Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the invariant of inter-frame processing for constructing the image similarity metric

The relevance of modeling digital images is determined by the need to implement approaches in the study of localization and identification of objects in order to reduce the amount of data. In this paper, the study object is the topology of a discrete two-dimensional image within the framework of the problem of determining the invariants of diffeomorphic transformations. Geographic information objects (GIOs) refer to objects that are on a given surface or objects that locally change the surface. With regard to objects, it is assumed that the change in their geolocation in the process of forming both single images and an extended series of frames obtained in the process of continuous monitoring is insignificant. In the process of scanning the surface, possible changes in the position of the image source are taken into account, for example, such as yawing, rolling, and pitch in the case of unmanned aerial vehicles (UAVs). These maneuvers are represented as a group of diffeomorphisms that are controlled by the internal gyroscopes of the carrier and the external navigation system. Based on the studies reported here, the initial ontology of digital images (ODI) has been determined by using the model of color spaces and functions of a special kind. The presence of an ontology makes it possible to build an adequate topology of color distribution in the image and take into account the specificity of the distribution of different colors in a digital image. The study results indicate that a promising method is to determine the similarity by constructing a color atlas structure graph (CASG) based on ODI and by determining invariants as a fragment of CASG inherited by all images in the sequence. The scope and conditions for the practical use of the result include its application to the analysis of images by methods of artificial intelligence
EulerFD: An Efficient Double-Cycle Approximation of Functional Dependencies

Functional dependencies (FDs) have been extensively employed in discovering inferential relationships in databases, which provide feasible approaches for many data mining tasks, such as data obfuscation, query optimization, and schema normalization. Since the explosive growth of data leads to a rapid increase of FDs on large datasets, existing algorithms that pay more attention to the exact FD discovery cannot extract FDs efficiently. To bridge this gap, we propose an Efficient double-cycle approximation of Functional Dependency (EulerFD) discovery algorithm, which ensures both efficiency and accuracy of FD discovery. EulerFD induces FDs from invalid ones as invalidating an FD only requires comparing and verifying some pairs of tuples (that violate the dependency) while validating an FD requires examining and verifying all tuples. Considering the abundant tuple pairs in large datasets, a novel sampling strategy is employed in EulerFD to quickly extract invalid FDs by revising the sampling range according to previous sampling results. Furthermore, EulerFD evaluates the stopping criteria in a double-cycle structure as feedback for further sampling. The sampling strategy and the double-cycle structure complement each other to achieve a more efficient sampling effect. Experimental results on real-world and synthetic datasets, especially the massive datasets from DMS of Alibaba Cloud, justify the design and verify the efficiency and effectiveness of the proposed EulerFD.
KG-MDL: Mining Graph Patterns in Knowledge Graphs with the MDL Principle

Nowadays, increasingly more data are available as knowledge graphs (KGs). While this data model supports advanced reasoning and querying, they remain difficult to mine due to their size and complexity. Graph mining approaches can be used to extract patterns from KGs. However this presents two main issues. First, graph mining approaches tend to extract too many patterns for a human analyst to interpret (pattern explosion). Second, real-life KGs tend to differ from the graphs usually treated in graph mining: they are multigraphs, their vertex degrees tend to follow a power-law, and the way in which they model knowledge can produce spurious patterns. Recently, a graph mining approach named GraphMDL+ has been proposed to tackle the problem of pattern explosion, using the Minimum Description Length (MDL) principle. However, GraphMDL+, like other graph mining approaches, is not suited for KGs without adaptations. In this paper we propose KG-MDL, a graph pattern mining approach based on the MDL principle that, given a KG, generates a human-sized and descriptive set of graph patterns, and so in a parameter-less and anytime way. We report on experiments on medium-sized KGs showing that our approach generates sets of patterns that are both small enough to be interpreted by humans and descriptive of the KG. We show that the extracted patterns highlight relevant characteristics of the data: both of the schema used to create the data, and of the concrete facts it contains. We also discuss the issues related to mining graph patterns on knowledge graphs, as opposed to other types of graph data.
Performance testing of a mid-infrared spectroscopic system for clinical chemistry applications utilising an ultra-broadband tunable EC-QCL radiation source

Mid-infrared (MIR) spectroscopy is a valuable analytical method for patient monitoring within point-of-care diagnostics. For implementation, quantum cascade lasers (QCL) appear to be most suited regarding miniaturization, complexity and eventually also costs. External cavity (EC) - QCLs offer broad tuning ranges and recently, ultra-broadly tunable systems covering spectral ranges around the mid-infrared fingerprint region became commercially available. Using such a system, transmission spectra from the wavenumber interval of 780 to 1920 cm-1, using a thermoelectrically cooled MCT-detector, were recorded while switching the aqueous glucose concentrations between 0, 50 and 100 mg/dL. In order to optimize the system performance, a multi-parameter study was carried out, varying laser pulse width, duty cycle, sweep speed and the optical sample pathlength for scoring the absorbance noise. Exploratory factor analysis with pattern recognition tools (PCA, LDA) was used for the raw data, providing more than 10 significantly contributing factors. With the glucose signal causing 20 % of the total variance, further factors include short-term drift possibly related to thermal effects, long-term drift due to varying atmospheric water vapour in the lab, as well as wavenumber shifts and drifts of the single tuners. For performance testing, the noise equivalent concentration was estimated based on cross-validated Partial-Least Squares (PLS) predictions and the a-posteriori obtained scores of the factor analysis. Based on the optimized parameters, a noise equivalent glucose concentration of 1.5 mg/dL was achieved.
Modified decoding algorithm of LLR-SPA

In wireless sensor networks, the energy consumption is mainly occurred in the stage of information transmission. The Low Density Parity Check code can make full use of the channel information to save energy. Because of the widely used decoding algorithm of the Low Density Parity Check code, this paper proposes a new decoding algorithm which is based on the LLR-SPA (Sum-Product Algorithm in Log-Likelihood-domain) to improve the accuracy of the decoding algorithm. In the modified algorithm, a piecewise linear function is used to approximate the complicated Jacobi correction term in LLR-SPA decoding algorithm. Construct the tangent by the tangency point to the function of Jacobi correction term, which is based on the first order Taylor Series. In this way, the proposed piecewise linear approximation offers almost a perfect match to the function of Jacobi correction term. Meanwhile, the proposed piecewise linear approximation could avoid the operation of logarithmic which is more suitable for practical application. The simulation results show that the proposed algorithm could improve the decoding accuracy greatly without noticeable variation of the computational complexity. Copyright © 2014 IFSA Publishing, S. L.
Travel time reliability estimating methodology in transportation networks based on Copula function

Concentrating on the issue of travel time reliability estimating for routes,OD pairs and traffic network,the research proposed a new reliability estimating algorithm which incorporated the stochastic characteristic of link travel time.Based on the Copula theory,this methodology constructed a Copula function to determine the joint density function of travel time to produce travel time reliability value of routes and OD pairs.This method could put the actual measured data into model parameters calibration and it took the correlation problem of different links on the same route into consideration.Three kinds of traffic network topological structure cases testified the effectiveness of this new algorithm model.Results show that the ignorance of involving the links correlation effect into consideration will generate a higher reliability value.
Integrated Put-Call Algebraic Formula

The option side of Put-Call Parity has been explored by prior studies but its algebraic side has not been illustrated before. This article converts Put-Call Parity parameters into algebraic ones in order to derive transformed arbitrage-free formula. Interestingly, inverse Laplace transform and Put-Call Parity are similar in the sense that parameters of both formulae are at least zero. The results illustrated that transformed arbitrage-free formula is discrete in nature.
The role of mass and environment in the build up of the quenched galaxy population since cosmic noon

We conduct the first study of how the relative quenching probability of galaxies depends on environment over the redshift range 0.5 < z < 3, using data from the UKIDSS Ultra-Deep Survey. By constructing the stellar mass functions for quiescent and post-starburst (PSB) galaxies in high, medium and low density environments to z = 3, we find an excess of quenched galaxies in dense environments out to at least z ∼ 2. Using the growth rate in the number of quenched galaxies, combined with the star-forming galaxy mass function, we calculate the probability that a given star-forming galaxy is quenched per unit time. We find a significantly higher quenching rate in dense environments (at a given stellar mass) at all redshifts. Massive galaxies (M* > 1010.7 M⊙) are on average 1.7 ± 0.2 times more likely to quench per Gyr in the densest third of environments compared to the sparsest third. Finally, we compare the quiescent galaxy growth rate to the rate at which galaxies pass through a PSB phase. Assuming a visibility timescale of 500 Myr, we find that the PSB route can explain ∼ 50% of the growth in the quiescent population at high stellar mass (M* > 1010.7 M⊙) in the redshift range 0.5 < z < 3, and potentially all of the growth at lower stellar masses.
Phase gradient autofocus within FFBP flow for spotlight SAR processing

Fast Factorized Back-Projection(FFBP)is an effective algorithm constructed based on aperture factorization.It is shown that the image quality is barely sacrificed compared with Back-Projection(BP). Different from frequency domain algorithms,SAR images reconstructed by FFBP cannot satisfy the requirement of autofocus.To solve this problem,we propose an improved FFBP(IFFBP)by introducing a fire-new coordinate system in this paper.Then,IFFBP can provide autofocus with Fourier transform pairs(FTP)between the image domain and the range compressed phase history domain.With a different framework of the overlapped subaperture in different stages,phase gradient autofocus(PGA)can be embedded into IFFBP to obtain a robust estimation of the phase error.Finally,the processed results of the collected data set validate this approach in spotlight SAR imagery.
Comparative analysis of fruit firmness and genes associated with cell wall metabolisms in three cultivated strawberries during ripening and postharvest

Cultivated strawberry (Fragaria × ananassa), a world-famous fruit, is subjected to rapid softening during ripening, resulting in a shorter shelf life and severe economic losses during storage and transportation. However, there is limited understanding of the molecular mechanism underlying differences in fruit firmness during ripening and postharvest among cultivated strawberries. Here, we explored this molecular mechanism by comparing three cultivated strawberries via firmness measurement, transcriptome analysis, quantitative real-time PCR, and correlation analysis, and revealed that FaEXP7, FaPG2, FaPLA, and Faβ-Gal4, as potential softening activators expressed before harvest to determine fruit with more softened texture and shorter shelf life, and the extremely high expression levels of FaCEL1-1 and FaCEL1-3 during ripening might be accelerators to intensify this situation. Additionally, both the enzyme activities of FaCEL and the expression pattern of FaCEL1-3 showed a significantly negative correlation with fruit firmness after harvest, suggesting that FaCEL1-3 might play a key role in promoting strawberry fruit softening not only during ripening but also postharvest. These results showed that the difference in fruit firmness and shelf life among cultivated strawberries was controlled by the temporal expression pattern of a legion of cell wall-associated genes during ripening and postharvest.
Social Engineering Attack concepts, frameworks, and Awareness: A Systematic Literature Review

: The growth of data exchange and the dependency on the digital world through cyberspace raise security risks. Social engineering attacks occupy high percentage of total cybercrimes. It is also classified as the major cause of financial losses in cyberspace. This shows the need to clarify social engineering definition and clarify the proposed frameworks solutions by di ff erent researchers. This paper explores the previous researches that try to extract di ff erent concepts and perspectives which are called lifecycles, phases, frameworks, models
Hiding Data in Binary Images Using Block-Diagonal Partition Pattern

: Many pieces of information are exchanged in the form of binary pictures across many domains as a result of the paradigm shift from paper to electronic. This covers banking, economic dealings, military communication, and imaging in the medical field. In some circumstances, it is necessary to communicate sensitive information about photographs in a covert manner. On the other hand, unreadable watermarks are necessary to verify the authenticity and originality of an image.The two most commonly used methods for this purpose are steganography and watermarking, both of which ensure the imperceptibility of hidden information.It is challenging for binary images to hide data and to keep changes unnoticeable. A novel data hiding method for binary images is presented in this paper which uses Block-Diagonal Partition Pattern(BDPP). In this method, 3X3 pixels image blocks are partitioned diagonally. Black and white pixel counts within each partition before and after embedding are used to identify potential blocks. The connectivity between the pixels in each division is also examined in order to determine whether a block is suitable for carrying data bits that account for distortion. The chosen block’s central pixel serves as the carrier for data bits. Since every block carrying the data satisfies the embeddability requirements both before and after concealing, the original picture is not required to retrieve the concealed data. Data is twice encrypted using this method to boost data security. The results show that our technique produces little distortion while being safe and having great hiding capabilities.
Theory of dynamical relaxation oscillations and frequency locking in a synchronously-pumped laser diode

It is shown that the rate equations for the plasma density and the light intensity, together with a delayed feedback of the light in the external resonator, can be used to describe the nonlinear dynamics of actively mode-locked laser diodes. In particular, the relaxation oscillations of the laser intensity and the plasma density around the stationary pulse solutions are calculated for a laser diode in a long resonator with a periodically pulsed pump current. For small negative detuning between the round-trip period and the pump period, slow dynamical relaxation oscillations that give rise to megahertz-sidemodes in the intensity correlation spectrum occur. For larger negative detuning the delayed feedback gives rise to a frequency locking between the light pulse frequency and the modulation frequency. >
Superconductivity in single crystals of a quasi-one dimensional infinite chain cuprate Sr$_x$Ca$_{1-x}$CuO$_2$ at 90 K

Although there is no complete theory of high temperature superconductivity, the importance of CuO$_2$ planes in cuprate superconductors is confirmed from both theory and experiments. Strong Coulomb repulsion between electrons on the CuO$_2$ plane makes the resultant electron system highly correlated and a difficult problem to solve since exact solutions of many-body Hamiltonian in two dimensions do not exist. If however, superconductivity can arise in structures having chains rather than planes and having a high critical temperature, then the high temperature superconductivity problem could become more tractable since exact solutions in one dimension do exist. In this paper, we report the observation of bulk superconductivity in single crystals of a cuprate Sr$_x$Ca$_{1-x}$CuO$_2$ at very high critical temperature, T$_c$, of $\sim$ 90 K whose structure reveals the presence of infinite double chains of Cu-O-Cu-O instead of CuO$_2$ planes, thus, ensuring quasi-one dimensional superconductivity. Bulk superconducting behaviour was observed in \textit{dc} magnetisation, \textit{ac} susceptibility as well as resistance measurements. The observation of bulk superconductivity in Sr$_x$Ca$_{1-x}$CuO$_2$ having chains of Cu-O-Cu-O rather than planes of CuO$_2$ at a high T$_c$ of 90 K is expected to profoundly impact our understanding of high temperature superconductivity.
CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems

Learning large-scale industrial recommender system models by fitting them to historical user interaction data makes them vulnerable to conformity bias. This may be due to a number of factors, including the fact that user interests may be difficult to determine and that many items are often interacted with based on ecosystem factors other than their relevance to the individual user. In this work, we introduce CAM2, a conformity-aware multi-task ranking model to serve relevant items to users on one of the largest industrial recommendation platforms. CAM2 addresses these challenges systematically by leveraging causal modeling to disentangle users’ conformity to popular items from their true interests. This framework is generalizable and can be scaled to support multiple representations of conformity and user relevance in any large-scale recommender system. We provide deeper practical insights and demonstrate the effectiveness of the proposed model through improvements in offline evaluation metrics compared to our production multi-task ranking model. We also show through online experiments that the CAM2 model results in a significant 0.50% increase in aggregated user engagement, coupled with a 0.21% increase in daily active users on Facebook Watch, a popular video discovery and sharing platform serving billions of users.
Option-Driven Design: Context, Tradeoffs, and Considerations for Accessibility

In Option-Driven Design, users must interact with options and settings for systems to adapt to their needs. This approach places the burden on both the user and the system to make the interaction between user and system fit. The user must know and find which options they need and then adjust them. In addition, the system must be capable of robust change, similar to system change in ability-based design. In this micro-paper I outline the context for option-driven design, followed by several design negotiations, tradeoffs, and suggestions worth considering with this approach.
Number Theoretical Locally Recoverable Codes

In this paper we give constructions for infinite sequences of finite non-linear locally recoverable codes $\mathcal C\subseteq \prod\limits^N_{i=1}\mathbb F_{q_i}$ over a product of finite fields arising from basis expansions in algebraic number fields. The codes in our sequences have increasing length and size, constant rate, fixed locality, and minimum distance going to infinity.
Variational Relational Point Completion Network for Robust 3D Classification

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40 dataset) containing over 200,000 high-quality scans, which render partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. Moreover, we can achieve robust 3D classification for partial point clouds with the help of VRCNet, which can highly increase classification accuracy.
Anisotropic linear and nonlinear excitonic optical properties of buckled monolayer semiconductors

The optical properties of two-dimensional materials are exceptional in several respects. They are highly anisotropic and frequently dominated by excitonic effects. Dipole-allowed second order non-linear optical properties require broken inversion symmetry. Hence, several two-dimensional materials show strong in-plane (IP) non-linearity but negligible out-of-plane (OOP) response due to vertical symmetry. By considering buckled hexagonal monolayers, we analyze the critical role of broken vertical symmetry on their excitonic optical response. Both linear as well as second order shift current and second harmonic response are studied. We demonstrate that substantial OOP non-linear response can be obtained, in particular, through off-diagonal tensor elements coupling IP excitation to OOP response. Our findings are explained by excitonic selection rules for OOP response and the impact of dielectric screening on excitons is elucidated.