Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Long Short-Term Memory Cell Architectures: Application to Light Field Face Recognition

With the emergence of lenslet light field cameras able to capture rich spatio-angular information from multiple directions, new frontiers in visual recognition performance have been opened. Since multiple 2D viewpoint images can be rendered from a light field, those multiple images, or descriptions extracted from them, can be organized as a pseudo-video sequence so that a LSTM network learns a model describing that sequence. This paper proposes three novel LSTM cell architectures able to create richer and more effective description models for visual recognition tasks, by jointly learning from two sequences simultaneously acquired. The novel key idea is to jointly process two sequences of rendered 2D images or their descriptions, e.g. representing the scene horizontal and vertical parallaxes, and thus with some specific dependency between them, that would not be exploited otherwise. To show the efficiency of the novel LSTM cell architectures, these architectures have been integrated into an end-to-end deep learning face recognition framework, which creates this join spatio-angular light field description. The LSTM network, using the proposed LSTM cell architectures, receives as input a sequence of VGG-Face descriptions computed for parallax related, horizontal and vertical 2D face viewpoint images, derived from the input light field image. A comprehensive evaluation in terms of recognition accuracy, computational complexity, memory efficiency, and parallelization ability has been performed with the IST EURECOM LFFD database using three new and challenging evaluation protocols. The obtained results show the superior performance of the proposed face recognition solutions adopting the novel LSTM cell architectures over ten state-of-the-art benchmarking recognition solutions.
Characterizing maser polarization: effects of saturation, anisotropic pumping, and hyperfine structure (Corrigendum)

Context. The polarization of masers contains information on the magnetic field strength and direction of the regions they occur in. Many maser polarization observations have been performed over the last 30 years. However, versatile maser polarization models that can aide in the interpretation of these observations are not available. Aims. We developed a program suite that can compute the polarization by a magnetic field of any non-paramagnetic maser species at arbitrarily high maser saturation. Furthermore, we investigated the polarization of masers by non-Zeeman polarizing effects. We present a general interpretive structure for maser polarization observations. Methods. We expanded existing maser polarization theories of non-paramagnetic molecules and incorporated them in a numerical modeling program suite. Results. We present a modeling program called CHAracterizes Maser Polarization (CHAMP) that can examine the polarization of masers of arbitrarily high maser saturation and high angular momentum. Hyperfine multiplicity of the maser-transition can also be incorporated. The user is able to investigate non-Zeeman polarizing mechanisms such as anisotropic pumping and polarized incident seed radiation. We present an analysis of the polarization of v = 1 SiO masers and the 22 GHz water maser. We comment on the underlying polarization mechanisms, and also investigate non-Zeeman effects. Conclusions. We identify the regimes where different polarizing mechanisms will be dominant and present the polarization characteristics of the SiO and water masers. From the results of our calculations, we identify markers to recognize alternative polarization mechanisms. We show that comparing randomly generated linear versus circular polarization (pL − pV) scatter-plots at fixed magnetic field strength to the observationally obtained pL − pV scatter can be a promising method of ascertaining the average magnetic field strength of a large number of masers.
Semantic Segmentation of Seismic Images

Almost all work to understand Earth's subsurface on a large scale relies on the interpretation of seismic surveys by experts who segment the survey (usually a cube) into layers; a process that is very time demanding. In this paper, we present a new deep neural network architecture specially designed to semantically segment seismic images with a minimal amount of training data. To achieve this, we make use of a transposed residual unit that replaces the traditional dilated convolution for the decode block. Also, instead of using a predefined shape for up-scaling, our network learns all the steps to upscale the features from the encoder. We train our neural network using the Penobscot 3D dataset; a real seismic dataset acquired offshore Nova Scotia, Canada. We compare our approach with two well-known deep neural network topologies: Fully Convolutional Network and U-Net. In our experiments, we show that our approach can achieve more than 99 percent of the mean intersection over union (mIOU) metric, outperforming the existing topologies. Moreover, our qualitative results show that the obtained model can produce masks very close to human interpretation with very little discontinuity.
Dielectric helical resonators

This paper discusses the possibility of constructing a resonant element from dielectric material shaped into a helix. The modeling conducted shows that the mode associated with ceramic quarter-wave resonators is present within a coiled dielectric helix, the properties of which are strongly dependent upon the pitch and the thickness of the coils. Some of the helices studies in this paper possess high-quality factors due to the low filling factors associated with the resonant mode. It is intended to use these initial results as design criteria to compare the dielectric helix with existing resonators. The modeling conducted suggests that the analogy with a conductive helix does not apply.
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints

In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Indeed, current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints. Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training. We analyze the following problem: "given a dataset, algorithm, and fixed resource budget, what is the best achievable performance? " We focus on the number of optimization iterations as the representative resource. Under such a setting, we show that it is critical to adjust the learning rate schedule according to the given budget. Among budget-aware learning schedules, we find simple linear decay to be both robust and high-performing. We support our claim through extensive experiments with state-of-the-art models on ImageNet (image classification), Kinetics (video classification), MS COCO (object detection and instance segmentation), and Cityscapes (semantic segmentation). We also analyze our results and find that the key to a good schedule is budgeted convergence, a phenomenon whereby the gradient vanishes at the end of each allowed budget. We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.
Real moduli space of stable rational curves revisted

We give a description of the operad formed by the real locus of the moduli space of stable genus zero curves with marked points $\overline{{\mathcal M}_{0,{n+1}}}({\mathbb R})$ in terms of a homotopy quotient of an operad of associative algebras. We use this model to find different Hopf models of the algebraic operad of Chains and homologies of $\overline{{\mathcal M}_{0,{n+1}}}({\mathbb R})$. In particular, we show that the operad $\overline{{\mathcal M}_{0,{n+1}}}({\mathbb R})$ is not formal. The manifolds $\overline{{\mathcal M}_{0,{n+1}}}({\mathbb R})$ are known to be Eilenberg-MacLane spaces for the so called pure Cacti groups. As an application of the operadic constructions we prove that for each $n$ the cohomology ring $H(\overline{{\mathcal M}_{0,{n+1}}}({\mathbb R}),{\mathbb{Q}})$ is a Koszul algebra and that the manifold $\overline{{\mathcal M}_{0,{n+1}}}({\mathbb R})$ is not formal but is a rational $K(\pi,1)$ space. We give the description of the Lie algebras associated with the lower central series filtration of the pure Cacti groups.
GraphSE²: An Encrypted Graph Database for Privacy-Preserving Social Search

In this paper, we propose GraphSE\textsuperscript2, an encrypted graph database for online social network services to address massive data breaches. GraphSE\textsuperscript2 ~preserves the functionality of social search, a key enabler for quality social network services, where social search queries are conducted on a large-scale social graph and meanwhile perform set and computational operations on user-generated contents. To enable efficient privacy-preserving social search, GraphSE\textsuperscript2 ~provides an encrypted structural data model to facilitate parallel and encrypted graph data access. It is also designed to decompose complex social search queries into atomic operations and realise them via interchangeable protocols in a fast and scalable manner. We build GraphSE\textsuperscript2 ~with various queries supported in the Facebook graph search engine and implement a full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that GraphSE\textsuperscript2 ~is practical for querying a social graph with a million of users.
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization

It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inference
Optimal Neutron-star Mass Ranges to Constrain the Equation of State of Nuclear Matter with Electromagnetic and Gravitational-wave Observations

Exploiting a very large library of physically plausible equations of state (EOSs) containing more than 107 members and yielding more than 109 stellar models, we conduct a survey of the impact that a neutron-star radius measurement via electromagnetic observations can have on the EOS of nuclear matter. Such measurements are soon to be expected from the ongoing Neutron Star Interior Composition Explorer mission and will complement the constraints on the EOS from gravitational-wave detections. Thanks to the large statistical range of our EOS library, we can obtain a first quantitative estimate of the commonly made assumption that the high-density part of the EOS is best constrained when measuring the radius of the most massive, albeit rare, neutron stars with masses M ≳ 2.1 M⊙. At the same time, we find that radius measurements of neutron stars with masses M ≃ 1.7–1.85 M⊙ can provide the strongest constraints on the low-density part of the EOS. Finally, we quantify how radius measurements by future missions can further improve our understanding of the EOS of matter at nuclear densities.
Language-Conditioned Graph Networks for Relational Reasoning

Solving grounded language tasks often requires reasoning about relationships between objects in the context of a given task. For example, to answer the question "What color is the mug on the plate? " we must check the color of the specific mug that satisfies the "on" relationship with respect to the plate. Recent work has proposed various methods capable of complex relational reasoning. However, most of their power is in the inference structure, while the scene is represented with simple local appearance features. In this paper, we take an alternate approach and build contextualized representations for objects in a visual scene to support relational reasoning. We propose a general framework of Language-Conditioned Graph Networks (LCGN), where each node represents an object, and is described by a context-aware representation from related objects through iterative message passing conditioned on the textual input. E.g., conditioning on the "on" relationship to the plate, the object "mug" gathers messages from the object "plate" to update its representation to "mug on the plate", which can be easily consumed by a simple classifier for answer prediction. We experimentally show that our LCGN approach effectively supports relational reasoning and improves performance across several tasks and datasets. Our code is available at http://ronghanghu.com/lcgn.
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models

Most objects in the visual world are partially occluded, but humans can recognize them without difficulty. However, it remains unknown whether object recognition models like convolutional neural networks (CNNs) can handle real-world occlusion. It is also a question whether efforts to make these models robust to constant mask occlusion are effective for real-world occlusion. We test both humans and the above-mentioned computational models in a challenging task of object recognition under extreme occlusion, where target objects are heavily occluded by irrelevant real objects in real backgrounds. Our results show that human vision is very robust to extreme occlusion while CNNs are not, even with modifications to handle constant mask occlusion. This implies that the ability to handle constant mask occlusion does not entail robustness to real-world occlusion. As a comparison, we propose another computational model that utilizes object parts/subparts in a compositional manner to build robustness to occlusion. This performs significantly better than CNN-based models on our task with error patterns similar to humans. These findings suggest that testing under extreme occlusion can better reveal the robustness of visual recognition, and that the principle of composition can encourage such robustness.
Capturing Evolution Genes for Time Series Data

The modeling of time series is becoming increasingly critical in a wide variety of applications. Overall, data evolves by following different patterns, which are generally caused by different user behaviors. Given a time series, we define the evolution gene to capture the latent user behaviors and to describe how the behaviors lead to the generation of time series. In particular, we propose a uniform framework that recognizes different evolution genes of segments by learning a classifier, and adopt an adversarial generator to implement the evolution gene by estimating the segments' distribution. Experimental results based on a synthetic dataset and five real-world datasets show that our approach can not only achieve a good prediction results (e.g., averagely +10.56% in terms of F1), but is also able to provide explanations of the results.
Another Look at ALGORAND

ALGORAND is a celebrated public ledger technology designed by Dr. Micali and his collaborators. By the end of year 2018, it has raised US$64 million in funding from venture capital firms. In this paper, we identify several design flaws of the ALGORAND protocol. In particular, we show that the claimed (proved) fork-free property is not true and several assumptions in ALGORAND are not realistic in practice. The ALGORAND wiki page this https URL claims that ``the probability of a fork in the protocol is estimated at 1/1,000,000,000 and therefore blocks can be considered final upon validation''. However, our first attack in this paper shows that a malicious adversary who controls less than 1/3 of the users (or money units) could fork the ALGORAND chain very easily. Our second attack shows that a malicious adversary could use a bribery attack to fork the ALGORAND chain very easily also. Furthermore, we show that the celebrated Byzantine Agreement component in ALGORAND is not necessary. The Byzantine Agreement is the most expensive part and one of the most innovative parts in the ALGORAND protocol. It is used to avoid forks in ALGORAND. We show that a simple majority vote could be used to achieve the same property that Byzantine Agreement achieves in ALGORAND under the same network assumption.
The 2dF Galaxy Redshift Survey: the power spectrum and the matter content of the Universe

The 2dF Galaxy Redshift Survey has now measured in excess of 160 000 galaxy redshifts. This paper presents the power spectrum of the galaxy distribution, calculated using a direct Fourier transform based technique. We argue that, within the k-space region 0.02 less than or similar to k less than or similar to 0.15 h Mpc(-1), the shape of this spectrum should be close to that of the linear density perturbations convolved with the window function of the survey. This window function and its convolving effect on the power spectrum estimate are analysed in detail. By convolving model spectra, we are able to fit the power-spectrum data and provide a measure of the matter content of the Universe. Our results show that models containing baryon oscillations are mildly preferred over featureless power spectra. Analysis of the data yields 68 per cent confidence limits on the total matter density times the Hubble parameter Omega (m) h = 0.20 +/- 0.03, and the baryon fraction Omega (b)/Omega (m) = 0.15 +/- 0.07, assuming scale-invariant primordial fluctuations.
Investigation of two-photon emission in strong field QED using channeling in a crystal

We investigate the 2nd order process of two photons being emitted by a high-energy electron dressed in the strong background electric field found between the planes in a crystal. The strong crystalline field combined with ultra relativistic electrons is one of very few cases where the Schwinger field can be experimentally achieved in the electron's rest frame. The radiation being emitted, the so-called channeling radiation, is a well studied phenomenon. However only the first order diagram corresponding to emission of a single photon has been studied so far. We elaborate on how the 2 photon emission process should be understood in terms of a two-step versus a one-step process, i.e., if one can consider one photon being emitted after the other, or if there is also a contribution where the two photons are emitted 'simultaneously'. From the calculated full probability we see that the two-step contribution is simply the product of probabilities for single photon emission while the additional one-step terms are, mainly, interferences due to several possible intermediate virtual states. These terms can contribute significantly when the crystal is thin. Therefore, in addition, we see how one can, for a thick crystal, calculate multiple photon emissions quickly by neglecting the one-step terms, which represents a solution of the problem of quantum radiation reaction in a crystal beyond the usually applied constant field approximation. We explicitly calculate an example of 180 GeV electrons in a thin Silicon crystal and argue why it is, for experimental reasons, more feasible to see the one-step contribution in a crystal experiment than in a laser experiment.
The diameter of lattice zonotopes

We establish sharp asymptotic estimates for the diameter of primitive zonotopes when their dimension is fixed. We also prove that, for infinitely many integers $k$, the largest possible diameter of a lattice zonotope contained in the hypercube $[0,k]^d$ is uniquely achieved by a primitive zonotope. As a consequence, we obtain that this largest diameter grows like $k^{d/(d+1)}$ up to an explicit multiplicative constant, when $d$ is fixed and $k$ goes to infinity, providing a new lower bound on the largest possible diameter of a lattice polytope contained in $[0,k]^d$.
Feedback control of charged ideal fluids

The theory of controlled mechanical systems of Bloch et al (2001 IEEE Trans. Control Syst. Technol. 45 2253–70; Bloch et al 2001 Int. J. Robust Nonlinear Control 11 191–214; Bloch et al 1992 Automatica 28 745–56; Bloch et al 1997 Current and Future Directions in Applied Mathematics) and Krishnaprasad P S (1985 Nonlinear Anal. Theor. Methods Appl. 9 1011–35) is extended to the case of ideal incompressible fluids consisting of charged particles in the presence of an external magnetic field. The resulting control is of feedback type and depends on the Eulerian state of the controlled system. Moreover, the control is set up so that the corresponding closed loop equations are Lie–Poisson. This implies that the energy–momentum method of Arnold V (1966 Ann. Inst. Fourier 16 319–61) and Holm et al (1985 Phys. Rep. 123 1–116) can be used to find a stabilizing control. As an example the case of planar parallel shear flow with an inflection point is treated. A state dependent feedback control is constructed which stabilizes the system for an arbitrarily long channel.
Pressure-induced formation of rhodium zigzag chains in the honeycomb rhodate 
Li2RhO3

We use powder x-ray diffraction to study the effect of pressure on the crystal structure of the honeycomb rhodate Li$_2$RhO$_3$. We observe low-pressure ($P$$ $$P_{c2}$ = 14 GPa) regions corresponding to the monoclinic $C2/m$ symmetry, while a phase mixture is observed at intermediate pressures. At $P$$>$$P_{c2}$, the honeycomb structure becomes distorted and features short Rh--Rh bonds forming zigzag chains stretched along the crystallographic $a$ direction. This is in contrast to dimerized patterns observed in triclinic high-pressure polymorphs of $\alpha$-Li$_2$IrO$_3$ and $\alpha$-RuCl$_3$. Density-functional theory calculations at various pressure conditions reveal that the observed rhodium zigzag-chain pattern is not expected under hydrostatic pressure but can be reproduced by assuming anisotropic pressure conditions.
Long-Term Dietary Folate Deficiency Accelerates Progressive Hearing Loss on CBA/Ca Mice

Dietary folic acid deficiency induced early hearing loss in C57BL/6J mice after 2-months, corroborates the epidemiological association previously described between vitamin deficiency and this sensory impairment. However, this strain is prone to early hearing loss, and hence we decided to analyze whether the effects exerted by folate deprivation follow the same pattern in a mouse strain such as CBA/Ca, which is resistant to hearing impairment. Here, we show results of a long-term study on hearing carried out on CBA/Ca mice subjected to dietary folate deprivation. Systemic changes included decreased serum folate levels, hyperhomocysteinemia and signs of anemia in the group fed with folate-deficient (FD) diet. Initial signs of hearing loss were detected in this strain after 8-months of vitamin deficiency, and correlated with histological damage in the cochleae. In conclusion, the data presented reinforce the importance of adequate folic acid levels for the auditory system and suggest that the impact of dietary deficiencies may depend on the genetic background.