Large Universe Research Articles

Efficient random walks for generating random fuzzy measures in Möbius representation in large universe

Random generation of fuzzy measures plays a pivotal role in large-scale decision-making and optimization. Random walks ensure uniform generation and adequate coverage. The Möbius representation of set functions is a valuable tool for establishing the sparse structure of fuzzy measures, with its non-negativity closely linked to monotonicity and convexity/supermodularity checking. We propose three efficient methods for monotonicity verification and convexity/supermodularity verification applicable to random walks in Möbius representations, specifically tailored for universal sets larger than ten inputs and k-order fuzzy measures. We first present the baseline methods by directly inspecting monotonicity and convexity constraints. Building on the observation that the majority of initially generated values exhibit non-negativity, we intentionally track negative Möbius values to enhance the computational performance of these baseline approaches. Further, we introduce the more agile methods that employ insertion and merge sorting techniques for both monotonicity and convexity checks in random walks that involve small perturbations of fuzzy measures. To address sparsity in large-scale scenarios, we focus on two major types of measures: k-additive and k-interactive measures, demonstrating their effectiveness through theoretical analysis and experimental results.

Improved estimation of the correlation matrix using reinforcement learning and text-based networks

We propose a data-driven methodology to shrink the correlation matrix and, hence, the covariance matrix using reinforcement learning (RL). Our approach does not impose any assumptions on the stock returns and can be applied to any covariance matrix target. It focuses on the special case of the global minimum variance portfolio and investigates the economic value of our methodology by utilizing text-based networks (Hoberg and Phillips, 2016). The portfolio selection problem, hence, boils down to determining the optimal shrinkage policy using RL. The empirical analysis utilizes a large universe of stocks covering more than 400 assets and 20 years as a testing period. Overall, the proposed portfolio rule outperforms state-of-the-art shrinkage techniques in terms of out-of-sample volatility, Sharpe ratio, and downside risk net of transaction costs. Our research highlights the effectiveness of our RL-driven approach and underscores the value of alternative data sources in ex-ante forming robust portfolio rules.

An anonymous and large-universe data-sharing scheme with traceability for medical cloud storage

The application of medical cloud storage technology in healthcare and the sharing of electronic medical records (EMR) bring convenience for patients and medical institutes. However, two barriers limit further expansions of the above (i.e. key escrow and key abuse issues). In this paper, we construct a large universe data-sharing scheme based on attribute-based encryption. We design all attribute authorities simultaneously to participate in the key computation, and the user performs aggregation. By issuing anonymous credentials to the recipients, their identity information is protected. To achieve complete tracing of the traitor, we blend the two mechanisms, white-box traceability and black-box traceability, together. Detailed security proofs have been carried out for various types of possible attackers, and theoretical analyses have verified the security of the proposed scheme. We performed performance evaluations in conjunction with existing schemes, and numerical experience shows that the burden on the user side is also minimal.

Firewalls from wormholes in higher genus

An old black hole can tunnel into a white hole/firewall by emitting large baby universes. This phenomenon was investigated in Jackiw-Teitelboim (JT) gravity for genus one. In this paper, the focus is on higher genus corresponding to emitting more than one baby universe (n > 1). The probability of encountering a firewall or tunneling into a white hole after emitting n baby universes is proportional to e−2nSEe4πEn−1E2n2−n−9/2t4n2−2n−5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {e}^{-2 nS(E)}{e}^{4\\pi \\sqrt{E}\\left(n-1\\right)}{E}^{2{n}^2-n-9/2}{t}^{4{n}^2-2n-5} $$\\end{document}, where t is the age of the black hole, and S and E represent the entropy and energy of the black hole, respectively.

An attribute-based access control scheme using blockchain technology for IoT data protection

With the wide application of the Internet of Things (IoT), storing large amounts of IoT data and protecting data privacy has become a meaningful issue. In general, the access control mechanism is used to prevent illegal users from accessing private data. However, traditional data access control schemes face some non-ignorable problems, such as only supporting coarse-grained access control, the risk of centralization, and high trust issues. In this paper, an attribute-based data access control scheme using blockchain technology is proposed. To address these problems, attribute-based encryption (ABE) has become a promising solution for encrypted data access control. Firstly, we utilize blockchain technology to construct a decentralized access control scheme, which can grant data access with transparency and traceability. Furthermore, our scheme also guarantees the privacy of policies and attributes on the blockchain network. Secondly, we optimize an ABE scheme, which makes the size of system parameters smaller and improves the efficiency of algorithms. These optimizations enable our proposed scheme supports large attribute universe requirements in IoT environments. Thirdly, to prohibit attribute impersonation and attribute replay attacks, we design a challenge-response mechanism to verify the ownership of attributes. Finally, we evaluate the security and performance of the scheme. And comparisons with other related schemes show the advantages of our proposed scheme. Compared to existing schemes, our scheme has more comprehensive advantages, such as supporting a large universe, full security, expressive policy, and policy hiding.

Gaussian universality of perceptrons with random labels.

While classical in many theoretical settings-and in particular in statistical physics-inspired works-the assumption of Gaussian i.i.d. input data is often perceived as a strong limitation in the context of statistics and machine learning. In this study, we redeem this line of work in the case of generalized linear classification, also known as the perceptron model, with random labels. We argue that there is a large universality class of high-dimensional input data for which we obtain the same minimum training loss as for Gaussian data with corresponding data covariance. In the limit of vanishing regularization, we further demonstrate that the training loss is independent of the data covariance. On the theoretical side, we prove this universality for an arbitrary mixture of homogeneous Gaussian clouds. Empirically, we show that the universality holds also for a broad range of real data sets.

Amplifying Sine Unit: An Oscillatory Activation Function for Deep Neural Networks to Recover Nonlinear Oscillations Efficiently

Many industrial and real-life problems exhibit highly nonlinear periodic behaviors and the conventional methods may fall short of finding their analytical or closed-form solutions. Such problems demand cutting-edge computational tools with increased functionality and reduced cost. Recently, deep neural networks have gained massive research interest due to their ability to handle large data and universality to learn complex functions. In this work, we put forward a methodology based on deep neural networks with responsive layers structure to deal nonlinear oscillations in microelectromechanical systems. We incorporated some oscillatory and non-oscillatory activation functions such as growing cosine unit known as GCU, Sine, Mish and Tanh in our designed network to have a comprehensive analysis on their performance for highly nonlinear problems. Integrating oscillatory activation functions with deep neural networks definitely outperform in predicting the periodic patterns of underlying systems. To support oscillatory actuation for nonlinear systems, we have proposed a novel oscillatory activation function called Amplifying Sine Unit denoted as ASU which is more efficient than GCU for complex vibratory systems such as microelectromechanical systems. Experimental results show that the designed network with our proposed activation function ASU is more reliable and robust to handle the challenges posed by oscillations

Fully collusion resistant trace-and-revoke functional encryption for arbitrary identities

Functional Encryption (FE) has been extensively studied in the recent years, mainly focusing on the feasibility of constructing FE for general functionalities, as well as some realizations for restricted functionalities of practical interest, such as inner-product. However, little consideration has been given to the issue of key leakage on FE. The property of FE that allows multiple users to obtain the same functional keys from the holder of the master secret key raises an important problem: if some users leak their keys or collude to create a pirate decoder, how can we identify at least one of those users, given some information about the compromised keys or the pirate decoder? Moreover, how do we disable the decryption capabilities of those users (i.e. traitors)?Two recent works have offered potential solutions to the above traitor scenario. However, the two solutions satisfy weaker notions of security and traceability, can only tolerate bounded collusions (i.e., there is an a priori bound on the number of keys the pirate decoder obtains), or can only handle a polynomially large universe of possible identities. In this paper, we study trace-and-revoke mechanism on FE and provide the first construction of trace-and-revoke FE that supports arbitrary identities, is both fully collusion resistant and fully anonymous. Our construction relies on a generic transformation from revocable predicate functional encryption with broadcast (RPFE with broadcast, which is an extension of revocable predicate encryption with broadcast proposed by Kim and J. Wu at ASIACRYPT'2020) to trace-and-revoke FE. Since this construction admits a generic construction of trace-and-revoke inner-product FE (IPFE), we instantiate the trace-and-revoke IPFE from the well-studied Learning with Errors (LWE). This is achieved by proposing a new LWE-based attribute-based IPFE (ABIPFE) scheme to instantiate RPFE with broadcast.

On selection and conditioning in multiple testing and selective inference

Summary We investigate a class of methods for selective inference that condition on a selection event. Such methods follow a two-stage process. First, a data-driven collection of hypotheses is chosen from some large universe of hypotheses. Subsequently, inference takes place within this data-driven collection, conditioned on the information that was used for the selection. Examples of such methods include basic data splitting as well as modern data-carving methods and post-selection inference methods for lasso coefficients based on the polyhedral lemma. In this article, we take a holistic view of such methods, considering the selection, conditioning and final error control steps together as a single method. From this perspective, we demonstrate that multiple testing methods defined directly on the full universe of hypotheses are always at least as powerful as selective inference methods based on selection and conditioning. This result holds true even when the universe is potentially infinite and only implicitly defined, such as in the case of data splitting. We provide general theory and intuition before investigating in detail several case studies where a shift to a nonselective or unconditional perspective can yield a power gain.

Highly Sensitive Temperature Sensor Based on Vernier Effect Using a Sturdy Double-cavity Fiber Fabry-Perot Interferometer.

Temperature measuring is a daily procedure carried out worldwide in practically all environments of human activity, but it takes particular relevance in industrial, scientific, medical, and food processing and production areas. The characteristics and performance of the temperature sensors required for such a large universe of applications have opened the opportunity for a comprehensive range of technologies and architectures capable of fulfilling the sensitivity, resolution, dynamic range, and response time demanded. In this work, a highly sensitive fiber optic temperature sensor based on a double-cavity Fabry-Perot interferometer (DCFPI) is proposed and demonstrated. Taking advantage of the Vernier effect, we demonstrate that it is possible to improve the temperature sensitivity exhibited by the polymer-capped fiber Fabry-Perot interferometer (PCFPI) up to 39.8 nm/°C. The DCFPI is sturdy, reconfigured, and simple to fabricate, consisting of a semi-spherical polymer cap added to the surface of the ferrule of a commercial single-mode fiber connector (SMF FC/PC) placed in front of a mirror at a proper distance. The length of the air cavity (Lair) was adjusted to equal the thickness of the polymer cap (Lpol) plus a distance δ to generate the most convenient Vernier effect spectrum. The DCFPI was packaged in a machined, movable mount that allows the adjustment of the air cavity length easily but also protects the polymer cap and simplifies the manipulation of the sensor head.

The relic radiation blunder and the cosmometric contradiction in Big Bang cosmology

The cosmic microwave background radiation is routinely cited as evidence for a hot Big Bang. Its near isotropy harmonizes with the cosmological principle. However, in prototypical Big Bang models, all matter originates from a primeval fireball that also emits the light that is redshifted into these microwaves. Since light escapes from its source faster than matter can move, it would need to return for it to still be visible to material observers, but the universe is considered ‘flat’ and non-reflective. This prevents us from observing the redshifted glow of a primeval fireball. This is concealed by considering the light to expand with the ‘Hubble flow’ while disregarding that it would escape at _c_. This “relic radiation blunder” reflects the assumption that model universes in General Relativity are filled with a spatially homogeneous fluid. For radiation, this becomes inappropriate when it is no longer scattered. What we actually observe remains unexplained. Moreover, current standard cosmology allows an expanding view into a large pre-existing universe, while for some aspects it assumes the universe to have been smaller before. This creates geometric, i.e., “cosmometric” contradictions such as between the observed source of the cosmic microwaves and the much smaller and closer assumed emitting source of the same. The criticism expressed here goes against the ‘hard core’ of an established research program. Experts in the field normally view these cores as untouchable. This attitude blocks foundational advances in science.

A Policy-Hiding Attribute-Based Access Control Scheme in Decentralized Trust Management

Internet of Medical Things (IoMT) technologies significantly improve the quality of health care, especially at the time when COVID-19 is becoming a worldwide pandemic. Due to the complexity of devices and user nodes in the IoMT system, there should be some ways to ensure the security and quality of the service or information. Decentralized trust management techniques are efficient means of promoting application security and reliability in these cases. However, the majority of currently utilized access control schemes cannot be applied in decentralized trust management systems or perform poorly owing to the numerous restrictions of decentralized systems. In this paper, we present a policy-hiding and multi-authority key generation CP-ABE scheme (PM-CPABE) for decentralized trust management systems, which could provide fine-grained access control capabilities. Meanwhile, the proposed scheme does not require any fully trusted entity, thus it can be well adapted to decentralized trust management systems. The scheme also implements policy hiding to protect user privacy. In addition, it supports large universe and outsourced decryption. The security analyses and performance comparisons give evidence of our scheme is security and efficient.

Universality in long-distance geometry and quantum complexity

In physics, two systems that radically differ at short scales can exhibit strikingly similar macroscopic behaviour: they are part of the same long-distance universality class1. Here we apply this viewpoint to geometry and initiate a program of classifying homogeneous metrics on group manifolds2 by their long-distance properties. We show that many metrics on low-dimensional Lie groups have markedly different short-distance properties but nearly identical distance functions at long distances, and provide evidence that this phenomenon is even more robust in high dimensions. An application of these ideas of particular interest to physics and computer science is complexity geometry3–7—the study of quantum computational complexity using Riemannian geometry. We argue for the existence of a large universality class of definitions of quantum complexity, each linearly related to the other, a much finer-grained equivalence than typically considered. We conjecture that a new effective metric emerges at larger complexities that describes a broad class of complexity geometries, insensitive to various choices of microscopic penalty factors. We discuss the implications for recent conjectures in quantum gravity.

Experimental investigation of the shear response of large-scale fibre-reinforced concrete panels

Although fibre-reinforced concrete has been the subject of extensive research for decades, its mechanical behaviour in shear is still not well understood. Furthermore, hardly any large-scale shear tests on elements with moderate fibre contents have been conducted to date. The present paper addresses this knowledge gap by presenting and discussing the results of an experimental campaign on large-scale fibre-reinforced concrete shear panels. Six panels with dimensions of 2.00 m × 2.00 m × 0.27 m were tested in the Large Universal Shell Element Tester at ETH Zurich by applying homogeneous shear. In four of these experiments, the in-plane shear was combined with homogeneous uniaxial compression by simulating the longitudinal restraint provided by the chords in a girder. Five tests focused on concrete of normal strength with moderate fibre contents and one test studied the behaviour of ultra high-performance fibre-reinforced concrete. The combined application of digital image correlation instrumentation and distributed fibre optical sensing allowed gaining a deeper insight into the structural behaviour of the panels. Overall, the test results showed the high efficiency of the fibres as shear reinforcement in large-scale web elements. However, failure of the panels without any steel reinforcing bars was rather brittle, with only few cracks forming over the element. The provision of a moderate amount of distributed longitudinal bar reinforcement has been found to be highly beneficial to the crack behaviour and thus significantly increase the ductility.

The Big Bang theory: two fatal flaws

The cosmic microwave background radiation is routinely cited as evidence for a hot Big Bang. Its isotropy harmonizes with the cosmological principle. However, in prototypical Big Bang models, all matter originates from a primeval fireball that also emits the light that is redshifted into these microwaves. Since light escapes from its source faster than matter can move, it would need to return for it to still be visible to material observers, but the universe is considered ‘flat’ and non-reflective. This prevents us from observing the redshifted glow of the primeval fireball. Like its observability, its homogeneity would also be transient. This is concealed by considering the light to expand with the ‘Hubble flow’ while disregarding that it escapes at _c_. This blunder reflects the practice of treating model universes in General Relativity as filled with a spatially homogeneous fluid. For radiation, this becomes inappropriate when it is no longer scattered. What we actually observe remains unexplained. Moreover, the calculation of line-of-sight distances allows an expanding view into a large pre-existing universe. For other aspects, the universe is assumed to have been smaller before. This creates contradictions such as between the observed source of the cosmic microwaves and their much smaller and closer assumed emitting source. The criticism expressed here goes against the ‘hard core’ of an established research program. Those cores are treated as inviolable, which blocks fundamental progress. Such blockage can persist for generations even if the theory that is promulgated as the best we have is actually irrational.

A controllable delegation scheme for the subscription to multimedia platforms

With the development of the Internet, various multimedia platforms have sprung up like mushrooms after rain. Through subscription, people can enjoy the rich resources provided by the multimedia platform. To attract more subscribers, the multimedia platform will give users the ability to delegate access rights. Because in this way, the subscriber can share resources with his/her family or friends. However, the delegation ability should be controllable. Otherwise, there will be a risk of delegation abuse and damage to the interests of the multimedia platform. Therefore, we propose a new Key-Policy Attribute-Based Encryption (KP-ABE) scheme supporting key delegation ability, traceability, and single-layer delegation, which can work as a controllable delegation scheme for the subscription to multimedia platforms. Firstly, KP-ABE can realize access control to resources and protect the security of resources through encryption. Secondly, the key delegation ability can meet the requirement of users to share their access rights. Lastly, traceability and single-layer delegation can limit the key delegation ability of users, so as to resist key delegation abuse. In addition, the proposed scheme supports large universe and tree access structures and is established on prime-order groups, which makes it scalable, expressive, and efficient.

Is the empirical out-of-sample variance an informative risk measure for the high-dimensional portfolios?

The main contribution of this paper is the derivation of the asymptotic behavior of the out-of-sample variance, the out-of-sample relative loss, and of their empirical counterparts in the high-dimensional setting, i.e., when both ratios p/n and p/m tend to some positive constants as m→∞ and n→∞, where p is the portfolio dimension, while n and m are the sample sizes from the in-sample and out-of-sample periods, respectively. The results are obtained for the traditional estimator of the global minimum variance (GMV) portfolio and for the two shrinkage estimators introduced by Frahm and Memmel (2010) and Bodnar et al. (2018). We show that the behavior of the empirical out-of-sample variance may be misleading in many practical situations, leading, for example, to a comparison of zeros. On the other hand, this will never happen with the empirical out-of-sample relative loss, which seems to provide a natural normalization of the out-of-sample variance in the high-dimensional setup. As a result, an important question arises if the out-of-sample variance can safely be used in practice for portfolios constructed from a large asset universe.

Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies, and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, countries, or revenue buckets. We also compare the model results to those of other providers and find our estimates to be more accurate. Explainability tools based on Shapley values allow the constructed model to be fully interpretable, the user being able to understand which factors split explains the GHG emissions for each particular company.

A new approach of soft rough sets and a medical application for the diagnosis of Coronavirus disease

<abstract><p>Rough set and soft set theories presents the mathematical foundations for studying decision making problems in different contexts. Some authors have established their own approaches regarding this theory, such as the "soft pre-rough approximation" and "soft $ \beta $-rough approaximation". In this study, the rationale and results of these two approaches were rigorously analyzed and it was concluded that they are the same. In addition, it was proven that some of the results established with the aforementioned approaches are not true, so we present two proposed modifications to the soft rough approximations, one of which represents an improvement in accuracy with respect to the exposed methods. The approaches addressed in this document were implemented to diagnose COVID-19 in a contextualized situation of a group of patients in Colombia, showing that our proposal obtained the highest accuracy. In addition, an algorithm was designed, which allows analyzing data with a larger universe and set of parameters than those presented in the theoretical and practical examples.</p></abstract>

AN INVESTIGATION OF CLASSROOM TEACHERS' SELF-EFFICACY LEVELS RELATED TO VALUES EDUCATION

Aim: This study aims to examine the self-efficacy levels of classroom teachers about values education. Method: This study was conducted with the survey model, one of the quantitative research methods. The population of the study consists of classroom teachers in Mersin province in the 2023-2024 academic year. According to the data announced by Mersin Governorate, 31,080 teachers constituted the population of the study in June 2023. It was stated that it is generalisable if 370 samples can be reached from the whole universe, taking into account the margin of error at a certain level. In this direction, the participants of the study were selected by convenient/accidental sampling technique. This sampling technique provides convenience to researchers in terms of time, material and labour force in large universes. Of the teachers participating in the study, 205 were women and 203 were men. In the study, the Values Education Self-Efficacy Scale of Classroom Teachers developed by Bayat and Yazar (2022) was used to determine the self-efficacy levels of classroom teachers regarding values education. The data collected with the participation of the teachers were transferred to SPSS 24.0 package programme and analysed. Results: According to the findings, no significant difference was found in terms of gender and educational status variables in any sub-dimension of the scale and in total. In addition, significant differences were found in the sub-dimensions of teacher behaviours and cooperation. In the sub-dimension of teacher behaviours, the scores of teachers with a seniority of 10-20 years and teachers with a seniority of 20 years or more differed significantly from those of teachers with a seniority of 0-5 years. Conclusion: According to the findings, the sub-dimensions of the scale and the total score do not show a significant difference according to gender. For this reason, it can be interpreted that gender variable is not effective on classroom teachers' values education self-efficacy. On the other hand, it can be interpreted that the self-efficacy of the classroom teachers participating in the study towards values education is at a high level with a mean score of 137,60.