Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Energy Forms and Lipschitz Spaces on the Koch Curve

In the quadratic case p = 2, constructions of this type are standard and have been done for various fractals K like the Sierpinski gasket, the Koch curve and more general simple nested fractals: in these cases, the energy functionals E (2) are Dirichlet forms, whose domains DE(2) are the fractal analogue of the Sobolev space W . Always in the case p = 2, these spaces W (K) ≡ DE(2)(K) have been put in relation with the theory of Lipschitz spaces Lipα,Df (2,∞, K); in particular, in Jonsson [5] first and later also in Lancia and Vivaldi [9], Paluba [13] and Kumagai [8], for various examples of fractals K the following characterization has been given
P4sec: Automated Deployment of 802.1X, IPsec, and MACsec Network Protection in P4-Based SDN

802.1X, MACsec, and IPsec are widespread network security mechanisms that control network access and add encryption and authentication to L2 and L3 networking. They are standardized by IEEE and IETF, and are part of most open-source and commercial network hardware and software appliances. However, lots of manual configuration is needed for their application in traditional networks. In this work, we present P4sec, a three-tier control architecture for automated configuration of these security protocols in networks with multiple sites. P4sec leverages P4-programmable switches and operates them through distributed controllers. We briefly introduce data plane programming with P4 and give an overview of 802.1X, MACsec, and IPsec. We explain the three-tier control architecture P4sec and validate it by a prototype which is published under the Apache v2 license on GitHub. Finally, we discuss opportunities and challenges.
The Metabolism and Immune Environment in Diffuse Large B-Cell Lymphoma

Cells utilize different metabolic processes to maintain their growth and differentiation. Tumor cells have made some metabolic changes to protect themselves from malnutrition. These metabolic alterations affect the tumor microenvironment and macroenvironment. Developing drugs targeting these metabolic alterations could be a good direction. In this review, we briefly introduce metabolic changes/regulations of the tumor macroenvironment and microenvironment and summarize potential drugs targeting the metabolism in diffuse large B-cell lymphoma.
Quantum Procedures for Nested Search Problems with Applications in Cryptanalysis

In this paper we study search problems that arise very often in cryptanalysis: nested search problems, where each search layer has known degrees of freedom and/or constraints. A generic quantum solu-tion for such problems consists of nesting Grover’s quantum search al-gorithm or amplitude ampliﬁcation (QAA) by Brassard et al., obtaining up to a square-root speedup on classical algorithms. However, the analysis of nested Grover or QAA is complex and introduces technicalities that in previous works are handled in a case-by-case manner. Moreover, straightforward nesting introduces an overhead factor of ( π/ 2) (cid:96) in the complexity (for (cid:96) layers). In this paper, we aim to remedy both these issues and introduce a generic framework and tools to transform a classical nested search into a quantum procedure. It improves the state-of-the-art in three ways: 1) our framework results in quantum procedures that are signiﬁcantly simpler to describe and analyze; 2) it reduces the overhead factor from ( π/ 2) (cid:96) to √ (cid:96) ; 3) it is simpler to apply and optimize, without needing manual quantum analysis. We give a generic complexity formula that improves the state-of-the-art both for concrete instantiations and in the asymptotic setting. For concrete instances, we show that numerical optimizations enable further improvements over this formula, which results in a further decrease in the gap to an exact quadratic speedup. We demonstrate our framework with applications in cryptanalysis and improve the complexity of quantum attacks on reduced-round AES.
TGh: A TEE/GC Hybrid Enabling Confidential FaaS Platforms

Trusted Execution Environments (TEEs) suffer from performance issues when executing certain management instructions, such as creating an enclave, context switching in and out of protected mode, and swapping cached pages. This is especially problematic for short-running, interactive functions in Function-as-a-Service (FaaS) platforms, where existing techniques to address enclave overheads are insufficient. We find FaaS functions can spend more time managing the enclave than executing application instructions. In this work, we propose a TEE/GC hybrid (TGh) protocol to enable confidential FaaS platforms. TGh moves computation out of the enclave onto the untrusted host using garbled circuits (GC), a cryptographic construction for secure function evaluation. Our approach retains the security guarantees of enclaves while avoiding the performance issues associated with enclave management instructions.
Overlap Functions-Based Fuzzy Mathematical Morphological Operators and Their Applications in Image Edge Extraction

: As special aggregation functions, overlap functions have been widely used in the soft computing ﬁeld. In this work, with the aid of overlap functions, two new groups of fuzzy mathematical morphology (FMM) operators were proposed and applied to image processing, and they obtained better results than existing algorithms. First, based on overlap functions and structuring elements, the ﬁrst group of new FMM operators (called OSFMM operators) was proposed, and their properties were systematically analyzed. With the implementation of OSFMM operators and the fuzzy C-means (FCM) algorithm, a new image edge extraction algorithm (called the OS-FCM algorithm) was proposed. Then, the second group of new FMM operators (called ORFMM operators) was proposed based on overlap functions and fuzzy relations. Another new image edge extraction algorithm (called OR-FCM algorithm) was proposed by using ORFMM operators and FCM algorithm. Finally, through the edge segmentation experiments of multiple standard images, the actual segmentation effects of the above-mentioned two algorithms and relevant algorithms were compared. The acquired results demonstrate that the image edge extraction algorithms proposed in this work can extract the complete edge of foreground objects on the basis of introducing the least noise.
The uniqueness of word superiority effect in Chinese reading

Compared with alphabetic languages, Chinese has many unique characteristics so that the processing of Chinese may also exhibit some larger differences. This paper examined the uniqueness of word superiority effect during Chinese reading. Using a force choice paradigm, we found a word superiority effect in Chinese. However, unlike English, when compared with the non-word condition, word superiority effect was found in the word condition but only when the character frequency was low and when compared with the character condition the word superiority effect was found only at the left character location of a two-character word. These differences reflect the difference in processing of Chinese words and English words.
Efficient and Secure k-NN Classification from Improved Data-Oblivious Programs and Homomorphic Encryption

The k -nearest neighbors classiﬁer is a simple machine learning algorithm with applications in image recognition, ﬁnance, medical diagnosis and so on. It involves a measurement which is compared against a database of preclassiﬁed vectors, so that the result depends on the k vectors in the database that are closest to the measurement. In the client-server model, this classiﬁcation process can be outsourced to an external party that oﬀers machine learning as a service, where the measurement is sent in the form of a query. However, this raises privacy concerns if sensitive information is contained in the query. We design a secure and non-interactive version of the k -nearest neighbors classiﬁer, based on fully homomorphic encryption, which does not leak any information about the query to the server. Our algorithm is instantiated with the TFHE homomorphic encryption scheme, and the selection of the top-k elements is done with a novel strategy based on a type of data-oblivious algorithm—sorting networks. Compared to prior work from PoPETs 2021, the asymptotic complexity is improved from O ( d 2 ) to O ( d log 2 k ), where d is the number of entries in the k -NN model. Experimental results show that the proposed protocol can be up to 16 times faster (not accounting for diﬀerence in CPU) than previous approaches for a moderately sized database.
Entropy Stable DGSEM Schemes of Gauss Points Based on Subcell Limiting

The discontinuous Galerkin spectral element method (DGSEM) is a compact and high-order method applicable to complex meshes. However, the aliasing errors in simulating under-resolved vortex flows and non-physical oscillations in simulating shock waves may lead to instability of the DGSEM. In this paper, an entropy-stable DGSEM (ESDGSEM) based on subcell limiting is proposed to improve the non-linear stability of the method. First, we discuss the stability and resolution of the entropy-stable DGSEM based on different solution points. Second, a provably entropy-stable DGSEM based on subcell limiting is established on Legendre–Gauss (LG) solution points. Numerical experiments demonstrate that the ESDGSEM-LG scheme is superior in non-linear stability and resolution, and ESDGSEM-LG with subcell limiting is robust in shock-capturing.
DECONVOLUTION FROM NON-STANDARD ERROR DENSITIES UNDER REPLICATED MEASUREMENTS

We propose a nonparametric density estimator based on data that are repeatedly observed with independent measurement errors. We particularly focus on cases where the Fourier transform of the error density has some zeros and shows oscillations. Our estimator attains the same rates of convergence as obtains under smooth error densities whose Fourier transform have the corresponding tails but no zeros. We prove minimax results for estimating the distribution function and for support estimation in the same model. A simulation study supports our findings.
Turbulence laws in natural bed flows

Characterization of turbulence in natural bed streams is one of the most fascinating problems of fluid dynamics. In this study, a statistical description of turbulence in a natural pebble bed flow is presented applying the laws of turbulence. A laboratory experiment was conducted to measure the three-dimensional instantaneous velocity components in a flow over heterogeneous coarse sediments that simulated a natural bed. The analysis reveals that the spectra (in Fourier space) show a power-law scaling, $E(k)\sim k^{{\it\alpha}}$ , suggesting the presence of inertial range turbulence. The exponent ${\it\alpha}$ is slightly shallower than the Kolmogorov $5/3$ scaling law, with this deviation possibly due to the bed roughness heterogeneity and to fluctuation anisotropy. The Taylor frozen-in approximation is broken at smaller scales towards the roughness crest level; therefore, a new statistical tool for the validation of this approximation is proposed. The Kolmogorov $4/5$ -law for the longitudinal increments and simultaneously the Monin–Yaglom $4/3$ -law for the nonlinear normal fluxes (both in physical space) are preserved, providing an accurate estimation of the turbulent kinetic energy dissipation rate. The heterogeneity of the bed acts to induce the transport of finite kinetic helicity to the outer layer through persistently prolonged vortices. An associated $2/15$ -law for the cascade of helicity has been locally found. These findings open a new direction in turbulence research for flows over highly rough beds.
Robotic Object Perception Based on Multispectral Few-Shot Coupled Learning

In order to enable intelligent robots to recognize unknown objects as accurately as human beings, object perception research is of great significance in service and industrial robot application scenarios. However, object perception using spectral measurements under few-shot learning usually leads to a poor result because of inadequate training samples. To overcome this problem, this work proposes a novel few-shot learning with coupled dictionary learning (FSL-CDL) framework. First, a hybrid feature fusion method is developed to extract the multiple dimension-reduced features of original spectral measurements to build the hybrid features. Then, based on the hybrid features, a multitask coupled learning method is developed to effectively recognize unknown objects under few-shot learning. In this method, two coupling patterns, i.e., interspectroscopy coupling and intraspectroscopy coupling, effectively bridge the gap between two spectral measurements. Finally, the proposed FSL-CDL is compared with other advanced algorithms on the SMM50 dataset, and reaches 97.5% and 98.4% recognition accuracy under one-shot and five-shot learning, respectively, which are better than other algorithms. Besides, FSL-CDL can be extended to other perception tasks which contains multiple heterogeneous measurements.
Rapid Fault Healing After Seismic Slip

Fault strength recovery (healing) following an earthquake is a key process in controlling the recurrence of future events; however, the rates and mechanisms of fault healing are poorly constrained. Here, by performing high‐velocity friction experiments at seismic slip rates (0.57 m/s), we show that granite and gabbro fault gouges recover their strength rapidly after experiencing dynamic weakening. The healing rates are one to two orders of magnitude faster than those observed in typical frictional healing experiments performed at slow slip velocities (micrometers to millimeters per second). Analysis of the sheared gouges using Raman spectroscopy suggests that enhanced healing after seismic slip is associated with thermally activated chemical bonding at frictional contacts in the gouge. Our results indicate that seismogenic faults can potentially regain their strength early during interseismic periods, which would imply that healing may not be the dominant control on earthquake recurrence, with other processes, such as far‐field tectonic loading or frictional stability transitions, possibly dictating the occurrence of future events.
A possible contribution of the locus coeruleus to arousal enhancement with mild exercise: evidence from pupillometry and neuromelanin imaging

Abstract Acute mild exercise has been observed to facilitate executive function and memory. A possible underlying mechanism of this is the upregulation of the ascending arousal system, including the catecholaminergic system originating from the locus coeruleus (LC). Prior work indicates that pupil diameter, as an indirect marker of the ascending arousal system, including the LC, increases even with very light-intensity exercise. However, it remains unclear whether the LC directly contributes to exercise-induced pupil-linked arousal. Here, we examined the involvement of the LC in the change in pupil dilation induced by very light-intensity exercise using pupillometry and neuromelanin imaging to assess the LC integrity. A sample of 21 young males performed 10 min of very light-intensity exercise, and we measured changes in the pupil diameters and psychological arousal levels induced by the exercise. Neuromelanin-weighted magnetic resonance imaging scans were also obtained. We observed that pupil diameter and psychological arousal levels increased during very light-intensity exercise, which is consistent with previous findings. Notably, the LC contrast, a marker of LC integrity, predicted the magnitude of pupil dilation and psychological arousal enhancement with exercise. These relationships suggest that the LC-catecholaminergic system is a potential a mechanism for pupil-linked arousal induced by very light-intensity exercise.
A Novel Ship Speed and Heading Estimation Approach Using Radar Sequential Images

This paper proposes an approach to estimate ship dynamic features (i.e., speed and heading) using radar sequential images, which can be used to assess the ship-ship collision risk in vessel traffic service (VTS) center. The proposed approach estimates the ship speed and heading in three steps, including ship detection, tracking, and data fusion. First, the ship target, together with its center coordinate, is detected by introducing the background subtraction detector in each radar image. Second, the ship position in one frame is mapped to the consecutive frame, and ship trajectory distribution is established using the Kalman filter and Hungarian algorithms. Third, fuzzy logic is introduced to fuse data from radar and AIS. Specifically, input variables include two parameters, which are the ship speed/heading difference in consecutive frames using radar and AIS, respectively. The variance of ship speed distribution is 1.30 at 10s sampling frequency, and the variance of ship heading distribution is 26.53, the experimental results indicate that the proposed approach can accurately estimate ship speed and heading in the radar sequential images of the Yangtze River. The strength of the proposed approach is its capability to estimate the ship speed and heading quantitatively from radar sequential images, which is interactive and interpretable for VTS operators.
Sheaf and cosheaf methods for analyzing multi-model systems

There is an interplay between models, specified by variables and equations, and their connections to one another. This dichotomy should be reflected in the abstract as well. Without referring to the models directly -- only that a model consists of spaces and maps between them -- the most readily apparent feature of a multi-model system is its topology. We propose that this topology should be modeled first, and then the spaces and maps of the individual models be specified in accordance with the topology. Axiomatically, this construction leads to sheaves and cosheaves. Sheaf theory provides a toolbox for constructing predictive models described by systems of equations. Sheaves and cosheaves are mathematical objects that manage the combination of bits of local information into a consistent whole. The power of this approach is that complex models can be assembled from smaller, easier-to-construct models. The models discussed in this chapter span the study of continuous dynamical systems, partial differential equations, probabilistic graphical models, and discrete approximations of these models.
Dual-Band End-Fire Four-Element MIMO Antenna Array Using Split-Ring Structure for mm-Wave 5G Applications

This paper presents a dual-band split-ring structure that provides end-fire radiation characteristics for multi-input multi-output (MIMO) mm-wave 5G applications. The proposed single antenna element consists of a main split-ring resonator (SRR) directly fed using a high impedance microstrip transmission line. A lamda/4 transformer is employed to match the high impedance transmission line with a 50-ohm line. Two symmetric parasitic SRRs are used as directing elements to give end-fire radiation properties for the introduced antenna at the first resonant frequency. The parasitic SRRs are also utilized to produce the second resonant frequency by establishing a capacitive link with the main SRR. The single element antenna radiates at 24.5 and 28.5 GHz. A cross-shaped MIMO structure composed of four elements of the proposed antenna is introduced to improve the isolation of the MIMO system due to its orthogonal polarization characteristics. The proposed MIMO demonstrates acceptable values of gain and radiation efficiency, reaching 9.8 dB and 93%, respectively, at the first resonant frequency, and 8.7 dB and 95%, respectively, at the second band. MIMO performance is also studied, and the proposed MIMO system provides a very low envelope correlation coefficient of lower than $10^{-4}$ over the two operating frequency bands. A diversity gain is also calculated, it is around 10 dB over the two operating bands. The proposed high-isolation dual-band cross-shaped MIMO array is a promising candidate for millimeter-wave 5G applications.
MONTE CARLO SIMULATION OF THE FULL MULTIPLICITY DISTRIBUTIONS MEASURED WITH A PASSIVE COUNTER

The objective of the work here is to validate the Monte Carlo code MCNP-PoliMi using comparisons with experimental data for the specific problem of neutron multiplicity counting with He-3 detectors. Measurements of various fissionable samples have been made with an active well coincidence counter (AWCC) operating in passive mode at the PERLA laboratory at Joint Research Center site in Ispra, Italy. The MCNP-PoliMi postprocessor was upgraded to explicitly simulate the deadtime in the PERLA measurement electronics. The results from MCNP-PoliMi have been compared to the measured data from 252 Cf sources and several plutonium and plutonium oxide samples. Very good agreement between the simulated and measured multiplicity distributions has been observed for multiplet orders as high as 25.
AHANet: Adaptive Hybrid Attention Network for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Imaging

Alzheimer’s disease (AD) is a progressive neurological problem that causes brain atrophy and affects the memory and thinking skills of an individual. Accurate detection of AD has been a challenging research topic for a long time in the area of medical image processing. Detecting AD at its earliest stage is crucial for the successful treatment of the disease. The proposed Adaptive Hybrid Attention Network (AHANet) has two attention modules, namely Enhanced Non-Local Attention (ENLA) and Coordinate Attention. These modules extract global-level features and local-level features separately from the brain Magnetic Resonance Imaging (MRI), thereby boosting the feature extraction power of the network. The ENLA module extracts spatial and contextual information on a global scale while also capturing important long-range dependencies. The Coordinate Attention module captures local features from the input images. It embeds positional information into the channel attention mechanism for enhanced feature extraction. Moreover, an Adaptive Feature Aggregation (AFA) module is proposed to fuse features from the global and local levels in an effective way. As a result of incorporating the above architectural enhancements into the DenseNet architecture, the proposed network exhibited better performance compared to the existing works. The proposed network was trained and tested on the ADNI dataset, yielding a classification accuracy of 98.53%.
A USEFUL BP ALGORITHM TO OVERCOME LOCAL MINIMA

BP algorlthm is actually a nonlinear optimal problem and produces inevitably Iocal minima.The paper proposes the rules to judge local minima and introduces momentum function in BP algorithm. Thealgorithm may iump local minima,Simulation result is given to show the effectiveness and the usage of the al- gorithm.
General Nonlocal Probability of Arbitrary Order

Using the Luchko’s general fractional calculus (GFC) and its extension in the form of the multi-kernel general fractional calculus of arbitrary order (GFC of AO), a nonlocal generalization of probability is suggested. The nonlocal and general fractional (CF) extensions of probability density functions (PDFs), cumulative distribution functions (CDFs) and probability are defined and its properties are described. Examples of general nonlocal probability distributions of AO are considered. An application of the multi-kernel GFC allows us to consider a wider class of operator kernels and a wider class of nonlocality in the probability theory.