Miracle-dz/UniFAR-FTUs huggingface dataset:
A new interval type 2 fuzzy-based pixel wise information extraction for face recognition

A new efficient and robust approach based on pixel wise information extraction and fuzzy logic concept is presented here for the application of face recognition (FR). As all the pixels of an individual face image do not participate equally in identifying a face image from a given set of classes. Therefore, in this paper, we use a new interval type 2 fuzzy based pixel wise information extraction (NIntTy2FPIE) on input face images for computing the pixel wise association of individual pixels of a face image in a given dataset. Next, computational cost is reduced by principle component analysis (PCA) and classification is done using a variant of nearest neighbour classifier (NNC), called k-NNC. Experiments performed on ORL, Yale and Georgia Tech and AR face database show that our method outperforms with many state-of-art methods and also proves that the proposed method with k-NNC is much more efficient and robust.
Application of shared control strategy in the design of a robotic device

A new paradigm in the design and control of assistive robotic devices is the notion of Shared Control. Shared control devices can be categorized as a class of robotics where both the human and mechanism are in direct cooperation in accomplishing a given task. The information and power are distributed and interconnected between the agents. This way one may be able to accomplish tasks which takes advantage of the information and power capabilities of both agents. This paper presents an application of the above concept in the design of a surgical tool which is used in the minimally-invasive surgery. The paper presents a summary of the design integration and performance analysis of such concept.
Effective and General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping

: In instruction conditioned navigation, agents interpret natural language and their surroundings to navigate through an environment. Datasets for studying this task typically contain pairs of these instructions and reference trajectories. Yet, most evaluation metrics used thus far fail to properly account for the latter, relying instead on insufﬁcient similarity comparisons. We address fundamental ﬂaws in previously used metrics and show how Dynamic Time Warping (DTW), a long known method of measuring similarity between two time series, can be used for evaluation of navigation agents. For such, we deﬁne the normalized Dynamic Time Warping (nDTW) metric, that softly penalizes deviations from the reference path, is naturally sensitive to the order of the nodes composing each path, is suited for both continuous and graph-based evaluations, and can be efﬁciently calculated. Further, we deﬁne SDTW, which constrains nDTW to only successful paths. 1 We collect human similarity judgments for simulated paths and ﬁnd nDTW correlates better with human rankings than all other metrics. We also demonstrate that using nDTW as a reward signal for Reinforcement Learning navigation agents improves their performance on both the Room-to-Room (R2R) and Room-for-Room (R4R) datasets. The R4R results in particular highlight the superiority of SDTW over previous success-constrained metrics.
Deep Model Compression via Filter Auto-sampling

The recent WSNet [1] is a new model compression method through sampling ﬁlter weights from a compact set and has demonstrated to be effective for 1D convolution neural networks (CNNs). However, the weights sampling strategy of WSNet is handcrafted and ﬁxed which may severely limit the expression ability of the resulted CNNs and weaken its compression ability. In this work, we present a novel auto-sampling method that is applicable to both 1D and 2D CNNs with signiﬁcant performance improvement over WSNet. Speciﬁcally, our proposed auto-sampling method learns the sampling rules end-to-end instead of being independent of the network architecture design. With such differentiable weight sampling rule learning, the sampling stride and channel selection from the compact set are optimized to achieve better trade-off between model compression rate and performance. We demonstrate that at the same compression ratio, our method outperforms WSNet by 6 . 5 % on 1D convolution. Moreover, on ImageNet, our method outperforms MobileNetV2 full model by 1 . 47% in classiﬁcation accuracy with 25% FLOPs reduction. With the same backbone architecture as baseline models, our method even outperforms some neural architecture search (NAS) based methods such as AMC [2] and MNasNet [3].
A variational approach to the Hermitian-Einstein metrics and the Quot-scheme limit of Fubini-Study metrics

This is a sequel of our paper [arXiv:1809.08425] on the Quot-scheme limit and variational properties of Donaldson's functional, which established its coercivity for slope stable holomorphic vector bundles over smooth projective varieties. Assuming that the coercivity is uniform in a certain sense, we provide a new proof of the Donaldson-Uhlenbeck-Yau theorem, in such a way that the analysis involved in the proof is elementary except for the asymptotic expansion of the Bergman kernel.
Virtual Adversarial Lipschitz Regularization

Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality. However, Wasserstein GANs require the critic to be K-Lipschitz, which is often enforced implicitly by penalizing the norm of its gradient, or by globally restricting its Lipschitz constant via weight normalization techniques. Training with a regularization term penalizing the violation of the Lipschitz constraint explicitly, instead of through the norm of the gradient, was found to be practically infeasible in most situations. With a novel generalization of Virtual Adversarial Training, called Virtual Adversarial Lipschitz Regularization, we show that using an explicit Lipschitz penalty is indeed viable and leads to state-of-the-art performance in terms of Inception Score and Fréchet Inception Distance when applied to Wasserstein GANs trained on CIFAR-10.
Parameterized normalization: application to wavelet networks

We present a method to handle the standard data normalization process, which is often used in neural network applications. Normalization is studied in the case of wavelet networks, and we derive a dynamic interpretation of its influence, which can be extended to several neural models. We show that data normalization may be simulated and parameterized to avoid data preprocessing, so that the normalization process becomes either tunable or dynamically adaptable. Both cases are illustrated with benchmark applications. The main benefit of the proposed methods is a big reduction of the time of convergence on a satisfactory classification rate.
Quantum discord-breaking and discord-annihilating channels

Quantum discord-breaking channels were previously defined as the local channels that act on subsystem $A$ to produce classical-quantum states across system $AB$. However, unlike entanglement, discord is asymmetric. Here, we characterise the discord-breaking channels that act on subsystem $B$, thus completing the overall description of discord-breaking channels. We then introduce the notion of discord-annihilating channels, which act globally on system $AB$ to destroy quantum discord, and find their closed form. Discord-annihilating channels have clear operational description, involving subspace projections on subsystem $A$ and conditional preparation of fixed states on subsystem $B$.
Asymptotic reversibility of thermal operations for interacting quantum spin systems via generalized quantum Stein’s lemma

For quantum spin systems in any spatial dimension with a local, translation-invariant Hamiltonian, we prove that asymptotic state convertibility from a quantum state to another one by a thermodynamically feasible class of quantum dynamics, called thermal operations, is completely characterized by the Kullback–Leibler (KL) divergence rate, if the state is translation-invariant and spatially ergodic. Our proof consists of two parts and is phrased in terms of a branch of the quantum information theory called the resource theory. First, we prove that any states, for which the min and max Rényi divergences collapse approximately to a single value, can be approximately reversibly converted into one another by thermal operations with the aid of a small source of quantum coherence. Second, we prove that these divergences collapse asymptotically to the KL divergence rate for any translation-invariant ergodic state. We show this via a generalization of the quantum Stein’s lemma for quantum hypothesis testing beyond independent and identically distributed situations. Our result implies that the KL divergence rate serves as a thermodynamic potential that provides a complete characterization of thermodynamic convertibility of ergodic states of quantum many-body systems in the thermodynamic limit, including out-of-equilibrium and fully quantum situations.
“So that's what that is”: Examining the impact of analogy on consumers' knowledge development for really new products

The ever-accelerating pace of technological change has heralded an increasing number of new product introductions involving products that defy classification within existing categories. With the advent of these so-called “really new products,” new questions about the influence of prior knowledge on consumer learning emerge. Chief among these is whether and to what extent prior knowledge plays a role in the comprehension of such products. Applying analogical learning theory to address this question, this investigation presents evidence indicating that analogy provides an effective link to the structural knowledge needed for consumers to learn about truly novel innovations. Reflecting this, subjects who engaged in analogical processing of new product information were more focused in their processing than subjects who processed the same information in the absence of analogy. Moreover, there was evidence to suggest that analogical processing itself results in the generation of positive affect. © 2002 Wiley Periodicals, Inc.
DC electrical degradation of YSZ: Voltage‐controlled electrical metallization of a fast ion conducting insulator

DC electrical degradation as a form of dielectric and resistance breakdown is a common phenomenon in thin-film devices including resistance-switching memory. To obtain design data and to probe the degradation mechanism, highly accelerated lifetime tests (HALT) are often conducted at higher temperatures with thicker samples. While the mechanism is well established in semiconducting oxides such as perovskite titanates, it is not in stabilized zirconia and other fast oxygen-ion conductors that have little electronic conductivity. Here we model the mechanism by an oxygen-driven, transport-limited, metal-insulator transition, which finds support in rich experimental observations - including in situ videos and variable temperature studies - of yttria-stabilized zirconia. They are contrasted with the findings in semiconducting titanates and resistance memory, and provide new insight into ceramic processing with extremely rapid heating and cooling such as flash sintering and melt processing.
Kingman’s coalescent with erosion

Consider the Markov process taking values in the partitions of N such that each pair of blocks merges at rate one, and each integer is eroded, i.e., becomes a singleton block, at rate d. This is a special case of exchangeable fragmentation-coalescence process called Kingman's coalescent with erosion. We provide a new construction of the stationary distribution of this process as a sample from a standard flow of bridges. This allows us to give a representation of the asymptotic frequencies of this stationary distribution in terms of a sequence of hierarchically independent diffusions. Moreover, we introduce a new process called Kingman's coalescent with immigration, where pairs of blocks coalesce at rate one, and new blocks of size one immigrate at rate d. By coupling Kingman's coalescents with erosion and with immigration, we are able to show that the size of a block chosen uniformly at random from the stationary distribution of the restriction of Kingman's coalescent with erosion to {1,. ,n} converges to the total progeny of a critical binary branching process.
Benchmarking Physical Performance of Neural Inference Circuits

Numerous neural network circuits and architectures are presently under active research for application to artificial intelligence and machine learning. Their physical performance metrics (area, time, energy) are estimated. Various types of neural networks (artificial, cellular, spiking, and oscillator) are implemented with multiple CMOS and beyond-CMOS (spintronic, ferroelectric, resistive memory) devices. A consistent and transparent methodology is proposed and used to benchmark this comprehensive set of options across several application cases. Promising architecture/device combinations are identified.
Global Stabilization of 2D Forced Viscous Burgers' Equation Around Nonconstant Steady State Solution by Nonlinear Neumann Boundary Feedback Control: Theory and Finite Element Analysis

Global stabilization of viscous Burgers' equation around constant steady state solution has been discussed in the literature. The main objective of this paper is to show global stabilization results for the 2D forced viscous Burgers' equation around a nonconstant steady state solution using nonlinear Neumann boundary feedback control law, under some smallness condition on that steady state solution. On discretizing in space using $C^0$ piecewise linear elements keeping time variable continuous, a semidiscrete scheme is obtained. Moreover, global stabilization results for the semidiscrete solution and optimal error estimates for the state variable in $L^\infty(L^2)$ and $L^\infty(H^1)$-norms are derived. Further, optimal convergence result is established for the boundary feedback control law. All our results in this paper preserve exponential stabilization property. Finally, some numerical experiments are documented to confirm our theoretical findings.
Equivalent dual theories for 3D 
N=2
 supergravity

N=2 three dimensional Supergravity with internal $R-$symmetry generators can be understood as a two dimensional chiral Wess-Zumino-Witten model. In this paper, we present the reduced phase space description of the theory, which turns out to be flat limit of a generalised Liouville theory, up to zero modes. The reduced phase space description can also be explained as a gauged chiral Wess-Zumino-Witten model. We show that both these descriptions possess identical gauge and global (quantum N=2 superBMS$_3$) symmetries.
Iterative soft onion peeling for multi-access and broadcast channels

The concept of iterative multiuser detection for a multiple access adder AWGN channel is coupled with turbo code used for each user. The iterative decoding exchanges the likelihood information both among the two subcodes of each user and among the users. The information exchange among users is performed through subtraction of the mean values of contributions of other users from each symbol. In addition, the variance of the equivalent noise is reestimated for each symbol and each user at each step. When the iterative procedure is applied to equal-power equal-rate case, it fails to converge at aggregate rates significantly beyond 0.5 bit/symbol, due to excessive S/(N+other users) ratio in the first iteration. We are interested in a multiple access scheme which is symmetric with respect to signaling method used by each user, but with significantly higher aggregate capacity. We overcome this problem by splitting each user into several subusers of equal rate but unequal power. Assignment of different powers to different users creates an "onion structure". This structure is used implicitly by the iterative decoding process, as the higher power users converge first, and then the lower power users converge as the residual multi-user noise reduces. This breaks the 0.5 bit/symbol barrier and enables to reach 2 bits/symbol and beyond, at the expense of a linear increase in the number of iterations required. The approach of unequal power assignment can be directly applied to multiple users with power disparities, where each user can be interpreted as an independent user. Application to a degraded broadcast channel is addressed.
Em direção à construção automática de Mapas Conceituais a partir de textos

From the assumption that the construction, evaluation and evolution of the concept maps are mechanisms that push the learner to become an active subject of the learning process, supporting collaborative action between teachers and learners, this paper proposes a computational approach for the construction of concept maps from documents in Portuguese of Brazil in order to overcome the difficulties of building concept maps from the "scratch" and to enable a way of giving new meanings to textual contents.
All-optical tuning of a diamond micro-disk resonator on silicon

High quality integrated diamond photonic devices have previously been demonstrated in applications from non-linear photonics to on-chip quantum optics. However, the small sample sizes of single crystal material available, and the difficulty in tuning its optical properties, are barriers to the scaling of these technologies. Both of these issues can be addressed by integrating micron scale diamond devices onto host photonic integrated circuits using a highly accurate micro-assembly method. In this work a diamond micro-disk resonator is integrated with a standard single mode silicon-on-insulator waveguide, exhibiting an average loaded Q-factor of 3.1x10^4 across a range of spatial modes, with a maximum loaded Q-factor of 1.05x10^5. The micron scale device size and high thermal impedance of the silica interface layer allow for significant thermal loading and continuous resonant wavelength tuning across a 450 pm range using a mW level optical pump. This diamond-on-demand integration technique paves the way for tunable devices coupled across large scale photonic circuits.
Homogeneous transitions during inflation: A description in quantum cosmology

The usual description of inflationary fluctuations uses the framework of quantum field theory (QFT) in curved spacetime, in which quantum fluctuations are superimposed on a classical background spacetime. Even for large fluctuations, such as those envisioned during a regime of eternal inflation, this framework is frequently used. In the present work we go one step beyond this description by quantising both the scalar field and the scale factor of the universe. Employing the Lorentzian path integral formulation of semi-classical gravity we restrict to a simplified minisuperspace setting by considering homogeneous transitions. This approach allows us to determine the dominant geometry and inflaton evolution contributing to such amplitudes. We find that for precisely specified initial scale factor and inflaton values (and uncertain momenta), two distinct saddle point geometries contribute to the amplitude, leading to interference effects. However, when the momenta of both scale factor and inflaton are specified with sufficient certainty, only a single saddle point is relevant and QFT in curved spacetime is applicable. In particular we find that for inflaton transitions up the potential, meaningful results are only obtained when the initial uncertainty in the inflaton value is large enough, allowing the dominant evolution to be a complexified slow-roll solution \emph{down} from a comparatively unlikely position higher up in the potential.
Unsupervised Adversarial Attacks on Deep Feature-based Retrieval with GAN

Studies show that Deep Neural Network (DNN)-based image classification models are vulnerable to maliciously constructed adversarial examples. However, little effort has been made to investigate how DNN-based image retrieval models are affected by such attacks. In this paper, we introduce Unsupervised Adversarial Attacks with Generative Adversarial Networks (UAA-GAN) to attack deep feature-based image retrieval systems. UAA-GAN is an unsupervised learning model that requires only a small amount of unlabeled data for training. Once trained, it produces query-specific perturbations for query images to form adversarial queries. The core idea is to ensure that the attached perturbation is barely perceptible to human yet effective in pushing the query away from its original position in the deep feature space. UAA-GAN works with various application scenarios that are based on deep features, including image retrieval, person Re-ID and face search. Empirical results show that UAA-GAN cripples retrieval performance without significant visual changes in the query images. UAA-GAN generated adversarial examples are less distinguishable because they tend to incorporate subtle perturbations in textured or salient areas of the images, such as key body parts of human, dominant structural patterns/textures or edges, rather than in visually insignificant areas (e.g., background and sky). Such tendency indicates that the model indeed learned how to toy with both image retrieval systems and human eyes.
Does Fever Make Difference

Brugada syndrome is increasingly recognized as a cause of cardiac death. Many of these patients do not get diagnosed due to dynamic and often hidden nature. We have come a long way in understanding the disease process, and its electrophysiology appears to be intimately linked with sodium channel mutations or disorders. The cardiac rhythm in these patients can deteriorate into fatal ventricular arrhythmias. This makes it important for the clinician to be aware of the condition in which arrhythmogenicity of Brugada syndrome is revealed or even potentiated. We present such an instance where our patient’s Brugada syndrome was unmasked by fever.
Crypto Système à Clé Publique de McEliece basé sur les Codes Cycliques de Hamming

Le developpement rapide des reseaux mondiaux et les immenses possibilites offertes par les transactions electroniques en communication continues, posent aujourd'hui de maniere cruciale le probleme de la protection de l'informations contre les erreurs de transmission d'une part, et d'autre part il faut que ces donnees soit non intelligibles sauf a l'auditoire voulu. Afin de pallier a ces deux contraintes on utilise le codage de l'information pour combattre les erreurs de transmissions et, le chiffrement des donnees est souvent utilise pour lutter contre tout systeme d'espionnage. Dans cette communication, on essaye d'introduire un crypto systeme a clef publique utilisant des codes correcteurs d'erreurs (c'est un systeme deux en un). Le systeme etudie est le crypto systeme de McEliece utilisant le code cycle de Hamming corrigeant une erreur simple.
High-Speed Mid-Infrared Interband Cascade Photodetector Based on InAs/GaAsSb Type-II Superlattice

High-speed mid-wave infrared (MWIR) photodetectors have applications in the areas such as free space optical communication and frequency comb spectroscopy. However, most of the research on the MWIR photodetectors have been focused on how to increase the quantum efficiency and reduce the dark current, in order to improve the detectivity (D*), and the 3 dB bandwidth performance of the corresponding MWIR photodetectors is still not fully studied. In this work, we report and characterize a MWIR interband cascade photodetector based on InAs/GaAsSb type-II superlattice with a 50% cutoff wavelength at ∼5.3 μm at 300 K. The 3 dB cutoff frequency is 2.4 GHz at 300 K, for a 40 μm circular diameter device under −5 V applied bias. Limitations on the detector high-speed performance are also discussed.