Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of ammonium application on the oxalate content of tea plants (Camellia sinensis L.)

Abstract Tea plants (Camellia sinensis L.) were hydroponically cultured for 9 d in a solution containing ammonium as a sole nitrogen source (0 (control), 50, 100, or 200 mg N L-1 ), and harvested after 0 (control), 1,3,6, and 9 d of treatments to investigate the effect of ammonium on the content and metabolism of oxalate in tea plants. Total and water-soluble oxalate contents in young leaves and roots treated with 50, 100, and 200 mg N L-1 decreased in proportion to the amount of ammonium applied, with levels significantly lower than those without ammonium application. In old leaves, both total and water-soluble oxalate contents showed constant values during the treatment period. The content of total ascorbic acid (AsA) in young leaves increased with the amount of ammonium in the medium. The activity of oxalate oxidase (OxO) in young leaves also increased with the concentration of ammonium in the medium and with the duration of the treatment. However, in roots, total AsA content and OxO activity were not affected. These results suggest that in young tea leaves, the application of ammonium suppressed the synthesis of oxalate via AsA oxidation and stimulated the decomposition of oxalate by the increase of the OxO activity, resulting in the accumulation of AsA and a decrease in oxalate content. On the other hand, in old leaves and young roots, the responses to ammonium were different from those in young leaves, suggesting that the oxalate metabolism may be controlled by different mechanisms in different parts of the tea plants.
Sex determination in the Lesser Flamingo (Phoenicopterus minor) using morphological measurements

Morphological measurements and blood samples were taken from 154 Lesser Flamingos Phoenicopterus minor, including adults (>3 years old), immature sub-adults (2–3 years old) and first-year juvenile birds of both sexes, captured at Lake Bogoria, Kenya (0°11'–20' N, 036°06' E) during 2001 and 2002. PCR amplification of the CHD-Z and CHD-W genes using DNA extracted from the blood samples was used to determine the sex of each bird. There were significant differences in mass and tarsus length among the three age groups, indicating that Lesser Flamingos continue to grow in skeletal size and mass between fledging and the attainment of adult plumage at 3–4 years of age. On average, males were significantly larger than females in all age groups, although there was substantial overlap between the sexes in all morphological measurements. The element with the least amount of overlap was head-and-bill length. Discriminant functions utilising head-and-bill length that correctly predict the sex of juvenile and immature birds with approximately 93% accuracy are presented. By adding total tarsus length, the sex of wild adult Lesser Flamingos is correctly predicted with approximately 98% accuracy. The same discriminant function developed for wild adult birds predicted the sex of 19 captive adult Lesser Flamingos of known sex with 100% accuracy.
Effects of synthetic conjugated linoleic acid (CLA) or bio-formed CLA as high CLA beef on rat growth and adipose tissue development

Two rat feeding experiments were conducted, concurrently, for 48 d, to determine the effects of feeding synthetic food-grade conjugated linoleic acid (CLA) containing 53% cis 9, trans 11 CLA and 44% trans 10, cis 12 CLA or bio-formed CLA as high CLA beef on adipose tissue development. In exp. 1, 30 (10/diet) weaned male Wistar rats (51 ± 0.65 g) were fed, ad libitum, a control diet, control with sufficient added synthetic CLA to achieve CLA concentrations of 1.1% of diet DM or the control, where the soybean oil in the diet was replaced with sunflower oil. In exp. 2, 20 (10/diet) weaned male Wistar rats (52.5 ± 2.5 g) were fed, ad libitum, diets in which freeze dried beef replaced the casein as the protein source. The meat in the two diets was derived either from steers raised without dietary oil or from beef from steers fed oil to increase CLA content by 144% from 3.36 to 8.20 mg g-1 lipid (high CLA beef). At the end of the experiment the rats were humanely sacrificed and the organs, muscles and the retro.
Ground water and vegetation in two peat bogs in northern Minnesota

Plant cover and water quality of bog waters are related to the surrounding ground-water flow systems in two bogs--one perched above and isolated from the regional ground-water system, the other nonperched and continuous with the regional system. The nonperched bog has higher pH, higher specific conductivity, and greater variety in plant cover than the perched bog.
Effects of Irradiance and Temperature on the Release and Growth of Carpospores from Gracilaria cornea J. Agardh (Gracilariales, Rhodophyta)

Abstract Carpospore liberation, germination and carposporeling growth were investigated in Gracilaria cornea J. Agardh at different combinations of temperature and irradiance in laboratory cultures. Maximum number of carpospores released per cystocarp approached 2500 at 25 °C and 10 μmol m−2 s−1. Student Newman Keuls multiple comparisons showed that the number of carpospores released were significantly different (P < 0.05) between low (10, 25, 50 μmol m−2 s−1) and high (75 and 100 μmol m−2 s−1) irradiances at 25 °C. The highest carposporeling specific growth rates ranged from 4.6 to 5.2% d−1, corresponding to a doubling time of 19.2 to 21.7 days and occurred at 25 and 28 °C at the lowest irradiance levels tested (10 and 25 μmol m−2 s−1). At 31 °C spore release and sporeling growth were significantly depressed regardless of irradiance. Favourable conditions for carpospore release were found at 25 °C and low irradiances (10, 25 and 50 μmol m−2 s−1). The best conditions for sporeling growth in G. cornea were at 25–28 °C and low irradiance. These temperatures corresponded to the environmental conditions during spring on the G. cornea beds from the Yucatan coast.
VIABILITY AND STABILITY OF YEAST CELLS AND FILAMENTOUS FUNGUS SPORES DURING FREEZE-DRYING: EFFECTS OF PROTECTANTS AND COOLING RATES

Two parameters of a one step freeze-drying process, cooling rate and protecting media, are studied in an effort to improve the viability and the stability of the preserved fungal strains. Cooling rates of 1.6 C, 3 C and 40 C/min are applied on cells of Saccharomyces cerevisiae and Brettanomyces bruxellensis and on spores of Trichoderma viride and Arthrobotrys arthrobotryoides preserved in 93 suspending media containing polymers, sugars, albumin, milk, honey, polyols, amino acids alone or in combination. The viability rate of Saccharomyces cerevisiae cells is increased from 30% to 96-98% by using an appropriate protecting medium containing 10% skim milk with 2 compounds among honey, sodium glutamate, trehalose or raffinose in the freeze-drying process carried out at a 3 C/min cooling rate. In the same conditions Arthrobotrys arthrobotryoides spores, the most sensitive strain among the four tested, provides 60-65% viability, while this strain does not survive a classical freeze-drying in 10% skim milk. Moreover, with the improved method the stability of properties of Penicillium expansum is demonstrated.
LEARNING OPTIMAL TRAFFIC ROUTING BEHAVIORS USING MARKOVIAN FRAMEWORK IN MICROSCOPIC SIMULATION

This article applies the existing Markovian trafﬁc assignment framework to novel trafﬁc control strategies. In the Markovian trafﬁc assignment framework, transition matrices are used to derive the trafﬁc ﬂow allocation. In contrast to the static trafﬁc assignment, the framework only requires ﬂow split ratio at every intersection, bypassing the need of computing path ﬂow allocation. Consequently, compared to static trafﬁc assignment, drivers’ routing behaviors can be modeled with fewer variables. As a result, it could be used to improve the efﬁciency of trafﬁc management, especially in large scale applications. To begin with, the article introduces Markovian trafﬁc assignment and connects it to the classic static trafﬁc assignment. Then, the framework is extended to dynamic trafﬁc assignment using microscopic trafﬁc simulator Simulation of Urban Mobility (SUMO). In a case study, the framework is applied to a standard benchmark network, where optimal routing behaviors are independently learned through grid search, random search, and evolution strategies, under three different reward functions (network outﬂow, total vehicle hours of travel, and average marginal regret). The case study shows that the this novel trafﬁc control strategy is promising, as Markov chain theory supports the ability to scale up to larger networks.
BIIT: Standardization of Blockchain-based I2oT Systems in the I4 Era

In Industry 4.0 (I4), the Industrial Internet of Things (I2 oT) data streams are prone to significant data manipulation risks. The integration of Blockchains (BC) with I2 oT may become a solution preventing from this problem. This paper provides a blockchain-agnostic Blockchain I2oT (BI2oT) architecture called BIIT that allows developing a broad range of BC applications fully integrating Internet of Things (IoT). The mechanisms introduced in BIIT aim at solutions that provide data reliability, limit the computational overhead, and enhance energy efficiency. BIIT is evaluated through real-world experimentation.
PhD Forum Abstract: Towards Deep Learning in Signal Translation for Cross Configurations in device-free WiFi Sensing

Wireless Sensing has been a topic in contemporary research for years and popularity is still intact as its ubiquitous and non-invasive nature. Human activity recognition using wireless sensing is leveraged by the deep learning advancements. However, maintaining the robustness of the sensing model in sensitive wireless environments is a non-trivial task to achieve. We propose a deep learning based signal translation framework for multiple variations observed in sensing environments such as transceiver variance and configuration of the user who performing the activities. Our approach has two major pipelines, translation pipeline which translates from novel configuration to a reference configuration and an inference pipeline which classifies the activities performed by the user. We further expect to extend this model to more complex multi user interacting sensing environments and to In-Situ setups.
Classifying Melanoma Skin Lesions Using Convolutional Spiking Neural Networks With Unsupervised STDP Learning Rule

Deep learning methods have made some achievements in the automatic skin lesion recognition, but there are still some problems such as limited training samples, too complicated network structure, and expensive computational costs. Considering the inherent power-efficiency, biological plausibility and good image recognition performance of spiking neural networks (SNNs), in this paper we make malignant melanoma and benign melanocytic nevi skin lesions classification using convolutional SNNs with unsupervised spike-timing-dependent plasticity (STDP) learning rule. Efficient temporal coding, event driven learning rule and winner-take-all (WTA) mechanism together ensure sparse spike coding and efficient learning of our networks which achieve an average accuracy of 83.8%. We further propose to use feature selection to select more diagnostic features to improve the classification performance of our networks. Our SNNs with feature selection reach an average accuracy of 87.7%. Experimental results show that comparing to CNNs that need to be trained from scratch, our SNNs (with and without feature selection) not only achieve much better classification accuracies but also have much better runtime efficiency. Moreover, although the pretrained CNNs models can achieve similar running time, our proposed SNNs are more stable and easier to use than the pretrained CNNs because we do not need to try many pretrained models any more, and our SNNs also have much better classification accuracies than the pretrained CNNs. In addition, our networks have only three convolutional layers, and the complexity of the model and the parameters that need to be trained in the networks are greatly reduced. Our works show that STDP-based SNNs are very beneficial for the implementation of automated skin lesion classifiers on small portable devices.
“Identity Bracelets” for Deep Neural Networks

The power of deep learning and the enormous effort and money required to build a deep learning model makes stealing them a hugely worthwhile and highly lucrative endeavor. Worse still, model theft requires little more than a high-school understanding of computer functions, which ensures a healthy and vibrant black market full of choice for any would-be pirate. As such, estimating how many neural network models are likely to be illegally reproduced and distributed in future is almost impossible. Therefore, we propose an embedded ‘identity bracelet’ for deep neural networks that acts as proof of a model’s owner. Our solution is an extension to the existing trigger-set watermarking techniques that embeds a post-cryptographic-style serial number into the base deep neural network (DNN). Called a DNN-SN, this identifier works like an identity bracelet that proves a network’s rightful owner. Further, a novel training method based on non-related multitask learning ensures that embedding the DNN-SN does not compromise model performance. Experimental evaluations of the framework confirm that a DNN-SN can be embedded into a model when training from scratch or in the student network component of Net2Net.
Secondary seed dispersal by dung beetles in a Colombian rain forest: effects of dung type and defecation pattern on seed fate

Abstract: In rain forests the fate of seeds defecated by mammals is often affected by dung beetles, but these effects can vary with mammal species. In a Colombian forest, differences in Scarabaeinae assemblages attracted to spider monkey (Ateles hybridus) and howler monkey (Alouatta seniculus) defecations were assessed. In total, 791 beetles of 35 species were captured. Mean number of individuals was similar for both dung types; mean number of species was higher for Alouatta traps. The effects of dung type (Alouatta vs. Ateles) and defecation pattern (clumped vs. scattered) on the fate of 220 Rollinia edulis (Annonaceae) seeds were determined. Burial by beetles occurred for 61% of the seeds (mean depth 2.6 cm). Seeds in Alouatta dung and in clumped defecations had higher burial rates. Horizontal movement (mean 11 cm) occurred for 80% of the seeds; no significant effects of experimental factors were detected. Survival was higher for buried vs. surface seeds. In a separate experiment, no differences were detected between dung types in the proportion of seeds buried by beetles. Changes in dung texture might have been responsible for the contrasting results. When assessing the effectiveness of an endozoochorous plant–mammal interaction, secondary dispersal by dung beetles should be addressed too.
Manufacturing Transition of High-Performance MEMS Gyroscopes

We present the progress of a high-performance inertial sensor manufacturing processes implemented in a volume compatible 200mm wafer diameter production environment. The baseline process, derived from Teledyne DALSA's MIDIS™ process platform, incorporates critical design elements specifically designed for navigation-grade device realization. The process enables considerable flexibility to support a broad range of device designs, facilitating the rapid transition of new device concepts to volume production. The recent work for the Si Disc Resonator Gyro (SiDRG) demonstrates the integration of die-level thermal stabilization elements and the manufacturing at high yield of wafer-level vacuum packaged gyro resonators with high quality factor and small frequency splits. Initial measurement of Allen Deviation on unscreened parts has shown promising performance levels of 0.028 degrees/hour.
Demo Abstract: Wireless Glasses for Non-contact Facial Expression Monitoring

Facial expression monitoring is crucial in fields including mental health care, driver assistant systems, and advertising. However, existing systems typically rely on cameras that capture entire faces, or contact-based bio-signal sensors, which are neither comfortable nor portable. In this demonstration, we present a wireless glasses system for non-contact facial expression monitoring. The system is composed of an IR camera and an embedded processing unit mounted on a 3D-printed glasses frame, and a novel data processing pipeline running across the glasses platform and a computer. Our system performs high-accuracy and real-time facial expression detection with a running time of up to 9 hours. We will show the fully-functioning wearable system in this demonstration.
Optimizing the Flexibility of SDN Control Plane

Facing fast changing network traffic, Software Defined Networking (SDN) endows the capability of efficient traffic forwarding and control plane resource management which results in flexibility, in comparison to networking with legacy rigid hardware. Meanwhile, flexibility has become an implicit target of many novel network algorithms and designs. This paper answers an interesting research question: "Can we optimize the flexibility as an objectiveƒ" We study the impact of Data Centers (DCs) location on the flexibility of dynamic control plane. The flexibility is revealed when the control plane can adapt itself with controller migration and switch re-assignment in a timely manner for a new group of flows to satisfy the requirements of flow setup. We propose a model, named FLEXDC, for static DC placement and dynamic controller placement to optimize the control plane’s flexibility. We also design heuristics to speed up the decision process. Our simulation over real network topology with synthetic flows shows the improved flexibility of the dynamic control plane. Furthermore, we can save up to 2 DCs while achieving the same flexibility, compared with a naive approach.
A Graph Approach to Dynamic Fusion of Sensors

PGM-based or PGM-inspired simultaneous localization and mapping (SLAM) has been successfully adopted for both online and offline pose estimation and map construction using multiple sensor modalities. One particular problem of this graph framework is the computational complexity: the longer the experiment, the larger the problem to solve; the more the sensor modalities, the larger the problem to solve. Several improvements have been made to constrain the time complexity. In this work, an information-theoretic method is applied to prune/simplify the graph while maintaining the a posteriori information represented by the underlying graph. In particular, Chow-Liu tree is used to connect the nodes of the Markov blanket of the node to be eliminated and the factors of the graph is reconstructed by computing mutual information between each and every pair of the nodes within the blanket and forming the network topology in a maximum spanning tree (MST). Synthetic results show that the pruned and reconstructed graph achieves similar estimation accuracy compared with the original graph, which is clearly signified by the Kullback-Leibler divergence metric.
Autonomous UAV-based Forest Mapping Below the Canopy

We present a system for forest mapping from an autonomous UAV, flying below the tree canopy. The UAV is equipped with four stereo cameras, which provide information for navigation, mapping, and collision avoidance. The main focus of the paper is positioning and forest mapping, but the full system, including both hardware and algorithms, is described.
Recognition of Audio Depression Based on Convolutional Neural Network and Generative Antagonism Network Model

This paper proposes an audio depression recognition method based on convolution neural network and generative antagonism network model. First of all, preprocess the data set, remove the long-term mute segments in the data set, and splice the rest into a new audio file. Then, the features of speech signal, such as Mel-scale Frequency Cepstral Coefficients (MFCCs), short-term energy and spectral entropy, are extracted based on audio difference normalization algorithm. The extracted matrix vector feature data, which represents the unique attributes of the subjects’ own voice, is the data base for model training. Then, based on the combination of CNN and GAN, DR AudioNet is used to build the model of depression recognition research. With the help of DR AudioNet, the former model is optimized and the recognition classification is completed through the normalization characteristics of the two adjacent segments before and after the current audio segment. The experimental results on AViD-Corpus and DAIC-WOZ datasets show that the proposed method effectively reduces the depression recognition error compared with other existing methods, and the RMSE and MAE values obtained on the two datasets are better than the comparison algorithm by more than 5%.
A Versatile Out-of-Band Software-Defined Networking Solution for the Internet of Things

The Internet of Things (IoT) is gradually incorporating multiple environmental, people, or industrial monitoring deployments with diverse communication and application requirements. The main routing protocols used in the IoT, such as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), are focusing on the many-to-one communication of resource-constraint devices over wireless multi-hop topologies, i.e., due to their legacy of the Wireless Sensor Networks (WSN). The Software-Defined Networking (SDN) paradigm appeared as a promising approach to implement alternative routing control strategies, enriching the set of IoT applications that can be delivered, by enabling global protocol strategies and programmability of the network environment. However, SDN can be associated with significant network control overhead. In this paper, we propose VERO-SDN, an open-source SDN solution for the IoT, bringing the following novelties in contrast to the related works: (i) programmable routing control with reduced control overhead through inherent protocol support of a long-range control channel; and (ii) a modular SDN controller and an OpenFlow-like protocol improving the quality of communication in a wide range of IoT scenarios through supporting two alternative topology discovery and two flow establishment mechanisms. We carried out simulations with various topologies, network sizes and high-volume transmissions with alternative communication patterns. Our results verified the robust performance and reduced control overhead of VERO-SDN for alternative IoT deployments, e.g., achieved up to 47% reduction in network’s overall end-to-end delay time compared to RPL and a packet delivery ratio of over 99.5%.
Vein Recognition Algorithm Based on Transfer Nonnegative Matrix Factorization

Most of the existing vein recognition algorithms are only effective for specified datasets, and once replacing the vein image acquisition device, i.e., the properties of the collected vein images are changed, the performance of the algorithm will be degraded greatly. Therefore, a transfer Nonnegative Matrix Factorization (NMF) based vein recognition algorithm is proposed, which makes vein features more universal. Its contributions are mainly reflected in the following two aspects: 1) The orthogonal constraint is imposed on the model to reduce the redundancy between feature bases and increase the difference between the features of different veins; 2) The differences between the vein features in different datasets are reduced based on Maximum Mean Difference (MMD) constraint, i.e., the knowledge of the source dataset is transferred to the target dataset well, and the universality of vein features can be improved. Experimental results show that the proposed algorithm outperforms state of the art methods on two dorsal hand vein datasets and two finger vein datasets.