Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Changes in Gut Microbiota Diversity and Composition in the Greater Horseshoe Bat

A large number of microorganisms colonize the intestines of animals. The gut microbiota plays an important role in nutrient metabolism and affects a number of physiological mechanisms in the host. Studies have shown that seasonal changes occur in the intestinal microbes of mammals that hibernate seasonally. However, these studies only focused on ground squirrels and bears. It remains unclear how hibernation might affect the intestinal microbes of bats. In this study, we measured microbial diversity and composition in the gut of Rhinolophus ferrumequinum in different periods (early spring, early summer, late summer, torpor, and interbout arousal) using 16S ribosomal RNA gene amplicon sequencing and PICRUSt to predict functional profiles. We found seasonal changes in the diversity and composition of the gut microbes in R. ferrumequinum. The diversity of gut microbiota was highest in the late summer and lowest in the early summer. The relative abundance of Proteobacteria was highest in the early summer and significantly lower in other periods. The relative abundance of Firmicutes was lowest in the early summer and significantly increased in the late summer, followed by a significant decrease in the early winter and early spring. The relative abundance of Tenericutes was significantly higher in the early spring compared with other periods. The results of functional prediction by PICRUSt showed seasonal variations in the relative abundance of metabolism-related pathways, including lipid metabolism, carbohydrate metabolism, and energy metabolism. Functional categories for carbohydrate metabolism had significantly lower relative abundance in early winter-torpor compared with late summer, while those associated with lipid metabolism had significantly higher relative abundance in the early winter compared with late summer. Overall, our results show that seasonal physiological changes associated with hibernation alter the gut microbial community of R. ferrumequinum. Hibernation may also alter the metabolic function of intestinal microbes, possibly by converting the gut microflora from carbohydrate-related to lipid-related functional categories. This study deepens our understanding of the symbiosis between hibernating mammals and gut microbes.
Development of a Taekwondo Combat Model Based on Markov Analysis

The purpose of the present study was to examine male and female Olympic taekwondo competitors’ movement patterns according to their tactical actions by applying a Markov processes analysis. To perform this study, 11,474 actions by male competitors and 12,980 actions by female competitors were compiled and analyzed. The results yielded 32 significant sequences among male competitors and 30 among female competitors. Male competitors demonstrated 11 sequences initiated by an attack, 11 initiated by a counterattack, and 10 initiated by a defensive action. Female competitors demonstrated nine sequences initiated by an attack, 11 initiated by a counterattack, and 10 initiated by a defensive move. The five most popular sequences were the opening and dodge, the direct attack and simultaneous counterattack, the dodge with a direct attack, the indirect attack and simultaneous counterattack, and the simultaneous counterattack with a direct attack. Markov chains help provide coaches and researchers with relevant information about the frequency of actions, both in terms of their frequency of occurrence and the order of their occurrence, during a real competition. It is suggested that coaches and athletes focus on these patterns when training for a real competition.
The Association Between Schizophrenia Risk Variants and Creativity in Healthy Han Chinese Subjects

Although previous evidence has suggested that there is a genetic link between schizophrenia and creativity, the specific genetic variants that underlie the link are still largely unknown. To further explore the potential genetic link between schizophrenia and creativity, in a sample of 580 healthy Han Chinese subjects, this study aimed to (1) validate the role of Neuregulin 1 (NRG1) rs6994992 (one schizophrenia risk variant that has been previously linked to creativity in the European population) in the relationship between schizophrenia and creativity and (2) explore the associations between 10 other schizophrenia risk variants and creativity. For NRG1 rs6994992, the result validated its association with creativity measures. However, since NRG1 rs6994992 is not a schizophrenia risk variant in the Han Chinese population, the validated association suggested that ethnic difference may exist in the relationship between NRG1 rs6994992, schizophrenia and creativity. For other schizophrenia risk variants, the result only demonstrated a nominal association between ZNF536 rs2053079 and creativity measures which would not survive correction for multiple testing. No association between polygenic risk score for these 10 schizophrenia risk variants and creativity measures was observed. In conclusion, this study provides limited evidence for the associations between these schizophrenia risk variants and creativity in healthy Han Chinese subjects. Future studies are warranted to better understand the potential genetic link between schizophrenia and creativity.
RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties

In recent decades, robot-assisted rehabilitation therapy has been widely researched and proven to be effective in the motor function recovery of disabled individuals. In this paper, an adaptive backstepping sliding mode control approach combined with neural uncertainty observer is developed for upper-limb exoskeleton, which can help the human operator perform repetitive rehabilitation training. Firstly, a comprehensive overview about the therapeutic exoskeleton hardware and real-time control system is introduced. Then, the neural adaptive backstepping sliding mode controller (NABSMC) is developed based on radial basis function network (RBFN) to improve the trajectory tracking accuracy with external disturbances and dynamics errors. Next, the closed-loop stability of the proposed controller is demonstrated according to the Lyapunov stability theory. Finally, further experimental investigation are conducted on three volunteers to compare the control performance of NABSMC strategy with an optimal backstepping sliding mode control (OBSMC) strategy. The comparison results show that the proposed NABSMC algorithm is capable of achieving higher trajectory tracking accuracy and better step response characteristic during repetitive passive rehabilitation training.
Investigating the Impact of Latency on Network Performance in Software Defined Network: Simulation Based Approach

Software Defined Network is intelligent network in which the state of network is handled by controllers, and the underlying network infrastructure is abstracted from the applications. The communication between the controller and the OpenFlow switches uses the OpenFlow protocol which is responsible for forwarding packets as per the controller’s instruction. The controller has a global view of the entire network which has a negative impact on the performance of the network. The end to end delay degrades the performance of the controllers. The decoupling of the control plane from the data plane causes latency and delays between the data plane and the control plane. Latency is the main challenge in the data centre and it causes delays in packet delivery and congestion. There is a need to further investigate the impact of latency on the performance of the network. In this study, we evaluated the performance of the OpenFlow controllers such as the NOX, POX, and Floodlight in MININET simulation. The schemes were evaluated based on delay, throughput, packet loss, and respond time of the controllers. The results show that the floodlight is the best performing controller.
T4P4S: A Target-independent Compiler for Protocol-independent Packet Processors

Although the programmability of control planes has been thoroughly examined in the past years, only a limited number of studies go beyond the consideration that the data plane is only a collection of simple packet forwarding devices. Even OpenFlow, a popular, very expressive data plane programming language, is still restricted to supporting a subset of existing protocol headers. To overcome such limitations, new data plane programming models have recently emerged. One of the them is P4, a high-level language for programming packet processors that enables great flexibility in the description of packet structures and processing pipelines. In this paper, we propose T4P4S11T4P4S stands for Translator for P4 Switches and its implementation is available at https://github.com/P4ELTE/t4p4s. , a multi-target compiler generating high performance switch programs from P4 descriptions. To support multiple targets, a networking hardware abstraction layer (NetHAL) is defined; the compiler generates a core switch code which is then linked with a target-specific NetHAL implementation. To avoid performance degradation, the boundaries of this separation should be chosen carefully, since the core program is only responsible for target-independent optimization, while the implementation of NetHAL should cover target-dependent enhancements. To analyze the performance, thorough measurements have been carried out, showing that the switch generated by T4P4S can easily scale beyond 100 Gbps.
Nem: Toward Fine-grained Load Balancing through RNIC EC Offloading

Modern datacenter networks employ Load-balancing (LB) in the large-scale multi-tier topology to ensure high network utilization as well as low flow completion time. This paper presents the design and evaluation of Nem, a robust Erasure Coding (EC) based load balancing scheme at end-host to spread data across multiple paths. Our design is based on two key insights. First, both theory and implementation have shown that redundancy is a powerful technique to reduce latency in networked system. Second, the commercial RDMA network interface card supports EC offload which can dramatically reduce the CPU consumption. Nem is an optimal user-level LB design, which leveraging redundant fine-grained data blocks and high speed lossless RDMA network to realize effective load balancing transmission. Evaluation over many workloads shows that Nem is adaptive to the asymmetric networks, and achieves better performance compared to the state-of-art host-based load balancing mechanism.
Automatic detection of the support points in relational clustering

The task of clustering is at the same time challenging and very important in Artificial Intelligence. One of the most popular family of clustering algorithms is the prototype-based approach. Prototype-based algorithms compute a representation of the clusters in the form of a set of prototypes, usually vectors approximating each cluster’s barycenter. However, the objects in a data set are not necessarily vectors, especially in real-world applications. These non-vectorial data sets are often represented by the dissimilarities, distances, or relations between all pairs of objects. They are usually referred as relational data sets. For this kind of data, the algorithms must be adapted to different measures of distance. There are a few state-of-the-art algorithms adapted to relational data sets through the use of barycentric coordinates formalism, in which the objects of a relational data sets are embedded in a space defined by the distances between a subset of the objects, called support points. In this paper, we propose an approach that is able to automatically select the optimal set of support points. We also extend the method to relational data streams, in order to detect variations in the intrinsic dimensionality of the representation space over time. We have compared experimentally the quality of the proposed algorithms on real and artificial data sets. We show that the automatic selection of support points allows an optimal quality in a minimal computation time.
Deep Convolutional Neural Networks for Text Localisation in Figures From Biomedical Literature

Text contained within figures is an important source of information in biomedical literature. Despite this, end-to-end text extraction from biomedical figures remains a challenging task. This paper presents a novel approach to address the founding block of this task, text detection, not only from biomedical figures but also from images in general. Particularly, the paper proposes an approach that simplifies the text detection problem into a reconstruction problem using a deep convolutional neural network. Designed to overcome the specific challenges of text detection from biomedical figures, our proposed model reports promising results on the DETEXT dataset.
Improving the quality of enzyme prediction by using feature selection and dimensionality reduction

The coming of the post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is called a feature-based approach and has been the focus of several works in Bioinformatics. In this work, we investigated the characteristics of proteins that improve the results found in the related works that used Support Vector Machine (SVM) in the enzymes class prediction. During the investigation, two approaches were evaluated: with and without dimensionality reduction, which was performed through the statistical technique Factor Analysis. Both approaches exceeded the results found in the related work, reaching F-measure averages of 85.10% and 83.90%, respectively, in the approachs with and without dimensionality reduction.
A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images

Lesion border detection is considered a crucial step in diagnosing skin cancer. However, performing such a task automatically is challenging due to the low contrast between the surrounding skin and lesion, ambiguous lesion borders, and the presence of artifacts such as hair. In this paper we propose a two-stage approach for skin lesion border detection: (i) segmenting the skin lesion dermoscopy image using U-Net, and (ii) extracting the edges from the segmented image using a novel approach we call FuzzEdge. The proposed approach is compared with another published skin lesion border detection approach, and the results show that our approach performs better in detecting the main borders of the lesion and is more robust to artifacts that might be present in the image. The approach is also compared with the manual border drawings of a dermatologist, resulting in an average Dice similarity of 87.7%.
Gate-Layer Autoencoders with Application to Incomplete EEG Signal Recovery

Autoencoders (AE) have been used successfully as unsupervised learners for inferring latent information, learning hidden features and reducing the dimensionality of the data. In this paper, we propose a new AE architecture: Gate-Layer AE (GLAE). The novelty of GLAE lies in its ability to encourage learning of the relationships among different input variables, which affords it with an inherent ability to recover missing variables from the available ones and to act as a concurrent multi-function approximator.GLAE uses a network architecture that associates each input with a binary gate acting as a switch that turns on or off the flow to each input unit, while synchronising its action with data flow to the network. We test GLAE with different coding sizes and compare its performance against the Classic AE, Denoising AE and Variational AE. The evaluation uses Electroencephalograph (EEG) data with an aim to reconstruct the EEG signal when some data are missing. The results demonstrate GLAE’s superior performance in reconstructing EEG signals with up to 25% missing data in an input stream.
Improved Techniques for Building EEG Feature Filters

Recent advances in the generative adversarial network (GAN) based image translation have shown its potential of being an image style transformer. Similarly, defined as a style transformer for physiological signals, a feature filter is used to filter privacy-related features while still keeping useful features. However, existing feature filter techniques have three problems: (1) the privacy-related features cannot be filtered out to the extent we need through a simple Conv-Deconv generator structure, and (2) the generator cannot control the semantics (maintain desired features) of given physiological signals. To address these problems, we utilize deeper neural networks and adopt techniques from domain adaptation. This includes semantic loss and a GAN based model structure with two generators, two discriminators and a classifier to form a game of five. Our results on the UCI EEG dataset demonstrate that our model can simultaneously (1) achieve the state-of-the-art accuracy removal for the privacy-related feature, (2) reduce the desired feature removal accuracy drop, and (3) make the filtered signals can be interpreted or visually checked.
Who should bid higher, NS or WE, in a given Bridge dealƒ

The paper proposes a neural model for a direct comparison of the two so-called Double Dummy Bridge Problem (DDBP) instances, along with a practical use-case for determining which pair, NS or WE, should propose the higher deal during a bidding phase in a Bridge game. The proposed system is composed of two identical subnetworks combined by a comparator layer placed on top of them. The base of each subnetwork is a shallow autoencoder (AE) which is further connected with a Multilayer Perceptron. The system is trained in two phases - an unsupervised one - used to create a meaningful feature-based input representation in AE compression layer, and a supervised one - meant for fine-tuning of the whole model. Training and test data are composed of pairs of Bridge deals in which the second deal in a pair is the first one rotated by 90 degrees. Since the task is to point which of the two deals promise a higher contract for the NS pair, due to deal rotation within a pair, the system effectively answers the title question "Who should bid higher, NS or WE, in a given dealƒ". The proposed approach is experimentally compared with two other methods: one relying on a neural system solving the DDBP and the other one employing several estimators of hand strength used by experienced players. The results clearly indicate that both neural network approaches outperform the usage of human-scoring systems by a large margin, most notably in the trump (suit) contract.
Skeletonization Combined with Deep Neural Networks for Superpixel Temporal Propagation

Medial axis representation (a.k.a. shape skeleton) seems to be present in visual processing, but its relevance has remained unclear. Here, we show the potentials of the medial axis transformation in the temporal propagation of superpixels. We combine (i) state-of-the-art deep neural network ‘sensors’ for optical flow and for depth estimation and (ii) a superpixel algorithm with (iii) the medial axis transformation to obtain frame-to-frame propagation of visual objects. We study the precision of this deep learning facilitated superpixel temporal propagation. We discuss the advantages of the method compared to the temporal propagation of the superpixels themselves.
The Emergent-Context Emergent-Input Framework for Temporal Processing

Many temporal processing tasks face great challenges. The handcrafted features and structure used in these temporal processing methods result in the brittle systems. To avoid handcrafted designs, we analyze and propose the EmergentContext Emergent-Input (ECEI) framework in this work. The Developmental Network (DN-1) can be considered as the first ECEI framework that used emergent motor vectors as temporal context (state) for top-down attention. This method suggests that the developmental root of temporal states is at the (open) motor end and always expressible by the motor end, not originally internal and hidden. Indeed, it has enabled the control of a Turing machine to incrementally emerge inside the network. However, DN-1 used a handcrafted partition of motor neurons as concept regions, which does not allow context to automatically emerge. The same is true for hidden (internal) areas other than the motor area. We analyze why this limitation is fundamentally damaging. To overcome this limitation, Developmental Network 2 (DN-2) is proposed with some new biology inspired mechanisms. DN2 is an ECEI framework which can directly use any naturally emerged motor patterns, allowing a freedom of thought that has been largely overlooked. The phoneme recognition experiments are used to investigate DN-2’s performance with two different settings about the motor area: handcrafted motor concept zones and emergent motor area. Based on the experimental results, we compared and analyzed such two mechanisms. This work is a step toward our goal of empowering DN-2 to not only free thoughts, but such free-thoughts are also necessary for handling increasingly complicated temporal processing tasks.
Towards A Deep Learning Question-Answering Specialized Chatbot for Objective Structured Clinical Examinations

Medical students undergo exams, called "Objective Structured Clinical Examinations" (OSCEs), to assess their medical competence in clinical tasks. In these OSCEs, a medical student interacts with a standardized patient, asking questions to complete a clinical assessment of the patient’s medical case. In real OSCEs, standardized patients or "Actors" are recruited and trained to answer questions about symptoms mentioned in a script designed by the medical examiner. Developing a virtual conversational patient for OSCEs would lead to significant logistical savings. In this work, we develop a deep learning framework to improve the virtual patient’s conversational skills. First, deep neural networks learned domain specific word embeddings. Then, long short-term memory networks derived sentence embeddings before a convolutional neural network model selected an answer to a given question from a script. Empirical results on a homegrown corpus showed that this framework outperformed other approaches, and reached an accuracy of 81%.
Hardware Architecture For A Shift-Based Parallel Odd-Even Transposition Sorting Network

Sorting is one of the most frequently executed routines on modern computers. Such algorithms are classically implemented as software programs and can contribute significantly to the overall execution time of a process. In this respect, implementing sorting algorithms in hardware can dramatically increase the overall performance of the applications embodying them. This paper proposes an optimized hardware architecture for a parallel Odd-Even transposition sorting network, on field programmable gate array (FPGA) based embedded systems. This implementation introduces a modification of the classical Odd-Even Transposition sorting algorithm. This modification is a shift-based approach offering high flexibility for general purpose applications. The proposed architecture results in increasing overall performance by minimizing hardware resource utilization, increasing the operating frequency and reducing complexity. Simulation and synthesis results demonstrates that the proposed architecture is minimal in size, can operate on odd and even length arrays, capable of sorting arrays of length larger than two times the number of available processors, and can begin the sorting process at data input.