First Order
Recently Published Documents





2021 ◽  
Vol 27 (11) ◽  
pp. 1149-1151
Nelson Baloian ◽  
José Pino

Modern technologies and various domains of human activities increasingly rely on data science to develop smarter and autonomous systems. This trend has already changed the whole landscape of the global economy becoming more AI-driven. Massive production of data by humans and machines, its availability for feasible processing with advent of deep learning infrastructures, combined with advancements in reliable information transfer capacities, open unbounded horizons for societal progress in close future. Quite naturally, this brings also new challenges for science and industry. In that context, Internet of things (IoT) is an enormously huge factory of monitoring and data generation. It enables countless devices to act as sensors which record and manipulate data, while requiring efficient algorithms to derive actionable knowledge. Billions of end-users equipped with smart mobile phones are also producing immensely large volumes of data, being it about user interaction or indirect telemetry such as location coordinates. Social networks represent another kind of data-intensive sources, with both structured and unstructured components, containing valuable information about world’s connectivity, dynamism, and more. Last but not least, to help businesses run smoothly, today’s cloud computing infrastructures and applications are also serviced and managed through measuring huge amounts of data to leverage in various predictive and automation tasks for healthy performance and permanent availability. Therefore, all these technology areas, experts and practitioners, are facing innovation challenges on building novel methodologies, accurate models, and systems for respective data-driven solutions which are effective and efficient. In view of the complexity of contemporary neural network architectures and models with millions of parameters they derive, one of such challenges is related to the concept of explainability of the machine learning models. It refers to the ability of the model to give information which can be interpreted by humans about the reasons for the decision made or recommendation released. These challenges can only be met with a mix of basic research, process modeling and simulation under uncertainty using qualitative and quantitative methods from the involved sciences, and taking into account international standards and adequate evaluation methods. Based on a successful funded collaboration between the American University of Armenia, the University of Duisburg-Essen and the University of Chile, in previous years a network was built, and in September 2020 a group of researchers gathered (although virtually) for the 2nd CODASSCA workshop on “Collaborative Technologies and Data Science in Smart City Applications”. This event has attracted 25 paper submissions which deal with the problems and challenges mentioned above. The studies are in specialized areas and disclose novel solutions and approaches based on existing theories suitably applied. The authors of the best papers published in the conference proceedings on Collaborative Technologies and Data Science in Artificial Intelligence Applications by Logos edition Berlin were invited to submit significantly extended and improved versions of their contributions to be considered for a journal special issue of J.UCS. There was also a J.UCS open call so that any author could submit papers on the highlighted subject. For this volume, we selected those dealing with more theoretical issues which were rigorously reviewed in three rounds and 6 papers nominated to be published. The editors would like to express their gratitude to J.UCS foundation for accepting the special issues in their journal, to the German Research Foundation (DFG), the German Academic Exchange Service (DAAD) and the universities and sponsors involved for funding the common activities and thank the editors of the CODASSCA2020 proceedings for their ongoing encouragement and support, the authors for their contributions, and the anonymous reviewers for their invaluable support. The paper “Incident Management for Explainable and Automated Root Cause Analysis in Cloud Data Centers” by Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, and Nicholas Kushmerick addresses an increasingly important problem towards autonomous or self-X systems, intelligent management of modern cloud environments with an emphasis on explainable AI. It demonstrates techniques and methods that greatly help in automated discovery of explicit conditions leading to data center incidents. The paper “Temporal Accelerators: Unleashing the Potential of Embedded FPGAs” by Christopher Cichiwskyj and Gregor Schiele presents an approach for executing computational tasks that can be split into sequential sub-tasks. It divides accelerators into multiple, smaller parts and uses the reconfiguration capabilities of the FPGA to execute the parts according to a task graph. That improves the energy consumption and the cost of using FPGAs in IoT devices. The paper “On Recurrent Neural Network based Theorem Prover for First Order Minimal Logic” by Ashot Baghdasaryan and Hovhannes Bolibekyan investigates using recurrent neural networks to determine the order of proof search in a sequent calculus for first-order minimal logic with a history mechanism. It demonstrates reduced durations in automated theorem proving systems.  The paper “Incremental Autoencoders for Text Streams Clustering in Social Networks” by Amal Rekik and Salma Jamoussi proposes a deep learning method to identify trending topics in a social network. It is built on detecting changes in streams of tweets. The method is experimentally validated to outperform relevant data stream algorithms in identifying “hot” topics. The paper “E-Capacity–Equivocation Region of Wiretap Channel” by Mariam Haroutunian studies a secure communication problem over the wiretap channel, where information transfer from the source to a legitimate receiver needs to be realized maximally secretly for an eavesdropper. This is an information-theoretic research which generalizes the capacity-equivocation region and secrecy-capacity function of the wiretap channel subject to error exponent criterion, thus deriving new and extended fundamental limits in reliable and secure communication in presence of a wiretapper. The paper “Leveraging Multifaceted Proximity Measures among Developers in Predicting Future Collaborations to Improve the Social Capital of Software Projects” by Amit Kumar and Sonali Agarwal targets improving the social capital of individual software developers and projects using machine learning. Authors’ approach applies network proximity and developer activity features to build a classifier for predicting the future collaborations among developers and generating relevant recommendations. 

2021 ◽  
Vol 27 (11) ◽  
pp. 1193-1202
Ashot Baghdasaryan ◽  
Hovhannes Bolibekyan

There are three main problems for theorem proving with a standard cut-free system for the first order minimal logic. The first problem is the possibility of looping. Secondly, it might generate proofs which are permutations of each other. Finally, during the proof some choice should be made to decide which rules to apply and where to use them. New systems with history mechanisms were introduced for solving the looping problems of automated theorem provers in the first order minimal logic. In order to solve the rule selection problem, recurrent neural networks are deployed and they are used to determine which formula from the context should be used on further steps. As a result, it yields to the reduction of time during theorem proving.

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Yong-Heng Lu ◽  
Yao Wang ◽  
Feng Mei ◽  
Yi-Jun Chang ◽  
Hang Zheng ◽  

Abstract First- and second-order topological phases, capable of inherent protection against disorder of materials, have been recently experimentally demonstrated in various artificial materials through observing the topologically protected edge states. Topological phase transition represents a new class of quantum critical phenomena, which is accompanied by the changes related to the bulk topology of energy band structures instead of symmetry. However, it is still a challenge to directly observe the topological phase transitions defined in terms of bulk states. Here, we theoretically and experimentally demonstrate the direct observation of multifarious topological phase transitions with real-space indicator in a single photonic chip, which is formed by integration of 324 × 33 waveguides supporting both first- and second-order topological phases. The trivial-to-first-order, trivial-to-second-order and first-to-second-order topological phase transitions signified by the band gap closure can all be directly detected via photon evolution in the bulk. We further observe the creation and destruction of gapped topological edge states associated with these topological phase transitions. The bulk-state-based route to investigate the high-dimensional and high-order topological features, together with the platform of freely engineering topological materials by three-dimensional laser direct writing in a single photonic chip, opens up a new avenue to explore the mechanisms and applications of artificial devices.

Gabriele Pulcini

AbstractIn Schwichtenberg (Studies in logic and the foundations of mathematics, vol 90, Elsevier, pp 867–895, 1977), Schwichtenberg fine-tuned Tait’s technique (Tait in The syntax and semantics of infinitary languages, Springer, pp 204–236, 1968) so as to provide a simplified version of Gentzen’s original cut-elimination procedure for first-order classical logic (Gallier in Logic for computer science: foundations of automatic theorem proving, Courier Dover Publications, London, 2015). In this note we show that, limited to the case of classical propositional logic, the Tait–Schwichtenberg algorithm allows for a further simplification. The procedure offered here is implemented on Kleene’s sequent system G4 (Kleene in Mathematical logic, Wiley, New York, 1967; Smullyan in First-order logic, Courier corporation, London, 1995). The specific formulation of the logical rules for G4 allows us to provide bounds on the height of cut-free proofs just in terms of the logical complexity of their end-sequent.

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Márcio M. Cunha ◽  
Edilberto O. Silva

In this manuscript, we study the relativistic quantum mechanics of an electron in external fields in the spinning cosmic string spacetime. We obtain the Dirac equation and write the first- and second-order equations from it, and then, we solve these equations for bound states. We show that there are bound state solutions for the first-order equation Dirac. For the second-order equation, we obtain the corresponding wave functions, which depend on the Kummer functions. Then, we determine the energies of the particle. We examine the behavior of the energies as a function of the physical parameters of the model, such as rotation, curvature, magnetic field, Aharonov-Bohm flux, and quantum numbers. We find that, depending on the values of these parameters, there are energy nonpermissible levels.

Yujia Zhang ◽  
Yang Kong ◽  
Xiliang Guo ◽  
Jiang Wang ◽  
Zhongming Ren ◽  

In this work, the static magnetic field was applied to plasma arc melting (PAM) method. The influence on sulfur removal from iron by Ar-20%H2 (PAM) was investigated experimentally. The results show that, sulfur content decreases more noticeably with the magnet field. The mechanism of sulfur removal by hydrogen plasma arc melting (HPAM) was found to obey a first-order rate law. From kinetic analysis, the adoption of magnet field enhanced purification efficiency about 45% under the same conditions. The numerical simulations were necessarily carried out to investigate the fluid flows of the melt. The results show that the magnet field changes the flow regime and strengthens the flow velocity of the melt. Moreover, the dead zone in melt by typical HPAM was eliminated. It is reasonable that dissolved sulfur atoms transferring to the top interface to react with active hydrogen atoms was promoted. In addition, during solidification process, thermoelectric magnetic (TEM) flows promote solute atoms that discharged from the solid–liquid interface moving to the upside of specimen. As a consequence, iron with much lower sulfur content could be obtained at the bottom of specimens, and sulfur removal was enhanced further during solidification under a static magnetic field.

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Ahmed Kajouni ◽  
Ahmed Chafiki ◽  
Khalid Hilal ◽  
Mohamed Oukessou

This paper is motivated by some papers treating the fractional derivatives. We introduce a new definition of fractional derivative which obeys classical properties including linearity, product rule, quotient rule, power rule, chain rule, Rolle’s theorem, and the mean value theorem. The definition D α f t = lim h ⟶ 0 f t + h e α − 1 t − f t / h , for all t > 0 , and α ∈ 0,1 . If α = 0 , this definition coincides to the classical definition of the first order of the function f .

Ernesto Copello ◽  
Nora Szasz ◽  
Álvaro Tasistro

Abstarct We formalize in Constructive Type Theory the Lambda Calculus in its classical first-order syntax, employing only one sort of names for both bound and free variables, and with α-conversion based upon name swapping. As a fundamental part of the formalization, we introduce principles of induction and recursion on terms which provide a framework for reproducing the use of the Barendregt Variable Convention as in pen-and-paper proofs within the rigorous formal setting of a proof assistant. The principles in question are all formally derivable from the simple principle of structural induction/recursion on concrete terms. We work out applications to some fundamental meta-theoretical results, such as the Church–Rosser Theorem and Weak Normalization for the Simply Typed Lambda Calculus. The whole development has been machine checked using the system Agda.

Andrew P. Roberts ◽  
Xiang Zhao ◽  
Pengxiang Hu ◽  
Alexandra Abrajevitch ◽  
Yen‐Hua Chen ◽  

2021 ◽  
Kaidong Song ◽  
Bing Ren ◽  
Yingnan Zhai ◽  
Wenxuan Chai ◽  
Yong Huang

Abstract Three-dimensional (3D) bioprinting has emerged as a powerful engineering approach for various tissue engineering applications, particularly for the development of 3D cellular structures with unique mechanical and/or biological properties. For the jammed gelatin microgel-gelatin solution composite bioink, comprising a discrete phase of microgels (enzymatically gelled gelatin microgels) and a cross-linkable continuous gelatin precursor solution-based phase containing transglutaminase (TG), its rheology properties and printability change gradually due to the TG enzyme-induced cross-linking process. The objective of this study is to establish a direct mapping between the printability of the gelatin microgel-gelatin solution based cross-linkable composite bioink and the TG concentration and cross-linking time, respectively. Due to the inclusion of TG in the composite bioink, the bioink starts cross-linking once prepared and is usually prepared right before a printing process. Herein, the bioink printability is evaluated based on the three metrics: injectability, feature formability, and process-induced cell injury. In this study, the rheology properties such as the storage modulus and viscosity have been first systematically investigated and predicted at different TG concentrations and times during the cross-linking process using the first-order cross-linking kinetics model. The storage modulus and viscosity have been satisfactorily modeled as exponential functions of the TG concentration and time with an experimentally calibrated cross-linking kinetic rate constant. Furthermore, the injectability, feature formability, and process-induced cell injury have been successfully correlated to the TG concentration and cross-linking time via the storage modulus, viscosity, and/or process-induced shear stress. By combing the good injectability, good feature formability, and satisfactory cell viability zones, a good printability zone (1.65, 0.61, and 0.31 hours for the composite bioinks with 1.00, 2.00, and 4.00% w/v TG, respectively) has been established during the printing of mouse fibroblast-based 2% gelatin B microgel-3% gelatin B solution composite bioink. This printability zone approach can be extended to the use of other cross-linkable bioinks for bioprinting applications.

Sign in / Sign up

Export Citation Format

Share Document