Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Category Theory in Model Based Enterprise

A mathematical framework based on category theory is proposed to formally describe and explore procedures of modeling engineering products and processes that comprise operation of a model-oriented enterprise. The framework is intended to provide interoperability across a variety of engineering modeling languages and tools, supplying them with a common abstract foundation capable to represent, generate, and verify diverse design and production knowledge. The framework is leveraged via algebraic representation of product configurations as diagrams in categories with models as objects and descriptions of actions involved into products assembly as morphisms. Relevance of the framework is justified by appealing to systems engineering standards such as IEC 81346. Category theoretical methods for solving direct assembly problems that consist in constructing a product model from a given configuration are presented. Specifically, solutions are obtained via the universal construction called a colimit of a diagram. Much attention is then paid to stating and solving inverse assembly problems that consist in recovery and subsequent optimization of the configuration from the product model and assembly actions. Inverse problem solving is in demand for generative design, viz. an emerging fully automatic product development and manufacturing technology. Example solutions to direct and inverse problems are described in categories that represent two major areas of model-based enterprise operation: solid body geometric modeling of mechanical products and discrete-event simulation of production processes.
Cloud Storage Protection Scheme Based on Fully Homomorphic Encryption

Cloud computing allows enterprises and individuals to have a less physical infrastructure of software and hardware. Nevertheless, there are some concerns regarding privacy protection which may turn out to be a strong barrier. Traditional encryption schemes have been used to encrypt the data before sending them to the cloud. However, the private key has to be provided to the server before any calculations on the data. To solve this security problem, this paper proposes a fully homomorphic encryption scheme for securing cloud data at rest. The scheme is based on prime modular operation, its security depends on factoring multiple large prime numbers (p1, p2,.pn) up to n, which is formed from very large prime numbers up to hundreds of digits as this is an open problem in mathematics. In addition, the elements of the secret key are derived from a series of mathematical operations and the calculation of an Euler coefficient within the modular of integers. Furthermore, it adds the complexity of noise to the plaintext using the number of users of the Cloud Service Provider. Moreover, its randomness is evaluated by the National Institute of Standards and Technology statistical tests, and the results demonstrating that the best statistical performance was obtained with this algorithm.
Internet of things and its applications

IOT is creating impeccable things by improving the performance of system in the field of communications in many technical applications in expedite manner and taken the system performance to next level. implementing IOT in today's world out do's the response time of the system to the normal system response in R&D applications. this paper depicts the challenges and the problems in various domains and gave the solution by imbibing IOT Technology by standardizing the system processes to meet the industrial as well as domestic needs.
The association between femoroacetabular impingement and sitting positions: A three-dimensional simulation study

To investigate the relationship between femoroacetabular impingement (FAI) and different sitting positions. We retrospectively reviewed 24 patients with FAI. Femoral and pelvic parameters were measured, and 3D simulation software was used to create and analyze a 3D model of the hip. The data were divided into impingement and non-impingement groups according to whether impingement occurred in the normal or cross-legged sitting positions. The parameters were compared between these groups, and the effect of each parameter on impingement volume was estimated. Femoral anteversion and the difference in sacral slope (SS) between standing and sitting positions (ΔSS) were significantly lower in the impingement group than in the non-impingement group (both p
From derived-band envelope-following responses to individualized models of near- and supra-threshold hearing deﬁcits

Auditory models which include frequency-dependent proﬁles of near and supra-threshold hearing deﬁcits can aid the design of individualized hearing-aid algorithms. However, determining individual auditory-nerve (AN) ﬁber loss parameters is controversial as diagnostic metrics are presently based on auditory brainstem responses (ABRs) or envelope following responses (EFRs). These measures do not necessarily yield a frequency-speciﬁc quantiﬁcation and might be affected by both outer-hair-cell and AN damage. We developed a derived-band EFR (DBEFR) metric to offer a frequency-speciﬁc assessment and complemented these with click-evoked otoacoustic emissions and audiometry. Cochlear-gain-loss proﬁles were derived from the latter measurements and inserted into individualized models, in which different synaptopathy proﬁles were introduced and DBEFRs simulated. Using a clustering technique, the best match between experimental and simulated synaptopathy proﬁles was determined and validated using the ABR data collected from the same listener. Results showed promise in offering a method to determine individualized sensorineural hearing-loss proﬁle given a limited number of objective metrics.
Omics versus Questionnaires: Using methylation data to assess the environmental impact of smoking on obesity-related traits

Variation in complex traits related to obesity, such as body weight and body mass index, has a genetic basis with heritabilities between 40 and 70%. Nonetheless, the so-called global obesity pandemic is usually associated with environmental changes related to diet, lifestyle, and sociocultural and socioeconomic changes. However, most genetic studies do not include all relevant environmental covariates so their contribution, alongside genetics, to variation in obesity-related traits can not be assessed. Similarly, some studies have described interactions between a few individual genes linked to obesity and different environmental variables but the total contribution to differences between individuals is unknown. In this study we explored the effect of smoking and gene-by-smoking interactions on obesity related traits from a genome-wide perspective to estimate the amount of variance they explain by modelling them using self-reported data and a proxy created using methylation data. Our results indicate that exploiting omic measures as proxies for environmental variation can improve our models for complex traits such as obesity and can be used as a substitute of environmental measures when they are not available or jointly to improve their accuracy.
Markov Chain Abstractions of Electrochemical Reaction-Diffusion in Synaptic Transmission for Neuromorphic Computing

Progress in computational neuroscience towards understanding brain function is challenged both by the complexity of molecular-scale electrochemical interactions at the level of individual neurons and synapses and the dimensionality of network dynamics across the brain covering a vast range of spatial and temporal scales. Our work abstracts an existing highly detailed, biophysically realistic 3D reaction-diffusion model of a chemical synapse to a compact internal state space representation that maps onto parallel neuromorphic hardware for efficient emulation at a very large scale and offers near-equivalence in input-output dynamics while preserving biologically interpretable tunable parameters.
Low-Complexity Distance-Based Scheduling for Multi-User XL-MIMO Systems

We introduce Distance-Based Scheduling (DBS), a new technique for user selection in downlink multi-user communications with extra-large (XL) antenna arrays. DBS categorizes users according to their <italic>equivalent distance</italic> to the antenna array. Such categorization effectively accounts for inter-user interference while largely reducing the computational burden. Results show that (<inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>) DBS achieves the same performance as the reference zero-forcing beamforming scheme with a lower complexity; (<italic>ii</italic>) a simplified version of DBS achieves a similar performance when realistic spherical-wavefront (SW) propagation features are considered; (<italic>iii</italic>) SW propagation brings additional degrees of freedom, which allows for increasing the number of served users.
Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.
Detection of cosmic magnification via galaxy shear-galaxy number density correlation from HSC survey data

Xiangkun Liu, ∗ Dezi Liu, Zucheng Gao, Chengliang Wei, Guoliang Li, 4 Liping Fu, Toshifumi Futamase, and Zuhui Fan † South-Western Institute for Astronomy Research, Yunnan University, Kunming 650500, P.R. China School of Physics, Peking University, Beijing 100871, P.R. China Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing, 210023, P.R. China School of Astronomy and Space Science, University of Science and Technology of China, Hefei, 230026, P.R. China The Shanghai Key Lab for Astrophysics, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, P.R. China Department of Astrophysics and Meteorology, Kyoto Sangyo University, Kita-ku, Kyoto 603-8555, Japan (Dated: April 29, 2021)
SELF & FEIL: Emotion and Intensity Lexicons for Finnish

This paper introduces a Sentiment and Emotion Lexicon for Finnish (SELF) and a Finnish Emotion Intensity Lexicon (FEIL). We describe the lexicon creation process and evaluate the lexicon using some commonly available tools. The lexicon uses annotations projected from the NRC Emotion Lexicon with carefully edited translations. To our knowledge, this is the first comprehensive sentiment and emotion lexicon for Finnish.
A fully backward representation of semilinear PDEs applied to the control of thermostatic loads in power systems

Abstract We propose a fully backward representation of semilinear PDEs with application to stochastic control. Based on this, we develop a fully backward Monte-Carlo scheme allowing to generate the regression grid, backwardly in time, as the value function is computed. This offers two key advantages in terms of computational efficiency and memory. First, the grid is generated adaptively in the areas of interest, and second, there is no need to store the entire grid. The performances of this technique are compared in simulations to the traditional Monte-Carlo forward-backward approach on a control problem of thermostatic loads.
Classification and comparison of license plates localization algorithms

The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the application of new information and communication technologies in the transport sector, to make the infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the key module of these systems, in which the License Plate Localization (LPL) is the most important stage, because it determines the speed and robustness of this module. Thus, during this step the algorithm must process the image and overcome several constraints as climatic and lighting conditions, sensors and angles variety, LPs’ no-standardization, and the real time processing. This paper presents a classification and comparison of License Plates Localization (LPL) algorithms and describes the advantages, disadvantages and improvements made by each of them.
ZePHyR: Zero-shot Pose Hypothesis Rating

Pose estimation is a basic module in many robot manipulation pipelines. Estimating the pose of objects in the environment can be useful for grasping, motion planning, or manipulation. However, current state-of-the-art methods for pose estimation either rely on large annotated training sets or simulated data. Further, the long training times for these methods prohibit quick interaction with novel objects. To address these issues, we introduce a novel method for zero-shot object pose estimation in clutter. Our approach uses a hypothesis generation and scoring framework, with a focus on learning a scoring function that generalizes to objects not used for training. We achieve zero-shot generalization by rating hypotheses as a function of unordered point differences. We evaluate our method on challenging datasets with both textured and untextured objects in cluttered scenes and demonstrate that our method significantly outperforms previous methods on this task. We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation. Our work allows users to estimate the pose of novel objects without requiring any retraining. Additional information can be found on our website https://bokorn.github. io/zephyr/
Detection of Signal in the Spiked Rectangular Models

We consider the problem of detecting signals in the rank-one signal-plus-noise data matrix models that generalize the spiked Wishart matrices. We show that the principal component analysis can be improved by pre-transforming the matrix entries if the noise is non-Gaussian. As an intermediate step, we prove a sharp phase transition of the largest eigenvalues of spiked rectangular matrices, which extends the Baik-Ben Arous-P\'ech\'e (BBP) transition. We also propose a hypothesis test to detect the presence of signal with low computational complexity, based on the linear spectral statistics, which minimizes the sum of the Type-I and Type-II errors when the noise is Gaussian.
Learning Fair Canonical Polyadical Decompositions using a Kernel Independence Criterion

This work proposes to learn fair low-rank tensor decompositions by regularizing the Canonical Polyadic Decomposition factorization with the kernel Hilbert-Schmidt independence criterion (KHSIC). It is shown, theoretically and empirically, that a small KHSIC between a latent factor and the sensitive features guarantees approximate statistical parity. The proposed algorithm surpasses the state-of-the-art algorithm, FATR (Zhu et al., 2018), in controlling the trade-off between fairness and residual fit on synthetic and real data sets.
Giant anharmonicity controls terahertz vibrations and the boson peak anomaly in disordered materials

The boson peak (BP) is a universal feature in the Raman and inelastic scattering spectra of both disordered and crystalline materials. The current paradigm presents the boson peak as the result of a Ioffe-Regel crossover between ballistic (phonon) and diffusive-type excitations, where the loss of coherence of phonons is described as a purely harmonic process due to structural disorder. This “harmonic disorder” paradigm for the BP has never been challenged or tested at the atomistic level. Here, through a set of atomistically-resolved characterizations of amorphous metallic alloys, we uncover a robust inverse proportionality between the intensity of boson peak and the activation energy of excitations in the potential energy landscape (PEL). Larger boson peak is linked with shallower basins and lower activation barriers and, consequently, with strongly anharmonic sectors of the PEL. Numerical evidence from atomistic simulations indicates that THz atomic vibrations contributing the most to the BP in atomic glasses are strongly anharmonic, as evidenced through very large values of the atomicand mode-resolved Grüneisen parameter found for the atomic vibrations that constitute the BP. These results provide a direct bridge between the vibrational spectrum and the topology of the PEL in solids, and point towards a new “giant anharmonicity” paradigm for both generic disordered materials and for the phonon-glass problem in emerging materials for energy applications. In this sense, disorder and anharmonicity emerge as the two sides of the same coin. 1 ar X iv :2 10 4. 13 74 0v 1 [ co nd -m at . d is -n n] 2 8 A pr 2 02 1
D-OccNet: Detailed 3D Reconstruction Using Cross-Domain Learning

Deep learning based 3D reconstruction of single view 2D image is becoming increasingly popular due to their wide range of real-world applications, but this task is inherently challenging because of the partial observability of an object from a single perspective. Recently, state of the art probability based Occupancy Networks reconstructed 3D surfaces from three different types of input domains: single view 2D image, point cloud and voxel. In this study, we extend the work on Occupancy Networks by exploiting cross-domain learning of image and point cloud domains. Specifically, we first convert the single view 2D image into a simpler point cloud representation, and then reconstruct a 3D surface from it. Our network, the Double Occupancy Network (D-OccNet) outperforms Occupancy Networks in terms of visual quality and details captured in the 3D reconstruction.
3D Microstructure Segmentation by Topological Persistence

Tomography is a widely used tool for analyzing microstructures in three dimensions (3D). The analysis, however, faces diﬃculty because the constituent materials produce similar grey-scale values. Sometimes, this prompts the image segmentation process to assign a pixel/voxel to the wrong phase (active material or pore). Consequently, errors are introduced in the microstructure characteristics calculation. In this work we develop a ﬁltering algorithm based on topological persistence, a technique used in topological data analysis . One problem faced when evaluating ﬁltering algorithms is that real image data in general are not equipped with the ‘ground truth’ information about the microstructure characteristics. For this study, we construct synthetic images for which the ground truth values are known. Speciﬁcally, we compare interconnected pore tortuosity and phase fraction. Experimental results show that our ﬁltering algorithm provides a signiﬁcant improvement in reproducing tortuosity close to the ground truth, even when the grey-scale values of the phases are similar.
Linear Nakayama algebras which are higher Auslander algebras

Abstract An artin algebra A is said to be a higher Auslander algebra provided the global dimension is finite and bounded by the dominant dimension. We say that a linear Nakayama algebra is concave, provided its Kupisch series first increases, then decreases. We are going to classify the concave Nakayama algebras which are higher Auslander algebras. Let us stress that the classification strongly depends on the parity of the global dimension of A.
Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks

Unsupervised domain adaptation seeks to mitigate the distribution discrepancy between source and target domains, given labeled samples of the source domain and unlabeled samples of the target domain. Generative adversarial networks (GANs) have demonstrated significant improvement in domain adaptation by producing images which are domain specific for training. However, most of the existing GAN based techniques for unsupervised domain adaptation do not consider semantic information during domain matching, hence these methods degrade the performance when the source and target domain data are semantically different. In this paper, we propose an end-to-end novel semantic consistent generative adversarial network (SCGAN). This network can achieve source to target domain matching by capturing semantic information at the feature level and producing images for unsupervised domain adaptation from both the source and the target domains. We demonstrate the robustness of our proposed method which exceeds the state-of-the-art performance in unsupervised domain adaptation settings by performing experiments on digit and object classification tasks.
Policy manifold search: exploring the manifold hypothesis for diversity-based neuroevolution

Neuroevolution is an alternative to gradient-based optimisation that has the potential to avoid local minima and allows parallelisation. The main limiting factor is that usually it does not scale well with parameter space dimensionality. Inspired by recent work examining neural network intrinsic dimension and loss landscapes, we hypothesise that there exists a low-dimensional manifold, embedded in the policy network parameter space, around which a high-density of diverse and useful policies are located. This paper proposes a novel method for diversity-based policy search via Neuroevolution, that leverages learned representations of the policy network parameters, by performing policy search in this learned representation space. Our method relies on the Quality-Diversity (QD) framework which provides a principled approach to policy search, and maintains a collection of diverse policies, used as a dataset for learning policy representations. Further, we use the Jacobian of the inverse-mapping function to guide the search in the representation space. This ensures that the generated samples remain in the high-density regions, after mapping back to the original space. Finally, we evaluate our contributions on four continuous-control tasks in simulated environments, and compare to diversity-based baselines.
The Manhattan curve, ergodic theory of topological flows and rigidity

For every non-elementary hyperbolic group, we introduce the Manhattan curve associated to any pair of left-invariant hyperbolic metrics which are quasi-isometric to a word metric. It is convex; we show that it is continuously differentiable and moreover is a straight line if and only if the corresponding two metrics are roughly similar, i.e., they are within bounded distance after multiplying by a positive constant. Further, we prove that the Manhattan curve associated to two strongly hyperbolic metrics is twice continuously differentiable. The proof is based on the ergodic theory of topological flows associated to general hyperbolic groups and analyzing the multifractal structure of Patterson-Sullivan measures. We exhibit some explicit examples including a hyperbolic triangle group and compute the exact value of the mean distortion for pairs of word metrics.