Miracle-dz/UniFAR-FTUs huggingface dataset:
An improved bag of words method for appearance based visual loop closure detection

Loop closure detection is an essential component of many robotics applications such as SLAM (Simultaneous Localization and Mapping) and place recognition. This paper presents an appearance based loop closure detection algorithm based on bag of words method and inverse depth of feature words. Our proposed approach represent the feature points and descriptors in the images as the visual words according to the off-line generated visual directory to simplify the similarity comparison between the images. For the evaluation criteria, on the basis of tf-idf scores of visual words, the inverse depth of words are also introduced into the process of loop closure detection. Considering the temporal consistency of image sequences, the co-visibility graphs are also applied to verify the real loop from all candidates. At last, some experiment results are showed and analyzed to illustrate the feasibility and performance of our algorithm in different situations and environments.
Nonintrusive Load Monitoring Algorithm Using Mixed-Integer Linear Programming

This paper presents a nonintrusive load monitoring (NILM) algorithm based on mixed-integer linear programming. The formulation deals with the problem of multiple switching that arises when disaggregating individual appliance’s consumptions from a compound power measurement. Mixed-integer linear constraints are used to efficiently represent the load signatures of each appliance. Also, a window-based strategy is used to enhance the computational performance of the proposed NILM algorithm. The disaggregation can be made using only active power measurements at a low sampling rate, which is available in most energy meters. Moreover, if available, other signatures can be added to the model to improve its accuracy, such as reactive power signatures or harmonics. The performance of the algorithm is evaluated using three test cases from the almanac of minutely power dataset. The proposed method is also compared with a disaggregation method called aided linear integer programming. Results demonstrate the ability of the proposed method to accurately identify and allocate individual energy signatures in a computationally efficient manner, which makes it suitable for inexpensive home energy management.
Ultra-fast tracking control of high-order discrete-time multi-agent systems with H∞ performance specification

This paper focuses on the ultra-fast tracking control for high-order discrete-time multi-agent systems with H∞ performance specification. Under an undirected graph, it is certified that the ultra-fast tracking control problem of multi-agent system with a certain H∞ performance specification is equivalent to ultra-fast tracking control problems of N subsystems with decomposed H∞ performance specifications. Moreover, a novel control protocol with the predictive information is proposed to solve the ultra-fast tracking control problems for the above subsystems. Some sufficient conditions for ultra-fast tracking control design are given herein. A simulation is performed to illustrate the effectiveness of the theoretical results.
A microstrip patch antenna operation on cylindrical structures based on silicon rubber

In this paper, a patch antenna on the flexible substrate is designed for many wireless applications especially Internet of Things (IoT). The paper presents the design of a microstrip patch antenna on the flexible substrate operated at 2.45GHz ISM band. In order to have better flexibility, a thin silicon rubber is used as a substrate. First, the antenna is designed on the planar silicon rubber. Then, the silicon rubber is bended on the cylindirical structures which have different diameters. Numerical results show that the resonance frequency of the antenna changes with bending the silicon rubber. The antenna simulations were performed in CST software. Simulation results suggest that silicone rubber material may be used for angled surface of the antenna.
A greedy algorithm for sparse unmixing

Hyperspectral imaging sensors provide image data containing both spatial and detailed spectral information. However, due to low spatial resolution, the pixels in hyperspectral images are actually mixtures of the spectral signatures of the materials. Sparse unmixing assumes that these mixed pixels are sparse linear combinations of different material spectra which are in a spectral library. However, spectral libraries contain materials that have similar spectral characteristics. That's why greedy algorithms may choose incorrect material from the spectral library that is similar to the material in the mixed pixel in the first iteration. This leads to incorrect material selections in the subsequent iterations. In this study, an orthogonal matching pursuit (OMP) variant method is proposed to deal with this issue. Our proposed method is compared to OMP-Star and SunGP methods. Experiments on simulated and real hyperspectral data show that satisfactory results have been achieved by using the proposed method.
Global Scale Dissemination of ST93: A Divergent Staphylococcus aureus Epidemic Lineage That Has Recently Emerged From Remote Northern Australia

Background: In Australia, community-associated methicillin-resistant Staphylococcus aureus (MRSA) lineage sequence type (ST) 93 has rapidly risen to dominance since being described in the early 1990s. We examined 459 ST93 genome sequences from Australia, New Zealand, Samoa, and Europe to investigate the evolutionary history of ST93, its emergence in Australia and subsequent spread overseas. Results: Comparisons with other S. aureus genomes indicate that ST93 is an early diverging and recombinant lineage, comprising of segments from the ST59/ST121 lineage and from a divergent but currently unsampled Staphylococcal population. However, within extant ST93 strains limited genetic diversity was apparent with the most recent common ancestor dated to 1977 (95% highest posterior density 1973–1981). An epidemic ST93 population arose from a methicillin-susceptible progenitor in remote Northern Australia, which has a proportionally large Indigenous population, with documented overcrowded housing and a high burden of skin infection. Methicillin-resistance was acquired three times in these regions, with a clade harboring a staphylococcal cassette chromosome mec (SCCmec) IVa expanding and spreading to Australia’s east coast by 2000. We observed sporadic and non-sustained introductions of ST93-MRSA-IVa to the United Kingdom. In contrast, in New Zealand, ST93-MRSA-IVa was sustainably transmitted with clonal expansion within the Pacific Islander population, who experience similar disadvantages as Australian Indigenous populations. Conclusion: ST93 has a highly recombinant genome including portions derived from an early diverging S. aureus population. Our findings highlight the need to understand host population factors in the emergence and spread of antimicrobial resistant community pathogens.
Exploring the Crosstalk Between LMNA and Splicing Machinery Gene Mutations in Dilated Cardiomyopathy

Mutations in the LMNA gene, which encodes for the nuclear lamina proteins lamins A and C, are responsible for a diverse group of diseases known as laminopathies. One type of laminopathy is Dilated Cardiomyopathy (DCM), a heart muscle disease characterized by dilation of the left ventricle and impaired systolic function, often leading to heart failure and sudden cardiac death. LMNA is the second most commonly mutated gene in DCM. In addition to LMNA, mutations in more than 60 genes have been associated with DCM. The DCM-associated genes encode a variety of proteins including transcription factors, cytoskeletal, Ca2+-regulating, ion-channel, desmosomal, sarcomeric, and nuclear-membrane proteins. Another important category among DCM-causing genes emerged upon the identification of DCM-causing mutations in RNA binding motif protein 20 (RBM20), an alternative splicing factor that is chiefly expressed in the heart. In addition to RBM20, several essential splicing factors were validated, by employing mouse knock out models, to be embryonically lethal due to aberrant cardiogenesis. Furthermore, heart-specific deletion of some of these splicing factors was found to result in aberrant splicing of their targets and DCM development. In addition to splicing alterations, advances in next generation sequencing highlighted the association between splice-site mutations in several genes and DCM. This review summarizes LMNA mutations and splicing alterations in DCM and discusses how the interaction between LMNA and splicing regulators could possibly explain DCM disease mechanisms.
Distributed representations of bids in lowest unique bid auctions

In this paper, we study multi-item lowest unique bid auctions (LUBA) with resubmission under budget constraints. A representation of bids that can perceive the rich strategic meaning in the world of multi-item LUBA with resubmission is learned by an efficient method-Skip-gram model. We created and maintained a dataset which contains the relevant information of LUBA, including the winning bid combination, budget constraints, and the number of participated bidders. An Android-based application is developed and contributes the construction of the dataset. The quantitative analysis displays that the representation cluster can reflect similarities in terms of numerical information, cultural and aesthetic preference, and other bidder's behaviors. The learned representation can serve as a guide for bidders who seek to maximize their payoff and feed into a sequence generation model, such as recurrent neural network, to produce the bids combination.
Improvement of industrial system identification by outlier detection

This paper focuses on improving data environment in the context of industrial process control. Considering the negative influence of outliers in target set, this paper proposes a novel outlier detection method named triSVDD, where three varying SVDD models are built successively. With data processed by triSVDD, performance of data-based techniques dedicating to system identification can be improved significantly. A realistic application, i.e. Transonic Wind Tunnel system is used to verify the significance of triSVDD in terms of system identification. Finally, performance with respect to only outlier detection is also investigated and compared with two well-known competitors.
Facial expression recognition with FHOG features

Facial expression recognition is a popular computer vision subject that has many applications such as humancomputer interaction and behavior analysis. As for many computer vision problems, lighting and contrast differences increase the difficulty of the problem. Especially the non-planar structure of the face, protruding regions such as the nose and chin and recessed regions such as eye sockets cause variations in lighting. Another problem with facial expression recognition problems is that the multi-scale detection methods do not align the faces accurately. This leads to comparing features that are extracted from different facial regions, which degrades performance. FHOG features are a contrast-sensitive variation of histogram oriented gradients (HOG) features, which perform well at object detection applications. In this study, the performance of FHOG features at facial expression recognition is investigated. Additionally, aligning with respect to the facial landmarks is proposed to prevent performance degradation due to misalignment. The proposed method is shown to deliver 93% accuracy in facial expression recognition in the extended Cohn-Kanade dataset.
Application-agnostic remote access for Bluetooth Low Energy

Today, the smart devices based on short-range wireless communication technologies such as Bluetooth or ZigBee are an important part of the Internet of Things (IoT). Such devices are not reachable outside of their Wireless Personal Area Network (WPAN). However, users' needs are sometimes incompatible with this limited reachability. Our goal is to break this barrier and provide location-independent connectivity. In this paper, we introduce a new abstraction layer, called RCM (Remote Connection Manager) enabling transparent communication between an application and a remote short-range device. We implemented our solution for Bluetooth Low Energy technology and demonstrated its feasibility through experiments performed on real devices.
Delayed data offloading based on full-duplex D2D communications in a cellular network

In this paper, we propose a new delayed data offloading scheme by exploiting full-duplex (FD) device-to-device (D2D) communications. To this end, we consider a scenario in which a common file is requested by a subset of users in different times and with different maximum tolerable delays. Moreover, a maximum specific bandwidth is dedicated for data transmission. In order to send the file in the specific bandwidth, we use multicasting alongside FD D2D communications. Thanks to the fact that users have different deadlines, we divide them into non-overlapping groups, i.e., coalitions, to receive the file in distinct intervals. In order to guarantee the tolerable delay of all users, a coalition formation method is proposed to find efficient cooperation strategy for users. Furthermore, FD communications enables us to schedule multi-hop transmissions at each coalition. To evaluate the performance of the proposed offloading method, we compare the time-integrated bandwidth of our proposed scheme with the case that no D2D communications is permitted.
A novel dual-band rectifier circuit with enhanced bandwidth for RF energy harvesting applications

In recent years, a rapid development of low-power sensor networks, enabling machine-to-machine communication in applications such as environmental monitoring, has been observed. Contemporary sensors are normally supplied by an external power source, typically in a form of a battery, which limits their lifespan and increases the maintenance costs. This problem can be addressed by harvesting and converting ambient RF energy into DC power. In the paper, a novel dual-band rectifier circuit with high efficiency and enhanced bandwidth for RF energy harvesting applications is proposed along with its design procedure. The rectifier consists of two branches fed through a junction and two voltage doublers with a common DC output. The proposed structure is designed to work in 0.6 GHz to 1 GHz and 1.75 GHz to 2.45 GHz ranges with an average RF- to-DC conversion efficiency of at least 50 percent. Compact dimensions of 19 mm × 17 mm (a footprint of only 345 mm2) have been obtained through appropriate folding of the impedance transformers. As demonstrated, the proposed design outperforms state-of-the-art rectifiers in terms of the operational bandwidth, efficiency, and the range of acceptable load impedances.
Regression-based speech enhancement by convolutional neural network

In this study, a regression-based convolutional neural network (CNN) model is proposed for speech enhancement. The main purpose is to remove the noise on the conversations. A babble noise is added to the speech samples of different persons and samples with different signal to noise ratio (SNR) are obtained. The logarithmic power spectrum (LPS) coefficients of noisy and clean speech signal samples are calculated. Then a regression model is established between the convolutional neural network and the logarithmic power spectrum coefficients of noisy and clean speech. The results are evaluated by perceptual evaluation of speech quality (PESQ) and short time objective intelligibility (STOI). The results are presented in tabular form.
Research review on control strategies of energy storage inverter

This paper introduces the control strategy of energy storage inverter. Firstly, it briefly expounds the background and significance of the research on energy storage inverter's control strategies. Then this paper briefly introduces the current situation of energy storage inverter and its control at home and abroad. It focuses on several basic control strategies at the microgrid level and the energy storage inverter level, finally, the existing problems and future development trend of the research on energy storage inverter are pointed out.
Uncovering hidden social relationships through location-based services: The Happn case study

Every day we publish an impressive amount of information about ourselves online: Pictures, videos, tweets, posts, and check-ins. These (big) data tell the story of our public lives and, at first, are seemingly innocuous. Nonetheless, they reveal a great deal of information about the things that we wished to remain private too. The goal of this work is to demonstrate how an attacker can exploit these data to jeopardize our privacy. In particular, we focus on Happn, a location-based dating app that counts millions of users worldwide. Happn's goal is to let us find other users in our surroundings. It shows us their first name, age, and gender, their pictures, and their short bio. In addition, it tells us their approximate distance from our current location. By exploiting this information we show how an attacker (a stalker, a data broker, an advertisement company, etc.) can expose the private lives of Happn users and pinpoint their geographic position accurately and in real-time. In fact, we demonstrate how he can get even more detailed knowledge about the users by targeting all the population from a specific region (a city or a country) to discover their daily routines, where they work, where they live, and where they have fun. Finally, we show how to find out the social relationships between the Happn users: Uncovering who their friends, colleagues, relatives, and flatmates are.
A lane keeping system with a weighted preview measurement

This paper proposes a new lane keeping system (LKS) based on a weighted preview measurement. The paper first identifies shortcomings of existing methods that use the sole measurement of lateral displacement error at a preview distance or at the center of gravity. Then, the novel idea of computing a weighted average of both measurements is proposed. The stability of the resulting LKS is analyzed and the improved performance of the resulting LKS is supported by simulation experiments.
Consecutive crystallographic reorientations and superplasticity in body-centered cubic niobium nanowires

Niobium nanowires show superplasticity through the close synergy of multiple deformation modes with consecutive reorientations. Plasticity of metallic nanowires is often controlled by the activities of single deformation mode. It remains largely unclear whether multiple deformation modes can be activated in an individual metallic nanowire and how much plasticity they can contribute. In situ nanomechanical testing reveals a superior plastic deformation ability of body-centered cubic (BCC) niobium nanowires, in which a remarkable elongation of more than 269% is achieved before fracture. This superplastic deformation originates from a synergy of consecutively nucleated multiple reorientation processes that occur for more than five times via three distinct mechanisms, that is, stress-activated phase transformation, deformation twinning, and slip-induced crystal rotation. These three coupled mechanisms work concurrently, resulting in sequential reorientations and therefore superplastic deformation of Nb nanowires. Our findings reveal a superior mechanical property of BCC Nb nanowires through the close coordination of multiple deformation modes, which may have some implications in other metallic nanowire systems.
Agile IoT service communication and orchestration platform using event driven SOA paradigm

Large scale IoT applications involve cross-domain or even cross-organizational users, business processes and physical entities, which requires to integrate multiple distributed applications to achieve certain business goals. This paper proposed an event-driven and service-oriented agile IoT services communication and orchestration platform, which can easily support the sensory data on-demand distribution and realize event-driven dynamic collaboration across services domains and even across organizations. It also can greatly enhance the flexibility and agility to response quickly to the dynamic changes in the physical world in a timely manner. Finally, shows the demo for Internet of heating network in smart city.
Long-Lasting Alterations in Gene Expression of Postsynaptic Density 95 and Inotropic Glutamatergic Receptor Subunit in the Mesocorticolimbic System of Rat Offspring Born to Morphine-Addicted Mothers

Prenatal exposure to morphine causes altered glutamatergic neurotransmission, which plays an important pathophysiological role for neurobiological basis of opiate-mediated behaviors in such offspring. However, it is still not clear whether such alteration involves gene expression of ionotropic glutamate receptor subunits. In this study, we further studied whether prenatal morphine exposure resulted in long-term changes in the gene expression of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor, N-methyl-d-aspartate (NMDA) receptor, and postsynaptic density 95 in the mesocorticolimbic area (an essential integration circuitry for drug craving behavior), nucleus accumbens (NAc), ventral tegmental area (VTA), and prefrontal cortex (PFC), of rat offspring from morphine-addicted mothers. Experimental results showed that prenatal morphine exposure led to a persistent downregulation of gene expression in the AMPA and NMDA receptor subunit, with a differential manner of decreased magnitudes, at the age of postnatal days 14 (P14) and P30. However, in PFC, the gene expression of the AMPA receptor subunit was not synchronized in observed rat offspring subjected to prenatal morphine exposure. An upregulation of gene expression in the AMPA receptor subunit 3 (GluR3) was persistently observed at P14 and P30. Furthermore, the gene expressions of PSD-95 in NAc, VTA, and PFC were all decreased concurrently. Collectively, the results suggest that prenatal exposure to morphine may initiate molecular mechanisms leading to a long-lasting, differential alteration in gene expression of the inotropic glutamate receptor subunit and PSD-95 in the mesocorticolimbic circuitry in rat offspring. This study raises a possibility in which differential changes in gene expression with a long-lasting manner may play a role for the development of nearly permanent changes in opiate-mediated behaviors, at least in part for the neurobiological pathogenesis in offspring.
Modeling Semantic Encoding in a Common Neural Representational Space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models.