Miracle-dz/UniFAR-FTUs huggingface dataset:
A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph

Many visual simultaneous localization and mapping (SLAM) systems have been shown to be accurate and robust, and have real-time performance capabilities on both indoor and ground datasets. However, these methods can be problematic when dealing with aerial frames captured by a camera mounted on an unmanned aerial vehicle (UAV) because the flight height of the UAV can be difficult to control and is easily affected by the environment. For example, the UAV may be shaken or experience a rapid drop in height due to sudden strong wind, which may in turn lead to lost tracking. What is more, when photographing a large area, the UAV flight path is usually planned in advance and the UAV does not generally return to the previously covered areas, so if the tracking fails during the flight, many areas of the map will be missing. To cope with the case of lost tracking, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph. Specifically, submaps are repeatedly generated, from the initialization process to the place where the tracking is lost, and a corresponding undirected connected graph is built by considering these submaps as nodes and the common map points within two submaps as edges. The common map points are then determined by the bag-of-words (BoW) method, and the submaps are merged if they are found to be connected with the online map in the undirect connected graph. To demonstrate the performance of the proposed method, we first investigated the performance on a UAV dataset, and the experimental results showed that, in the case of several tracking failures, the integrity of the mapping was significantly better than that of the current mainstream SLAM method. We also tested the proposed method on both ground and indoor datasets, where it again showed a superior performance.
On the Use of Interpretable Machine Learning for the Management of Data Quality

Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process data. IoT devices are connected to Edge Computing (EC) nodes to report the collected data, thus, we have to secure data quality not only at the IoT but also at the edge of the network. In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based for any data processing activity. Our aim is to secure data quality, at least, for those features that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining in every dataset, thus, they can be adopted for dimensionality reduction. We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. Our scheme is capable of timely retrieving the final result and efficiently select the appropriate features. We evaluate our model through extensive simulations and present numerical results. Our aim is to reveal its performance under various experimental scenarios that we create varying a set of parameters adopted in our mechanism.
Intersectional Affirmative Action Policies for Top-k Candidates Selection

We study the problem of selecting the top-k candidates from a pool of applicants, where each candidate is associated with a score indicating his/her aptitude. Depending on the specific scenario, such as job search or college admissions, these scores may be the results of standardized tests or other predictors of future performance and utility. We consider a situation in which some groups of candidates experience historical and present disadvantage that makes their chances of being accepted much lower than other groups. In these circumstances, we wish to apply an affirmative action policy to reduce acceptance rate disparities, while avoiding any large decrease in the aptitude of the candidates that are eventually selected. Our algorithmic design is motivated by the frequently observed phenomenon that discrimination disproportionately affects individuals who simultaneously belong to multiple disadvantaged groups, defined along intersecting dimensions such as gender, race, sexual orientation, socio-economic status, and disability. In short, our algorithm's objective is to simultaneously: select candidates with high utility, and level up the representation of disadvantaged intersectional classes. This naturally involves trade-offs and is computationally challenging due to the the combinatorial explosion of potential subgroups as more attributes are considered. We propose two algorithms to solve this problem, analyze them, and evaluate them experimentally using a dataset of university application scores and admissions to bachelor degrees in an OECD country. Our conclusion is that it is possible to significantly reduce disparities in admission rates affecting intersectional classes with a small loss in terms of selected candidate aptitude. To the best of our knowledge, we are the first to study fairness constraints with regards to intersectional classes in the context of top-k selection.
Investigation of Phase Distortion on Perceived Speech Quality for Hearing-impaired Listeners

Phase serves as a critical component of speech that influences the quality and intelligibility. Current speech enhancement algorithms are beginning to address phase distortions, but the algorithms focus on normal-hearing (NH) listeners. It is not clear whether phase enhancement is beneficial for hearing-impaired (HI) listeners. We investigated the influence of phase distortion on speech quality through a listening study, in which NH and HI listeners provided speech-quality ratings using the MUSHRA procedure. In one set of conditions, the speech was mixed with babble noise at 4 different signal-to-noise ratios (SNRs) from -5 to 10 dB. In another set of conditions, the SNR was fixed at 10 dB and the noisy speech was presented in a simulated reverberant room with T60s ranging from 100 to 1000 ms. The speech level was kept at 65 dB SPL for NH listeners and amplification was applied for HI listeners to ensure audibility. Ideal ratio masking (IRM) was used to simulate speech enhancement. Two objective metrics (i.e., PESQ and HASQI) were utilized to compare subjective and objective ratings. Results indicate that phase distortion has a negative impact on perceived quality for both groups and PESQ is more closely correlated with human ratings.
Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning

Reinforcement learning algorithms can show strong variation in performance between training runs with different random seeds. In this paper we explore how this affects hyperparameter optimization when the goal is to find hyperparameter settings that perform well across random seeds. In particular, we benchmark whether it is better to explore a large quantity of hyperparameter settings via pruning of bad performers, or if it is better to aim for quality of collected results by using repetitions. For this we consider the Successive Halving, Random Search, and Bayesian Optimization algorithms, the latter two with and without repetitions. We apply these to tuning the PPO2 algorithm on the Cartpole balancing task and the Inverted Pendulum Swing-up task. We demonstrate that pruning may negatively affect the optimization and that repeated sampling does not help in finding hyperparameter settings that perform better across random seeds. From our experiments we conclude that Bayesian optimization with a noise robust acquisition function is the best choice for hyperparameter optimization in reinforcement learning tasks.
NuSTAR View of TeV Blazar Mrk 501

We report the results of flux and spectral variability studies of all seven Nuclear Spectroscopic Telescope Array (NuSTAR) observations of TeV γ-ray emitting blazar Markarian (or Mrk) 501. We found strong evidence of intraday variability in 3–79 keV X-ray light curves (LCs) of Mrk 501 during four out of these seven observations. We examined spectral variability using a model-independent hardness-ratio analysis and found a general “harder-when-brighter” behaviour in two observations. We also investigated the nature of 3–79 keV X-ray spectra of TeV blazar Mrk 501 and found that five out of seven spectra are well described by the curved log-parabola models with photon indices (at 10 keV) α∼ 2.12–2.32 and a curvature β∼ 0.15–0.28. The two other spectra are somewhat better represented by simple power-law models with photon indices 2.70 and 2.75. We briefly discuss available physical models to explain our results.
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning

The Generative Adversarial Networks (GAN) framework is a well-established paradigm for probability matching and realistic sample generation. While recent attention has been devoted to studying the theoretical properties of such models, a full theoretical understanding of the main building blocks is still missing. Focusing on generative models with Optimal Transport metrics as discriminators, in this work we study how the interplay between the latent distribution and the complexity of the pushforward map (generator) affects performance, from both statistical and modelling perspectives. Motivated by our analysis, we advocate learning the latent distribution as well as the pushforward map within the GAN paradigm. We prove that this can lead to significant advantages in terms of sample complexity.
A survey for variable young stars with small telescopes – III. Warm spots on the active star V1598 Cyg

Magnetic spots on low-mass stars can be traced and characterized using multiband photometric light curves. Here, we analyse an extensive data set for one active star, V1598 Cyg, a known variable K dwarf which is either pre-main sequence and/or in a close binary system. Our light curve contains 2854 photometric data points, mostly in V, Rc, Ic, but also in U, B, and Hα, with a total baseline of about 4 yr, obtained with small telescopes as part of the HOYS project. We find that V1598 Cyg is a very fast rotator with a period of 0.8246 d and varying amplitudes in all filters, best explained as a signature of strong magnetic activity and spots. We fit the photometric amplitudes in V, Rc, Ic and use them to estimate spot properties, using a grid-based method that is also propagating uncertainties. We verify the method on a partial data set with high cadence and all five broad-band filters. The method yields spot temperatures and fractional spot coverage with typical uncertainties of 100 K and 3–4 per cent, respectively. V1598 Cyg consistently exhibits spots that are a few hundred degrees warmer than the photosphere, most likely indicating that the light curve is dominated by chromospheric plage. The spot activity varies over our observing baseline, with a typical time-scale of 0.5–1 yr, which we interpret as the typical spot lifetime. Combining our light curve with archival data, we find a six year cycle in the average brightness, that is probably a sign of a magnetic activity cycle.
Efficient and Privacy-Preserving Online Fingerprint Authentication Scheme over Outsourced Data

With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric data bring out increasing concerns due to the highly sensitivity of biometric data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted outsourced data. In the proposed e-Finga scheme, the user’s fingerprint registered in trust authority can be outsourced to different servers with user’s authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology for secure euclidean distance calculation to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode data in the outsourcing scenarios. Through detailed security analysis, we show that e-Finga can resist various security threats. In addition, we implement e-Finga over a workstation with a real fingerprint database, and extensive simulation results demonstrate that the proposed e-Finga scheme can serve efficient and accurate online fingerprint authentication.
Quickest Detection of Moving Anomalies in Sensor Networks

The problem of sequentially detecting a moving anomaly is studied, in which the anomaly affects different parts of a sensor network over time. Each network sensor is characterized by a pre- and post-change distribution. Initially, the observations of each sensor are generated according to the corresponding pre-change distribution. After some <italic>unknown</italic> but <italic>deterministic</italic> time instant, a moving anomaly emerges, affecting different sets of sensors as time progresses. Our goal is to design a stopping procedure to detect the emergence of the anomaly as quickly as possible, subject to <italic>false alarms</italic> constraints. The problem is studied in a quickest change detection framework where it is assumed that the evolution of the anomaly is <italic>unknown</italic> but <italic>deterministic</italic>. A modification of Lorden’s detection delay is proposed to account for the trajectory of the anomaly that maximizes the detection delay of a detection procedure. It is established that a <italic>Cumulative Sum</italic>-type test solves the resulting sequential detection problem exactly when the sensors are <italic>homogeneous</italic>. For the case of <italic>heterogeneous</italic> sensors, the proposed detection scheme can be modified to provide a first-order asymptotically optimal algorithm.
High-resolution tomography for galaxy spectroscopic surveys with angular redshift fluctuations

In the context of next-generation spectroscopic galaxy surveys, new statistics of the distribution of matter are currently being developed. Among these, we investigated the angular redshift fluctuations (ARF), which probe the information contained in the projected redshift distribution of galaxies. Relying on the Fisher formalism, we show how ARF will provide complementary cosmological information compared to traditional angular galaxy clustering. We tested both the standard ΛCDM model and the wCDM extension. We find that the cosmological and galaxy bias parameters express different degeneracies when inferred from ARF or from angular galaxy clustering. As such, combining both observables breaks these degeneracies and greatly decreases the marginalised uncertainties by a factor of at least two on most parameters for the ΛCDM and wCDM models. We find that the ARF combined with angular galaxy clustering provide a great way to probe dark energy by increasing the figure of merit of the w0 − wa parameter set by a factor of more than ten compared to angular galaxy clustering alone. Finally, we compared ARF to the CMB lensing constraints on the galaxy bias parameters. We show that a joint analysis of ARF and angular galaxy clustering improves constraints by ∼40% on galaxy bias compared to a joint analysis of angular galaxy clustering and CMB lensing.
Malicious Node Detection in MANET

A mobile ad hoc network consists of mobile wireless nodes. MANET is a self organized and self configurable network where the mobile nodes move arbitrarily. The mobile nodes can receive and forward packets as a router. Routing is a critical issue in MANET.In this paper a new scheme has been proposed for security. A comparative study for the performance of the protocols has been done varying in pause time using Network Simulator-2. Performances of the protocols have been compared by taking three metrics as; Packet Delivery Ratio, End to End Delay and Throughput.
Biomedical and clinical English model packages for the Stanza Python NLP library

Abstract Objective The study sought to develop and evaluate neural natural language processing (NLP) packages for the syntactic analysis and named entity recognition of biomedical and clinical English text. Materials and Methods We implement and train biomedical and clinical English NLP pipelines by extending the widely used Stanza library originally designed for general NLP tasks. Our models are trained with a mix of public datasets such as the CRAFT treebank as well as with a private corpus of radiology reports annotated with 5 radiology-domain entities. The resulting pipelines are fully based on neural networks, and are able to perform tokenization, part-of-speech tagging, lemmatization, dependency parsing, and named entity recognition for both biomedical and clinical text. We compare our systems against popular open-source NLP libraries such as CoreNLP and scispaCy, state-of-the-art models such as the BioBERT models, and winning systems from the BioNLP CRAFT shared task. Results For syntactic analysis, our systems achieve much better performance compared with the released scispaCy models and CoreNLP models retrained on the same treebanks, and are on par with the winning system from the CRAFT shared task. For NER, our systems substantially outperform scispaCy, and are better or on par with the state-of-the-art performance from BioBERT, while being much more computationally efficient. Conclusions We introduce biomedical and clinical NLP packages built for the Stanza library. These packages offer performance that is similar to the state of the art, and are also optimized for ease of use. To facilitate research, we make all our models publicly available. We also provide an online demonstration (http://stanza.run/bio).
SAFER: Sparse secure Aggregation for FEderated leaRning

Federated learning enables one to train a common machine learning model across separate, privately-held datasets via distributed model training. During federated training, only intermediate model parameters are transmitted to a central server which aggregates these parameters to create a new common model, thus exposing only intermediate parameters rather than the training data itself. However, some attacks (e.g. membership inference) are able to infer properties of local data from these intermediate model parameters. Hence, performing the aggregation of these client-specific model parameters in a secure way is required. Additionally, the communication cost is often the bottleneck of the federated systems, especially for large neural networks. So, limiting the number and the size of communications is necessary to efficiently train large neural architectures. In this article, we present an efficient and secure protocol for performing secure aggregation over compressed model updates in the context of collaborative, few-party federated learning, a context common in the medical, healthcare, and biotechnical use-cases of federated systems. By making compression-based federated techniques amenable to secure computation, we develop a secure aggregation protocol between multiple servers with very low communication and computation costs and without preprocessing overhead. Our experiments demonstrate the efficiency of this new approach for secure federated training of deep convolutional neural networks.
Towards Cooperative Transport of a Suspended Payload via Two Aerial Robots with Inertial Sensing

This paper addresses the problem of cooperative transport of a point mass hoisted by two aerial robots. Treating the robots as a leader and a follower, the follower stabilizes the system with respect to the leader using only feedback from its Inertial Measurement Units (IMU). This is accomplished by neglecting the acceleration of the leader, analyzing the system through the generalized coordinates or the cables’ angles, and employing an observation model based on the IMU measurements. A lightweight estimator based on an Extended Kalman Filter (EKF) and a controller are derived to stabilize the robot-payload-robot system. The proposed methods are verified with extensive flight experiments, first with a single robot and then with two robots. The results show that the follower is capable of realizing the desired quasi-static trajectory using only its IMU measurements. The outcomes demonstrate promising progress towards the goal of autonomous cooperative transport of a suspended payload via small flying robots with minimal sensing and computational requirements.
Simultaneously Learning Corrections and Error Models for Geometry-Based Visual Odometry Methods

This letter fosters the idea that deep learning methods can be used to complement classical visual odometry pipelines to improve their accuracy and to associate uncertainty models to their estimations. We show that the biases inherent to the visual odometry process can be faithfully learned and compensated for, and that a learning architecture associated with a probabilistic loss function can jointly estimate a full covariance matrix of the residual errors, defining an error model capturing the heteroscedasticity of the process. Experiments on autonomous driving image sequences assess the possibility to concurrently improve visual odometry and estimate an error associated with its outputs.
How do different spiral arm models impact the ISM and GMC population?

The nature of galactic spiral arms in disc galaxies remains elusive. Regardless of the spiral model, arms are expected to play a role in sculpting the star-forming interstellar medium (ISM). As such, different arm models may result in differences in the structure of the ISM and molecular cloud properties. In this study, we present simulations of galactic discs subject to spiral arm perturbations of different natures. We find very little difference in how the cloud population or gas kinematics vary between the different grand design spirals, indicating that the ISM on cloud scales cares little about where spiral arms come from. We do, however, see a difference in the interarm/arm mass spectra, and minor differences in tails of the distributions of cloud properties (as well as radial variations in the stellar/gaseous velocity dispersions). These features can be attributed to differences in the radial dependence of the pattern speeds between the different spiral models, and could act as a metric of the nature of spiral structure in observational studies.
Stochastic approximation algorithms for superquantiles estimation

This paper is devoted to two dierent two-time-scale stochastic ap- proximation algorithms for superquantile estimation. We shall investigate the asymptotic behavior of a Robbins-Monro estimator and its convexied version. Our main contribution is to establish the almost sure convergence, the quadratic strong law and the law of iterated logarithm for our estimates via a martingale approach. A joint asymptotic normality is also provided. Our theoretical analysis is illustrated by numerical experiments on real datasets.