Miracle-dz/UniFAR-FTUs huggingface dataset:
Change Detection between Multimodal Remote Sensing Data Using Siamese CNN

Detecting topographic changes in the urban environment has always been an important task for urban planning and monitoring. In practice, remote sensing data are often available in different modalities and at different time epochs. Change detection between multimodal data can be very challenging since the data show different characteristics. Given 3D laser scanning point clouds and 2D imagery from different epochs, this paper presents a framework to detect building and tree changes. First, the 2D and 3D data are transformed to image patches, respectively. A Siamese CNN is then employed to detect candidate changes between the two epochs. Finally, the candidate patch-based changes are grouped and verified as individual object changes. Experiments on the urban data show that 86.4\% of patch pairs can be correctly classified by the model.
Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

This study demonstrates the feasibility of proactive received power prediction by leveraging spatiotemporal visual sensing information towards reliable millimeter-wave (mmWave) networks. As the received power on a mmWave link can attenuate aperiodically owing to human blockages, a long-term series of the future received power cannot be predicted by analyzing the received signals prior to the blockage occurring. We propose a novel mechanism that predicts the time series of received power from the next moment to as many as several hundred milliseconds ahead. The key idea is to leverage camera imagery and machine learning (ML). Time-sequential images may involve the spatial geometry and mobility of obstacles representing mmWave signal propagation. ML is used to construct a prediction model from a dataset of sequential images labeled with received power in several hundred milliseconds ahead of the time at which each image is obtained. The simulation and experimental evaluations conducted using IEEE 802.11ad devices and a depth camera demonstrated that the proposed mechanism employing convolutional long short-term memory predicted a time series of received power up to 500 ms ahead, with an inference time of less than 3 ms and a root-mean-square error of 3.4 dB.
SAR Imagery for Detecting Sea Surface Slicks: Performance Assessment of Polarization-Dependent Parameters

Remote sensing technology is an essential link in the global monitoring of the ocean surface, and radars are efficient sensors for detecting marine pollution. When used operationally by authorities, a tradeoff must usually be made between the covered area and the quantity of information collected by the radar. To identify the most appropriate imaging mode, a methodology based on receiver operating characteristic curve analysis has been applied to an original data set collected by two airborne systems operating at L-band, both characterized by a very low instrument noise floor. The data set was acquired during controlled releases of mineral and vegetable oil at sea. Various polarization-dependent quantities are investigated, and their ability to detect slick-covered areas is assessed. A relative ordering of the main polarimetric parameters is reported in this paper. When the sensor has a sufficiently low noise floor, HV is recommended because it provides the strongest slick–sea contrast. Otherwise, VV is found to be the most relevant parameter for detecting slicks on the ocean surface. Among all the investigated quad-polarimetric settings, no significant added value compared to single-polarized data was found. More specifically, it is demonstrated, by increasing the instrument noise level, that the studied polarimetric quantities which combine the four polarimetric channels have performances of detection mainly driven by the instrument noise floor, namely, the noise equivalent sigma zero. This result, obtained by progressively adding noise to the raw synthetic aperture radar (SAR) data, indicates that the polarimetric discrimination between clean sea and polluted area results mainly from the differentiated behavior between single-bounce scattering and noise. It is thus demonstrated, using SAR data collected with a low instrument noise floor, that there is no deviation from Bragg scattering for radar scattering from ocean surface covered by mineral and vegetable oil.
Computational Studies on the Suppression of Parasitic Oscillations in Gyrotron Beam Tunnels

Parasitic oscillations frequently appear in several modern gyrotron beam tunnels. In this paper, the CST Studio Suite is used to study the performance of dielectric-loaded beam tunnel designs in attenuating the spurious modes. Two common designs are considered and the effect of the ceramic material’s dielectric constant on the beam tunnel’s performance is studied. For this purpose, the ohmic quality factor <inline-formula> <tex-math notation="LaTeX">${Q}_{\textsf {ohm}}$ </tex-math></inline-formula> and the normalized shunt impedance <inline-formula> <tex-math notation="LaTeX">${R}/{Q}$ </tex-math></inline-formula> are calculated to measure this performance. These studies focus on the backward propagating axisymmetric parasitic modes.
A Statistical Approach to Error Correction for isiZulu Spellcheckers

Spellcheckers have become important due to the increase of text-based communication at work and in society on social media. There is, however, very little support for spellchecking in agglutinating Sub-Saharan African (Bantu) languages. While error detection has shown to yield acceptable results for at least isiZulu, error correction has not even been investigated. The aim of this paper is to solve the spelling correction problem by means of a statistical approach such that it can provide candidate corrections to misspelled isiZulu words (non-word errors). Trigrams learned from a corpus, their probabilities, minimum edit distance, and additional optimisations are used in the error corrector. The corrector was evaluated for the four types of non-word errors (substitution, insertions, deletions, and transpositions). It achieved an 89% language recall rate, 84% error recall, 85% language precision, and 88% error precision for error correction. The error corrector was found to have an overall suggestions accuracy rate of 95% and relevance of 61%, performing best for transposition errors. The error corrector has been added to an existing open source isiZulu error detector. This facilitates uptake and, moreover, fills a feature gap that has numerous benefits for society, both for isiZulu speakers and learners, and for bootstrapping spellcheckers for related languages.
Maritime Moving Target Localization Using Passive GNSS-Based Multistatic Radar

This paper examines the potential in extracting the instantaneous location of maritime moving targets using a passive multistatic radar with Global Navigation Satellite Systems (GNSS) as illuminators of opportunity and a single receiver. This paper presents a theoretical framework for the localization of a moving target from a set of bistatic range measurements. The algorithm and its predicted accuracy are presented. The localization is achieved by what is essentially a multilateration technique, which can be applied while the transmitting platform is also in motion. The algorithms and the accuracy predictions, as a function of the number of transmitters, have been experimentally confirmed via a dedicated experimental campaign, where two different maritime targets were detected by up to 12 GNSS satellites belonging to different satellite constellations (GPS, GLONASS, and Galileo) simultaneously. To the best of the author’s knowledge, these are the first results of their kind and on this scale not only for GNSS-based passive radar but also for multistatic radar in general.
Roles of Three HSF Domain-Containing Proteins in Mediating Heat-Shock Protein Genes and Sustaining Asexual Cycle, Stress Tolerance, and Virulence in Beauveria bassiana

Heat-shock transcription factors (HSFs) with a HSF domain are regulators of fungal heat-shock protein (HSP) genes and many others vectoring heat-shock elements, to which the domain binds in response to heat shock and other stress cues. The fungal insect pathogen Beauveria bassiana harbors three HSF domain-containing orthologous to Hsf1, Sfl1, and Skn7 in many fungi. Here, we show that the three proteins are interrelated at transcription level, play overlapping or opposite roles in activating different families of 28 HSP genes and mediate differential expression of some genes required for asexual developmental and intracellular Na+ homeostasis. Expression levels of skn7 and sfl1 largely increased in Δhsf1, which is evidently lethal in some other fungi. Hsf1 was distinct from Sfl1 and Skn7 in activating most HSP genes under normal and heat-shocked conditions. Sfl1 and Skn7 played overlapping roles in activating more than half of the HSP genes under heat shock. Each protein also activated a few HSP genes not targeted by two others under certain conditions. Deletion of sfl1 resulted in most severe growth defects on rich medium and several minimal media at optimal 25°C while such growth defects were less severe in Δhsf1 and minor in Δskn7. Conidiation level was lowered by 76% in Δskn7, 62% in Δsfl1, and 39% in Δhsf1. These deletion mutants also showed differential changes in cell wall integrity, antioxidant activity, virulence and cellular tolerance to osmotic salt, heat shock, and UV-B irradiation. These results provide a global insight into vital roles of Hsf1, Sfl1, and Skn7 in B. bassiana adaptation to environment and host.
Commutation for Functions of Small Arity Over a Finite Set

Commutation is defined for multi-variable functions on a finite base set. For a set F of functions the centralizer F* of F is the set of functions which commute with all functions in F. For a function f a minor of f is a function obtained from f by iden- tifying some of its variables. An important observation is that the centralizer f* of f is a subclone of the centralizer of any minor of f, which motivates the study of the centralizers of functions of small arity. In this paper we determine the centralizers of all 2-variable functions over the two-element set. Then, as a generalization of AND on the 2-element set we consider the function Min on the k-element set, k > 1, and characterize the centralizer of Min using a term from lattice theory.
MRI-Based Brain Volumetry at a Single Time Point Complements Clinical Evaluation of Patients With Multiple Sclerosis in an Outpatient Setting

Purpose: Thalamic atrophy and whole brain atrophy in multiple sclerosis (MS) are associated with disease progression. The motivation of this study was to propose and evaluate a new grouping scheme which is based on MS patients' whole brain and thalamus volumes measured on MRI at a single time point. Methods: In total, 185 MS patients (128 relapsing-remitting (RRMS) and 57 secondary-progressive MS (SPMS) patients) were included from an outpatient facility. Whole brain parenchyma (BP) and regional brain volumes were derived from single time point MRI T1 images. Standard scores (z-scores) were computed by comparing individual brain volumes against corresponding volumes from healthy controls. A z-score cut-off of −1.96 was applied to separate pathologically atrophic from normal brain volumes for thalamus and whole BP (accepting a 2.5% error probability). Subgroup differences with respect to the Symbol Digit Modalities Test (SDMT) and the Expanded Disability Status Scale (EDSS) were assessed. Results: Except for two, all MS patients showed either no atrophy (group 0: 61 RRMS patients, 10 SPMS patients); thalamic but no BP atrophy (group 1: 37 RRMS patients; 18 SPMS patients) or thalamic and BP atrophy (group 2: 28 RRMS patients; 29 SPMS patients). RRMS patients without atrophy and RRMS patients with thalamic atrophy did not differ in EDSS, however, patients with thalamus and BP atrophy showed significantly higher EDSS scores than patients in the other groups. Conclusion: MRI-based brain volumetry at a single time point is able to reliably distinguish MS patients with isolated thalamus atrophy (group 1) from those without brain atrophy (group 0). MS patients with isolated thalamus atrophy might be at risk for the development of widespread atrophy and disease progression. Since RRMS patients in group 0 and 1 are clinically not distinguishable, the proposed grouping may aid identification of RRMS patients at risk of disease progression and thus complement clinical evaluation in the routine patient care.
The influence of action-effect anticipation on bistable perception: differences between onset rivalry and ambiguous motion

Abstract Perception is strongly shaped by the actions we perform. According to the theory of event coding, and forward models of motor control, goal-directed action preparation activates representations of desired effects. These expectations about the precise stimulus identity of one’s action-outcomes (i.e. identity predictions) are thought to selectively influence perceptual processing of action-contingent effects. However, the existing evidence for such identity-prediction effects is scarce and mixed. Here, we developed a new paradigm to capture such effects and examined whether action-outcome predictions can bias the perception of binocular onset rivalry (Experiments 1a and 1b) and bistable motion (Experiment 2). Participants performed learning tasks in which they were exposed to action-outcome associations. On test trials, actions were followed by bistable stimuli that could be perceived as being either congruent or incongruent with the aforementioned associations (i.e. rivalrous oriented gratings in Experiments 1a and 1b and spheres with ambiguous rotation directions in Experiment 2). Across three experiments, we show that, whilst exposure to action-effect associations can bias the apparent motion direction of ambiguous spheres, it fails to influence perceptual selection of grating orientations in binocular onset rivalry. This pattern of results extends previous work on ambiguous motion by demonstrating that action-induced modulations do not generalize to all types of bistable percepts.
Simultaneous Carriage of mcr-1 and Other Antimicrobial Resistance Determinants in Escherichia coli From Poultry

The use of antimicrobial growth promoters (AGPs) in sub-therapeutic doses for long periods promotes the selection of resistant microorganisms and the subsequent risk of spreading this resistance to the human population and the environment. Global concern about antimicrobial resistance development and transference of resistance genes from animal to human has been rising. The goal of our research was to evaluate the susceptibility pattern to different classes of antimicrobials of colistin-resistant Escherichia coli from poultry production systems that use AGPs, and characterize the resistance determinants associated to transferable platforms. E. coli strains (n = 41) were obtained from fecal samples collected from typical Argentine commercial broiler farms and susceptibility for 23 antimicrobials, relevant for human or veterinary medicine, was determined. Isolates were tested by PCR for the presence of mcr-1, extended spectrum β-lactamase encoding genes and plasmid-mediated quinolone resistance (PMQR) coding genes. Conjugation and susceptibility patterns of the transconjugant studies were performed. ERIC-PCR and REP-PCR analysis showed a high diversity of the isolates. Resistance to several antimicrobials was determined and all colistin-resistant isolates harbored the mcr-1 gene. CTX-M-2 cefotaximase was the main mechanism responsible for third generation cephalosporins resistance, and PMQR determinants were also identified. In addition, co-transference of the qnrB determinant on the mcr-1-positive transconjugants was corroborated, which suggests that these resistance genes are likely to be located in the same plasmid. In this work a wide range of antimicrobial resistance mechanisms were identified in E. coli strains isolated from the environment of healthy chickens highlighting the risk of antimicrobial abuse/misuse in animals under intensive production systems and its consequences for public health.
Soundscape of an Archaeological Site Recreated with Audio Augmented Reality

This article investigates the use of an audio augmented reality (AAR) system to recreate the soundscape of a medieval archaeological site. The aim of our work was to explore whether it is possible to enhance a tourist's archaeological experience, which is often derived from only scarce remains. We developed a smartphone-based AAR system, which uses location and orientation sensors to synthesize the soundscape of a site and plays it to the user via headphones. We recreated the ancient soundscape of a medieval archaeological site in Croatia and tested it in situ on two groups of participants using the soundwalk method. One test group performed the soundwalk while listening to the recreated soundscape using the AAR system, while the second control group did not use the AAR equipment. We measured the experiences of the participants using two methods: the standard soundwalk questionnaire and affective computing equipment for detecting the emotional state of participants. The results of both test methods show that participants who were listening to the ancient soundscape using our AAR system experienced higher arousal than those visiting the site without AAR.
Frame Theory for Optimal Sensor Augmentation Problem of AOA Localization

The sensor–target placement plays a pivotal role in improving the localization accuracy; the commonly used evaluation function for this problem is built by Cramer–Rao lower bound (CRLB) or Fisher information matrix (FIM). Frame theory, which was developed recently, can provide a more easily tractable evaluation function, especially for some complex cases. In this letter, we derive an evaluation function by frame theory and prove its equivalence with that by CRLB and FIM. For angle-of-arrival based localization system, we present the optimal sensor augmentation problem with several fixed sensors and an arbitrary number of new sensors, and for arbitrary but fixed distances between new sensors and target. The conclusions are verified by several simulations.
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution

Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1)the neural architectures found are solely optimized for high predictive performance, without penalizing excessive resource consumption, (2) most architecture search methods require vast computational resources. We address the first shortcoming by proposing LEMONADE, an evolutionary algorithm for multi-objective architecture search that allows approximating the entire Pareto-front of architectures under multiple objectives, such as predictive performance and number of parameters, in a single run of the method. We address the second shortcoming by proposing a Lamarckian inheritance mechanism for LEMONADE which generates children networks that are warmstarted with the predictive performance of their trained parents. This is accomplished by using (approximate) network morphism operators for generating children. The combination of these two contributions allows finding models that are on par or even outperform both hand-crafted as well as automatically-designed networks.
An Empirical Study on Network Anomaly Detection Using Convolutional Neural Networks

Deep learning has been widely applied to network anomaly detection to improve performance. In our past research, we empirically evaluated a set of deep learning models, including Fully Connected Network (FCN), Variational Auto Encoder (VAE), and Sequence to Sequence model with Long Short-Term Memory (Seq2Seq-LSTM), for network anomaly detection. Additionally, we evaluate Convolution Neural Networks (CNNs) for network anomaly detection in this paper. We set up three simple CNN models with different internal depths (shallow CNN, moderate CNN, and deep CNN) to see the impact of the depth to the performance. We evaluate the models using three different types of traffic datasets. Our experimental results show that deeper structures do not make any performance improvement. In addition, we observed that the evaluated CNN models occasionally outperform the VAE models, but do not work better than the other deep learning models based on FCN and Seq2Seq-LSTM.
TATC: Predicting Alzheimer's Disease with Actigraphy Data

With the increase of elderly population, Alzheimer's Disease (AD), as the most common cause of dementia among the elderly, is affecting more and more senior people. It is crucial for a patient to receive accurate and timely diagnosis of AD. Current diagnosis relies on doctors' experience and clinical test, which, unfortunately, may not be performed until noticeable AD symptoms are developed. In this work, we present our novel solution named time-aware TICC and CNN (TATC), for predicting AD from actigraphy data. TATC is a multivariate time series classification method using a neural attention-based deep learning approach. It not only performs accurate prediction of AD risk, but also generates meaningful interpretation of daily behavior pattern of subjects. TATC provides an automatic, low-cost solution for continuously monitoring the change of physical activity of subjects in daily living environment. We believe the future deployment of TATC can benefit both doctors and patients in early detection of potential AD risk.
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation

Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon. A large body of previous research studied long-term recommendation through dimensionality reduction techniques applied to the historical user-item interactions. A recently introduced session-based recommendation setting highlighted the importance of modeling short-term user preferences. In this task, Recurrent Neural Networks (RNN) have shown to be successful at capturing the nuances of user's interactions within a short time window. In this paper, we evaluate RNN-based models on both short-term and long-term recommendation tasks. Our experimental results suggest that RNNs are capable of predicting immediate as well as distant user interactions. We also find the best performing configuration to be a stacked RNN with layer normalization and tied item embeddings.
GRAINS

We present a generative neural network that enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently. Our key observation is that indoor scene structures are inherently hierarchical. Hence, our network is not convolutional; it is a recursive neural network, or RvNN. Using a dataset of annotated scene hierarchies, we train a variational recursive autoencoder, or RvNN-VAE, which performs scene object grouping during its encoding phase and scene generation during decoding. Specifically, a set of encoders are recursively applied to group 3D objects based on support, surround, and co-occurrence relations in a scene, encoding information about objects’ spatial properties, semantics, and relative positioning with respect to other objects in the hierarchy. By training a variational autoencoder (VAE), the resulting fixed-length codes roughly follow a Gaussian distribution. A novel 3D scene can be generated hierarchically by the decoder from a randomly sampled code from the learned distribution. We coin our method GRAINS, for Generative Recursive Autoencoders for INdoor Scenes. We demonstrate the capability of GRAINS to generate plausible and diverse 3D indoor scenes and compare with existing methods for 3D scene synthesis. We show applications of GRAINS including 3D scene modeling from 2D layouts, scene editing, and semantic scene segmentation via PointNet whose performance is boosted by the large quantity and variety of 3D scenes generated by our method.