Miracle-dz/UniFAR-FTUs huggingface dataset:
An FE-EBC Method for Electromagnetic Scattering from Inhomogeneous Objects

In this paper, we present a finite-element-extended boundary condition (FE-EBC) method for an efficient calculation of the electromagnetic wave scattering from inhomogeneous magneto-dielectric objects. To this end, we apply the hierarchical Legendre polynomial basis functions on large curved inhomogeneous hexahedral elements and propose an efficient numerical algorithm for a fast computation of the finite-element matrix entries in the presence of the material and Jacobian inhomogeneities. Also, we present a multiple-harmonic expansion on the surface boundary to represent the surface magnetic field accurately. The accuracy, efficiency and convergence of the method are studied through some numerical examples.
Simple, exact model of quasisingle field inflation

In this note we present a simple but exact model of quasi-single field inflation \cite{Chen:2009zp, Chen:2009we}, in which the couplings between perturbations are completely controlled, and for instance can be made constant with any desired value. This provides a way to numerically implement quasi-single field inflation and to test its predictions in various regimes. We confirm that the numerics agree to good accuracy with the analytical predictions for the isosceles bispectrum and the full shape function.
Resolved [C ii] Emission from z > 6 Quasar Host–Companion Galaxy Pairs

We report on ≈0. ″35 (≈2kpc) resolution observations of the [C ii] and dust continuum emission from five z > 6 quasar host–companion galaxy pairs obtained with the Atacama Large Millimeter/submillimeter Array. The [C ii] emission is resolved in all galaxies, with physical extents of 3.2–5.4 kpc. The dust continuum is on-average 40% more compact, which results in larger [C ii] deficits in the center of the galaxies. However, the measured [C ii] deficits are fully consistent with those found at lower redshifts. Four of the galaxies show [C ii] velocity fields that are consistent with ordered rotation, while the remaining six galaxies show no clear velocity gradient. All galaxies have high (∼80−200 km s−1) velocity dispersions, consistent with the interpretation that the interstellar medium (ISM) of these high-redshift galaxies is turbulent. By fitting the galaxies with kinematic models, we estimate the dynamical mass of these systems, which ranges between (0.3 − >5.4) × 1010 M⊙. For the three closest-separation galaxy pairs, we observe dust and [C ii] emission from gas in between and surrounding the galaxies, which is an indication that tidal interactions are disturbing the gas in these systems. Although gas exchange in these tidal interactions could power luminous quasars, the existence of quasars in host galaxies without nearby companions suggests that tidal interactions are not the only viable method for fueling their active centers. These observations corroborate the assertion that accreting supermassive black holes do not substantially contribute to the [C ii] and dust continuum emission of the quasar host galaxies, and showcase the diverse ISM properties of galaxies when the universe was less than one billion years old.
Genetic Network Architecture Search

We propose a method for learning the neural network architecture that based on Genetic Algorithm (GA). Our approach uses a genetic algorithm integrated with standard Stochastic Gradient Descent(SGD) which allows the sharing of weights across all architecture solutions. The method uses GA to design a sub-graph of Convolution cell which maximizes the accuracy on the validation-set. Through experiments, we demonstrate this methods performance on both CIFAR10 and CIFAR100 dataset with an accuracy of 96% and 80.1%. The code and result of this work available in GitHub:this https URL.
A dynamically configurable control architecture for autonomous mobile robots

This paper presents a dynamically configurable architecture for the control of autonomous mobile robots based on hierarchic structure whose three levels, organization, coordination and functional layer, are organized linguistically. The main contribution is the concept of dynamic reconfigurability where the notion of architecture coordinator plays a crucial role. The design of the functional layer establishes the primitives for dynamic configuration.
A Pvalue-guided Anomaly Detection Approach Combining Multiple Heterogeneous Log Parser Algorithms on IIoT Systems

Industrial Internet of Things (IIoT) is becoming an attack target of advanced persistent threat (APT). Currently, IIoT logs have not been effectively used for anomaly detection. In this paper, we use blockchain to prevent logs from being tampered with and propose a pvalue-guided anomaly detection approach. This approach uses statistical pvalues to combine multiple heterogeneous log parser algorithms. The weighted edit distance is selected as a score function to calculate the nonconformity score between a log and a predefined event. The pvalue is calculated based on the non-conformity scores which indicate how well a log matches an event. This approach is tested on a large number of real-world HDFS logs and IIoT logs. The experiment results show that abnormal events could be effectively recognized by our pvalue-guided approach.
Mean-field Analysis of Multipopulation Dynamics with Label Switching

The mean-field analysis of a multi-population agent-based model is performed. The model couples a particle dynamics driven by a nonlocal velocity with a Markow-type jump process on the probability that each agent has of belonging to a given population. A general functional analytic framework for the well-posedness of the problem is established, and some concrete applications are presented, both in the case of discrete and continuous set of labels. In the particular case of a leader-follower dynamics, the existence and approximation results recently obtained in [2] are recovered and generalized as a byproduct of the abstract approach proposed.
An Approximate Bayesian Approach to Surprise-Based Learning

Surprise-based learning allows agents to rapidly adapt to non-stationary stochastic environments characterized by stationary periods separated by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting old observations and integrating them with the new ones. The modulation depends on a probability ratio, called the "Bayes Factor Surprise" that tests the prior belief against the current belief. We demonstrate that in several existing approximate algorithms the Bayes Factor Surprise modulates the rate of adaptation to new observations. We derive three novel surprised-based algorithms, one in the family of particle filters, one in the family of variational learning, and the other in the family of message passing, that are biologically plausible, have constant scaling in observation sequence length and particularly simple update dynamics for any distribution in the exponential family. Empirical results show that these surprise-based algorithms estimate parameters better than alternative approximate approaches and reach levels of performance comparable to computationally more expensive algorithms. The Bayes Factor Surprise is related to but different from Shannon Surprise. In two hypothetical experiments, we make testable predictions for physiological or behavioral indicators that dissociate the Bayes Factor Surprise from Shannon Surprise. The theoretical insight of casting various approaches as surprise-based learning, as well as the proposed online algorithms, may be applied to the analysis of animal and human behavior, and to reinforcement learning in non-stationary environments.
Determination of protein-protein interaction through Artificial Neural Network and Support Vector Machine: A Comparative study

Protein-protein interactions (PPI) plays considerable role in most of the cellular processes and study of PPI enhances understanding of molecular mechanism of the cells. After emergence of proteomics, huge amount of protein sequences were generated but there interaction patterns are still unrevealed. Traditionally various techniques were used to predict PPI but are deficient in terms of accuracy. To overcome the limitations of experimental approaches numerous computational approaches were developed to find PPI. However previous computational approaches were based on descriptors, various external factors and protein sequences. In this article, a sequence based prediction model is proposed by using various machine learning approaches. A comparative study was done to understand efficiency of various machine learning approaches. Large amount of yeast PPI data have been analyzed. Same data has been incorporated for different classification approach like Artificial Neural Network (ANN) and Support Vector Machine (SVM), and compared their results. Existing methods with additional features were implemented to enhance the accuracy of the result. Thus it was concluded that efficiency of this model was more admirable than those existing sequence-based methods; therefore it can be effective for future proteomics research work.
A Survey on Spatial Modulation in Emerging Wireless Systems: Research Progresses and Applications

Spatial modulation (SM) is an innovative and promising digital modulation technology that strikes an appealing tradeoff between spectral efficiency and energy efficiency with a simple design philosophy. SM enjoys plenty of benefits and shows great potential to fulfill the requirements of future wireless communications. The key idea behind SM is to convey additional information typically through the ON/OFF states of transmit antennas and simultaneously save the implementation cost by reducing the number of radio-frequency chains. As a result, the SM concept can have widespread effects on diverse applications and can be applied in other signal domains, such as frequency/time/code/angle domain or even across multiple domains. This survey provides a comprehensive overview of the latest results and progresses in SM research. Specifically, the fundamental principles, variants of system design, and enhancements of SM are described in detail. Furthermore, the integration of the SM family with other promising techniques, applications to emerging communication systems, and extensions to new signal domains are also extensively studied.
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization

As a real scenes sensing approach, depth information obtains the widespread applications. However, resulting from the restriction of depth sensing technology, the depth map captured in practice usually suffers terrible noise and missing values at plenty of pixels. In this paper, we propose a fast low-rank matrix completion via dual-graph regularization for depth restoration. Specifically, the depth restoration can be transformed into a low-rank matrix completion problem. In order to complete the low-rank matrix and restore it to the depth map, the proposed dual-graph method containing the local and non-local graph regularizations exploits the local similarity of depth maps and the gradient consistency of depth-color counterparts respectively. In addition, the proposed approach achieves the high speed depth restoration due to closed-form solution. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods with respect to both objective and subjective quality evaluations, especially for serious depth degeneration.
Vector Colorings of Random, Ramanujan, and Large-Girth Irregular Graphs

We prove that in sparse Erdős-Renyi graphs of average degree $d$, the vector chromatic number (the relaxation of chromatic number coming from the Lovasz theta function) is typically $\tfrac{1}{2}\sqrt{d} + o_d(1)$. This fits with a long-standing conjecture that various refutation and hypothesis-testing problems concerning $k$-colorings of sparse Erdős-Renyi graphs become computationally intractable below the `Kesten-Stigum threshold' $d_{KS,k} = (k-1)^2$. Along the way, we use the celebrated Ihara-Bass identity and a carefully constructed non-backtracking random walk to prove two deterministic results of independent interest: a lower bound on the vector chromatic number (and thus the chromatic number) using the spectrum of the non-backtracking walk matrix, and an upper bound dependent only on the girth and universal cover. Our upper bound may be equivalently viewed as a generalization of the Alon-Boppana theorem to irregular graphs
Polarization reversal of scattered thermal dust emission in protoplanetary disks at submillimetre wavelengths

Investigation of the polarized light of protoplanetary disks is key for constraining dust properties, disk morphology, and embedded magnetic fields. However, different polarization mechanisms and the diversity of dust grain shapes and compositions lead to ambiguities in the polarization pattern. The so-called “self-scattering” of thermal, re-emitted radiation in the infrared and millimetre and submillimetre wavelengths is discussed as a major polarization mechanism. If the net flux of the radiation field is in the radial direction, it is commonly assumed that the polarization pattern produced by scattering in a protoplanetary disk shows concentric rings for disks seen in face-on orientation. We show that a change of 90° of the polarization vector orientation may occur and mimic the typical pattern of dichroic emission of dust grains aligned by a toroidal magnetic field in disks seen close to face-on. Furthermore, this effect of polarization reversal is a fast-changing function of wavelength and grain size, and is thus a powerful tool to constrain grain composition and size distribution present in protoplanetary disks. In addition, the effect may also provide unique constraints for the disk inclination, especially if the disk is seen close to face-on.
Solvable Lie Algebras of Vector Fields and a Lie's Conjecture

We present a local and constructive differential geometric description of finite-dimensional solvable and transitive Lie algebras of vector fields. We show that it implies a Lie's conjecture for such Lie algebras. Also infinite-dimensional analytical solvable and transitive Lie algebras of vector fields whose derivative ideal is nilpotent can be adapted to this scheme.
Interacting young M-dwarfs in triple system – Par 1802 binary system case study

The binary star Par 1802 in the Orion Nebula presents an interesting puzzle in the field of stellar dynamics and evolution. Binary systems such as Par 1802 are thought to form from the same natal material and thus the stellar members are expected to have very similar physical attributes. However, Par 1802’s stars have significantly different temperatures despite their identical (within $3\, {\rm per\, cent}$) masses of about 0.39 M⊙. The leading proof-of-concept idea is that a third companion gravitationally induced the two stars to orbit closer than their Roche limit, which facilitated heating through tidal effects. Here we expand on this idea and study the three-body dynamical evolution of such a system, including tidal and pre-main-sequence evolution. We also include tidal heating and mass transfer at the onset of Roche limit crossing. We show, as a proof-of-concept, that mass transfer combined with tidal heating can naturally explain the observed temperature discrepancy. We also predict the orbital configuration of the possible tertiary companion. Finally, we suggest that the dynamical evolution of such a system has pervasive consequences. We expect an abundance of systems to undergo mass transfer during their pre-main-sequence time, which can cause temperature differences.
Deep Convolutional Compression For Massive MIMO CSI Feedback

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, the huge CSI feedback overhead becomes restrictive and degrades the overall spectral efficiency. In this paper, we propose a deep learning based channel state matrix compression scheme, called DeepCMC, composed of convolutional layers followed by quantization and entropy coding blocks. Simulation results demonstrate that DeepCMC significantly outperforms the state of the art compression schemes in terms of the reconstruction quality of the channel state matrix for the same compression rate, measured in bits per channel dimension.
An operator derivation of the Feynman–Vernon theory, with applications to the generating function of bath energy changes and to an-harmonic baths

We present a derivation of the Feynman–Vernon approach to open quantum systems in the language of super-operators. We show that this gives a new and more direct derivation of the generating function of energy changes in a bath, or baths. As found previously, this generating function is given by a Feynman–Vernon-like influence functional, with only time shifts in the kernels coupling the forward and backward paths. We further show that the new approach extends to an-harmonic and possible non-equilibrium baths, provided that the interactions are bi-linear, and that the baths do not interact between themselves. Such baths are characterized by non-trivial cumulants. Every non-zero cumulant of certain environment correlation functions is thus a kernel in a higher-order term in the Feynman–Vernon action.
C^3 Framework: An Open-source PyTorch Code for Crowd Counting

This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F). The contributions of C$^3$F are in three folds: 1) Some solid baseline networks are presented, which have achieved the state-of-the-arts. 2) Some flexible parameter setting strategies are provided to further promote the performance. 3) A powerful log system is developed to record the experiment process, which can enhance the reproducibility of each experiment. Our code is made publicly available at \url{this https URL}. Furthermore, we also post a Chinese blog\footnote{\url{this https URL}} to describe the details and insights of crowd counting.
Data Encoding for Byzantine-Resilient Distributed Optimization

We study distributed optimization in the presence of Byzantine adversaries, where both data and computation are distributed among <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> worker machines, <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> of which may be corrupt. The compromised nodes may collaboratively and arbitrarily deviate from their pre-specified programs, and a designated (master) node iteratively computes the model/parameter vector for <italic>generalized linear models</italic>. In this work, we primarily focus on two iterative algorithms: <italic>Proximal Gradient Descent</italic> (PGD) and <italic>Coordinate Descent</italic> (CD). Gradient descent (GD) is a special case of these algorithms. PGD is typically used in the data-parallel setting, where data is partitioned across different samples, whereas, CD is used in the model-parallelism setting, where data is partitioned across the parameter space. At the core of our solutions to both these algorithms is a method for Byzantine-resilient matrix-vector (MV) multiplication; and for that, we propose a method based on data encoding and error correction over real numbers to combat adversarial attacks. We can tolerate up to <inline-formula> <tex-math notation="LaTeX">$t\leq \lfloor \frac {m-1}{2}\rfloor $ </tex-math></inline-formula> corrupt worker nodes, which is information-theoretically optimal. We give deterministic guarantees, and our method does not assume any probability distribution on the data. We develop a <italic>sparse</italic> encoding scheme which enables computationally efficient data encoding and decoding. We demonstrate a trade-off between the corruption threshold and the resource requirements (storage, computational, and communication complexity). As an example, for <inline-formula> <tex-math notation="LaTeX">$t\leq \frac {m}{3}$ </tex-math></inline-formula>, our scheme incurs only a <italic>constant</italic> overhead on these resources, over that required by the plain distributed PGD/CD algorithms which provide no adversarial protection. To the best of our knowledge, ours is the first paper that connects MV multiplication with CD and designs a specific encoding matrix for MV multiplication whose structure we can leverage to make CD secure against adversarial attacks. Our encoding scheme extends <italic>efficiently</italic> to <italic>(i)</italic> the data streaming model, in which data samples come in an online fashion and are encoded as they arrive, and <italic>(ii)</italic> making <italic>stochastic gradient descent</italic> (SGD) Byzantine-resilient. In the end, we give experimental results to show the efficacy of our proposed schemes.
Verifying Outsourced Decryption of CP-ABE with Signature

Attribute-based encryption enables users to encrypt and decrypt messages based on user attributes. The computational complexities of decryption are very high. By Outsourcing decryption to the third party, it can address the problem. But the verifiability of results returned from the third party has yet to be addressed. In this paper, we introduce short signature and give a new model of verifiable outsourced decryption of ABE cipertexts. By using signature, we achieve the verification and provide good performance and propose a concrete scheme. We prove that our scheme is both secure and verifiable. Moreover, we show an implementation of our scheme and result of performance measurements.
Privacy-preserved data publishing of evolving online social networks

ABSTRACT The increasing growth of online social networks provides an unprecedented opportunity to study the complex interactions among human beings. Privacy-preserved network-data publishing is becoming increasingly popular in both industry and academia. This articles focuses on evolving social subscription networks (ESSN), which indicate social actors’ participation in certain media channels, such as Hollywood stars’ Twitter pages, during a series of time intervals. The discussion first introduces a new identity disclosure attack by exploring the subscribed channel sizes of a social actor and the actor’s frequency of joining/leaving the channels. For privacy protection, K-anonymity should be ensured for the whole evolving graph. However, unlike the conventional topology information, such as node degree, the ESSN data points are much more sparse. Moreover, during the construction of anonymous groups, the unpopular channel-related information is likely to be discarded. How to maximally preserve ESSN data utility during anonymization is an open problem. These authors propose an effective three-step framework to solve it: data space compression, anonymity construction, and realizable publishing. Also provided are comprehensive studies on the performance of this approach. Extensive results show that this approach is effective in terms of privacy, utility, and efficacy. To the best of the knowledge of these authors, this work is the first systematic study to the anonymization of time-evolving multi-relation graphs.