Miracle-dz/UniFAR-FTUs huggingface dataset:
No-Reference Quality Evaluation of Light Field Content Based on Structural Representation of The Epipolar Plane Image

As an emerging technology, Light Field (LF) has gained ever-increasing importance in the domain. In order to provide guidance for the development of perceptually accurate Light Field (LF) processing tools and supervise the entire streaming system, robust perceptual quality assessment metrics are required. Especially, No-Reference (NR) metrics are preferable to compare LF with different angular resolutions. Some metrics have been developed by extending commonly used 2D image quality metrics to the 4D LF domain with angular consistency terms. Nonetheless, although these models consistently show slightly improved performance, most of them are limited in evaluating the quality of LF using the sub-aperture views with additional terms on the angular domain. There is an evident lack of reliable quality metrics that are tailored to LF content. To remedy this lack, we propose a NR quality metric for LF contents based on representing EPI with structural descriptors, including the Histogram of Gradients and the Convolutional Sparse Coding based descriptors. The primary motivation resides in our observations that (1) LF related distortions on the angular domain are highly noticeable on the Epipolar Plane Image representations (EPI); (2) most of the distortions in EPI are structure-related. Extensive experiments on the MPI-LFA [1] LF image quality dataset demonstrate that our method provides competitive performance with the state-of-the-art NR image quality metrics.
Relative competitive performance of 63 species of terrestrial herbaceous plants

Abstract There is growing evidence that plant and animal species are arranged in hierarchies of relative competitive performance. More work is needed to determine which plant traits best predict relative competitive performance. We therefore measured relative competitive performance of 63 terrestrial herbaceous plant species using Trichostema brachiatum as a reference species (that is, phytometer or target species). The neighbour species came from a wide array of terrestrial vegetation types (e.g. rock barrens, alvars, old fields), and represented a wide array of growth forms (e.g. small rosette species such as Saxifraga virginiensis and large clonal graminoids such as Agropyron repens). The experiment was repeated with two pot sizes: large (control) and small (stress treatment). Relative competitive performance in large pots (controls) was highly correlated with that in small pots (stress treatment) (r = 0.90, p < 0.001). The hierarchy of relative competitive performance in the large pots was also highly correlated with the hierarchy in the small (stressed) pots (rs = 0.91, p < 0.001). Principal components analysis and multiple linear regression showed that plant size (measured by total biomass, above‐ground biomass, below‐ground biomass, canopy area, height and leaf area index) and leaf shape (measured as length to width ratio, length, width) were the two characteristics that best predicted relative competitive performance (large pots, r 2 = 0.55; small pots, r 2 = 0.48). Nomenclature:Gleason & Cronquist (1963); Morton & Venn (1990).
Predicting Traffic Demand Matrix by Considering Inter-flow Correlations

Accurate traffic demand matrix (TM) prediction is essential for effective traffic engineering and network management. Based on our analysis of real traffic traces from Wide Area Network (WAN), the traffic flows in TM are both time-varying (i.e. with intra-flow dependencies) and correlated with each other (i.e. with inter-flow correlations). However, existing works in TM prediction ignore inter-flow correlations. In this work, we propose a new model for TM prediction by considering both inter-flow correlation and intra-flow dependencies, namely CRNN model. By observing that most strongly-correlated traffic flows have the same source or destination, we employ convolutional structures to capture the neighboring correlation in TMs and extract even higher-order correlations. To model the trends in time dimension, like previous works we use recurrent structures to capture the temporal variations of traffic flows. Our evaluation on two WAN datasets shows that, when predicting the next TM, CRNN model reduces the Mean Squared Error (MSE) by up to 44.8% compared to state-of-the-art method; and the gap is even larger when predicting the next multiple TMs.
The specific status of Moniliformis (Acanthocephala) of Texas rats, and a review of the species of this genus in the western hemisphere.

The writer (1921) reported the common occurrence of a species of Moniliformis in various species of Rattus, particularly R. alexandrinus, in Houston, Texas and expressed uncertainty as to the relationships of these worms to various previously described members of the genus, both in this country and in Europe. Van Cleave (1925), after examination of some of the Texas forms and comparison of them with specimens from various other parts of the world, came to the conclusion that they belonged to the type species, M. moniliformis (Bremser, 1811), and that there was only one other valid species in the genus, M. clarki (Ward, 1917) from American squirrels and a mole. Meyer (1933) reviewed the genus again and recognized no less than nine species. He considered the Texas form distinct and named it M. dubius, and also considered a form from Brazilian rats, reported by Travassos (1917), as distinct and named it M. travassosi. Another form, obtained from the droppings of a Brazilian vampire he also recognized as a separate species, M. convolutus. Since the Texas form has been extensively studied by the writer and his students since 1921, and since there were certain errors in the original account of it which have influenced Meyer in his taxonomic conclusions, and have been widely quoted by other authors, it is desirable to amend the account and bring it up to date, and to discuss the specific status of this and other American forms.
ClothingNet: Cross-Domain Clothing Retrieval With Feature Fusion and Quadruplet Loss

Cross-domain clothing retrieval is an active research topic because of its massive potential applications in fashion industry. Due to the large number of garment categories or styles, and different clothing appearances caused by different camera angles, different shooting conditions, different messy background environments, or different postures of the dressed human body, the retrieval accuracy of traditional consumer-to-shop scheme is always low. In this paper, based on the framework of deep convolution neural network, a novel cross-domain clothing retrieval method is proposed by using feature fusion and quadruplet loss function, which is named as ClothingNet. First, the pre-trained deep neural network Resnet-50 is adopted to extract feature map of clothing images. The extracted high-level image features can thus be merged with middle-level features, and the final feature representation of clothing images can be obtained by constraining the fusion feature values to a certain range in term of L2 norm. This fusion feature provides a comprehensive description of the differences between clothing images. For effectively training our ClothingNet, the cross-domain clothing images are organized in form of a quadruplet for calculating its loss function, and the network parameters can be optimized according to back propagation scheme via stochastic gradient descent of loss function. Our proposed method is validated on two public datasets for clothing retrieval, DARN and DeepFashion, showing that the top-50 retrieval accuracy is 35.67% and 53.52% respectively. Experimental results illustrate the effectiveness of our clothing retrieval method.
An Unsupervised Segmentation Method For Remote Sensing Imagery Based On Conditional Random Fields

Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation.
The Vehicle’s Velocity Prediction Methods Based on RNN and LSTM Neural Network

The electrification of fuel vehicles has become a trend all around the world. For HEV and PHEV, the prediction of the future velocity affects the energy distribution and energy management directly. The vehicle’s velocity prediction models based on Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) Neural Network have been established in this study, to predict the future vehicle velocity. Through the test data validation, both models can improve the accuracy of vehicle velocity prediction. But as the prediction length increases, the root mean square error (RMSE) of the LSTM prediction model is smaller than the RNN model, which means the LSTM prediction model has better performance in vehicle velocity prediction.
Robust Saturated Tracking Control of an Autonomous Surface Vehicle

In this paper, we consider the problem of robust control for an autonomous surface vehicle (ASV) with bounded exogenous disturbances. The objective is to design a controller that ensures the system state tracks a sufficiently smooth desired trajectory generated by a suitable virtual ship. To solve this problem, based on the robust integral of the sign of the error (RISE) feedback, the developed controller is able to utilize a smooth saturated controller with a strong input-to-state stability backstepping term. The bounds on the control are known a priori and can be adjusted by changing the feedback gains. Moreover, A Lyapunov stability analysis is included to prove semi-global asymptotically tracking. A simulation is provided to demonstrate the proposed controller on an experimentally validated ASV model.
Entomophthora Coronata, the Etiologic Agent of a Phycomycosis of Horses

Mycologists are generally acquainted with species of Entomophthora as parasites of insects. In a separate paper (1) we are reporting four cases of a chronic granulomatous disease of the nasal mucosa and the lips of horses caused by Entomophthora coronata (Costantin) Kevorkian (4). Germination of conidia of this fungus may be by a single hypha, by multiple hyphae, by replication of a single conidium on a short conidiophore, or by replication of many conidia on many short conidiophores. In many cultures all conidia are covered by hair-like appendages. The versatility of this fungus, the taxonomic significance of this variability, and the proof that E. coronata can parasitize mammalian tissues should be of interest to mycologists. The fungus was easily demonstrated in the tissues of the host where it grew as a coenocytic mycelium. In culture E. coronata grows rapidly. A subculture on glucoseneopeptone agar incubated at 30°C begins to discharge spores in 10-15 hours and the colony reaches a diameter of 6 cm within 48 hours. The colony is colorless to yellowish white. It is at first flat and glabrous but it quickly produces radial and irregular folds and a white bloom on the surface which represents surface hyphae and short conidiophores (FIG. 1). The colony remains membranous without deep penetration of the agar substratum and without production of a conspicuous aerial mycelium. In a petri dish incubated in an inverted position and in agar slants incubated vertically, the glass surface opposite the colony is covered in 24-36 hours by a film of conidia which have been discharged forcibly from the tips of conidiophores and which germinate by production of hyphae or reproduce by replication on the nonnutritive glass surface (FIGS. 2-7). Hyphae vary in dimensions between 6 and 15 ,x. They contain droplets and granules of varying size which apparently are fat, glycogen and other metabolites. Many germinated spores and older hyphae appear
Detection of Insider Threats using Artificial Intelligence and Visualisation

Insider threats are one of the most damaging risk factors for the IT systems and infrastructure of a company or an organization; identification of insider threats has prompted the interest of the world academic research community, with several solutions having been proposed to alleviate their potential impact. For the implementation of the experimental stage described in this study, the Convolutional Neural Network (from now on CNN) algorithm was used and implemented via the Google Tensorflow program, which was trained to identify potential threats from images produced by the available dataset. From the examination of the images that were produced and with the help of Machine Learning, the question whether the activity of each user is classified as “malicious” or not for the Information System was answered.
Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure

In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the direct recursive structures and recursive algorithms for the DCT/IDCT with Arbitrary Length. Experimental results on clinical ultrasound images and comparisons with classical Wiener filter, non-local mean (NLM) filter and total variation (TV) algorithms are used to validate the improvements of the proposed approaches in both noise reduction and reconstruction performance for ultrasound images.
MIMO Ground-Radiation Antennas Using a Novel Closed-Decoupling-Loop for 5G Applications

This paper investigates the design of a compact multiple-input multiple-output (MIMO) ground-radiation antenna system for 5G terminal devices. Ground-radiation antennas are small loop-type resonators that can excite the ground plane as radiators while having advantages of small size and convenient tunability, but their compact MIMO systems remain as unsolved issues. In this study, a novel and straightforward decoupling technique based on a closed-decoupling-loop is presented for MIMO applications in the first of its kind. The proposed closed-decoupling loop can be viewed as a coupling transformer between two antenna elements by acting as a half-wavelength loop-type resonator and by introducing a current null at its center; in this way, a tremendous isolation improvement is accomplished even though the distance between two antennas is only 1 mm. Therefore, a compact <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> MIMO ground-radiation antenna system and its <inline-formula> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> MIMO system are demonstrated for 5G applications. Both simulation and measurement are taken to validate the proposed decoupling technique and the compact MIMO ground-radiation antenna system.
Synchronization of fractional order memristor-based inertial neural networks with time delay

In this paper, the synchronization problem of fractional order memristor-based inertial neural networks are investigated. To facilitate the analysis, the set-valued mapping theory is applied to the discontinuity problem caused by memristor. Since fractional order memristive inertial neural networks have two fractional derivative terms, it is hard to analyze. To solve this problem, a special feedback controller is designed in this paper to offset the fractional derivative term between 0 and 1 and the stability theory of the fractional autonomous system is used to analyze the fractional derivative term between 1 and 2. Then some sufficient conditions for the synchronization of fractional order memristive inertial neural networks are established. Finally, the effectiveness of the conclusion is verified by an example.
Peanet: The Products of Experts Autoencoder for Abnormal Detection

Recent researches have shown great progress in abnormal detection with the application of deep neural network. However, those works tend to solve the task concentrating on homogeneous features or with a decoupled model that combines features inefficiently. In this paper, we propose a method for abnormal detection that learns different features’ distributions in low-dimensionalities and combines them in an efficient way. The main architecture of our work consists of a two-stream AutoEncoder and LSTM architecture model to get the compressed low-dimensional spatial and temporal features respectively. Instead of standard Expectation-Maximization algorithm, we further design two estimation network to estimate probability densities and combine them with the Products of Experts. In addition, the experiments of our method on different dataset deliver on-par or superior performance compared to state-of-the-art methods in one-class and abnormal detection settings.
Activity and Density of Red Jungle Fowl Gallus gallus in a Dry Evergreen Forest in Thailand

ニワトリの野生原種とされるセキショクヤケイの生態学的・行動学的研究は，飼育下において行われることがあったが，本来の生息域においてはほとんど研究されてこなかった。 そこで著者らはタイ国王立森林局の許可を得て，2006年から09年まで常緑乾燥林のKao Ang Rue Nai自然保護区51. 7 haの調査地域に，総計427個のライブトラップを設置して調査を行った。 その結果，オス76羽，メス73羽，合計149羽のセキショクヤケイを捕獲し，体重・体長などのデータを採取，標識したのち放鳥した。 同一個体が捕獲されたトラップの最大間隔はメスでは1129 m，オスでは981 mであった。 雌雄に有意な差が認められなかったので，すべての個体の最大間隔の平均値を求めたところ380. 3 m (±305.1SD)であった。 またJolly-Seber法により，調査地域1ha当たりのセキショクヤケイの平均生息密度は，1.6-2. 0羽と推定された。 またセキショクヤケイのオスの体重は繁殖期に18. 0%減少するが，非繁殖期には21. 5%増加することが明らかになった。 一方メスの体重は，繁殖期に23. 6%減少し，非繁殖期に14. 2%増加した。 この結果は，繁殖にかかわる雄雌の負担の差を示すものと推測される。 なお保護区に生息するセキショクヤケイの平均寿命は4年を超えていると推測された。
Mating Time of the Rice-feeding and Water-oat-feeding Strains of the Rice Stem Borer, Chilo suppressalis(Walker) (Lepidoptera: Pyralidae).

The Rice Stem Borer, Chilo suppressalis (WALKER), is a serious pest of rice plants in Asia. In Japan, host plants of are mainly rice plants (Oryza sativa) and the water-oat (Zizania latifolia). Our previous study indicated significant differences in insecticide susceptibility and esterase isozyme patterns between the rice-feeding and water-oat-feeding strains. In the present study, the mating time of the two strains was investigated in the laboratory. The mating time was significantly different. The peak of mating time of the rice-feeder was 5h earlier than that of the water-oat-feeder, suggesting that the two strains are reproductively isolated.
Diurnal Cycle, Behaviour and Social Organization of the Budgerigar Melopsittacus Undulatus

The Budgerigar Melopsittacus undulatus was studied in the field in eastern Australia. Flocks occurred throughout the year, during most diurnal activities and at most stages of the life cycle. Daily activity began about sunrise and ceased during dusk. It consisted of feeding in the morning and afternoon and resting and preening in the foliage of trees during the middle of the day. Birds drank intermittently and at no specific time of the day. Different ages and sexes mixed in flocks and there was no obvious hierarchical construction nor high- and low-status groups within flocks or popula- tions. At times, flocks occurred that were composed predominantly of young birds. Eight discrete calls were identified. Slight differences between juveniles and adults, little sexual dimorphism and few discrete calls suggests that the social organization of the Budgerigar is simple compared to that of the Eastern Rosella Platycercus eximius. Behaviour of birds in the field is compared to that described from studies of domestic birds in cages.
A TDMA Protocol Based on Data Priority for In-Vivo Wireless NanoSensor Networks

Terahertz based in-vivo Wireless NanoSensor Networks (iWNSNs) implant nano-scale sensor nodes into the body, and use the human body as the network center to remotely monitor vital signs. Due to the characteristics of the terahertz channel in the body and the limited resources of nano-devices, the MAC protocol of the traditional Wireless Sensor Network is not suitable for iWNSNs. In this paper, a MAC protocol based on data priority, called DPB-MAC, is proposed. This protocol considers the difference of node data services in the network and prioritizes the services of the source node according to the timeliness of the data and the energy consumption of the node itself. Subsequently, a queuing mechanism is introduced on the basis of TDMA, and the time slots of the transmission phase are allocated according to the number of nodes in the network and the priority of the node data service, thereby avoiding conflicts between nodes. Simulations results show that the protocol can effectively reduce the average end-to-end delay and improve the average throughput.
CBRAM devices with a water casted solid polymer electrolyte for flexible electronic applications

In this work, an eco-friendly polymer consisting of Polyethylene Oxide (PEO) is employed to achieve the solid electrolyte layer of Conductive-Bridge Random Access Memory devices (CBRAM). In an attempt to move towards green solvent, water was used to fabricate the devices on polyimide substrates. The devices show promising electrical performance with low operating voltages (< 2.0 V), high endurance cycles with high OFF/ON resistance ratio (>104) and resistance to mechanical stresses. These results achieve a promising step toward the sustainable development of flexible electronic devices.
A 3mm2 Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm

This paper describes a 16nm programmable accelerator for unsupervised probabilistic machine perception tasks that performs Bayesian inference on probabilistic models mapped onto a 2D Markov Random Field, using MCMC. Exploiting two degrees of parallelism, it performs Gibbs sampling inference at up to 1380× faster with 1965× less energy than an Arm Cortex-A53 on the same SoC, and 1.5× faster with 6.3× less energy than an embedded FPGA in the same technology. At 0.8V, it runs at 450MHz, producing 44.6 MSamples/s at 0.88 nJ/sample.