cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Applications of Post-quantum Cryptography
Creators: name: Bagirovs, Emils
name: Provodin, Grigory
name: Sipola, Tuomo
name: Hautamäki, Jari
Publisher: name: arXiv
Year: 2024
Subjects: subject: Cryptography and Security (cs.CR)
subject: Emerging Technologies (cs.ET)
subject: FOS: Computer and information sciences

description: With the constantly advancing capabilities of quantum computers, conventional cryptographic systems relying on complex math problems may encounter unforeseen vulnerabilities. Unlike regular computers, which are often deemed cost-ineffective in cryptographic attacks, quantum computers have a significant advantage in calculation speed. This distinction potentially makes currently used algorithms less secure or even completely vulnerable, compelling the exploration of post-quantum cryptography (PQC) as the most reasonable solution to quantum threats. This review aims to provide current information on applications, benefits, and challenges associated with the PQC. The review employs a systematic scoping review with the scope restricted to the years 2022 and 2023; only articles that were published in scientific journals were used in this paper. The review examined the articles on the applications of quantum computing in various spheres. However, the scope of this paper was restricted to the domain of the PQC because most of the analyzed articles featured this field. Subsequently, the paper is analyzing various PQC algorithms, including lattice-based, hash-based, code-based, multivariate polynomial, and isogeny-based cryptography. Each algorithm is being judged based on its potential applications, robustness, and challenges. All the analyzed algorithms are promising for the post-quantum era in such applications as digital signatures, communication channels, and IoT. Moreover, some of the algorithms are already implemented in the spheres of banking transactions, communication, and intellectual property. Meanwhile, despite their potential, these algorithms face serious challenges since they lack standardization, require vast amounts of storage and computation power, and might have unknown vulnerabilities that can be discovered only with years of cryptanalysis.
description: Proceedings of the 23rd European Conference on Cyber Warfare and Security (ECCWS 2024)
Characteristics of ventilation in Callinectes sapidus (Rathbun)
Creators: name: Batterton, Carol Sue Vaughan
Publisher: name: The University of Texas at Austin
Year: 1977
Subjects: subject: Blue crab
subject: Respiration
subject: Measurement
subject: Ventilation volume
subject: Callinectes sapidus
subject: Blue crab--Respiration
subject: Blue crab--Respiration--Measurement

description: 1. Ventilation volume (V̇ [subscript G]) was monitored in the blue crab Callinectes sapidus using an electromagnetic flow probe. Mean resting value of ventilation volume for a 200g crab was 146±81ml•min⁻¹, oxygen consumption (V̇ [subscript O₂] was 85.6±35ml • kg⁻¹• hr⁻¹ and extraction efficiency was 48%±14. 2. The relationships between pulsatile ventilatory flow and pressure and pressure phasing in both gill chambers, pressure volume relationships, and ventilatory energy cost were investigated. 3. Blue crabs tolerated exposure to air for at least eight hours at room temperature, and were able to maintain oxygen consumption during exposure to air. 4. Response to 1% CO₂ was variable and inconsistent in S.W. (sea water) Callinectes sapidus. No response to 1% or 2% CO₂ was seen in F.W. (fresh water) blue crabs. 5. Blue crabs responded to hypoxia of 65, 40, and 25 torr by increasing V̇ [subscript G] and maintaining extraction efficiency at the pre-hypoxia value. V̇ [subscript O₂] decreased with decreasing O₂ tension of the water. 6. No significant change was seen in ventilatory parameters in response to salinity variations
Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification
Creators: name: Phadikar, Souvik
name: Sinha, Nidul
name: Ghosh, Rajdeep
Publisher: name: arXiv
Year: 2022
Subjects: subject: Signal Processing (eess.SP)
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important component of the Brain-Computer Interface (BCI) system that helps motor-disabled people interact with the outside world via external devices. The main issue in developing the EEG based BCI is the informative confusion due to the non-stationary characteristics of EEG data. In this work, an innovative idea of transforming an EEG signal into the weight vector of an unsupervised neural network called the autoencoder is proposed for the first time to solve that problem. Separate autoencoders are trained for the individual EEG data. The weight vectors are then optimized for the individual EEG signals. The EEG signals are thus represented in a new domain that is in the form of weight vectors of the individual autoencoder. The weight vectors are then used to extract features such as autoregressive coefficients (ARs), Shannon entropy (SE), and wavelet leader. A window-based feature extraction technique is implemented to capture the local features of the EEG data. Finally, extracted features are classified using a classifier network. The proposed approach is tested on two publicly accessible EEG datasets (BCI competition-III and Competition-IV) to ensure that it is as successful as and superior to the previously published methods. The proposed technique achieves a mean accuracy of 95.33 % for dataset-IIIa from BCI-III and a mean accuracy of 97% for dataset-IIa from BCI-IV for four-class EEG-based MI classification. The experimental outcomes show that the proposed approach is a promising way to increase BCI performance.
Cyber Protection Applications of Quantum Computing: A Review
Creators: name: Ahmed, Ummar
name: Sipola, Tuomo
name: Hautamäki, Jari
Publisher: name: arXiv
Year: 2024
Subjects: subject: Cryptography and Security (cs.CR)
subject: Emerging Technologies (cs.ET)
subject: FOS: Computer and information sciences

description: Quantum computing is a cutting-edge field of information technology that harnesses the principles of quantum mechanics to perform computations. It has major implications for the cyber security industry. Existing cyber protection applications are working well, but there are still challenges and vulnerabilities in computer networks. Sometimes data and privacy are also compromised. These complications lead to research questions asking what kind of cyber protection applications of quantum computing are there and what potential methods or techniques can be used for cyber protection? These questions will reveal how much power quantum computing has and to what extent it can outperform the conventional computing systems. This scoping review was conducted by considering 815 papers. It showed the possibilities that can be achievedif quantum technologies are implemented in cyber environments. This scoping review discusses various domains such as algorithms and applications, bioinformatics, cloud and edge computing, the organization of complex systems, application areas focused on security and threats, and the broader quantum computing ecosystem. In each of these areas, there is significant scope for quantum computing to be implemented and to revolutionize the working environment. Numerous quantum computing applications for cyber protection and a number of techniques to protect our data and privacy were identified. The results are not limited to network security but also include data security. This paper also discusses societal aspects, e.g., the applications of quantum computing in the social sciences. This scoping review discusses how to enhance the efficiency and security of quantum computing in various cyber security domains. Additionally, it encourages the reader to think about what kind of techniques and methods can be deployed to secure the cyber world.
description: Proceedings of the 23rd European Conference on Cyber Warfare and Security (ECCWS 2024)
Existence and uniqueness of traveling wave for accelerated Frenkel-Kontorova model
Creators: name: Forcadel, Nicolas
name: Ghorbel, Amin
name: Walha, Sonda
Publisher: name: arXiv
Year: 2014
Subjects: subject: Analysis of PDEs (math.AP)
subject: FOS: Mathematics

description: In this paper, we study the existence and uniqueness of traveling wave solution for the accelerated Frenkel-Kontorova model. This model consists in a system of ODE that describes the motion particles in interaction. The most important applications we have in mind is the motion of crystal defects called dislocations. For this model, we prove the existence of traveling wave solutions under very weak assumptions. The uniqueness of the velocity is also studied as well as the uniqueness of the profile which used different types of strong maximum principle. As far as we know, this is the first result concerning traveling waves for accelerated, spatially discrete system.
Exploring the Lived Experience of Black Young People in a Predominantly White Secondary School
Creators: name: Ajewole, Selone
Publisher: name: University of East London
Year: 2023
Subjects: predominantly White school
young people
lived experiences
Black
IPA

description: “Kasserian Ingera?” (“𝘈𝘯𝘥 𝘩𝘰𝘸 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘤𝘩𝘪𝘭𝘥𝘳𝘦𝘯?”) is a traditional greeting amongst the Maasai Warriors (African tribe), often followed by the customary response, “𝘈𝘭𝘭 𝘵𝘩𝘦 𝘤𝘩𝘪𝘭𝘥𝘳𝘦𝘯 𝘢𝘳𝘦 𝘸𝘦𝘭𝘭”. These common conversations signify the deep-rooted protection and nurture of the young and powerless and thus a hope for a promising future for society. In UK society however, the need for the Children’s Act 2004, in part due to societal failures to protect children, highlights misalignment between the UK and Maasai tribe ethos. The fact that Black young people (BYP) in UK schools are disproportionality affected by racism exemplifies our inadequate child protection efforts. The aim of this current research therefore was to explore the lived experiences of BYP in a predominantly White secondary school (PWSS). With a social constructivist and transformative epistemology, this study used qualitative methodology and Interpretative Phenomenological Analysis (IPA) to extract meaning from participant semi-structured interviews. Findings suggest that the nine BYP who participated in the study experienced challenges with their identity due to negative societal discourses around Blackness impacting their sense of belonging. Racial injustice (e.g. differential treatment and racial abuse) was commonplace. Subsequent behaviour governing to avoid White judgement ensued. Participants outlined how their PWSSs could embody cultural sensitivity and cited a majority of external factors as facilitators of a positive educational experience leaving BYP vulnerable should they dissipate. Critical Race Theory (CRT), The Social Graces framework and the Ecological model facilitated theoretical understanding of the findings. Implications for schools and Educational Psychologists (EP) were devised from researcher and participant insight prompting greater understanding of ethnic difference and culturally sensitive targeted responses. Emancipatory in nature, this study elicited the voices of BYP and reframed oppressive experiences. Ultimately however, findings cast doubt over whether like Maasai tribe, “𝘈𝘭𝘭 𝘵𝘩𝘦 𝘤𝘩𝘪𝘭𝘥𝘳𝘦𝘯 𝘢𝘳𝘦 𝘪𝘯𝘥𝘦𝘦𝘥 𝘸𝘦𝘭𝘭”.
Machine Learning Applications of Quantum Computing: A Review
Creators: name: Nguyen, Thien
name: Sipola, Tuomo
name: Hautamäki, Jari
Publisher: name: arXiv
Year: 2024
Subjects: subject: Machine Learning (cs.LG)
subject: Emerging Technologies (cs.ET)
subject: FOS: Computer and information sciences

description: At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional computational methods. Drawing upon an in-depth analysis of 32 seminal papers, this review delves into the interplay between quantum computing and machine learning, focusing on transcending the limitations of classical computing in advanced data processing and applications. This review emphasizes the potential of quantum-enhanced methods in enhancing cybersecurity, a critical sector that stands to benefit significantly from these advancements. The literature review, primarily leveraging Science Direct as an academic database, delves into the transformative effects of quantum technologies on machine learning, drawing insights from a diverse collection of studies and scholarly articles. While the focus is primarily on the growing significance of quantum computing in cybersecurity, the review also acknowledges the promising implications for other sectors as the field matures. Our systematic approach categorizes sources based on quantum machine learning algorithms, applications, challenges, and potential future developments, uncovering that quantum computing is increasingly being implemented in practical machine learning scenarios. The review highlights advancements in quantum-enhanced machine learning algorithms and their potential applications in sectors such as cybersecurity, emphasizing the need for industry-specific solutions while considering ethical and security concerns. By presenting an overview of the current state and projecting future directions, the paper sets a foundation for ongoing research and strategic advancement in quantum machine learning.
description: Proceedings of the 23rd European Conference on Cyber Warfare and Security (ECCWS 2024)
Relativistic heavy-ion collisions
Creators: name: Bhalerao, Rajeev S.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Nuclear Theory (nucl-th)
subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: The field of relativistic heavy-ion collisions is introduced to the high-energy physics students with no prior knowledge in this area. The emphasis is on the two most important observables, namely the azimuthal collective flow and jet quenching, and on the role fluid dynamics plays in the interpretation of the data. Other important observables described briefly are constituent quark number scaling, ratios of particle abundances, strangeness enhancement, and sequential melting of heavy quarkonia. Comparison is made of some of the basic heavy-ion results obtained at LHC with those obtained at RHIC. Initial findings at LHC which seem to be in apparent conflict with the accumulated RHIC data are highlighted.
description: Updated version of the lectures given at the First Asia-Europe-Pacific School of High-Energy Physics, Fukuoka, Japan, 14-27 October 2012. Published as a CERN Yellow Report (CERN-2014-001) and KEK report (KEK-Proceedings-2013-8), K. Kawagoe and M. Mulders (eds.), 2014, p. 219. Total 21 pages
Capacitação sobre as Fontes de Informação da BVS para a Rede Brasileira de Informação em Ciências da Saúde - 2021 - Seção V – Tema: Criação de Recursos Educacionais Abertos no sistema FI-Admin
Creators: name: BIREME/OPS/OMS
name: Biruel, Elisabeth
name: Suga, Sueli Mitiko Yano
Publisher: name: Zenodo
Year: 2021
Subjects: Metodologia BVS
Red BVS

description: [PT] (5 de 6) Capacitação sobre as Fontes de Informação da BVS para a Rede Brasileira - 2021 Tema: Criação de Recursos Educacionais Abertos e Recursos de Multimídia no sistema FI-admin - Apresentado 08 de julho de 2021 Público alvo: Bibliotecários da rede brasileira que cooperam com a rede LILACS e BVS Elisabeth Biruel apresentou sobre "Recursos Educacionais Abertos" Leonardo Ragacini apresentou sobre "Recursos Multimídia" 00:00 →Abertura da sala - Sueli Suga 02:07 →Diferença entre Recursos Educacionais e Recursos Multimídia - por Elisabeth Biruel 03:06 →Objetivos 04:42 →Estrutura e vínculos de todo material educativo 13:34 →Critérios LORI- avaliação de REAS 13:40 →Registrar um REA no FI-Admin 22:40 →Catalogo de Recursos de Multimídia apresentado por Leonardo Ragacini 23:16 →Como Identificar e avaliar a qualidade de um recurso multimídia? 25:16 →Critérios de seleção de recursos multimídia 29:36 →Registrar o multimídia no FI-Admin 43:25 →Abertura para perguntas e respostas Visite a pagina geral para ter acesso as capacitações sobre outras fontes de informação da BVS e LILACS: https://lilacs.bvsalud.org/sessoes-virtuais-lilacs/capacitacao-sobre-as-fontes-de-informacao-da-bvs-para-a-rede-brasileira-de-informacao-em-ciencias-da-saude-2021/ Siga a Rede LILACS no Twitter: https://twitter.com/redelilacs
The Positive Energy Theorem for Asymptotically Hyperboloidal Initial Data Sets With Toroidal Infinity and Related Rigidity Results
Creators: name: Alaee, Aghil
name: Hung, Pei-Ken
name: Khuri, Marcus
Publisher: name: arXiv
Year: 2022
Subjects: subject: Differential Geometry (math.DG)
subject: General Relativity and Quantum Cosmology (gr-qc)
subject: High Energy Physics - Theory (hep-th)
subject: FOS: Mathematics
subject: FOS: Physical sciences

description: We establish the positive energy theorem and a Penrose-type inequality for 3-dimensional asymptotically hyperboloidal initial data sets with toroidal infinity, weakly trapped boundary, and satisfying the dominant energy condition. In the umbilic case, a rigidity statement is proven showing that the total energy vanishes precisely when the initial data manifold is isometric to a portion of the canonical slice of the associated Kottler spacetime. Furthermore, we provide a new proof of the recent rigidity theorems of Eichmair-Galloway-Mendes [10] in dimension 3, with weakened hypotheses in certain cases. These results are obtained through an analysis of the level sets of spacetime harmonic functions.
description: 28 pages, final version
Barjols, atelier Fabre, enregistrement de la Marche du roi René interprétée par Marius et André Fabre
Creators: name: Musée National Des Arts Et Traditions Populaires (Paris)
name: Centre D'ethnologie Française
Publisher: name: École des Hautes Études en Sciences Sociales; Institut  interdisciplinaire d'anthropologie du contemporain (Paris); Laboratoire d'anthropologie et d'histoire de l'institution de la culture (Paris)
Year: 2024

Abstract
Topological materials for full-vector elastic waves
Creators: name: Wu, Ying
name: Lu, Jiuyang
name: Huang, Xueqin
name: Yang, Yating
name: Luo, Li
name: Yang, Linyun
name: Li, Feng
name: Deng, Weiyin
name: Liu, Zhengyou
Publisher: name: arXiv
Year: 2022
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: Applied Physics (physics.app-ph)
subject: FOS: Physical sciences

description: Elastic wave manipulation is important in a wide variety of scales in applications including information processing in tiny elastic devices and noise control in big solid structures. The recent emergence of topological materials opens a new avenue toward modulating elastic waves in solids. However, because of the full-vector feature, and the complicated couplings of the longitudinal and transverse components of elastic waves, manipulating elastic waves is generally difficult, compared with manipulating acoustic waves (scalar waves) and electromagnetic waves (vectorial waves but transverse only). Up to date, topological materials, including insulators and semimetals, have been realized for acoustic and electromagnetic waves. Although topological materials of elastic waves have also been reported, the topological edge modes observed all lie on the domain wall. A natural question can be asked: whether there exists an elastic metamaterial with the topological edge modes on its own boundary only? Here, we report a 3D metal-printed bilayer metamaterial, insulating topologically the elastic waves. By introducing the chiral interlayer couplings, the spin-orbit couplings for elastic waves are induced, which give rise to nontrivial topological properties. The helical edge states with the vortex feature are demonstrated on the boundary of the single topological phase. We further show a heterostructure of the metamaterial, which exhibits tunable edge transport. Our work may have potential in devices based on elastic waves in solids.
Additional file 1 of Improved non-invasive positron emission tomographic imaging of chemotherapy-induced tumor cell death using Zirconium-89-labeled APOMAB®
Creators: name: Liapis, Vasilios
name: Tieu, William
name: Rudd, Stacey E.
name: Donnelly, Paul S.
name: Wittwer, Nicole L.
name: Brown, Michael P.
name: Staudacher, Alexander H.
Publisher: name: figshare
Year: 2020
Subjects: subject: Cell Biology
subject: Molecular Biology
subject: Pharmacology
subject: Biotechnology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: Immunology
subject: FOS: Clinical medicine
subject: Cancer
subject: Plant Biology
subject: FOS: Biological sciences

description: Additional file 1.
Reconstructing an early Albian (Cretaceous) marine food web by employing macrofossil δ13Ccarb data
Creators: name: Mutterlose, JÃ¶rg
name: Ohnemus, Bernhard
Publisher: name: Geological Society of London
Year: 2023
Subjects: classificationCode: 3705
subject: Geology
subject: FOS: Earth and related environmental sciences

description: S1, Early Albian foods web
Simultaneous Resonant Excitation of Low-frequency Eccentric Wave and Tilt Wave on Tidally Deformed Disks
Creators: name: Kato, Shoji
Publisher: name: arXiv
Year: 2014
Subjects: subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: FOS: Physical sciences

description: Simultaneous excitation of low-frequency eccentric precessing mode (one-armed p-mode) and tilt mode on tidally deformed disks is considered. If the orbit of the secondary star is eccentric and its orbital plane is misaligned with the disk plane of the primary, the above-mentioned two low-frequency oscillation modes are simultaneously excited on the primary disk, the former having prograte precession and the latter having retrograde precession. This excitation of disk oscillations is due to a wave-wave resonant excitation process considered by Kato (2013). If parameter values relevant to Be/X-ray binary systems are adopted, the periods of these excited oscillations are around ten times of the orbital period of the secondary, which may be comparable with the time scale of giant outbursts observed in Be/X-ray systems.
description: 20 pages, 5 figures (to be published in PASJ 66, 2014)
Tomita-Takesaki theory and quantum concurrence
Creators: name: Chatterjee, Rupak
Publisher: name: arXiv
Year: 2024
Subjects: subject: Quantum Physics (quant-ph)
subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: The quantum entanglement measure of concurrence is shown to be directly calculable from a Tomita- Takesaki modular operator framework constructed from the local von Neumann algebras of observables for two quantum systems. Specifically, the Tomita-Takesaki modular conjugation operator $J$ that links two separate systems with respect to their von Neumann algebras is related to the quantum concurrence $C$ of a pure bi-variate entangled state composed from these systems. This concurrence relation provides a direct physical meaning to $J$ as both a symmetry operator and a quantitative measure of entanglement. This procedure is then demonstrated for a supersymmetric quantum mechanical system and a real scalar field interacting with two entangled spin-$\frac{1}{2}$ Unruh-DeWitt qubit detectors. For the latter system, the concurrence result is shown to be consistent with some known results on the Bell-CHSH inequality for such a system.
description: 10 pages
Reconstructing an early Albian (Cretaceous) marine food web by employing macrofossil δ13Ccarb data
Creators: name: Mutterlose, JÃ¶rg
name: Ohnemus, Bernhard
Publisher: name: Geological Society of London
Year: 2023
Subjects: classificationCode: 3705
subject: Geology
subject: FOS: Earth and related environmental sciences

description: S2, figure
title: Supply Drivers of US Inflation Since the Pandemic
Creators: name: Kabaca, Serdar
name: TUZCUOGLU, KEREM
Publisher: name: Bank of Canada
Year: 2023

description: This paper examines the contribution of several supply factors to US headline inflation since the start of the COVID-19 pandemic. We identify six supply shocks using a structural VAR model: labor supply, labor productivity, global supply chain, oil price, price mark-up and wage mark-up shocks. Our shock identification relies mainly on sign restrictions. But for the global supply chain shock, we propose a new identification scheme combining sign, narrative and variance decomposition restrictions. Historical decomposition results suggest that global supply chain and oil price shocks are the biggest supply contributors to the US inflation during the pandemic. In contrast, labor shortages only mildly contribute to inflation, but their impact on output is larger in that period. Additionally, price and wage mark-up shocks start to significantly contribute to inflation only towards the middle of 2022. Finally, our analysis, which also allows the identification of monetary policy and aggregate demand shocks, suggests that demand and supply factors are almost equally responsible for the movements in the inflation rate during the pandemic.
description: Cette étude examine la contribution de divers facteurs d’offre à l’inflation globale aux États-Unis depuis le début de la pandémie de COVID-19. Nous identifions six chocs d’offre à l’aide d’un modèle VAR structurel : chocs liés à l’offre de travail, à la productivité de la main d’œuvre, aux chaînes d’approvisionnement mondiales, aux cours du pétrole, aux majorations de prix et aux hausses salariales. Cette identification s’appuie principalement sur les restrictions de signe. Cependant, pour les chocs liés aux chaînes d’approvisionnement mondiales, nous proposons un nouveau schéma d’identification qui combine les approches de restrictions de signe, narrative et de décomposition de la variance. Les résultats de la décomposition historique donnent à penser que les chocs liés aux chaînes d’approvisionnement mondiales et aux cours du pétrole ont été les plus importants facteurs d’offre ayant contribué à l’inflation aux États-Unis durant la pandémie. Par contraste, les pénuries de main-d’œuvre n’ont que légèrement contribué à inflation, mais leur incidence sur la production a été plus grande durant la même période. En outre, ce n’est que vers le milieu de 2022 que les chocs liés aux majorations de prix et aux hausses salariales ont commencé à contribuer de façon significative à l’inflation. Enfin, notre analyse, qui permet aussi d’identifier les chocs de politique monétaire et de demande globale, montre que les facteurs d’offre et de demande ont contribué de manière presque égale aux variations du taux d’inflation durant la pandémie.
Re-infection by untreated partners of people treated for Chlamydia trachomatis and Neisseria gonorrhoeae: mathematical modelling study
Creators: name: Low, Nicola
name: Heijne, Janneke C. M.
name: Herzog, Sereina A.
name: Althaus, Christian L.
Publisher: name: arXiv
Year: 2013
Subjects: subject: Populations and Evolution (q-bio.PE)
subject: FOS: Biological sciences

description: Objectives: Re-infection after treatment for Chlamydia trachomatis or Neisseria gonorrhoeae reduces the effect of control interventions. We explored the impact of delays in partner treatment on the expected probability of re-infection of index cases using a mathematical model. Methods: We used previously reported parameter distributions to calculate the probability that index cases would be re-infected by their untreated partners. We then assumed different delays between index case and partner treatment to calculate the probabilities of re-infection. Results: In the absence of partner treatment, the medians of the expected re-infection probabilities are 19.4% (interquartile range (IQR) 9.2-31.6%) for chlamydia and 12.5% (IQR 5.6-22.2%) for gonorrhoea. If all current partners receive treatment three days after the index case, the expected re-infection probabilities are 4.2% (IQR 2.1-6.9%) for chlamydia and 5.5% (IQR 2.6-9.5%) for gonorrhoea. Conclusions: Quicker partner referral and treatment can substantially reduce re-infection rates for chlamydia and gonorrhoea. The formula we used to calculate re-infection rates can be used to inform the design of randomised controlled trials of novel partner notification technologies like accelerated partner therapy.
description: Short report, 1 figure
Influence of cross-sectional uncertainty on sensitivity studies of DUNE and T2HK experiments
Creators: name: Devi, Ritu
name: Singh, Jaydip
name: Potukuchi, Baba
Publisher: name: arXiv
Year: 2022
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: The ultimate objectives of ongoing and upcoming neutrino experiments are the precise measurement of neutrino mixing parameters and the confirmation of mass hierarchy. The systematic inaccuracy in the cross-section models introduces inaccuracy in the neutrino mixing parameters estimation. It is important to secure a large decrease of uncertainties, particularly those relating to cross-section, neutrino-nucleus interactions, and neutrino-energy reconstruction, in order to achieve these ambitious goals. In this research article, we use three alternative neutrino event generators, GENIE, NuWro, and GiBUU, to analyze sensitivity studies of T2HK, DUNE, and combined sensitivity of DUNE, and T2HK for mass hierarchy, CP violation, and octant degeneracy caused by cross-section uncertainties. The cross-section models of these generators are separate and independent.
Joint Stream: Malignant Region Learning for Breast Cancer Diagnosis
Creators: name: Rehman, Abdul
name: Hussein, Sarfaraz
name: Sultani, Waqas
Publisher: name: arXiv
Year: 2024
Subjects: subject: Image and Video Processing (eess.IV)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences

description: Early diagnosis of breast cancer (BC) significantly contributes to reducing the mortality rate worldwide. The detection of different factors and biomarkers such as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal growth factor receptor 2 (HER2) gene, Histological grade (HG), Auxiliary lymph node (ALN) status, and Molecular subtype (MS) can play a significant role in improved BC diagnosis. However, the existing methods predict only a single factor which makes them less suitable to use in diagnosis and designing a strategy for treatment. In this paper, we propose to classify the six essential indicating factors (ER, PR, HER2, ALN, HG, MS) for early BC diagnosis using H\&E stained WSI's. To precisely capture local neighboring relationships, we use spatial and frequency domain information from the large patch size of WSI's malignant regions. Furthermore, to cater the variable number of regions of interest sizes and give due attention to each region, we propose a malignant region learning attention network. Our experimental results demonstrate that combining spatial and frequency information using the malignant region learning module significantly improves multi-factor and single-factor classification performance on publicly available datasets.
description: Under Review (Biomedical Signal Processing and Control)
Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines
Creators: name: Parekh, Vivek
name: Flore, Dominik
name: Schöps, Sebastian
Publisher: name: arXiv
Year: 2022
Subjects: subject: Machine Learning (cs.LG)
subject: Computational Engineering, Finance, and Science (cs.CE)
subject: FOS: Computer and information sciences

description: Conventional magneto-static finite element analysis of electrical machine design is time-consuming and computationally expensive. Since each machine topology has a distinct set of parameters, design optimization is commonly performed independently. This paper presents a novel method for predicting Key Performance Indicators (KPIs) of differently parameterized electrical machine topologies at the same time by mapping a high dimensional integrated design parameters in a lower dimensional latent space using a variational autoencoder. After training, via a latent space, the decoder and multi-layer neural network will function as meta-models for sampling new designs and predicting associated KPIs, respectively. This enables parameter-based concurrent multi-topology optimization.
description: 4 pages, 15 This article will appear in the proceedings of the IEEE transactions on magnetics as a conference paper
One-Dimensional Quantum Systems with Ground-State of Jastrow Form Are Integrable
Creators: name: Yang, Jing
name: del Campo, Adolfo
Publisher: name: arXiv
Year: 2022
Subjects: subject: Mathematical Physics (math-ph)
subject: Quantum Gases (cond-mat.quant-gas)
subject: Exactly Solvable and Integrable Systems (nlin.SI)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: The exchange operator formalism (EOF) describes many-body integrable systems using phase-space variables involving an exchange operator that acts on any pair of particles. We establish an equivalence between models described by EOF and the complete infinite family of parent Hamiltonians (PHJ) describing quantum many-body models with ground-states of Jastrow form. This makes it possible to identify the invariants of motion for any model in the PHJ family and establish its integrability, even in the presence of an external potential. Using this construction we establish the integrability of the long-range Lieb-Liniger model, describing bosons in a harmonic trap and subject to contact and Coulomb interactions in one dimension. We give a variety of examples exemplifying the integrability of Hamiltonians in this family.
description: 5+15 pages, no figures. More examples as well as a general user-guide to construct PHJ family of integrable models are included
New insights on the thermal decomposition of lanthanide(III) and actinide(III) oxalates: from neodymium and cerium to plutonium
Creators: name: De Almeida, Lucie
name: Grandjean, Stéphane
name: Vigier, Nicolas
name: Patisson, Fabrice
Publisher: name: arXiv
Year: 2014
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: Lanthanides are often used as surrogates to study the properties of actinide compounds. Their behaviour is considered to be quite similar as they both possess f valence electrons and are close in size and chemical properties. This study examines the potential of using two lanthanides (neodymium and cerium) as surrogates for plutonium during the thermal decomposition of isomorphic oxalate compounds, in the trivalent oxidation state, into oxides. Thus, the thermal decomposition of neodymium, cerium and plutonium (III) oxalates are investigated by several coupled thermal analyses (TG-DTA/DSC-MS/FT-IR) and complementary characterisation techniques (XRD, UV-vis, FT-IR, SEM, carbon analyser) under both oxidizing and inert atmosphere. The thermal decomposition mechanisms determined in this study confirmed some previous results reported in the literature, among diverging propositions, while also elucidating some original mechanisms not previously considered. Calculated thermodynamic and kinetic parameters for the studied systems under both atmospheres are reported and compared with available literature data. Similarities and differences between the thermal behaviour of plutonium(III) and lanthanide(III) oxalates are outlined.
NAS-VAD: Neural Architecture Search for Voice Activity Detection
Creators: name: Rho, Daniel
name: Park, Jinhyeok
name: Ko, Jong Hwan
Publisher: name: arXiv
Year: 2022
Subjects: subject: Sound (cs.SD)
subject: Machine Learning (cs.LG)
subject: Audio and Speech Processing (eess.AS)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Various neural network-based approaches have been proposed for more robust and accurate voice activity detection (VAD). Manual design of such neural architectures is an error-prone and time-consuming process, which prompted the development of neural architecture search (NAS) that automatically design and optimize network architectures. While NAS has been successfully applied to improve performance in a variety of tasks, it has not yet been exploited in the VAD domain. In this paper, we present the first work that utilizes NAS approaches on the VAD task. To effectively search architectures for the VAD task, we propose a modified macro structure and a new search space with a much broader range of operations that includes attention operations. The results show that the network structures found by the propose NAS framework outperform previous manually designed state-of-the-art VAD models in various noise-added and real-world-recorded datasets. We also show that the architectures searched on a particular dataset achieve improved generalization performance on unseen audio datasets. Our code and models are available at https://github.com/daniel03c1/NAS_VAD.
description: Submitted to Interspeech 2022
NLSE for quantum plasmas with the radiation damping
Creators: name: Andreev, Pavel A.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Plasma Physics (physics.plasm-ph)
subject: FOS: Physical sciences

description: We consider contribution of the radiation damping in the quantum hydrodynamic equations for spinless particles. We discuss possibility of obtaining of corresponding non-linear Schrodinger equation (NLSE) for the macroscopic wave function. We compare contribution of the radiation damping with weakly (or semi-) relativistic effects appearing in the second order by v/c. The radiation damping appears in the third order by v/c. So it might be smaller than weakly relativistic effects, but it gives damping of the Langmuir waves which can be considerable.
description: 4 pages
Distribution-Free Location-Scale Regression
Creators: name: Siegfried, Sandra
name: Kook, Lucas
name: Hothorn, Torsten
Publisher: name: Taylor & Francis
Year: 2023
Subjects: Biotechnology
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Cancer

description: We introduce a generalized additive model for location, scale, and shape (GAMLSS) next of kin aiming at distribution-free and parsimonious regression modeling for arbitrary outcomes. We replace the strict parametric distribution formulating such a model by a transformation function, which in turn is estimated from data. Doing so not only makes the model distribution-free but also allows to limit the number of linear or smooth model terms to a pair of location-scale predictor functions. We derive the likelihood for continuous, discrete, and randomly censored observations, along with corresponding score functions. A plethora of existing algorithms is leveraged for model estimation, including constrained maximum-likelihood, the original GAMLSS algorithm, and transformation trees. Parameter interpretability in the resulting models is closely connected to model selection. We propose the application of a novel best subset selection procedure to achieve especially simple ways of interpretation. All techniques are motivated and illustrated by a collection of applications from different domains, including crossing and partial proportional hazards, complex count regression, nonlinear ordinal regression, and growth curves. All analyses are reproducible with the help of the tram add-on package to the R system for statistical computing and graphics.
Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs
Creators: name: Bourazas, Konstantinos
name: Sobas, Frederic
name: Tsiamyrtzis, Panagiotis
Publisher: name: Taylor & Francis
Year: 2023
Subjects: subject: Physiology
subject: FOS: Biological sciences
subject: Pharmacology
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified

description: The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, where the testing procedure is built on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The theoretic framework can accommodate any likelihood from the regular exponential family and the use of conjugate analysis allows closed form modeling. Power priors will offer the axiomatic framework to incorporate into the model different sources of information, when available. A simulation study evaluates the performance against competitors and examines aspects of prior sensitivity. Technical details and algorithms are provided as supplementary material.
Data-Centric Machine Learning in Quantum Information Science
Creators: name: Lohani, Sanjaya
name: Lukens, Joseph M.
name: Glasser, Ryan T.
name: Searles, Thomas A.
name: Kirby, Brian T.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Quantum Physics (quant-ph)
subject: Artificial Intelligence (cs.AI)
subject: Machine Learning (cs.LG)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a "toy model" demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.
A minimal model of predator-swarm interactions
Creators: name: Chen, Yuxin
name: Kolokolnikov, Theodore
Publisher: name: arXiv
Year: 2014
Subjects: subject: Adaptation and Self-Organizing Systems (nlin.AO)
subject: Mathematical Physics (math-ph)
subject: Dynamical Systems (math.DS)
subject: Chaotic Dynamics (nlin.CD)
subject: Biological Physics (physics.bio-ph)
subject: FOS: Physical sciences
subject: FOS: Mathematics

description: We propose a minimal model of predator-swarm interactions which captures many of the essential dynamics observed in nature. Different outcomes are observed depending on the predator strength. For a "weak" predator, the swarm is able to escape the predator completely. As the strength is increased, the predator is able to catch up with the swarm as a whole, but the individual prey are able to escape by "confusing" the predator: the prey forms a ring with the predator at the center. For higher predator strength, complex chasing dynamics are observed which can become chaotic. For even higher strength, the predator is able to successfully capture the prey. Our model is simple enough to be amenable to a full mathematical analysis which is used to predict the shape of the swarm as well as the resulting predator-prey dynamics as a function of model parameters. We show that as the predator strength is increased, there is a transition (due to a Hopf bifurcation) from confusion state to chasing dynamics, and we compute the threshold analytically. Our analysis indicates that the swarming behaviour is not helpful in avoiding the predator, suggesting that there are other reasons why the species may swarm. The complex shape of the swarm in our model during the chasing dynamics is similar to the shape of a flock of sheep avoiding a shepherd.
Localized associations in market based analysis
Creators: name: Li, Xiaojun
name: Mazvancheryl, Sanal
name: Prasad, Srinivas Y.
name: Rau, Pradeep
Publisher: name: American University
Year: 2023

description: Association rule mining approaches have been proposed in the data mining liter- ature to analyze market basket data. The outputs of such approaches are rules that identify pairs of associated products that imply the co-occurrence of particular prod- ucts in a basket. As established in the marketing research literature, several factors can influence the co-occurrence of products. First, marketing mix activities such as pricing and promotions in one product category may influence a consumer’s purchase decision of products in other categories. Second, due to consumer heterogeneity, the association of a set of products may vary across customer segments. Third, a basket of products may just be purchased together coincidentally since the consumer wants to spread the shopping cost of one trip. Finally, aggregate associations (or correlations) may dif- fer from localized associations. In the extreme case, we may see Simpson’s paradox, which means that a pair of products is positively correlated in every consumer seg- ment but shows negative correlation in the aggregate data or vice versa. This happens when market baskets from multiple sources are pooled into one aggregate database. Thus product associations may be distorted in two ways - change of magnitude, or change of direction. As a result, association rules discovered by existing association rule mining may be spurious. We develop an exploratory rule mining algorithm based on transaction attributes such as consumer demographics or marketing mix variables that identifies segments of the data (a subset of the baskets) which exhibit strong asso- ciations between pairs of products that are not seen in the aggregate data set. Results are presented using an IRI market basket data set that contains transactions including 22 categories over 2 years for 500 panelists.
Birational geometry via moduli spaces
Creators: name: Cheltsov, Ivan
name: Katzarkov, Ludmil
name: Przyjalkowski, Victor
Publisher: name: arXiv
Year: 2014
Subjects: subject: Algebraic Geometry (math.AG)
subject: FOS: Mathematics

description: In this paper we connect degenerations of Fano threefolds by projections. Using Mirror Symmetry we transfer these connections to the side of Landau--Ginzburg models. Based on that we suggest a generalization of Kawamata's categorical approach to birational geometry enhancing it via geometry of moduli spaces of Landau--Ginzburg models. We suggest a conjectural application to Hasset--Kuznetsov--Tschinkel program based on new nonrationality "invariants" we consider --- gaps and phantom categories. We make several conjectures for these invariants in the case of surfaces of general type and quadric bundles.
description: arXiv admin note: text overlap with arXiv:1405.2953