Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Structured Pruning and Architecture Searching for Group Convolution

Efficient inference of Convolutional Neural Networks is a thriving topic recently. It is desirable to achieve the maximal test accuracy under given inference budget constraints when deploying a pre-trained model. Network pruning is a commonly used technique but it may produce irregular sparse models that can hardly gain actual speed-up. Group convolution is a promising pruning target due to its regular structure; however, incorporating such structure into the pruning procedure is challenging. It is because structural constraints are hard to describe and can make pruning intractable to solve. The need for configuring group convolution architecture, i.e., the number of groups, to maximise test accuracy also increases difficulty. This paper presents an efficient method to address this challenge. We formulate group convolution pruning as finding the optimal channel permutation to impose structural constraints and solve it efficiently by heuristics. We also apply local search to exploring group configuration based on estimated pruning cost to maximise test accuracy. Compared to prior work, results show that our method produces competitive group convolution models for various tasks within a shorter pruning period and enables rapid group configuration exploration subject to inference budget constraints.
On List Equitable Total Colorings of the Generalized Theta Graph

Abstract In 2003, Kostochka, Pelsmajer, and West introduced a list analogue of equitable coloring called equitable choosability. A k-assignment, L, for a graph G assigns a list, L(v), of k available colors to each v ∈ V (G), and an equitable L-coloring of G is a proper coloring, f, of G such that f(v) ∈ L(v) for each v ∈ V (G) and each color class of f has size at most ⌈|V (G)|/k⌉. Graph G is equitably k-choosable if G is equitably L-colorable whenever L is a k-assignment for G. In 2018, Kaul, Mudrock, and Pelsmajer subsequently introduced the List Equitable Total Coloring Conjecture which states that if T is a total graph of some simple graph, then T is equitably k-choosable for each k ≥ max{xℓ(T ), Δ(T )/2 + 2} where Δ(T ) is the maximum degree of a vertex in T and xℓ(T ) is the list chromatic number of T . In this paper, we verify the List Equitable Total Coloring Conjecture for subdivisions of stars and the generalized theta graph.
Bounding Radon Number via Betti Numbers

We prove general topological Radon-type theorems for sets in $\mathbb R^d$ or on a surface. Combined with a recent result of Holmsen and Lee, we also obtain fractional Helly theorem, and consequently the existence of weak $\varepsilon$-nets as well as a $(p,q)$-theorem for those sets. More precisely, given a family $\mathcal F$ of subsets of $\mathbb R^d$, we will measure the homological complexity of $\mathcal F$ by the supremum of the first $\lceil d/2\rceil$ reduced Betti numbers of $\bigcap \mathcal G$ over all $\mathcal G \subsetneq \mathcal F$. We show that if $\mathcal F$ has homological complexity at most $b$, the Radon number of $\mathcal F$ is bounded in terms of $b$ and $d$. In case that $\mathcal F$ lives on a surface and the number of connected components of $\bigcap \mathcal G$ over all $\mathcal G\subsetneq \mathcal F$ is at most $b$, then the Radon number of $\mathcal F$ is bounded by a function depending only on $b$ and the surface itself. Concerning fractional Helly theorem, we get the following improvement using the recent result of the author and Kalai: Let $\mathcal F$ be a family of open sets in a surface $S$ such that for every $\mathcal G\subsetneq \mathcal F$, $\bigcap \mathcal G$ has at most $b$ path-connected components. Then the fractional Helly number of $\mathcal F$ is linear in $b$. Specifically, for $b=1$ we show that the fractional Helly number is at most three, which is optimal. This case further leads to solving a conjecture of Holmsen, Kim, and Lee about an existence of a $(p,q)$-theorem for open subsets of a surface.
Superconducting-Nanowire Single-Photon Spectrometer Exploiting Cascaded Photonic Crystal Cavities

Superconducting nanowire single-photon detectors promise efficient (~100%) and fast (~Gcps) detection of light at the single-photon level. They constitute one of the building blocks to realize integrated quantum optical circuits in a waveguide architecture. The optical response of single-photon detectors, however, is limited to measure only the presence of photons. It misses the capability to resolve the spectrum of a possible broadband illumination. In this work, we propose the optical design for a superconducting nanowire single-photon spectrometer in an integrated optical platform. We exploit a cascade of cavities with different resonance wavelengths side-coupled to a photonic crystal bus waveguide. This allows to demultiplex different wavelengths into different spatial regions, where individual superconducting nanowires that measure the presence of single photons are placed next to these cavities. We employ temporal coupled-mode theory to derive the optimal conditions to achieve a high absorption efficiency in the nanowire with fine spectral resolution. It is shown that the use of a mirror at the end of the cascaded system that terminates the photonic crystal bus waveguide increases the absorption efficiency up to unity, in principle, in the absence of loss. The expected response is demonstrated by full-wave simulations for both two-dimensional and three-dimensional structures. Absorption efficiencies of about 80% are achieved both in two-dimensional structures for four cascaded cavities and in three-dimensional structures for two cascaded cavities. The achieved spectral resolution is about 1 nm. We expect that the proposed setup, both analytically studied and numerically demonstrated in this work, offers a great impetus for future quantum nanophotonic on-chip technologies.
The Myths of Our Time: Fake News

While the purpose of most fake news is misinformation and political propaganda, our team sees it as a new type of myth that is created by people in the age of internet identities and artificial intelligence. Seeking insights on the fear and desire hidden underneath these modified or generated stories, we use machine learning methods to generate fake articles and present them in the form of an online news blog. This paper aims to share the details of our pipeline and the techniques used for full generation of fake news, from dataset collection to presentation as a media art project on the internet.
FLuID: A Meta Model to Flexibly Define Schema-level Indices for the Web of Data

Schema-level indices are vital for summarizing large collections of graph data. There is a large variety in how existing schema-level indices capture the schema of data instances. Each index has its value for a particular application scenario or information need. However, existing indices define only a single, fixed data structure that is tailored to a specific application scenario. Thus, the indices cannot be easily adapted or extended to changing requirements. In order to address these shortcomings, we propose a formal, parameterized meta model called FLuID (Flexible schema-Level Index model for the web of Data) that allows to quickly define, adapt, and compare different schema-level indices for distributed graph data. We conduct an extensive study of related works and abstract from the features of the existing index models to FLuID. In addition, FLuID provides novel features such as aggregation of instances over owl:sameAs. We conduct a detailed complexity analysis and show that indices defined with FLuID can be efficiently computed on average in $\Theta(n)$ w.r. t. $n$ being the number of triples in the input data graph. Furthermore, we implemented the FLuID meta model following an existing stream-based schema computation approach for the Web of Data. We empirically analyze different index models for different application scenarios, types of queries, datasets, and space requirements. This provides for the first time in-depth insights for understanding the influence of design choices of the index models and their usefulness in different scenarios.
Error Analysis of Approximate Array Multipliers

Approximate computing is a nascent energy-efficient computing paradigm suitable for error-tolerant applications. However, the value of approximation error depends on the applied inputs where individual output error may reach intolerable level while the average output error is acceptable. Thus, it is critical to show the response of approximate design for various applied inputs where understanding the interdependence between the inputs and the approximation error could be utilized to control the output quality. In this report, we exhaustively analyze the accuracy of 20 different designs of 8-bit approximate array multipliers. We designed these multipliers based on various configurations including the type of approximable component and how much of the result to approximate, i.e., approximation degree. Accuracy analysis shows a strong correlation between the applied inputs and the magnitude of the observed error, i.e., error distance, normalized error distance and peak-to-signal-noise ratio. We may utilize this input-dependency of approximation error, in controlling the quality of approximate computing by eliminating large magnitude errors and improving the quality of the final results.
Impact of a Spinning Supermassive Black Hole on the Orbit and Gravitational Waves of a Nearby Compact Binary

Recent theoretical studies suggest that stellar-mass binary black holes (BBHs) would merge more efficiently due to the Kozai–Lidov (K–L) mechanism if these binaries form in the vicinity of supermassive black holes (SMBHs). Since SMBHs are likely rotating rapidly, we continue our earlier study on the generalization of the K–L formalism to include the spin of the SMBH and study the evolution of a nearby BBH. We find that the eccentricity and orbital inclination of the BBH are significantly affected, because the spin (i) forces the orbital plane of the center-of-mass of the BBH around the SMBH to precess (the Lense–Thirring effect) and (ii) imposes an additional gravitomagnetic force on the BBH. As a result, the merger time of the BBH could be significantly different. We calculate the waveform from the BBH in one representative example and study its detectability with a milli-Hertz gravitational-wave detector, such as the Laser Interferometer Space Antenna (LISA). We find that the signal is distinguishable from that in the case without spin. Our results imply that the BBHs in the LISA band could potentially be used to probe the spin of the SMBHs in galaxy centers.
Coherent Elastic Neutrino-Nucleus Scattering as a Precision Test for the Standard Model and Beyond: The COHERENT Proposal Case

Several experimental proposals expect to confirm the recent measurement of the coherent elastic neutrino-nucleus scattering (CEvNS). Motivated in particular by the next generation experiments of the COHERENT collaboration, we study their sensitivity to different tests of the Standard Model and beyond. We analyze the resolution that can be achieved by each future proposed detector in the measurement of the weak mixing angle; we also perform a similar analysis in the context of Nonstandard Interaction (NSI) and in the case of oscillations into a sterile neutrino state. We show that future perspectives are interesting for these types of new physics searches.
A Comparative Study between the Internal and External Internet Broadcasting on AL-NAJAf City

Internet-based technology such as mobile and computers have been widely grown over the world in the recent years. Indoor Internet wireless has spread rapidly instead of a traditional method which using cable-based services to get the Internet services. Today hardly non-exist these devices in any house or office. A lot of people concerned about the effect these devices especially effect on children. Hundreds of reports talked about the Electromagnetic field (EMF) emitted by wireless devices and its effect on both human and animal. In Iraq the wireless broadcasting has been divided into two types, these kinds include indoor wireless broadcasting and outdoor wireless broadcasting by using towers. In this paper, comparing between two type of broadcasting from the perspective of power density which is accredited by most of the organization that specializes in EMF. In the indoor broadcasting, different types of wireless broadcasting devices take as a sample to measure the power density, whilst, the in outdoor broadcasting Previous measurements take to compare with indoor measurements. The measurements obtained from this study show that the effects of the internal broadcasting devices are more influential than the external broadcasting devices that connected to the towers, especially for very close distances. Therefore, this study emphasizes to remove these devices from the bedrooms and classrooms and patients rooms.
Processamento de linguagem natural em Português e aprendizagem profunda para o domínio de Óleo e Gás

Over the last few decades, institutions around the world have been challenged to deal with the sheer volume of information captured in unstructured formats, especially in textual documents. The so called Digital Transformation age, characterized by important technological advances and the advent of disruptive methods in Artificial Intelligence, offers opportunities to make better use of this information. Recent techniques in Natural Language Processing (NLP) with Deep Learning approaches allow to efficiently process a large volume of data in order to obtain relevant information, to identify patterns, classify text, among other applications. In this context, the highly technical vocabulary of Oil and Gas (O&G) domain represents a challenge for these NLP algorithms, in which terms can assume a very different meaning in relation to common sense understanding. The search for suitable mathematical representations and specific models requires a large amount of representative corpora in the O&G domain. However, public access to this material is scarce in the scientific literature, especially considering the Portuguese language. This paper presents a literature review about the main techniques for deep learning NLP and their major applications for O&G domain in Portuguese.
3-uniform hypergraphs with few Berge paths of length three between any two vertices

Recently, Berge theta hypergraphs have received special attention due to the similarity with Berge even cycles. Let $r$-uniform Berge theta hypergraph $\Theta_{\ell,t}^{B}$ be the $r$-uniform hypergraph consisting of $t$ internally disjoint Berge paths of length $\ell$ with the same pair of endpoints. In this work, we determine the Turan number of $3$-uniform Berge theta hypergraph when $\ell=3$ and $t$ is relatively small. More precisely, we provide an explicit construction giving \begin{align*} \textup{ex}_{3}(n,\Theta_{3,217}^{B})=\Omega(n^{\frac{4}{3}}). \end{align*} This matches an earlier upper bound by He and Tait up to an absolute constant factor. The construction is algebraic, which is based on some equations over finite fields, and the parameter $t$ in our construction is much smaller than that in random algebraic construction.
TopoTag: A Robust and Scalable Topological Fiducial Marker System

Fiducial markers have been playing an important role in augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and an object is required. Here we introduce TopoTag, a robust and scalable topological fiducial marker system, which supports reliable and accurate pose estimation from a single image. TopoTag uses topological and geometrical information in marker detection to achieve higher robustness. Topological information is extensively used for 2D marker detection, and further corresponding geometrical information for ID decoding. Robust 3D pose estimation is achieved by taking advantage of all TopoTag vertices. Without sacrificing bits for higher recall and precision like previous systems, TopoTag can use full bits for ID encoding. TopoTag supports tens of thousands unique IDs and easily extends to millions of unique tags resulting in massive scalability. We collected a large test dataset including in total 169,713 images for evaluation, involving in-plane and out-of-plane rotation, image blur, different distances, and various backgrounds, etc. Experiments on the dataset and real indoor and outdoor scene tests with a rolling shutter camera both show that TopoTag significantly outperforms previous fiducial marker systems in terms of various metrics, including detection accuracy, vertex jitter, pose jitter and accuracy, etc. In addition, TopoTag supports occlusion as long as the main tag topological structure is maintained and allows for flexible shape design where users can customize internal and external marker shapes. Code for our marker design/generation, marker detection, and dataset are available at http://herohuyongtao.github. io/research/publications/topo-tag/.
On Flying Backwards: Preventing Run-away of Small, Low-speed, Fixed-wing UAVs in Strong Winds

Small, low-speed fixed-wing Unmanned Aerial Vehicles (UAVs) operating autonomously, beyond-visual-line-of-sight (BVLOS) will inevitably encounter winds rising to levels near or exceeding the vehicles’ nominal airspeed. In this paper, we develop a nonlinear lateral-directional path following guidance law with explicit consideration of online wind estimates. Energy efficient airspeed reference compensation logic is developed for excess wind scenarios (i.e. when the wind speed rises above the airspeed), enabling either mitigation, prevention, or over-powering of excess wind induced run-away from a given path. The developed guidance law is demonstrated on a representative small, low-speed test UAV in two flight experiments conducted in mountainous regions of Switzerland with strong, turbulent wind conditions, gusts reaching up to 13 meters per second. We demonstrate track-keeping errors of less than 1 meter consistently maintained during a representative duration of gusting, excess winds and a mean ground speed undershoot of 0.5 meters per second from the commanded minimum forward ground speed demonstrated in over 5 minutes of the showcased flight results.
Impact of ion motion on atom-ion confinement-induced resonances in hybrid traps

We investigate confinement-induced resonances in atom-ion quantum mixtures confined in hybrid traps. Specifically, we consider an ion confined in a time-dependent radio-frequency Paul trap with linear geometry, while the atom is constrained to move into a quasi-one-dimensional optical waveguide within the ion trap. We evaluate the impact of the ion intrinsic micromotion on the resonance position. Thus, we solve the atom-ion dynamics semiclassically, namely the atom dynamics is governed by the three-dimensional time-dependent Schrodinger equation, whereas the ion motion is described by the classical Hamilton equations. We find that the energy of the ion provided by the oscillating radiofrequency fields can affect the resonance position substantially. Notwithstanding, the peculiar phenomenology of those resonances regarding perfect transmission and reflection is still observable. These findings indicate that the intrinsic micromotion of the ion is not detrimental for the occurrence of the resonance and that its position can be controlled by the radiofrequency fields. This provides an additional mean for tuning atom-ion interactions in low spatial dimensions. The study represents an important advancement in the scattering physics of compound atomic quantum systems in time-dependent traps.
Sequentially Aggregated Convolutional Networks

Modern deep networks generally implement a certain form of shortcut connections to alleviate optimization difficulties. However, we observe that such network topology alters the nature of deep networks. In many ways, these networks behave similarly to aggregated wide networks. We thus exploit the aggregation nature of shortcut connections at a finer architectural level and place them within wide convolutional layers. We end up with a sequentially aggregated convolutional (SeqConv) layer that combines the benefits of both wide and deep representations by aggregating features of various depths in sequence. The proposed SeqConv serves as a drop-in replacement of regular wide convolutional layers and thus could be handily integrated into any backbone network. We apply SeqConv to widely adopted backbones including ResNet and ResNeXt, and conduct experiments for image classification on public benchmark datasets. Our ResNet based network with a model size of ResNet-50 easily surpasses the performance of the 2.35× larger ResNet-152, while our ResNeXt based model sets a new state-of-the-art accuracy on ImageNet classification for networks with similar model complexity. The code and pre-trained models of our work are publicly available at https://github.com/GroupOfAlchemists/SeqConv.
Robust UOWC systems against bubble-induced impairments via transmit/receive diversities [Invited]

We systematically investigate the bubble-induced performance degradation for underwater wireless optical communication (UOWC) with different bubble sizes and positions. By using different transmit and receive diversities, we investigate the effectiveness of transmit/receive diversity on the mitigation of the bubble-induced impairment to the UOWC link. With the help of a 2 by 2 MIMO using repetition coding (RC) and maximum ratio combining (MRC), a robust 780-Mbit/s UOWC transmission is achieved. The corresponding outage probability can be significantly reduced from 34.6% for the system without diversity to less than 1%.
Topology of the icosidodecahedral arrangement.

The icosidodecahedral arrangement is introduced by M. Yoshinaga (arXiv:1902.06256) as the first known example that is a hyperplane arrangement whose Milnor fiber has torsions in first integral homology. In this note, we prove that the icosidodecahedral arrangement is $K(\pi,1)$, hence so is its Milnor fiber.
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting

When randomized ensemble methods such as bagging and random forests are implemented, a basic question arises: Is the ensemble large enough? In particular, the practitioner desires a rigorous guarantee that a given ensemble will perform nearly as well as an ideal infinite ensemble (trained on the same data). The purpose of the current paper is to develop a bootstrap method for solving this problem in the context of regression --- which complements our companion paper in the context of classification (Lopes 2019). In contrast to the classification setting, the current paper shows that theoretical guarantees for the proposed bootstrap can be established under much weaker assumptions. In addition, we illustrate the flexibility of the method by showing how it can be adapted to measure algorithmic convergence for variable selection. Lastly, we provide numerical results demonstrating that the method works well in a range of situations.