Miracle-dz/UniFAR-FTUs huggingface dataset:
Progressive Large-Scale Structure-from-Motion with Orthogonal MSTs

Pairwise image matching plays a vital role in Structure-from-Motion (SfM). Though the image-retrieval method accelerates the matching process, the number of neighbors is usually hard to determine. Insufficient feature matches could break the completeness of reconstructed scene, while redundant pairs may bring in many erroneous ones. In this paper, we propose a progressive SfM method to tackle the completeness, robustness and efficiency problems in a united framework, where two loops are contained. The outer loop is a feature matching loop, where the orthogonal MSTs (maximum spanning trees) of the image similarity graph is iteratively selected to perform the image matching. The inner loop is an incremental camera calibration loop, where the initial camera poses in each iteration are inherited from those calibrated in the last one. By progressively performing the image matching and calibration, we find both loops converge fast and a large number of redundant pairs are excluded. Experiments demonstrate the superior performance of our method in terms of both efficiency and robustness on various image datasets, and our method also has a large potential to tackle the ambiguity problems in SfM.
Computer vision system for fire detection and report using UAVs

Continuous technological progress has led to great changes in our society. UAVs (Unnamed Aerial Vehicles), commonly known as drones, are one of the most significant technological advancements of the last decade. UAVs offer a wide variety of new possibilities and have become a tool that is used on an everyday basis. UAVs can be used in fire control due to their ability to manoeuvre rapidly and their wide range of operation. This article presents a review of the main uses of UAVs in combatting fire. Special emphasis is placed on fire detection techniques using computer and infrared computer techniques, as well as the hardware systems that drones must incorporate to perform this task. This article also presents a simple proposal for fire detection and alert using UAVs and computer vision.
Utilizing Information from Task-Independent Aspects via GAN-Assisted Knowledge Transfer

Observed data often have multiple labels with respect to different aspects. For example, a picture can have one label specifying the contents in terms of the object category such as aeroplane, building, cat, etc. and in the meanwhile have another label describing the image style such as photo-realistic or artistic. The central idea of this work is that any annotation of the data contains precious knowledge and is not to be foregone: an analytic task focusing on one aspect of the data can benefit from the knowledge transferred from the other aspects. We propose a passive knowledge transfer scheme for deep neural network training based on the generative adversarial nets (GANs). The adversarial training scheme encourages the nets to encode data into representations that are both discriminative for the target aspect and invariant with respect to the irrelevant aspects. We show that the scheme mixes the conditional distributions of the encoded data on the irrelevant aspects, by the theory on the link between the GAN framework and the Wasserstein metric in distribution spaces. Moreover, we empirically verified the method by i) classifying images despite influence by geometric transform and ii) recognizing the movements (geometric transform) regardless the image contents.
A Compressive Sensing Based Multi-user Detection Algorithm for SIMa-NOMA Systems

Multi-User Detection (MUD) for uplink grant-free Non-orthogonal Multiple Access (NOMA) has received much attention recently. In this paper, we consider the scenario in which a Base Station (BS) is equipped with multiple antennas, and propose a Compressive Sensing-based Hard Fusion Algorithm (CS-HFA) to realize MUD. More explicitly, we detect the user activity information by a conventional CS algorithm at each antenna, and then amalgamate the detected user activity information to derive an active user set. Finally, we can use the obtained active user set to estimate the active users' data. The proposed CS- HFA with low complexity for uplink grant-free Single-Input Multiple-Output Non-Orthogonal Multiple Access (SIMO-NOMA) systems can achieve better BER performance than traditional CS-based MUD in the NOMA system with single antenna at the BS.
Metrological Characterization of a Vision-Based System for Relative Pose Measurements with Fiducial Marker Mapping for Spacecrafts

An improved approach for the measurement of the relative pose between a target and a chaser spacecraft is presented. The selected method is based on a single camera, which can be mounted on the chaser, and a plurality of fiducial markers, which can be mounted on the external surface of the target. The measurement procedure comprises of a closed-form solution of the Perspective from n Points (PnP) problem, a RANdom SAmple Consensus (RANSAC) procedure, a non-linear local optimization and a global Bundle Adjustment refinement of the marker map and relative poses. A metrological characterization of the measurement system is performed using an experimental set-up that can impose rotations combined with a linear translation and can measure them. The rotation and position measurement errors are calculated with reference instrumentations and their uncertainties are evaluated by the Monte Carlo method. The experimental laboratory tests highlight the significant improvements provided by the Bundle Adjustment refinement. Moreover, a set of possible influencing physical parameters are defined and their correlations with the rotation and position errors and uncertainties are analyzed. Using both numerical quantitative correlation coefficients and qualitative graphical representations, the most significant parameters for the final measurement errors and uncertainties are determined. The obtained results give clear indications and advice for the design of future measurement systems and for the selection of the marker positioning on a satellite surface.
High-Speed Configuration Strategy for Configurable Logic Block-Based TCAM Architecture on FPGA

Ternary content addressable memory (TCAM)-based high-speed search engines are extensively used to accelerate applications in network routing. Researchers have used field programmable gate arrays (FPGAs) to implement TCAM-based classification engines. The existing RAM-based architectures of TCAM undervalue the massive parallelism capabilities of FPGA which is evident from their relatively high latencies. This paper presents an FPGA-based TCAM design which exploits the rich logic resources available on contemporary FPGAs for an increased throughput parallel implementation compared to existing designs. The encoded bits of the traditional TCAM table are stored in the flip-flop resources, and the comparison operation is performed using the look-up table resources of the slices of configurable logic blocks. The structure and simple mapping methodology of the proposed design enables the implementation of a high-speed update operation in only a single cycle. We implemented our proposed TCAM design on a Virtex-6 XC6VLX760 FPGA device. Our proposed design achieves almost 30 % better throughput compared to existing TCAM designs for FPGAs with an efficient utilization of FPGA resources.
Adaptive Missing Data Imputation with Incremental Neuro-Fuzzy Gaussian Mixture Network (INFGMN)

Many real-world machine learning applications face a common problem: the occurrence of missing data due to losses or failures in the collection mechanisms. This article presents a new approach to deal with problems in which the data is missing completely at random (MCAR) or missing at random (MAR). This approach is based on an extension of the INFGMN (Incremental Neuro-Fuzzy Gaussian Mixture Network), using an approximated incremental version of the Expectation Maximization (EM) algorithm, to carry out the imputation process of the missing data during the execution of the recalling operation in the network layer of the INFGMN, making it capable of dealing with missing data. By adding the imputation mechanism to the INFGMN network, we obtained a neuro-fuzzy network that can produce reasonable estimates based on few training data, even in the occurrence of missing data. Unlike other neuro-fuzzy networks, the INFGMN neuro-fuzzy network does not require that the missing data be filled in prior to training and network use (this is done during its use and adaptively). The learning and modeling performance of the INFGMN in the presence of missing data are evaluated using several benchmark applications and we conclude that the proposed model can be used as a viable alternative to the existing ones for the data imputation.
Analysis of Streaming Clustering Using an Incremental Validity Index

In this paper, we study the internal incremental Davies-Bouldin (iiDB) cluster validity index in the context of streaming data analysis. We extend the original index to a more general version parameterized by the exponent of membership weights. Then we illustrate how the iiDB can be used to analyze and understand the performance of the Extended Robust Online Streaming Clustering (EROLSC) algorithm. We give examples that illustrate the appearance of a new cluster, the effect of different cluster sizes, handling of outlier data samples, and the effect of the input order on the resultant cluster history.
Multi-modal signal evolution in birds: re-examining a standard proxy for sexual selection

Sexual selection is proposed to be an important driver of speciation and phenotypic diversification in animal systems. However, previous phylogenetic tests have produced conflicting results, perhaps because they have focused on a single signalling modality (visual ornaments), whereas sexual selection may act on alternative signalling modalities (e.g. acoustic ornaments). Here, we compile phenotypic data from 259 avian sister species pairs to assess the relationship between visible plumage dichromatism—a standard index of sexual selection in birds—and macroevolutionary divergence in the other major avian signalling modality: song. We find evidence for a strong negative relationship between the degree of plumage dichromatism and divergence in song traits, which remains significant even when accounting for other key factors, including habitat type, ecological divergence and interspecific interactions. This negative relationship is opposite to the pattern expected by a straightforward interpretation of the sexual selection–diversification hypothesis, whereby higher levels of dichromatism indicating strong sexual selection should be related to greater levels of mating signal divergence regardless of signalling modality. Our findings imply a ‘trade-off’ between the elaboration of visual ornaments and the diversification of acoustic mating signals, and suggest that the effects of sexual selection on diversification can only be determined by considering multiple alternative signalling modalities.
Conserved thermal performance curves across the geographic range of a gametophytic fern

Abstract Species-level responses to environmental change depend on the collective responses of their constituent populations and the degree to which populations are specialized to local conditions. Manipulative experiments in common-garden settings make it possible to test for population variation in species’ responses to specific climate variables, including those projected to shift as the climate changes in the future. While this approach is being applied to a variety of plant taxa to evaluate their responses to climate change, these studies are heavily biased towards seed-bearing plant species. Given several unique morphological and physiological traits, fern species may exhibit very different responses from angiosperms and gymnosperms. Here, we tested the hypothesis that previously detected population differentiation in a fern species is due to differentiation in thermal performance curves among populations. We collected explants from six populations spanning the species’ geographic range and exposed them to 10 temperature treatments. Explant survival, lifespan and the change in photosynthetic area were analysed as a function of temperature, source population and their interaction. Overall results indicated that explants performed better at the lowest temperature examined, and the threshold for explant performance reflects maximum temperatures likely to be experienced in the field. Surprisingly, explant fitness did not differ among source populations, suggesting that temperature is not the driver behind previously detected patterns of population differentiation. These results highlight the importance of other environmental axes in driving population differentiation across a species range, and suggest that the perennial life history strategy, asexual mating system and limited dispersal potential of Vittaria appalachiana may restrict the rise and differentiation of adaptive genetic variation in thermal performance traits among populations.
Enhancing Automated Requirements Traceability by Resolving Polysemy

Requirements traceability provides critical support throughout all phases of software engineering. Automated tracing based on information retrieval (IR) reduces the effort required to perform a manual trace. Unfortunately, IR-based trace recovery suffers from low precision due to polysemy, which refers to the coexistence of multiple meanings for a term appearing in different requirements. Latent semantic indexing (LSI) has been introduced as a method to tackle polysemy, as well as synonymy. However, little is known about the scope and significance of polysemous terms in requirements tracing. While quantifying the effect, we present a novel method based on artificial neural networks (ANN) to enhance the capability of automatically resolving polysemous terms. The core idea is to build an ANN model which leverages a term's highest-scoring coreferences in different requirements to learn whether this term has the same meaning in those requirements. Experimental results based on 2 benchmark datasets and 6 long-lived open-source software projects show that our approach outperforms LSI on identifying polysemous terms and hence increasing the precision of automated tracing.
Current Perspectives on High-Throughput Sequencing (HTS) for Adventitious Virus Detection: Upstream Sample Processing and Library Preparation

A key step for broad viral detection using high-throughput sequencing (HTS) is optimizing the sample preparation strategy for extracting viral-specific nucleic acids since viral genomes are diverse: They can be single-stranded or double-stranded RNA or DNA, and can vary from a few thousand bases to over millions of bases, which might introduce biases during nucleic acid extraction. In addition, viral particles can be enveloped or non-enveloped with variable resistance to pre-treatment, which may influence their susceptibility to extraction procedures. Since the identity of the potential adventitious agents is unknown prior to their detection, efficient sample preparation should be unbiased toward all different viral types in order to maximize the probability of detecting any potential adventitious viruses using HTS. Furthermore, the quality assessment of each step for sample processing is also a critical but challenging aspect. This paper presents our current perspectives for optimizing upstream sample processing and library preparation as part of the discussion in the Advanced Virus Detection Technologies Interest group (AVDTIG). The topics include: Use of nuclease treatment to enrich for encapsidated nucleic acids, techniques for amplifying low amounts of virus nucleic acids, selection of different extraction methods, relevant controls, the use of spike recovery experiments, and quality control measures during library preparation.
Hierarchical Probabilistic Fusion Framework for Matching and Merging of 3-D Occupancy Maps

Fusing 3-D maps generated by multiple robots in real/semi-real time distributed mapping systems are addressed in this paper. A 3-D occupancy grid-based approach for mapping is utilized to satisfy the real/semi-real time and distributed operating constraints. This paper proposes a novel hierarchical probabilistic fusion framework, which consists of uncertainty modeling, map matching, transformation evaluation, and map merging. Before the fusion of maps, the map features and their uncertainties are explicitly modeled and integrated. For map matching, a two-level probabilistic map matching (PMM) algorithm is developed to include high-level structural and low-level voxel features. In the PMM, the structural uncertainty is first used to generate a coarse matching between the maps and its result is then used to improve the voxel level map matching, resulting in a more efficient and accurate matching between maps with a larger convergence basin. The relative transformation output from PMM algorithm is then evaluated based on the Mahalanobis distance, and the relative entropy filter is used subsequently to integrate the map dissimilarities more accurately, completing the map fusion process. The proposed approach is evaluated using map data collected from both simulated and real environments, and the results validate the accuracy, efficiency, and the support for larger convergence basin of the proposed 3-D occupancy map fusion framework.
Towards Computational Fact-Checking: Is the information checkable?

Fact-checking has recently become a real world hot topic, especially in what concerns political claims. Several big players, such as, for example, Google or Facebook, have started addressing/making contributions to make "fact-checking" possible/available to the general public. However, most, if not all fact-checking platforms are largely manual, in the sense that most of the contributions and of the actual checking is performed by humans. Automatic computational fact-checking is still very far from being reliable and available on a large scale.The current work is a contribution to the goal of automatic fact-checking by presenting features to distinguish checkable from uncheckable sentences and a fuzzy approach to computing sentence checkability, i.e., to answer the question: "is it possible to know if a sentence is worth to be checked? ". Even though, this is a hot topic, few to none solutions have been presented to automatically assess the worthiness and liability of the verification of a sentence. The solution that is proposed is mainly based on Natural Language Processing methods, linguistics and fuzzy logic.Assessing the checkability of a sentence can have many applications besides automatic fact-checking, like, for example: a broader fact-checking view (automatic checking of webpages and articles), the summarizing of information and the evaluation of factual information on texts. The main goal, however, was to focus on analysis of individual sentences, to provide an important tool to automatic fact-checking, by finding if a sentence is worth to, and can, be checked.
A hypothetical molecular mechanism for TRPV1 activation that invokes rotation of an S6 asparagine

The transient receptor potential channel vanilloid type 1 (TRPV1) is activated by a variety of endogenous and exogenous stimuli and is involved in nociception and body temperature regulation. Although the structure of TRPV1 has been experimentally determined in both the closed and open states, very little is known about its activation mechanism. In particular, the conformational changes that occur in the pore domain and result in ionic conduction have not yet been identified. Here we suggest a hypothetical molecular mechanism for TRPV1 activation, which involves rotation of a conserved asparagine in S6 from a position facing the S4–S5 linker toward the pore. This rotation is associated with hydration of the pore and dehydration of the four peripheral cavities located between each S6 and S4–S5 linker. In light of our hypothesis, we perform bioinformatics analyses of TRP and other evolutionary related ion channels, evaluate newly available structures, and reexamine previously reported water accessibility and mutagenesis experiments. These analyses provide several independent lines of evidence to support our hypothesis. Finally, we show that our proposed molecular mechanism is compatible with the prevailing theory that the selectivity filter acts as a secondary gate in TRPV1.
An Analysis of Network-Partitioning Failures in Cloud Systems

We present a comprehensive study of 136 system failures attributed to network-partitioning faults from 25 widely used distributed systems. We found that the majority of the failures led to catastrophic effects, such as data loss, reappearance of deleted data, broken locks, and system crashes. The majority of the failures can easily manifest once a network partition occurs: They require little to no client input, can be triggered by isolating a single node, and are deterministic. However, the number of test cases that one must consider is extremely large. Fortunately, we identify ordering, timing, and network fault characteristics that significantly simplify testing. Furthermore, we found that a significant number of the failures are due to design flaws in core system mechanisms.We found that the majority of the failures could have been avoided by design reviews, and could have been discovered by testing with network-partitioning fault injection. We built NEAT, a testing framework that simplifies the coordination of multiple clients and can inject different types of network-partitioning faults. We used NEAT to test seven popular systems and found and reported 32 failures.
Artificial light at night alters life history in a nocturnal orb-web spider

The prevalence of artificial light at night (ALAN) is increasing rapidly around the world. The potential physiological costs of this night lighting are often evident in life history shifts. We investigated the effects of chronic night-time exposure to ecologically relevant levels of LED lighting on the life history traits of the nocturnal Australian garden orb-web spider (Eriophora biapicata). We reared spiders under a 12-h day and either a 12-h natural darkness (∼0 lux) or a 12-h dim light (∼20 lux) night and assessed juvenile development, growth and mortality, and adult reproductive success and survival. We found that exposure to ALAN accelerated juvenile development, resulting in spiders progressing through fewer moults, and maturing earlier and at a smaller size. There was a significant increase in daily juvenile mortality for spiders reared under 20 lux, but the earlier maturation resulted in a comparable number of 0 lux and 20 lux spiders reaching maturity. Exposure to ALAN also considerably reduced the number of eggs produced by females, and this was largely associated with ALAN-induced reductions in body size. Despite previous observations of increased fitness for some orb-web spiders in urban areas and near night lighting, it appears that exposure to artificial night lighting may lead to considerable developmental costs. Future research will need to consider the detrimental effects of ALAN combined with foraging benefits when studying nocturnal insectivores that forage around artificial lights.