Miracle-dz/UniFAR-FTUs huggingface dataset:
Interference Management in NOMA-Based Fog-Radio Access Networks via Scheduling and Power Allocation

This paper analyzes the integration of Non-Orthogonal Multiple Access (NOMA) in a Fog Radio Access Network (FRAN) architecture with limited fronthaul capacity. More precisely, it proposes methods for optimizing the resource allocation for the downlink of a NOMA-based FRAN with multiple resource blocks (RB). The resource allocation problem is formulated as a mixed-integer optimization problem, which determines the user-to-RB assignment, the power allocated to each RB, and the power split levels of the NOMA users served by each RB. The optimization problem maximizes a network-wide rate-based utility function subject to fronthaul-capacity constraints. The paper proposes a feasible decoupled solution for such a non-convex optimization problem using a three-step hybrid centralized/distributed approach, which in part relies on the edge-devices computation capabilities. The paper proposes and compares two distinct methods for solving the assignment problem, namely a Hungarian-based method, and a Multiple Choice Knapsack-based method. The power allocation to RBs and the NOMA power split optimization are solved using the alternating direction method of multipliers (ADMM). Simulations results illustrate the advantages of the proposed methods compared to different baseline schemes, including the conventional Orthogonal Multiple Access (OMA), for different utility functions and different network environments.
Toward preserving privacy and functionality in geosocial networks

Storing user friend lists, preferences and messages, online social networks have become a significant source of sensitive personal information. A recent addition to this space, geosocial networks (GSNs) such as Yelp [1] or Foursquare [2], collect even user locations, through check-ins performed by users at visited venues. Overtly, personal information allows GSN providers to offer a variety of applications, including personalized recommendations and targeted advertising, and venue owners to promote their businesses through spatio-temporal incentives (e.g., rewarding frequent customers through accumulated badges). Providing personal information exposes however users to significant risks, as social networks have been shown to leak [3] and even sell [4] user data to third parties. There exists therefore a conflict. Without privacy people may be reluctant to use geosocial networks; without user information the provider and venues cannot support applications and have no incentive to participate. In this work we take first steps toward breaking this deadlock, by introducing the concept of location centric profiles (LCPs), aggregate statistics built from the profiles of users that have visited a certain location. As we know, location privacy has been extensively studied before [5]. This work significantly extends the state of the art by (i) providing constructs that preserve the privacy of users when reporting private profile information (e.g., age, gender, location), and (ii) ensuring that the solutions enable providers to collect information needed to develop existing services. We introduce ProfilR , a framework that allows the construction of LCPs based on the profiles of present users, while ensuring the privacy and correctness of participants. To relieve the GSN provider from costly involvement in venue specific activities, ProfilR stores and builds LCPs at venues.
DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

In the following we provide additional results, insights and visualizations for DeepLSD. Section A describes in details our network architecture, Section B introduces additional ablation studies and insights about our approach, Section C provides an evaluation of visual localization with points and lines on the full 7Scenes dataset, Section D gives additional results about vanishing point estimation from the detected line segments, Section E displays visualizations of the 3D reconstruction, Section F highlights some limitations of our method, and finally Section G offers examples of the line detections.
Accelerated Sampling Kaczmarz Motzkin Algorithm for Linear Feasibility Problem

The Sampling Kaczmarz-Motzkin (SKM) algorithm is a generalized method for solving large-scale linear system of inequalities. Having its root in the relaxation method of Agmon, Motzkin and the randomized Kaczmarz method, SKM outperforms the state-of-the-art methods in solving large-scale linear feasibility problems. Motivated by SKM’s success, in this work, we propose an Accelerated Sampling Kaczmarz-Motzkin (ASKM) algorithm which achieves better convergence compared to the standard SKM algorithm on ill conditioned problems. We provide a thorough convergence analysis for the proposed accelerated algorithm. We validate the convergence analysis with a set of numerical experiments on randomly generated linear system of inequalities.
Source Language Monolingual Data Target Language Monolingual Data Bilingual Word Embeddings Bilingual Phrase Embeddings Source Phrases Target Phrases Phrase Table Induction ( Section 3 . 1 ) Induced Phrase

Recent work achieved remarkable results in training neural machine translation (NMT) systems in a fully unsupervised way, with new and dedicated architectures that rely on monolingual corpora only. In this work, we propose to define unsupervised NMT (UNMT) as NMT trained with the supervision of synthetic bilingual data. Our approach straightforwardly enables the use of state-of-the-art architectures proposed for supervised NMT by replacing human-made bilingual data with synthetic bilingual data for training. We propose to initialize the training of UNMT with synthetic bilingual data generated by unsupervised statistical machine translation (USMT). The UNMT system is then incrementally improved using back-translation. Our preliminary experiments show that our approach achieves a new state-of-the-art for unsupervised machine translation on the WMT16 German– English news translation task, for both translation directions.
Text Style Transfer: A Review and Experiment Evaluation

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has burgeoned because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models and provide a comprehensive summary of the state of the art. We review the existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study where we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field.
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line

Leveraging line features to improve location accuracy of point-based visual-inertial SLAM (VINS) is gaining importance as they provide additional constraint of scene structure regularity, however, real-time performance has not been focused. This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line, developed based on state-of-the-art point-based VINS-Mono \cite{vins}. Observe that current works use LSD \cite{lsd} algorithm to extract lines, however, the LSD is designed for scene shape representation instead of specific pose estimation problem, which becomes the bottleneck for the real-time performance due to its expensive cost. In this work, a modified LSD algorithm is presented by studying hidden parameter tuning and length rejection strategy. The modified LSD can run three times at least as fast as the LSD. Further, by representing a line landmark with Plucker coordinate, the line reprojection residual is modeled as midpoint-to-line distance then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Plucker coordinate. Experiments in public EuRoc benchmark dataset show the location error of our method is down 12-16\% compared to VINS-Mono at the same work frequency on a low-power CPU @1.1 GHz without GPU parallelization. For the benefit of the community, we make public the source code: \textit{this https URL
Actionneurs hydrauliques, Commande

1 Modele de conduite 1.1 Equations de base, linearisation - reduction 1.1.1 Modulateur de puissance 1.1.2 Equation de continuite 1.1.3 Equation dynamique 1.2 Modele de conduite 1.2.1 Fonction de transfert 1.2.2 Modele d'etat 1.3 Analogie electrique. Considerations sur la non-linearite 1.3. 1 Non-linearite du distributeur de puissance 1.3. 2 Non-linearite de position 1.3. 3 Cas des verins non symetriques 2 Commande en position 2.1 Commande proportionnelle pure 2.1.1 Analyse 2.1.2 Effet des non-linearites 2.2 Correcteurs pour l'asservissement de position 2.2.1 Conception optimale 2.2.2 Actions de correction au niveau organique 2.2. 3 Correcteurs electriques type serie 2.2. 4 Correcteurs electriques type parallele 2.2. 5 Correcteurs electriques par anticipation 2.2. 6 Influence des elasticites parasites 2.2. 7 Commande non-lineaire 3 Commande en vitesse 4 Commande en effort 4.1 Modele de conduite 4.2 Commande proportionnelle 4.3 Charge bloquee 4.4 Correction Pour en savoir plus
Analysis of Commutativity with state-chart graph representation of concurrent programs

We present a new approach to check for Commutativity in concurrent programs from their runtime state-chart graphs. Two operations are said to be commutative if changing the order of their execution do not change the resultant effect on the working object. Commutative property is capable of boosting performance in concurrent transactions such that transactional concurrency is comparable to a non-blocking linearizable version of a similar data structure type. Transactional concurrency is a technique that analyses object semantics, as object states, to determine conflicts and recovery between conflicting operations [7]. Processes that commute at object level can be executed concurrently at transaction level without conflicting with one another. In our approach we generate graphs by tracking run-time execution of concurrent program and representing object states in all possible thread interleavings as states and transitions. Using state-chart notations, we capture the object states at each execution step and compare their states before and after transitions as processed by a known set of operations reordered in different ways. Considering the non-deterministic nature of concurrent programs, we exhaustively capture program states during method invocation, method response, atomic instruction execution, etc., for determining commutativity. This helps user to examine state transitions at a thread level granularity, across all possible interleavings. With this methodology user can not only verify commutativity, but also can visually check ways in which functions commute at object level, which is an edge over current state-of-the art tools. The object level commutative information helps in identifying faulty implementations and performance improving considerations. We use a graph database to represent state nodes that further assists to check for other concurrency properties using cypher queries.
The Green Bank North Celestial Cap Survey. VIII. 21 New Pulsar Timing Solutions

We present timing solutions for 21 pulsars discovered in 350 MHz surveys using the Green Bank Telescope (GBT). All were discovered in the Green Bank North Celestial Cap pulsar survey, with the exception of PSR J0957−0619, which was found in the GBT 350 MHz Drift-scan pulsar survey. The majority of our timing observations were made with the GBT at 820 MHz. With a spin period of 37 ms and a 528 days orbit, PSR J0032+6946 joins a small group of five other mildly recycled wide binary pulsars, for which the duration of recycling through accretion is limited by the length of the companion’s giant phase. PSRs J0141+6303 and J1327+3423 are new disrupted recycled pulsars. We incorporate Arecibo observations from the NANOGrav pulsar timing array into our analysis of the latter. We also observed PSR J1327+3423 with the Long Wavelength Array, and our data suggest a frequency-dependent dispersion measure. PSR J0957−0619 was discovered as a rotating radio transient, but is a nulling pulsar at 820 MHz. PSR J1239+3239 is a new millisecond pulsar (MSP) in a 4 days orbit with a low-mass companion. Four of our pulsars already have published timing solutions, which we update in this work: the recycled wide binary PSR J0214+5222, the noneclipsing black widow PSR J0636+5128, the disrupted recycled pulsar J1434+7257, and the eclipsing binary MSP J1816+4510, which is in an 8.7 hr orbit with a redback-mass companion.
Geometric multipole expansion and its application to neutral inclusions of general shape

The field perturbation induced by an elastic or electrical inclusion admits a multipole expansion in terms of the outgoing potential functions. In the classical expansion, basis functions are defined independently of the inclusion. In this paper, we introduce the new concept of the geometric multipole expansion for the two-dimensional conductivity problem (or, equivalently, anti-plane elasticity problem) of which basis functions are associated with the inclusion's geometry; the coefficients of the expansion are denoted by the Faber polynomial polarization tensors (FPTs). In the derivation we use the series expansion for the complex logarithm by the Faber polynomials that are associated with the exterior conformal mapping of the inclusion. The virtue of the proposed expansion is that one can express the field perturbation in a simple series form for an inclusion of arbitrary shape. Regarding the computation of the exterior conformal mapping, one can use the integral formula for the conformal mapping coefficients obtained in \cite{2018JLconductivity}. As an application, we construct multi-coated neutral inclusions of general smooth shape that have negligible perturbation for low-order polynomial loadings. These neutral inclusions are layered structures composed of level curves of an exterior conformal mapping; material parameters in each layer are determined such that the FPTs vanish or are small for low-order terms. We provide numerical examples to validate the results.
Evidencias psicométricas de la escala de autoestima de Rosenberg en adolecentes limeños

La Investigacion Tuvo Como Objetivos · · · · · · · · · analizar la Estructura interna de la Escala de Autoestima de Rosenberg (EAR) y comprobar v vv vv vv vv EL FUNCIONAMIENTO diferencial de los items de Acuerdo al sexo. Se DESARROLLO instrumentales estudio de las Naciones Unidas En Dos Muestras, La Primera conformada POR 450 (226 mujeres y 224 Varones) y La Segunda POR 481 (225 mujeres y 256) Varones adolescentes peruanos pertenecientes a Instituciones Educativas Publicas y Privadas de Lima Metropolitana. Se evaluo la Estructura interna Mediante el Analisis factorial exploratorio y confirmatorio, asimismo, se efectuo el Analisis de Consistencia interna, Parr Que Todos Los Modelos bueno FUE (H> 0,80). Por Otro Lado, se estimo el FUNCIONAMIENTO diferencial de las Naciones Unidas Traves de la invarianza factorial. Los Resultados Indican Que la EAR Debe Ser interpretada DE UN Acuerdo modelo bidimensional de la ONU, encontrandose ONU do Vez invarianza Fuerte parcial, debiendo liberarse los Puntos 1, 5 y 10, sugiriendo la Base de Borrarse el Punto 8. En La Unidad ONU Los de Resultados del Estudio, la EAR Cuenta con Evidencia empirica de Validez, Confiabilidad y sen FUNCIONAMIENTO invariante de algunos adj adj Articulos adj.
Multispectral AreaHyperspectral Area Training Phase Testing Phase Joint Feature Representation Classification Feature Stack Hyperspectral Out-of-samples Multispectral Out-of-samples Visualizing Learned Features on Training Data Visualizing Learned Features on Testing Data FJFL Learned Mapping Multis

This is the pre-acceptance version, to read the final version please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. With a large amount of open satellite multispectral imagery (e.g., Sentinel-2 and Landsat-8), considerable attention has been paid to global multispectral land cover classification. However, its limited spectral information hinders further improving the classification performance. Hyperspectral imaging enables discrimination between spectrally similar classes but its swath width from space is narrow compared to multispectral ones. To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification. By locally aligning the manifold structure of the two modalities, CoSpace linearly learns a shared latent subspace from hyperspectral-multispectral (HSMS) correspondences. The multispectral out-of-samples can be then projected into the subspace, which are expected to take advantages of rich spectral information of the corresponding hyperspectral data used for learning, and thus leads to a better classification. Extensive experiments on two simulated HSMS datasets (University of Houston and Chikusei), where HSMS data sets have trade-offs between coverage and spectral resolution, are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
Continuous blood pressure recordings simultaneously with functional brain imaging: studies of the glymphatic system

The lymph system is responsible for cleaning the tissues of metabolic waste products, soluble proteins and other harmful fluids etc. Lymph flow in the body is driven by body movements and muscle contractions. Moreover, it is indirectly dependent on the cardiovascular system, where the heart beat and blood pressure maintain force of pressure in lymphatic channels. Over the last few years, studies revealed that the brain contains the so-called glymphatic system, which is the counterpart of the systemic lymphatic system in the brain. Similarly, the flow in the glymphatic system is assumed to be mostly driven by physiological pulsations such as cardiovascular pulses. Thus, continuous measurement of blood pressure and heart function simultaneously with functional brain imaging is of great interest, particularly in studies of the glymphatic system. We present our MRI compatible optics based sensing system for continuous blood pressure measurement and show our current results on the effects of blood pressure variations on cerebral brain dynamics, with a focus on the glymphatic system. Blood pressure was measured simultaneously with near-infrared spectroscopy (NIRS) combined with an ultrafast functional brain imaging (fMRI) sequence magnetic resonance encephalography (MREG, 3D brain 10 Hz sampling rate).
Mat Mul Linear Linear Linear Scale SoftMax Mat

Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT’14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU.1
Policy URL Found ✓ “ Is Policy ? ” Classifier “ Is Policy ? ” Classifier

Every new privacy regulation brings along the question of whether it results in improving the privacy for the users or whether it creates more barriers to understanding and exercising their rights. The EU General Data Protection Regulation (GDPR) is one of the most demanding and comprehensive privacy regulations of all time. Hence, a few months after it went into effect, it is natural to study its impact over the landscape of privacy policies online. In this work, we conduct the first longitudinal, in-depth, and at-scale assessment of privacy policies before and after the GDPR. We gauge the complete consumption cycle of these policies, from the first user impressions until the compliance assessment. We create a diverse corpus of 3,086 English-language privacy policies for which we fetch the pre-GPDR and the post-GDPR versions. Via a user study with 530 participants on Amazon Mechanical Turk, we discover that the visual presentation of privacy policies has slightly improved in limited data-sensitive categories in addition to the top European websites. We also find that the readability of privacy policies suffers under the GDPR, due to almost a 30% more sentences and words, despite the efforts to reduce the reliance on passive sentences. We further develop a new workflow for the automated assessment of requirements in privacy policies, building on automated natural language processing techniques. We first use that workflow to perform ambiguity assessment of the policies’ content. We find evidence for positive changes triggered by the GDPR, with the ambiguity level, averaged over 8 metrics, improving in over 20.5% of the policies. Finally, we show that privacy policies cover more data practices, particularly around data retention, user access rights, and specific audiences, and that an average of 15.2% of the policies improved across 8 compliance metrics. Our analysis, however, reveals a large gap that exists between the current status quo and the ultimate goals of the GDPR. Thomas Linden: University of Wisconsin, E-mail: tlinden2@wisc.edu Hamza Harkous: École Polytechnique Fédérale de Lausanne, E-mail: hamza.harkous@gmail.com *Corresponding Author: Kassem Fawaz: University of Wisconsin, E-mail: kfawaz@wisc.edu
Back to Square One: Bias Detection, Training and Commonsense Disentanglement in the Winograd Schema

The Winograd Schema (WS) has been proposed as a test for measuring commonsense capabilities of models. Recently, pre-trained language model-based approaches have boosted performance on some WS benchmarks but the source of improvement is still not clear. We begin by showing that the current evaluation method of WS is sub-optimal and propose a modiﬁcation that makes use of twin sentences for evaluation. We also propose two new baselines that indicate the existence of biases in WS benchmarks. Finally, we propose a method for evaluating WS-like sentences in a zero-shot setting and observe that popular language models perform randomly in this setting. We conclude that much of the apparent progress on WS may not necessarily reﬂect progress in commonsense reasoning, but much of it comes from supervised data, which is not likely to account for all the required common-sense reasoning skills and knowledge.