Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensoring with the Frobenius endomorphism

Let $R$ be a commutative Noetherian Cohen-Macaulay local ring that has positive dimension and prime characteristic. Li proved that the tensor product of a finitely generated non-free $R$-module $M$ with the Frobenius endomorphism ${}^{\varphi^n}\! R$ is not maximal Cohen-Macaulay provided that $M$ has rank and $n\gg 0$. We replace the rank hypothesis with the weaker assumption that $M$ is locally free on the minimal prime ideals of $R$. As a consequence, we obtain, if $R$ is a one-dimensional non-regular complete reduced local ring that has a perfect residue field and prime characteristic, then ${}^{\varphi^n}\! R \otimes_{R}{}^{\varphi^n}\! R$ has torsion for all $n\gg0$. This property of the Frobenius endomorphism came as a surprise to us since, over such rings $R$, there exist non-free modules $M$ such that $M\otimes_{R}M$ is torsion-free.
A dynamic hysteresis model for customized glass transition in amorphous polymer towards multiple shape memory effects

Coexistence of multiple and discrete segments as well as their distinctive hysteresis relaxations enables amorphous shape memory polymers (SMPs) exhibiting complex disordered dynamics, which is critical for the glass transition behavior to determine the shape memory effect (SME), but remained largely unexplored. In this study, a dynamic hysteresis model is proposed to explore the working principle and collective dynamics in discrete segments of amorphous SMPs, towards a dynamic connection between complex relaxation hysteresis and glass transition behavior, which can be applied for design and realization of multiple SMEs in the amorphous SMPs. In combination of free volume theory and Adam-Gibbs domain size model, a phase transition model is formulated to identify the working principle of dynamic relaxation hysteresis in the glass transition of amorphous SMP. Furthermore, constitutive relationships among relaxation time, strain, storage modulus, loss angle and temperature have been established to describe the dynamic connection between complex relaxation hysteresis and customized glass transition, which is then utilized to achieve multiple SMEs based on the extended Maxwell model. Finally, effectiveness of the proposed models is verified using experimental results of SMPs with multiple SMEs reported in literature.
Analysis of nonlinear parity-time symmetric four-layer Bragg grating

The one dimensional PT (parity time) symmetric four-layer Bragg grating with gain and loss saturation effects is analyzed taking into account the evolution of the field amplitudes. The investigated structure has modulated real and imaginary parts of refractive index. The functional configurations of the Bragg grating have been considered, showing the behavior of this structure acting as a discrete device and as a component of an integrated circuit. The obtained characteristics illustrate the influence of the saturation effect on the intensity of the output wave at a given intensity of the incident plane wave for different values of gain and loss saturation intensities of the PT structure. The performed analysis shows the bistability of the structure and a strong influence of the incident wave intensity on the properties of light propagation nonreciprocity.
Torque Improvement of Dual Three-phase YASA Machine Based on Current Harmonics Injection

The method for enhancing the torque capability of the dual three-phase yokeless and segmented armature (YASA) axial flux permanent magnet machine by injecting the 5th and 7th current harmonics is proposed in this paper. The mathematical model of the dual three-phase YASA machine considering the 5th and 7th harmonics of the open-circuit flux-linkage is established based on vector space decomposition method. The parametric model and vector control systems with and without the 5th and 7th current harmonics injection of the dual three-phase YASA machine are built to verify the effectiveness of torque enhancement by current harmonics injection. The simulation results show that the torque per peak current of the analyzed YASA machine can be improved by 8.74% by injecting 5th and 7th current harmonics.
Board of Directors’ Interlocks: A Social Network Analysis Tutorial

ABSTRACT Objective: the literature on board interlocks has increased in recent years, focusing on understanding board composition and its relationships with other companies’ boards. Such studies usually require multiple procedures of data extraction, handling, and analysis to create and analyze social networks. However, these procedures are not standardized, and there is a lack of methodological instructions available to make this process easier for researchers. This tutorial intends to describe the logical steps taken to collect data, treat them, and map and measure the network properties to provide researchers with the sources to replicate it in their own research. We contribute to the literature in the management field by proposing an empirical methodological approach to conduct board interlocks’ research. Proposal: our tutorial describes and provides examples of data collection, directors’ data treatment, and the use of these data to map and measure network structural properties using an open-source tool - R statistical software. Conclusions: our main contribution is a tutorial detailing the steps required to map and analyze board interlocks, making this process easier, standardized, and more accessible for all researchers who wish to develop social network analysis studies.
Recent Advances on Convolutional Architectures in Medical Applications: Classical or Quantum?

Deep learning is one of the most significant advances in AI (AI). It is used in a variety of fields due to it has the ability to solve problems that cannot be handled by traditional technologies. The optimization of deep learning relevant to medical images is one of the most important recent advances in image analysis. Several developments have been done on Convolutional Neural Networks to achieve optimal accuracy and increase the learning speed. However, in this paper, we discuss the most recent innovations in convolutional neural networks within Classical method and Quantum method. We briefly provide a snapshot about the architecture, improvements, and principles of both (Classical and Quantum).
SubScatter: Sub-symbol WiFi Backscatter for High Throughput

Throughput is one of the key performance indicators in backscatter communication systems. Existing works either have limited throughput or modify the transmitter or receiver to fit the backscatter tag, thereby causing incompatibility with commodity radios. In this paper, we present SubScatter, which realizes a high throughput and keeps compatibility at the same time. For high throughput, SubScatter uses one CCK-modulated 802.11b WiFi symbol to carry eight tag bits by manipulating the phase of the backscattered signal in eight-time slots of a symbol separately. For compatibility with commercial-off-the-shelf (COTS) radios, SubScatter leverages only the physical service data unit (PSDU) to recover the backscatter modulation in which the tag bits are conveyed. And still, to fit the sub-symbol backscatter modulation, SubScatter calculates the Hamming distance between the binary envelope provided by the synchronization circuit and a reference sequence in real-time for synchronization. Extensive experiments in our prototype have proven the effectiveness of SubScatter. SubScatter achieves a throughput of about 11× over state-of-the-art backscatter systems compatible with COTS radios. The Hamming-distance-based synchronization outperforms the design of merely detecting the change of signal power and helps reduce the bit error rate from over 10% to below 1%.
New hypergeometric formulae to $\pi$ arising from M. Roberts hyperelliptic reductions

In this article we developed a special topic of our pure-mathematics papers concerning the hypergeometric theory. Based upon a Roberts's reduction approach of hyperelliptic integrals to elliptic ones and on the simultaneous multivariable hypergeometric series evaluation of them, several identities have been obtained expressing $\pi$ in terms of special values of elliptic, hypergeometric and Gamma functions. By them $\pi$ can be provided through either only one or two parameters and through the imaginary unit. In any case, such results, all unpublished and undoubtably new, will provide, beyond their own beauty, a useful tool in order to check the routines (more or less naive) which one can build for the practical computations of Lauricella's functions met frequently in researches on Mechanics or Elasticity.
Lava Mapping Using Sentinel-1 Data after the Occurrence of a Volcanic Eruption—The Case of Cumbre Vieja Eruption on La Palma, Canary Islands, Spain

Volcanic eruptions pose a great threat to humans. In this context, volcanic hazard and risk assessment constitute crucial issues with respect to mitigating the effects of volcanic activity and ensuring the health and safety of inhabitants. Lava flows directly affect communities living near active volcanoes. Nowadays, remote sensing advances make it possible to effectively monitor eruptive activity, providing immediate and accurate information concerning lava evolution. The current research focuses on the mapping of the surface deformation and the analysis of lava flow evolution occurred on the island of La Palma, during the recent (2021) eruptive phase of the volcano. Sentinel-1 data covering the island were collected throughout the entire eruptive period, i.e., September 2021 until January 2022. The processing was based on amplitude-based and phase-based detection methods, i.e., Synthetic Aperture Radar interferometry (InSAR) and offset tracking. In particular, ground deformation occurred on the island, while Line-Of-Sight (LOS) displacements were derived from Sentinel-1 interferograms. Moreover, the evolution of lava flow velocity was estimated using Sentinel-1 imagery along with offset tracking technique. The maximum lava flow velocity was calculated to be 2 m/day. It was proved that both approaches can provide rapid and useful information in emergencies, especially in inaccessible areas. Although offset tracking seems a quite promising technique for the mapping of lava flows, it still requires improvement.
A posteriori stochastic correction of reduced models in delayed‐acceptance MCMC, with application to multiphase subsurface inverse problems

Sample‐based Bayesian inference provides a route to uncertainty quantification in the geosciences and inverse problems in general but is very computationally demanding in the naïve form, which requires simulating an accurate computer model at each iteration. We present a new approach that constructs a stochastic correction to the error induced by a reduced model, with the correction improving as the algorithm proceeds. This enables sampling from the correct target distribution at reduced computational cost per iteration, as in existing delayed‐acceptance schemes, while avoiding appreciable loss of statistical efficiency that necessarily occurs when using a reduced model. Use of the stochastic correction significantly reduces the computational cost of estimating quantities of interest within desired uncertainty bounds. In contrast, existing schemes that use a reduced model directly as a surrogate do not actually improve computational efficiency in our target applications. We build on recent simplified conditions for adaptive Markov chain Monte Carlo algorithms to give practical approximation schemes and algorithms with guaranteed convergence. The efficacy of this new approach is demonstrated in two computational examples, including calibration of a large‐scale numerical model of a real geothermal reservoir, that show good computational and statistical efficiencies on both synthetic and measured data sets.
Efficient Neural Network Compression

Network compression reduces the computational complexity and memory consumption of deep neural networks by reducing the number of parameters. In SVD-based network compression the right rank needs to be decided for every layer of the network. In this paper we propose an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, our method considers the whole network to choose the right rank configuration. We propose novel accuracy metrics to represent the accuracy and complexity relationship for a given neural network. We use these metrics in a non-iterative fashion to obtain the right rank configuration which satisfies the constraints on FLOPs and memory while maintaining sufficient accuracy. Experiments show that our method provides better compromise between accuracy and computational complexity/memory consumption while performing compression at much higher speed. For VGG-16 our network can reduce the FLOPs by 25% and improve accuracy by 0.7% compared to the baseline, while requiring only 3 minutes on a CPU to search for the right rank configuration. Previously, similar results were achieved in 4 hours with 8 GPUs. The proposed method can be used for lossless compression of a neural network as well. The better accuracy and complexity compromise, as well as the extremely fast speed of our method make it suitable for neural network compression.
Remark on Weil’s conjectures

Abstract We introduce a cohomology theory for a class of projective varieties over a finite field coming from the canonical trace on a C * ${C^{*}}$ -algebra attached to the variety. Using the cohomology, we prove the rationality, functional equation and the Betti numbers conjectures for the zeta function of the variety.
Dark energy stars: Stable configurations

In present paper a spherically symmetric stellar configuration has been analyzed by assuming the matter distribution of the stellar configuration is anisotropic in nature and compared with the realistic objects, namely, the low mass X-ray binaries and X-ray pulsars. The analytic solution has been obtained by utilizing the dark energy equation of state for the interior solution corresponding to the Schwarzschild exterior vacuum solution at the junction interface. Several physical properties, like energy conditions, stability, mass–radius ratio, and surface redshift, are described through mathematical calculations as well as graphical plots. It is found that obtained mass–radius ratios of the compact star candidates like 4U 1820–30, PSR J 1614–2230, Vela X-1, and Cen X-3 are very much consistent with the observed data by Gangopadhyay et al. (Mon. Not. R. Astron. Soc. 431, 3216 (2013)). So our proposed model would be useful in the investigation of the possible clustering of dark energy.
Consumer-Grade Inertial Measurement Units Enhanced Indoor Magnetic Field Matching Positioning Scheme

Magnetic field matching (MFM) positioning is one of the mainstream methods of consumer indoor positioning, which has attracted great attention from academia and industry. However, efficient methods for constructing magnetic field maps and matching positioning methods with high stability still need to be developed. This study proposes an indoor MFM positioning scheme enhanced by consumer-grade inertial measurement units that can efficiently generate a magnetic field grid map and achieve robust matching positioning, without the need to actively calibrate the magnetometer bias. When generating the magnetic field map, the proposed method employs a pedestrian positioning and orientation system to efficiently collect data by releasing individual behavioral constraints and reducing the number of control points required. Moreover, a magnetometer bias autocalibration method, aided by the precise attitude, is proposed to simplify the data collection process. During real-time positioning, the position and attitude generated by pedestrian dead reckoning (PDR) are used to generate the differential magnetic field strength in the sensor frame; this achieves matching positioning that is independent of the magnetometer bias. Furthermore, an estimation method for the MFM position noise, based on the magnetic field gradient, is proposed to improve the accuracy of MFM/PDR integrated positioning. Several experiments were conducted to verify the feasibility and performance of the proposed scheme. Only slight differences were found between the magnetic field maps using three different smartphones, showing that the proposed scheme can efficiently generate a high-precision magnetic map. The positioning results of multiple tests conducted using eight different smartphones revealed that the proposed scheme achieves continuous and robust meter-level positioning accuracy.
Distribution and correlation-free two-sample test of high-dimensional means

We propose a two-sample test for high-dimensional means that requires neither distributional nor correlational assumptions, besides some weak conditions on the moments and tail properties of the elements in the random vectors. This two-sample test based on a nontrivial extension of the one-sample central limit theorem (Chernozhukov et al., 2017) provides a practically useful procedure with rigorous theoretical guarantees on its size and power assessment. In particular, the proposed test is easy to compute and does not require the independently and identically distributed assumption, which is allowed to have different distributions and arbitrary correlation structures. Further desired features include weaker moments and tail conditions than existing methods, allowance for highly unequal sample sizes, consistent power behavior under fairly general alternative, data dimension allowed to be exponentially high under the umbrella of such general conditions. Simulated and real data examples are used to demonstrate the favorable numerical performance over existing methods.
L1-Norm Tucker Tensor Decomposition

Tucker decomposition is a standard multi-way generalization of Principal-Component Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker decomposition has been shown to be sensitive against faulty data, due to its L2-norm-based formulation which places squared emphasis to peripheral/outlying entries. In this work, we explore L1-Tucker, an L1-norm based reformulation of Tucker decomposition, and present two algorithms for its solution, namely L1-norm Higher-Order Singular Value Decomposition (L1-HOSVD) and L1-norm Higher-Order Orthogonal Iterations (L1-HOOI). The proposed algorithms are accompanied by complexity and convergence analysis. Our numerical studies on tensor reconstruction and classification corroborate that L1-Tucker decomposition, implemented by means of the proposed algorithms, attains similar performance to standard Tucker when the processed data are corruption-free, while it exhibits sturdy resistance against heavily corrupted entries.
Optimization of DER Integrated Distribution System by Sequential Quadratic Programming (SQP)

One of the most critical challenges of the modern power distribution system is to optimize the network, reduce loss, and ultimately reduce the cost of operation. After years of research, it remains a difficult challenge for researchers. Fur-thermore, the increasing amount of distributed energy resources (DERs) in the distribution network has recently aggravated the situation. In this article, a Sequential Quadratic Programming (SQP) based nonlinear programming (NLP) model is proposed for optimal power flow (OPF) solution in a radial distribution network with penetration of DERs. The model has been tested with the IEEE 33 bus modified distribution network and the IEEE 123 bus distribution system. The results of these simulations have been verified with the interior point method (IPM) NLP algorithm (NLP-IPM), and the results have shown faster convergence than the NLP-IPM algorithm.
Black hole evaporation in an expanding universe

We calculate the quantum radiation power of black holes which are asymptotic to the Einstein–de Sitter universe at spatial and null infinities. We consider two limiting mass accretion scenarios, no accretion and significant accretion. We find that the radiation power strongly depends on not only the asymptotic condition but also the mass accretion scenario. For the no accretion case, we consider the Einstein–Straus solution, where a black hole of constant mass resides in the dust Friedmann universe. We find negative cosmological correction besides the expected redshift factor. This is given in terms of the cubic root of ratio in size of the black hole to the cosmological horizon, so that it is currently of order 10−5(M/106M⊙)1/3(t/14Gyr)−1/3 but could have been significant at the formation epoch of primordial black holes. Due to the cosmological effects, this black hole has not settled down to an equilibrium state. This cosmological correction may be interpreted in an analogy with the radiation from a moving mirror in a flat spacetime. For the significant accretion case, we consider the Sultana–Dyer solution, where a black hole tends to increase its mass in proportion to the cosmological scale factor. In this model, we find that the radiation power is apparently the same as the Hawking radiation from the Schwarzschild black hole of which mass is that of the growing mass at each moment. Hence, the energy loss rate decreases and tends to vanish as time proceeds. Consequently, the energy loss due to evaporation is insignificant compared to huge mass accretion onto the black hole. Based on this model, we propose a definition of quasi-equilibrium temperature for general conformal stationary black holes.
Optimization Techniques for Reliable Low Leakage GNRFET-Based 9T SRAM

We present a low-leakage graphene nano-ribbon transistors (GNRFETs)-based static random access memory (SRAM) cell in 16nm technology that operates near the sub-threshold region in this study. In comparison to conventional Si-CMOS technology, the proposed cell improves read stability and writeability by <inline-formula> <tex-math notation="LaTeX">$2.67\mathbf {\times }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4.14\mathbf {\times }$ </tex-math></inline-formula>, respectively. The proposed cell considerably outperforms the existing 9T SRAMs cell in terms of power consumption, latency, read stability, and write-ability, according to the HSPICE simulation. In addition, at the low supply voltage of 325mV, the multi-threshold approach and transistor optimizations are used to improve the read static noise margin (RSNM). The proposed cell’s overall power consumption is lowered by <inline-formula> <tex-math notation="LaTeX">$4161\mathbf {\times }$ </tex-math></inline-formula> when compared to a conventional 6T SRAM cell while using the multi-threshold approach in the write port. Device optimization and the multi-threshold approach, which enhances read and write performance, can considerably minimize read and write delays.
SNAF-based Interdependent E2E Network Resource Slicing Scheme for a Virtualized Network

To meet the requirements of assorted applications from different service providers operating on a shared infrastructure, the current generation of mobile cellular networks (5G) rely on network slicing. However, the synchronization of RAN and core network slicing has not been investigated as an interdependent resource allocation problem. This paper proposes a novel End to End (E2E) resource slicing and allocation scheme. The Slice to Node Access Factor (SNAF) based E2E Slice resource provisioning scheme is proposed based on diverse users' Quality of Service (QoS) requirements for transmission delay and data rate. The SNAF, in principle, ensures proper resource provisioning and traffic synchronization, and thus allocates radio resources based on the provisioned affordable traffic and backhaul resources, and vice versa. Based on the 5G air interface, we ran a system-level simulation to assess the performance of our solution from multiple angles. Simulation findings show that our proposed SNAF-based interdependent E2E resource allocation delivers improved E2E traffic-resource synchronization and enhances QoS satisfaction with minimum resource utilization when compared to benchmarked state-of-the-art methods.