therefore, the growth model with a fractional order provides more accuracy than a classical integer order., Comment: 15 pages

This study examines conversational business analytics, an approach that utilizes AI to address the technical competency gaps that hinder end users from effectively using traditional self-service analytics. By facilitating natural language interactions, conversational business analytics aims to empower end users to independently retrieve data and generate insights. The analysis focuses on Text-to-SQL as a representative technology for translating natural language requests into SQL statements. Developing theoretical models grounded in expected utility theory, this study identifies the conditions under which conversational business analytics, through partial or full support, can outperform delegation to human experts. The results indicate that partial support, focusing solely on information generation by AI, is viable when the accuracy of AI-generated SQL queries leads to a profit that surpasses the performance of a human expert. In contrast, full support includes not only information generation but also validation through explanations provided by the AI, and requires sufficiently high validation effectiveness to be reliable. However, user-based validation presents challenges, such as misjudgment and rejection of valid SQL queries, which may limit the effectiveness of conversational business analytics. These challenges underscore the need for robust validation mechanisms, including improved user support, automated processes, and methods for assessing quality independent of the technical competency of end users.

We consider a uniqueness problem concerning the Fourier coefficients of normalized Cauchy transforms. These problems inherently involve proving a simultaneous approximation phenomenon and establishing the existence of cyclic inner functions in certain sequence spaces. Our results have several applications in different directions. First, we offer a new non-probabilistic proof of a classic theorem by Kahane and Katzenelson on simultaneous approximation. Secondly, we demonstrate the absence of uniform admissible majorants of Fourier coefficients in de Branges-Rovnyak spaces., Comment: 16 pages

Software repositories is one of the sources of data in Empirical Software Engineering, primarily in the Mining Software Repositories field, aimed at extracting knowledge from the dynamics and practice of software projects. With the emergence of social coding platforms such as GitHub, researchers have now access to millions of software repositories to use as source data for their studies. With this massive amount of data, sampling techniques are needed to create more manageable datasets. The creation of these datasets is a crucial step, and researchers have to carefully select the repositories to create representative samples according to a set of variables of interest. However, current sampling methods are often based on random selection or rely on variables which may not be related to the research study (e.g., popularity or activity). In this paper, we present a methodology for creating representative samples of software repositories, where such representativeness is properly aligned with both the characteristics of the population of repositories and the requirements of the empirical study. We illustrate our approach with use cases based on Hugging Face repositories., Comment: The paper has been accepted for publication in the Proceedings of the 18th International Symposium on Empirical Software Engineering and Measurement (ESEM 2024)

The problem of modeling ropes arises in many applications, including providing haptic feedback to surgeons who are using surgical robots to realign the distal and proximal ends of split bones. Here, we consider a simplified, 2D variant of the haptic feedback estimation problem and discuss how visibility decompositions greatly simplify the problem. Then, we introduce an efficient, concise algorithm for modeling the dynamics of 2D ropes around polygonal obstacles in O($n$) time, where $n$ is the number of line segment obstacles.

The prevalence of digital media and evolving sociopolitical dynamics have significantly amplified the dissemination of hateful content. Existing studies mainly focus on classifying texts into binary categories, often overlooking the continuous spectrum of offensiveness and hatefulness inherent in the text. In this research, we present an extensive benchmark dataset for Amharic, comprising 8,258 tweets annotated for three distinct tasks: category classification, identification of hate targets, and rating offensiveness and hatefulness intensities. Our study highlights that a considerable majority of tweets belong to the less offensive and less hate intensity levels, underscoring the need for early interventions by stakeholders. The prevalence of ethnic and political hatred targets, with significant overlaps in our dataset, emphasizes the complex relationships within Ethiopia's sociopolitical landscape. We build classification and regression models and investigate the efficacy of models in handling these tasks. Our results reveal that hate and offensive speech can not be addressed by a simplistic binary classification, instead manifesting as variables across a continuous range of values. The Afro-XLMR-large model exhibits the best performances achieving F1-scores of 75.30%, 70.59%, and 29.42% for the category, target, and regression tasks, respectively. The 80.22% correlation coefficient of the Afro-XLMR-large model indicates strong alignments.

We prove an analogue of Miller's stable splitting of the unitary group $U(m)$ for spaces of commuting elements in $U(m)$. After inverting $m!$, the space $\text{Hom}(\mathbb{Z}^n,U(m))$ splits stably as a wedge of Thom-like spaces of bundles of commuting varieties over certain partial flag manifolds. Using Steenrod operations we prove that our splitting does not hold integrally. Analogous decompositions for symplectic and orthogonal groups as well as homological results for the one-point compactification of the commuting variety in a Lie algebra are also provided., Comment: To appear in Journal of the London Mathematical Society

It is generally known that in order to solve the split equality fixed-point problem (SEFPP), it is necessary to compute the norm of bounded and linear operators, which is a challenging task in real life, to address this issue, we studied the SEFPP involving the class of quasi-pseudocontractive mappings in Hilbert spaces and constructed novel algorithms in this regards, and we proved the algorithms' convergence both with and without prior knowledge of the operator norm for bounded and linear mappings. Additionally, we gave applications and numerical examples of our findings. A variety of well-known discoveries revealed in the literature are generalized by the findings presented in this work., Comment: 19 pages

We investigate asymptotic polynomial approximation for a class of weighted Bloch functions in the unit disc. Our main result is a structural theorem on asymptotic polynomial approximation in the unit disc, in the flavor of the classical Plessner Theorem on asymptotic values of meromorphic functions. This provides the appropriate set up for studying metric and geometric properties of sets E on the unit circle for which the following simultaneous approximation phenomenon occurs: there exists analytic polynomials which converge uniformly to zero on E and to a non-zero function in the weighted Bloch norm. We offer a characterization completely within the realm of real-analysis, establish a connection to removable sets for analytic Sobolev functions in the complex plane, and provide several necessary conditions in terms of entropy, Hausdorff content and condenser capacity. Furthermore, we demonstrate two principal applications of our developments, which go in different directions. First, we shall deduce a rather subtle consequence in the theme of smooth approximation in de Branges-Rovnyak spaces. Secondly, we answer some questions that were raised almost a decade ago in the theory of Universal Taylor series., Comment: 39

This report summarizes the results of kindergarten through fifth grade students' academic achievement outcomes in mathematics and science following four years of campus-level participation in the Launching Elementary Academic Foundations (LEAF) to STEM program. The LEAF to STEM Program consists of three key components that were adapted for K-5 teachers from a secondary STEM curricular intervention within the same charter system. The three key components were: 1) an elementary-focused project-based learning STEM curriculum, 2) ongoing professional development for participating teachers, and 3) peer-to-peer mentoring with secondary STEM teachers serving as mentors to elementary STEM teachers. The charter school system in which LEAF to STEM was implemented is ethnically and racially diverse, with a higher percentage of students of color than the state in which the system operates (86.1% vs. 71.9% students of color). The system serves a high population of students identified as at-risk, including 60% of students identified as eligible for Free and Reduced Lunch and 24% of students identified as English Language Learners. At the time of randomization, the charter system had a total of 14,572 grades K-5 students on 34 K-5 campuses. The cluster randomized-controlled trial assigned thirty-two K-5 campuses within a large charter system to a treatment/control (business-as-usual) condition using stratified sampling. Campuses were stratified into four groups based on whether a campus's total percentage of students classified as eligible for free-and-reduced price lunch (FRL) fell above or below the system-wide average percentage of FRL students. Within each of those levels, schools were further stratified into two additional groups, based on whether the percentage of a school's students meeting NWEA MAP Reading and Mathematics growth targets in the year prior to randomization to treatment was above or below the system-wide average. Student achievement in mathematics and science was measured using the NWEA MAP Rausch Interval Unit (RIT) scores for mathematics and science for all grades K through five students enrolled in campuses at the time of randomization. Mathematics baseline RIT scores, as well as RIT scores for years one (spring 2020), two (spring 2021), three (spring 2022), and four (spring 2023) of treatment were available for all students, whereas science RIT scores were only available for grades 4 and 5 students after one year (for both 4th and 5th grade students at the onset of the program) or two years of treatment (for 4th grade students only at the onset). Two-level hierarchical linear modeling (students nested in campuses) was employed to explore the impact of treatment on students' mathematics and science achievement (NWEA MAP scores) controlling for demographic background (i.e., ethnicity and gender) and prior achievement at the onset of the LEAF to STEM (beginning of year 1). Results indicated no statistically significant differences in mathematics and science achievement between students in the treatment and control conditions after four years of the exposure to the LEAF to STEM program. When broken down by the grade level, results still did not indicate any significant differences between treatment and control groups in mathematics and science achievement.

In this work, product tables in invoices are obtained autonomously via a deep learning model, which is named as ExTTNet. Firstly, text is obtained from invoice images using Optical Character Recognition (OCR) techniques. Tesseract OCR engine [37] is used for this process. Afterwards, the number of existing features is increased by using feature extraction methods to increase the accuracy. Labeling process is done according to whether each text obtained as a result of OCR is a table element or not. In this study, a multilayer artificial neural network model is used. The training has been carried out with an Nvidia RTX 3090 graphics card and taken $162$ minutes. As a result of the training, the F1 score is $0.92$., Comment: 6 pages, 4 figures, 3 tables

This article presents a two-stage approach for the processing of frequency-stacked mobile subbands. The frequency stacking is performed in the analog domain to enable the use of a wideband analog-to-digital converter (ADC), instead of employing multiple narrowband ADCs, to support multiple antenna elements for digital satellite beamforming. This analog front end provides a common broadband digital interface to the on-board processor and can be configured to support multiple satellite missions, reducing the cost of commissioning a digital processor for individual satellite missions. This article proposes a framework on the specification of digital prototype filter for the analysis of frequency-stacked mobile subbands. The computational complexity of the analysis operation, with two digital filter alternatives, are evaluated. A series of results, taken from our European Space Agency sponsored project, are presented here to demonstrate the applicability of the proposed two stage approach, reporting on the savings in power consumption when an Nth-band all-pass-based recursive filter having an infinite impulse response is used as the digital prototype filter.

As 5G continues to expand its coverage and use. Innovative ideas/technologies continue to be implemented within. New vulnerabilities appear, thus resulting in new methods of mitigation and detection to occur. With the architecture that 5G can implement, DDoS (Distributed Denial of Service) is at a higher risk. There are many methods and approaches to help combat this challenge, most of which are implemented in networks containing Wi-Fi (Wireless Fidelity). This article aims to discuss the possible approaches that could be included in 5G technology. The method we will discuss involves Machine Learning. We have used three classifiers to test on datasets (Na\"ive Bayes, UltraBoost, LogitBoost) with multiple cross-folds, verifying which would have the highest accuracy with multiple factors (such as the cross-folds, verifying whether the number of folds affects accuracy), expanding upon [25] by using feature selection to obtain more accurate results.

Background. The development of empirical studies in software engineering mainly relies on the data available on code hosting platforms, being GitHub the most representative. Nevertheless, in the last years, the emergence of Machine Learning (ML) has led to the development of platforms specifically designed for developing ML-based projects, being Hugging Face Hub (HFH) the most popular one. With over 250k repositories, and growing fast, HFH is becoming a promising ecosystem of ML artifacts and therefore a potential source of data for empirical studies. However, so far there have been no studies evaluating the potential of HFH for such studies. Objective. In this proposal for a registered report, we aim at performing an exploratory study of the current state of HFH in order to investigate its suitability to be used as a source platform for empirical studies. Method. We conduct a qualitative and quantitative analysis of HFH for empirical studies. The former will be performed by comparing the features of HFH with those of other code hosting platforms, such as GitHub and GitLab. The latter will be performed by analyzing the data available in HFH., Comment: Accepted registered report at the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)

We consider weak-star closed invariant subspaces of the shift operator in the classical Bloch space. We prove that any bounded analytic function decomposes into two factors, one which is cyclic and another one generating a proper invariant subspace, satisfying a permanence property, which in a certain way is opposite to cyclicity. Singular inner functions play the crucial role in this decomposition. We show in several different ways that the description of shift invariant subspaces generated by inner functions in the Bloch spaces deviates substantially from the corresponding description in the Bergman spaces, provided by the celebrated Korenblum-Roberts Theorem. Furthermore, the relationship between invertibility and cyclicity is also investigated and we provide an invertible function in the Bloch space which is not cyclic therein. Our results answer several open questions stated in the early nineties., Comment: 38 pages

The aim of this research is to examine various statistical approximation properties with respect to Kantorovich \textit{\text{\texthtq}}-Baskakov operators using wavelets. We discuss and investigate a weighted statistical approximation employing a Bohman-Korovkin type theorem as well as a statistical rate of convergence applying a weighted modulus of smoothness $\omega_{\rho_{\alpha}}$ correlated with the space $B_{\rho\alpha}(\mathbb{R_{+}})$ and Lipschitz type maximal functions. Both topics are covered in the article., Comment: 15 pages

We present results of implicit large eddy simulation (LES) and different Reynolds-averaged Navier-Stokes (RANS) models of the MTU 161 low pressure turbine at an exit Reynolds number of 90,000 and exit Mach number of 0.6. The LES results are based on a high order discontinuous Galerkin method and the RANS is computed using a classical finite-volume approach. The paper discusses the steps taken to create realistic inflow boundary conditions in terms of end wall boundary layer thickness and free stream turbulence intensity. This is achieved by tailoring the input distribution of total pressure and temperature, Reynolds stresses and turbulent length scale to a Fourier series based synthetic turbulence generator. With this procedure, excellent agreement with the experiment can be achieved in terms of blade loading at midspan and wake total pressure losses at midspan and over the channel height. Based on the validated setup, we focus on the discussion of secondary flow structures emerging due to the interaction of the incoming boundary layer and the turbine blade and compare the LES to two commonly used RANS models. Since we are able to create consistent setups for both LES and RANS, all discrepancies can be directly attributed to physical modelling problems. We show that both a linear eddy viscosity model and a differential Reynolds stress model coupled with a state-of-the-art correlation-based transition model fail, in this case, to predict the separation induced transition process around midspan. Moreover, their prediction of secondary flow losses leaves room for improvement as shown by a detailed discussion turbulence kinetic energy and anisotropy fields., Comment: invited submission to Flow, Turbulence and Combustion Special Issue: 13th ERCOFTAC Workshop on Direct and Large Eddy Simulation - DLES13

We investigate certain classes of shift invariant subspaces in growth spaces on the unit disc of the complex plane determined by a majorant $w$, which include the classical Korenblum growth spaces. Our main result provides a complete description of shift invariant subspaces generated by Nevanlinna class functions in growth spaces, where we show that they are of Beurling-type. In particular, our result generalizes the celebrated Korenblum-Roberts Theorem. It turns out that singular inner functions play the decisive role in our description, phrased in terms of certain $w$-entropy conditions on the carrier sets of the associated singular measures, which arise in connection to boundary zero sets for analytic functions in the unit disc having modulus of continuity not exceeding $w$ on the unit circle. Furthermore, this enables us to establish an intimate link between shift invariant subspace generated by inner functions and the containment of the above mentioned analytic function spaces in the corresponding model spaces., Comment: 29 pages

Dynamical properties of materials determined using force constant expansions in the HIPHIVE package and density functional theory as implemented in the Gpaw code. Thermal conductivity of alkaline earth tellurides was determined, and the narrower width between amplitudons and electrical modes exhibited supersonic cooling and heat transportation, and our findings are in perfect agreement with experimental research. When compared to the other binary crystals, BaTe appears to offer the dual advantage of prolonged photon lifetime at normal temperature as well as a speedier thermo-electric converter due to the tiny gap between amplitudons and electric modes.

With the large number of deep space (DS) missions anticipated by the end of this decade, reliable-high capacity DS communication systems are needed more than ever. Nevertheless, existing DS communication technologies are far from meeting such a goal. Improving current systems does not only demand a system engineering leadership, but more crucially a well investigation in the potentials of emerging technologies in overcoming the challenges of the unique-ultra long DS communication channel. This project starts with a survey that highlights current technologies, trends, and advancements, investigates potentials, and identify challenges, and in essence, provide perspectives and propose solutions. It focuses on free-space optical (FSO) communication as a potential technology that can overcome the shortcomings of current radio frequency (RF)-based communication systems. To the best of our knowledge, in addition, it provides for the very first time a thoughtful discussion about implementing orbital angular momentum (OAM) for DS, identifies major related challenges, and proposes some novel solutions. Furthermore, we discuss DS modulations and coding schemes, as well as emerging receiver technologies and communication protocols. We also elaborate on how all of these technologies guarantee reliability, improve efficiency, offer capacity boosts, and enhance security in the unique DS environment. In addition to that, an extended study on the design and performance analysis of deep space optical communication (DSOC) is included, with the most suggested modulation for such a link being pulse position modulation (PPM) and a focus on the communication between Earth and the planet Mars, which is an important destination for space exploration.

In this paper, we consider low-rank approximations for the solutions to the stochastic Helmholtz equation with random coefficients. A Stochastic Galerkin finite element method is used for the discretization of the Helmholtz problem. Existence theory for the low-rank approximation is established when the system matrix is indefinite. The low-rank algorithm does not require the construction of a large system matrix which results in an advantage in terms of CPU time and storage. Numerical results show that, when the operations in a low-rank method are performed efficiently, it is possible to obtain an advantage in terms of storage and CPU time compared to computations in full rank. We also propose a general approach to implement a preconditioner using the low-rank format efficiently.

The development of integrated sensing and communication (ISAC) systems has recently gained interest for its ability to offer a variety of services including resources sharing and new applications, for example, localization, tracking, and health care related. While the sensing capabilities are offered through many technologies, rending to their wide deployments and the high frequency spectrum they provide and high range resolution, its accessibility through the Wi-Fi networks IEEE 802.11ad and 802.11ay has been getting the interest of research and industry. Even though there is a dedicated standardization body, namely the 802.11bf task group, working on enhancing the Wi-Fi sensing performance, investigations are needed to evaluate the effectiveness of various sensing techniques. In this project, we, in addition to surveying related literature, we evaluate the sensing performance of the millimeter wave (mmWave) Wi-Fi systems by simulating a scenario of a human target using Matlab simulation tools. In this analysis, we processed channel estimation data using the short time Fourier transform (STFT). Furthermore, using a channel variation threshold method, we evaluated the performance while reducing feedback. Our findings indicate that using STFT window overlap can provide good tracking results, and that the reduction in feedback measurements using 0.05 and 0.1 threshold levels reduces feedback measurements by 48% and 77%, respectively, without significantly degrading performance., Comment: arXiv admin note: substantial text overlap with arXiv:2207.04859 by other authors

We present first results of an implicit large eddy simulation of the MTU T161 low pressure turbine at a Reynolds number of 90,000 and Mach number of 0.6, both based on isentropic exit conditions, using a high order discontinuous Galerkin method. The aim is to validate the numerical setup with respect to available experimental data. We discuss the steps taken to create realistic inflow boundary conditions in terms of end wall boundary layer thickness and free stream turbulence intensity. This is achieved by tailoring the input distribution of Reynolds stresses and turbulent length scale to a Fourier series based synthetic turbulence generator. Both blade loading and total pressure losses at midspan show excellent agreement with the measurements. Following a short discussion of the secondary flow structures emerging due to the interaction of the incoming boundary layer and the turbine blade, we show that this simulation is also able to reproduce loss distribution behind the blade over the whole channel height., Comment: submitted to conference proceedings of DLES13 (Direct and Large Eddy Simulation)

Quantum technologies (QT) promise to change the landscape of technologies disruptively in diverse industries. For this reason, many nations around the globe are investing to emerge within the global quantum ecosystem through initiating national programs and international partnerships. Nonetheless, some other countries are still running behind and yet their governments need to take series actions to help their private and public sectors adapt to the looming changes, considering the new regulations required and the huge influence that QT will present in the near future. In this opinion piece, we provide, for the best of our knowledge, the first generally applicable, yet comprehensive and brief, framework for leveraging the emerging quantum technologies to facilitate the establishment of national initiatives properly. The insights presented in this article were driven based on investigating various approaches, initiatives, and roadmaps adopted globally and meeting with local and regional leaders, professionals, and governmental officials. Furthermore, taken into account socioeconomic and institutional dimensions of the Libyan society, we project the framework for the Libyan nation. This opinion piece is intended to inspire researchers, technical industrial experts, stakeholders, and governmental bodies to find roles they need to play to bring QT forward.

With a large number of deep space (DS) missions anticipated by the end of this decade, reliable and high-capacity DS communications are needed more than ever. Nevertheless, existing technologies are far from meeting such a goal. Improving current systems does not only require engineering leadership, but also, very crucially, investigating potential technologies that overcome the unique challenges of ultra-long DS links. To the best of our knowledge, there has not been any comprehenive surveys of DS communications technologies over the last decade. Free space optical (FSO) is an emerging DS technology, proven to acquire lower communications systems size weight and power (SWaP) and achieve a very high capacity compared to its counterpart radio frequency (RF), the currently used DS technology. In this survey, we discuss the pros and cons of deep space optical communications (DSOC) and review their physical and networking characteristics. Furthermore, we provide, for the first time, thoughtful discussions about implementing orbital angular momentum (OAM) and quantum communications (QC) for DS. We elaborate on how these technologies among other field advances including interplanetary network (IPN) and RF/FSO systems improve reliability, capacity, and security. This paper provides a holistic survey of DSOC technologies gathering 247 fragmented pieces of literature and including novel perspectives aiming to set the stage for more developments in the field., Comment: 17 pages, 10 Figures

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi., Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCVi

Rendering to their abilities to provide ubiquitous connectivity, flexibly and cost effectively, unmanned aerial vehicles (UAVs) have been getting more and more research attention. To take the UAVs' performance to the next level, however, they need to be merged with some other technologies like non-orthogonal multiple access (NOMA) and millimeter wave (mmWave), which both promise high spectral efficiency (SE). As managing UAVs efficiently may not be possible using model-based techniques, another key innovative technology that UAVs will inevitably need to leverage is artificial intelligence (AI). Designing an AI-based technique that adaptively allocates radio resources and places UAVs in 3D space to meet certain communication objectives, however, is a tough row to hoe. In this paper, we propose a neuroevolution of augmenting topologies NEAT framework, referred to as ProSky, to manage NOMA-mmWave-UAV networks. ProSky exhibits a remarkable performance improvement over a model-based method. Moreover, ProSky learns 5.3 times faster than and outperforms, in both SE and energy efficiency EE while being reasonably fair, a deep reinforcement learning DRL based scheme. The ProSky source code is accessible to use here: https://github.com/Fouzibenfaid/ProSky, Comment: IEEE GC WS 2022

Faster-than-Nyquist (FTN) signaling is an attractive transmission technique which accelerates data symbols beyond the Nyquist rate to improve the spectral efficiency; however, at the expense of higher computational complexity to remove the introduced intersymbol interference (ISI). In this work, we introduce a novel FTN signaling transmission technique, named coordinate interleaved FTN (CI-FTN) signaling that exploits the ISI at the transmitter to generate constructive interference for every pair of the counter-clockwise rotated binary phase shift keying (BPSK) data symbols. In particular, the proposed CI- FTN signaling interleaves the in-phase (I) and the quadrature (Q) components of the counter-clockwise rotated BPSK symbols to guarantee that every pair of consecutive symbols has the same sign, and hence, has constructive ISI. At the receiver, we propose a low-complexity detector that makes use of the constructive ISI introduced at the transmitter. Simulation results show the merits of the CI-FTN signaling and the proposed low-complexity detector compared to conventional Nyquist and FTN signaling.

In the present manuscript, we present a new sequence of operators, $i.e.$, $\alpha$-Bernstein-Schurer-Kantorovich operators depending on two parameters $\alpha\in[0,1]$ and $\rho>0$ for one and two variables to approximate measurable functions on $[0: 1+q], q>0$. Next, we give basic results and discuss the rapidity of convergence and order of approximation for univariate and bivariate of these sequences in their respective sections. Further, Graphical and numerical analysis are presented. Moreover, local and global approximation properties are discussed in terms of first and second order modulus of smoothness, Peetre's K-functional and weight functions for these sequences in different spaces of functions., Comment: 3 figures

Our main purpose of this article is to study the convergence and other related properties of q-Bernstein-Kantorovich operators including the shifted knots of real positive numbers. We design the shifted knots of Bernstein-Kantorovich operators generated by the basic q-calculus. More precisely, we study the convergence properties of our new operators in the space of continuous functions and Lebesgue space. We obtain the degree of convergence with the help of modulus of continuity and integral modulus of continuity. Furthermore, we establish the quantitative estimates of Voronovskaja-type., Comment: The SCIE journal (FILOMAT) has already agreed (with major revisions) to publish in their forthcoming issue

For a given Beurling-Carleson subset $E$ of the unit circle $\mathbb{T}$ which has positive Lebesgue measure, we give explicit formulas for measurable functions supported on $E$ such that their Cauchy transforms have smooth extensions from $\mathbb{D}$ to $\mathbb{T}$. The existence of such functions has been previously established by Khrushchev in 1978, in non-constructive ways by the use of duality arguments. We construct several particular families of such Cauchy transforms with a few applications in operator and function theory in mind. In one application, we give a new proof of irreducibility of the shift operator on certain Hilbert spaces of functions. In another application, we establish a permanence principle for inner factors under convergence in certain topologies. The applications lead to a self-contained duality proof of the density of smooth functions in a very large class of de Branges-Rovnyak spaces. This extends the previously known approximation results., Comment: This is an updated version of a previous preprint, with some topics added in and some topics moved to a different preprint. Any comments from the community would be highly appreciated

Inequalities play important roles not only in mathematics, but also in other fields, such as economics and engineering. Even though many results are published on Hermite-Hadamard (H-H) type inequalities, new researcher to this fields often found it difficult to understand them. Thus, some important discoverers, such as the formulations of H-H type inequalities via various classes of convexity, through differentiable mappings and for fractional integrals, are presented. Some well-known examples from previous literature are used as illustrations., Comment: 29 pages

In this paper, deep neural network (DNN) is integrated with spatial modulation-orthogonal frequency division multiplexing (SM-OFDM) technique for end-to-end data detection over Rayleigh fading channel. This proposed system directly demodulates the received symbols, leaving the channel estimation done only implicitly. Furthermore, an ensemble network is also proposed for this system. Simulation results show that the proposed DNN detection scheme has a significant advantage over classical methods when the pilot overhead and cyclic prefix (CP) are reduced, owing to its ability to learn and adjust to complicated channel conditions. Finally, the ensemble network is shown to improve the generalization of the proposed scheme, while also showing a slight improvement in its performance., Comment: 4 pages, 5 figures, 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA

We study the invariant subspaces generated by inner functions for a class of $\mathcal{P}^t(\mu)$-spaces which can be identified as spaces of analytic functions in the unit disk $\mathbb{D}$, where $\mu$ is a measure supported in the closed unit disk and $\mathcal{P}^t(\mu)$ is the span of analytic polynomials in the usual Lebesgue space $L^t(\mu)$. Our measures define a range of spaces somewhere in between the Hardy and the Bergman spaces, and our results are thus a mixture of results from these two theories. For a large class of measures $\mu$ we characterize the cyclic inner functions, and exhibit some interesting properties of invariant subspaces generated by non-cyclic inner functions. Our study is motivated by a connection with the problem of smooth approximations in de Branges-Rovnyak spaces.

For the class of de Branges-Rovnyak spaces $\mathcal{H}(b)$ of the unit disk $\mathbb{D}$ defined by extreme points $b$ of the unit ball of $H^\infty$, we study the problem of approximation of a general function in $\mathcal{H}(b)$ by a function with an extension to the unit circle $\mathbb{T}$ of some degree of smoothness, for instance satisfying H\"older estimates or being differentiable. We will exhibit connections between this question and the theory of subnormal operators and, in particular, we will tie the possibility of smooth approximations to properties of invariant subspaces of a certain subnormal operator. This leads us to several computable conditions on $b$ which are necessary for such approximations to be possible. For a large class of extreme points $b$ we use our result to obtain explicit necessary and sufficient conditions on the symbol $b$ which guarantee the density of functions with differentiable boundary values in the space $\mathcal{H}(b)$. These conditions include an interplay between the modulus of $b$ on $\mathbb{T}$ and the spectrum of its inner factor.

Ultra-reliable low-latency communication (URLLC) requires short packets of data transmission. It is known that when the packet length becomes short, the achievable rate is subject to a penalty when compared to the channel capacity. In this paper, we propose to use faster-than-Nyquist (FTN) signaling to compensate for the achievable rate loss of short packet communications. We investigate the performance of a combination of a low complexity detector of FTN signaling used with nonbinary low-density parity-check (NB-LDPC) codes that is suitable for low-latency and short block length requirements of URLLC systems. Our investigation shows that such combination of low-complexity FTN signaling detection and NB-LDPC codes outperforms the use of close-to-optimal FTN signaling detectors with LDPC codes in terms of error rate performance and also has a considerably lower computational complexity., Comment: 6 pages, 5 figures, accepted for publication in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2021)

We give an abstract approach to approximations with a wide range of regularity classes $X$ in spaces of pseudocontinuable functions $K^p_\vartheta$, where $\vartheta$ is an inner function and $p>0$. More precisely, we demonstrate a general principle, attributed to A. B. Aleksandrov, which asserts that if a certain linear manifold $X$ is dense in the space of pseudocontinuable functions $K^{p_0}_\vartheta$, for some $p_0>0$, then $X$ is in fact dense in $K^p_{\vartheta}$, for all $p>0$. %This allows for generalizations of the recent result on density by functions with smooth boundary extensions. Moreover, for a rich class of Banach spaces of analytic functions $X$, we describe the precise mechanism that determines when $X$ is dense in a certain space of pseudocontinuable functions. As a consequence, we obtain an extension of Aleksandrov's density theorem to the class of analytic functions with uniformly convergent Taylor series., Comment: 15 pages

The sixth generation (6G), unlike any of the previous generations, is envisioned by 2030 to connect everything. Moreover, in addition to the new use cases, 6G is expected to support, it will need to provide a superior performance over 5G. The global connectivity, large network dimensions, users heterogeneity, extremely low-power consumption, high throughput, ultrahigh reliability, efficient network operation and maintenance, and low-latency requirements to be met by future networks inevitably necessitate the autonomy of 6G. Intelligence, facilitated mainly by the advancement of artificial intelligence (AI) techniques, is a key to achieve autonomy. In this paper, we provide a bird's-eye view of 6G, its vision, progress, and objectives. Furthermore, we present some technologies that would be mainly enabling intelligent globally connected world. In addition to discussing the role of AI for future wireless communications, we, unlike any other review papers, provide our original results which give early evidence for the viability of achieving 6G networks autonomy through leveraging AI advances. Furthermore, we, very importantly, identify 6G implementation challenges and key innovative techniques that promise to solve them. This article serves as a starting point for learners to acquire more knowledge about 6G and also for researchers to promote more development to the field.

Unmanned aerial vehicle (UAV) has recently attracted a lot of attention as a candidate to meet the 6G ubiquitous connectivity demand and boost the resiliency of terrestrial networks. Thanks to the high spectral efficiency and low latency, non-orthogonal multiple access (NOMA) is a potential access technique for future communication networks. In this paper, we propose to use the UAV as a moving base station (BS) to serve multiple users using NOMA and jointly solve for the 3D-UAV placement and resource allocation problem. Since the corresponding optimization problem is non-convex, we rely on the recent advances in artificial intelligence (AI) and propose AdaptSky, a deep reinforcement learning (DRL)-based framework, to efficiently solve it. To the best of our knowledge, AdaptSky is the first framework that optimizes NOMA power allocation jointly with 3D-UAV placement using both sub-6GHz and millimeter wave mmWave spectrum. Furthermore, for the first time in NOMA-UAV networks, AdaptSky integrates the dueling network (DN) architecture to the DRL technique to improve its learning capabilities. Our findings show that AdaptSky does not only exhibit a fast-adapting learning and outperform the state-of-the-art baseline approach in data rate and fairness, but also it generalizes very well. The AdaptSky source code is accessible to use here: https://github.com/Fouzibenfaid/AdaptSky

We use the principle of almost orthogonality to give a new and simple proof that a sparse Lerner operator is bounded on a matrix- or operator-weighted space $L_W^{2}(\mu)$, where $\mu$ is a doubling measure on $\R^d$ if and only if the weight $W$ satisfies the Muckenhoupt $A_2(\mu)$-condition, restricted to the sparse collection in question. Our method extends to the infinite-dimensional setting, thus allowing for applications to the multi-parameter setting. For the class of Muckenhoupt $A_2$-weights, we obtain bounds in terms of mixed $A_{2}(\mu)$-$A_{\infty}(\mu)$-conditions, which is independent of dimension and agrees with the best known bound in the finite-dimensional vectorial setting. As an application, we prove a matrix-weighted bound for the maximal Bergman projection, where we obtain a new sharper bound in terms of the B\'ekoll\'e-Bonami characteristic. Furthermore, we consider commutators of sparse Lerner operators on operator-valued weighted $L^{2}$-spaces and some applications to multi-parameters, Comment: 29 pages

We consider the Euler characteristics $\chi(M)$ of closed orientable topological $2n$-manifolds with $(n-1)$-connected universal cover and a given fundamental group $G$ of type $F_n$. We define $q_{2n}(G)$, a generalized version of the Hausmann-Weinberger invariant for 4-manifolds, as the minimal value of $(-1)^n\chi (M)$. For all $n\geq 2$, we establish a strengthened and extended version of their estimates, in terms of explicit cohomological invariants of $G$. As an application we obtain new restrictions for non-abelian finite groups arising as fundamental groups of rational homology 4-spheres., Comment: 25 pages (v4): revised statements of Theorems A and B. Improvements to exposition throughout following referee's reports; final version, accepted for publication in Forum of Mathematics, Sigma

In this paper, we are interested in the study of a problem with fractional derivatives having boundary conditions of integral types. The problem represents a Caputo type advection-diffusion equation where the fractional order derivative with respect to time with $1<\alpha <2$. The method of the energy inequalities is used to prove the existence and the uniqueness of solutions of the problem. The finite difference method is also introduced to study the problem numerically in order to find an approximate solution of the considered problem. Some numerical examples are presented to show satisfactory results., Comment: 24 pages

This paper gives an in-depth review of the most common iterative methods for unconstrained optimization using two functions that belong to a class of Rosenbrock functions as a performance test. This study covers the Steepest Gradient Descent Method, the Newton-Raphson Method, and the Fletcher-Reeves Conjugate Gradient method. In addition, four different step-size selecting methods including fixed-step-size, variable step-size, quadratic-fit, and golden section method were considered. Due to the computational nature of solving minimization problems, testing the algorithms is an essential part of this paper. Therefore, an extensive set of numerical test results is also provided to present an insightful and a comprehensive comparison of the reviewed algorithms. This study highlights the differences and the trade-offs involved in comparing these algorithms., Comment: Title of the paper was changed and simulations were presented in a clear manner

We provide an alternative Fourier analysis for multigrid applied to the Poisson problem in 1D, based on explicit derivation of spectra of the iteration matrix. The new Fourier analysis has advantages over the existing one. It is easy to understand and enables us to write the error equation in terms of the eigenvector of the stiffness matrix. When weighted-Jacobi is used as a smoother with two different weights, multigrid is an exact solver.

In this article, we define a new class of convexity called generalized $(h-m)$-convexity, which generalizes $h$-convexity and $m$-convexity on fractal sets $\mathbb{R}^{\alpha}$ $(0<\alpha\leq 1)$. Some properties of this new class are discussed. Using local fractional integrals and generalized $(h-m)$-convexity, we generalized Hermite-Hadamard (H-H) and Fej\'er-Hermite-Hadamard (Fej\'er-H-H) types inequalities. We also obtained a new result of the Fej\'er-H-H type for the function whose derivative in absolute value is the generalized $(h-m)$-convexity on fractal sets. Some applications to random variables and numerical integrations are studied., Comment: 15 pages

The nonlinear dispersive wave equation inside the cylindrical elastic rod is derived by applying the Navier-Bernoulli hypothesis and Love's relation in \cite{5}. The elastic rod is assumed to be composed of the Murnaghan's materials such as Lam$\acute{e}$'s coefficient, Poisson ratio and constitutive constant which are compressible in nature. In this research paper we apply the two integral architectures namely extended sine-Gordon method and modified exponential function method to study the dispersive wave and solved for the solitons and their classifications. The existence of the number of solutions are proved with respect to the linear equation obtained by balancing principle. The related two and three dimensional graphs are simulated and drawn to show the complex structures., Comment: 23 pages

We characterize the model spaces $K_\Theta$ in which functions with smooth boundary extensions are dense. It is shown that such approximations are possible if and only if the singular measure associated to the singular inner factor of $\Theta$ is concentrated on a countable union of Beurling-Carleson sets. In fact, we use a duality argument to show that if there exists a restriction of the associated singular measure which does not assign positive measure to Beurling-Carleson sets, then even larger classes of functions, such as H\"older classes and large collections of analytic Sobolev spaces, fail to be dense. In contrast to earlier results on density of functions with continuous extensions to the boundary in $K_\Theta$ and related spaces, the existence of a smooth approximant is obtained through a constructive method., Comment: 14 pages

An adapted bubble approach which is a modifiation of the residual-free bubbles (RFB) method, is proposed for the Helmhotz problem in 2D. A new two-level finite element method is introduced for the approximations of the bubble functions. Unlike the other equations such as the advection-diffusion equation, RFB method when applied to the Helmholtz equation, does not depend on another stabilized method to obtain approximations to the solutions of the sub-problems. Adapted bubbles (AB) are obtained by a simple modification of the sub-problems. This modification increases the accuracy of the numerical solution impressively. The AB method is able to solve the Helmholtz equation efficiently in 2D up to ch = 3.5 where c is the wave number and h is the mesh size. We provide analysis to show how the AB method mitigates the pollution error.