Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Image Segmentation Based on Attentions to Intra Scales and Inner Channels

Multi-scale features provide different context information of objects, which is significant for better performance in semantic segmentation tasks. But different scale features contribute equally to final predicitons. In this paper, we propose a new attention mechanism that not only learns weights between different scales but also allocates importance to subregions of inner channels. The network architecture is built on a state-of-the-art feedforward network to generate strong semantic feature maps, accompanying with a top-down pathway that incorporates larger scale feature maps through lateral connections. The proposed intra-scale attention module softly weights features between scales pixel by pixel. To enhance impact of each feature map in intermediate layers on performance, we further present an inner-channel attention module to pay attention to subregions within each channel. Moreover, an extra supervision is presented to achieve excellent performance. Importantly, the inner-channel attention module adaptively changes features as the layer goes deeper and could be inserted into any other layers. Extensive experiments are conducted on PASCAL VOC2012 to verify the network effectiveness. The experimental results show that both intra-scale and inner-channel attention modules could yield better performance.
Modeling and Passivity-PI Control of a Fuel Cell-Supercapacitor Hybrid Energy Storage System

This paper deals with the problem of controlling a hybrid energy storage system consisted of a Proton Exchange Membrane Fuel Cell (PEMFC) as the main power source and Supercapacitors (SCs) as the auxiliary power source for electrical applications. The energy control strategy aims at reasonably distributing the load power demand into the two different sources and making sure that each source can be optimally used. The energy controller is devised based on the Passivity-Based Control (PBC) method with the Interconnection and Damping Assignment (IDA) technique. Moreover, a complementary PI controller is designed to enhance the robustness of the closed-loop system under system model uncertainties. Simulation results show that the proposed control strategy could help the hybrid system fast response to electrical load demand with strong robustness.
Information Collection Strategies In Memetic Cooperative Neuroevolution For Time Series Prediction

Memetic algorithms have been a promising strategy to enhance neuroevolution in the past. Cooperative coevolution has been combined as memetic cooperative neuroevolution with application to chaotic time series prediction. Although the method has shown promising performance, there are limitations in the balance between global and local search. The previous study used a specific local search strategy for intensification that affected the diversity of solutions. In this study, we address this limitation by information (meme) collection strategies that maintains and refines a pool of memes during global search. We present two strategies where one is sequential and the other is concurrent meme collection implemented at different stages of evolution. In the majority of the given problems, the proposed strategies showed improvement in prediction accuracy over the related methods.
Mapping Free Energy Pathways for ATP Hydrolysis in the E. coli ABC Transporter HlyB by the String Method

HlyB functions as an adenosine triphosphate (ATP)-binding cassette (ABC) transporter that enables bacteria to secrete toxins at the expense of ATP hydrolysis. Our previous work, based on potential energy profiles from combined quantum mechanical and molecular mechanical (QM/MM) calculations, has suggested that the highly conserved H-loop His residue H662 in the nucleotide binding domain (NBD) of E. coli HlyB may catalyze the hydrolysis of ATP through proton relay. To further test this hypothesis when entropic contributions are taken into account, we obtained QM/MM minimum free energy paths (MFEPs) for the HlyB reaction, making use of the string method in collective variables. The free energy profiles along the MFEPs confirm the direct participation of H662 in catalysis. The MFEP simulations of HlyB also reveal an intimate coupling between the chemical steps and a local protein conformational change involving the signature-loop residue S607, which may serve a catalytic role similar to an Arg-finger motif in many ATPases and GTPases in stabilizing the phosphoryl-transfer transition state.
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments

The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of an initial network structure via the self-constructing network structure. ADL specifically addresses catastrophic forgetting by having a different-depth structure which is capable of achieving a trade-off between plasticity and stability. Network significance (NS) formula is proposed to drive the hidden nodes growing and pruning mechanism. Drift detection scenario (DDS) is put forward to signal distributional changes in data streams which induce the creation of a new hidden layer. The maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers. The efficacy of ADL is numerically validated under the prequential test-then-train procedure in lifelong environments using nine popular data stream problems. The numerical results demonstrate that ADL consistently outperforms recent continual learning methods while characterizing the automatic construction of network structures.
Taming Performance Variability

The performance of compute hardware varies: software run repeatedly on the same server (or a different server with supposedly identical parts) can produce performance results that differ with each execution. This variation has important effects on the reproducibility of systems research and ability to quantitatively compare the performance of different systems. It also has implications for commercial computing, where agreements are often made conditioned on meeting specific performance targets. Over a period of 10 months, we conducted a large-scale study capturing nearly 900,000 data points from 835 servers. We examine this data from two perspectives: that of a service provider wishing to offer a consistent environment, and that of a systems researcher who must understand how variability impacts experimental results. From this examination, we draw a number of lessons about the types and magnitudes of performance variability and the effects on confidence in experiment results. We also create a statistical model that can be used to understand how representative an individual server is of the general population. The full dataset and our analysis tools are publicly available, and we have built a system to interactively explore the data and make recommendations for experiment parameters based on statistical analysis of historical data.
Connectivity Based Method for Clustering Microbial Communities from Metagenomics Data of Water and Soil Samples

Understanding microbial community structure of metagenomics water and soil samples is a key process in discovering functions and impact of microorganisms on human and animal health. Evolution of Next Generation Sequencing (NGS) technology has encouraged researchers to sequence large quantity of microbial data from environmental sources. Clustering marker gene sequences into Operational Taxonomic Units (OTU) is the most significant task in microbial community analysis. Several methods have been developed over the years to improve OTU picking strategies. However, building strongly connected OTUs is a major issue in majority of these methods. Herein we present ConClust, a novel method for clustering OTUs that is based on quantifying connectivity among the sequences. Experimental analysis on two synthetic datasets and two real world datasets from water and soil samples demonstrate that our method can mine robust OTUs. Our method can be highly benelicial to study functions of known and unknown microbes and analyze their positive and negative effect on the environment as well as human and animal health.
Histological indicators of cutaneous lesions caused by electrocution, flame burn and impact abrasion

Cutaneous electrocution marks are the key indicator that aid forensic pathologists in establishing electrocution as the cause of death, especially when crime scene and internal autopsy findings do not provide significant information. The gross findings of electrocution mark are often confused with impact abrasion and the burns produced by high voltage flash are often indistinguishable with flame burns. The present study aims to identify cutaneous light microscopic histological indicators, which are peculiar to electrocution marks, burns and impact abrasions. Cutaneous tissue samples from injury sites and healthy areas were collected from 45 autopsy cases and examined under light microscope. The histological changes observed were intra-epidermal separation of cells, sub-epidermal separation, coagulative necrosis in the epidermis and dermis, epidermal and dermal cell nuclear elongation and streaming, pyknotic tightly packed epidermal nuclei, dark staining of epidermal nuclei, homogenization of dermal collagen, and vascular dilatation, congestion, hemorrhage and thrombosis. The study revealed that certain histological changes are helpful in differentiating electrocution mark from other types of injuries, which present themselves in various types morphologically and thus facilitate correct diagnosis during autopsy. Pyknotic and tightly packed epidermal nuclei was found characteristic of electrocution mark produced by high voltage current.
SVSR: A Program to Simulate Structural Variations and Generate Sequencing Reads for Multiple Platforms

Structural variation accounts for a major fraction of mutations in the human genome and confers susceptibility to complex diseases. Next generation sequencing along with the rapid development of computational methods provides a cost-effective procedure to detect such variations. Simulation of structural variations and sequencing reads with real characteristics is essential for benchmarking the computational methods. Here, we develop a new program, SVSR, to simulate five types of structural variations (indels, tandem duplication, CNVs, inversions, and translocations) and SNPs for the human genome and to generate sequencing reads with features from popular platforms (Illumina, SOLiD, 454, and Ion Torrent). We adopt a selection model trained from real data to predict copy number states, starting from the first site of a particular genome to the end. Furthermore, we utilize references of microbial genomes to produce insertion fragments and design probabilistic models to imitate inversions and translocations. Moreover, we create platform-specific errors and base quality profiles to generate normal, tumor, or normal-tumor mixture reads. Experimental results show that SVSR could capture more features that are realistic and generate datasets with satisfactory quality scores. SVSR is able to evaluate the performance of structural variation detection methods and guide the development of new computational methods.
Long non-coding RNA, CHRF, predicts poor prognosis of lung adenocarcinoma and promotes cell proliferation and migration

Research has demonstrated that long non-coding RNAs (lncRNAs) are crucial factors in carcinogenesis. LncRNA, cardiac hypertrophy-related factor (CHRF), has been demonstrated to act as an oncogene in a variety of types of tumor. However, its biological function in lung adenocarcinoma remains to be elucidated. The present study aimed to examine the level of CHRF expression in lung adenocarcinoma tissues and cell lines, and to analyze the association between CHRF and clinicopathological characteristics, as well prognosis of patients with lung adenocarcinoma. Loss-of-function assays were performed to determine the biological function of CHRF. The expression of CHRF was markedly upregulated in lung adenocarcinoma tissues and cell lines. Patients exhibiting upregulated CHRF also demonstrated advanced Tumor-Node-Metastasis stage, lymph node metastasis and larger tumor size compared with those exhibiting downregulated CHRF. Results of Cox proportional hazards regression analysis suggested that highly-expressed CHRF may be regarded as an independent prognostic factor of prognosis. In addition, loss-of-function assays indicated that downregulation of CHRF suppressed cell proliferation, migration and invasion, and induced cell cycle arrest and apoptosis. Western blotting revealed that the phosphoinositide-3-kinase/Akt signaling pathway activity is reduced in lung adenocarcinoma following the knockdown of CHRF. Together, these results indicate that lncRNA, CHRF, may serve a critical role in the development and progression of lung adenocarcinoma, and may act as a novel prognostic biomarker and therapeutic target in lung adenocarcinoma.
Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective

Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique.
d-FuzzStream: A Dispersion-Based Fuzzy Data Stream Clustering

Fuzzy clustering algorithms have recently been investigated as appropriate techniques to extract knowledge from Data Streams due to their unsupervised nature and flexibility to deal with changes in the distribution of data. While most fuzzy clustering algorithms for Data Streams are based on chunks, the FuzzStream algorithm, proposed before by the authors of this paper, pioneered a fuzzy extension of a different approach known as the Online-Offline Framework (OOF). The extended framework, named Fuzzy Online-Offline Framework (FOOF), includes two steps known as fuzzy abstraction and fuzzy clustering. The fuzzy abstraction step continuously summarizes data in a set of cluster features called Fuzzy Micro Cluster (FMiC). Then, these FMiCs are later clustered in the fuzzy clustering step to generate the data partition. Although FuzzStream has shown to be more robust than other OOF-based algorithms, the fuzzy abstraction process in the algorithm overly reduces the data summarization, almost producing one FMiC for each example, also suffering from high overlapping FMiCs. Furthermore, the algorithm has a long processing time due to its need to calculate membership matrices for every example. In this paper we propose the d-FuzzStream algorithm, an adaptation of FuzzStream using the concepts of fuzzy dispersion and fuzzy similarity in order to improve the data summarization while minimizing the complexity of the algorithm. Experiments showed that the proposed algorithm generates FMiCs with higher representativeness and lower execution time than its original version, still producing similar clustering results.
Using Complexity Measures to Evolve Synthetic Classification Datasets

Machine Learning studies usually involve a large volume of experimental work. For instance, any new technique or solution to a classification problem has to be evaluated concerning the predictive performance achieved in many datasets. In order to evaluate the robustness of the algorithm face to different class distributions, it would be interesting to choose a set of datasets that spans different levels of classification difficulty. In this paper, we present a method to generate synthetic classification datasets with varying complexity levels. The idea is to greedly exchange the labeling of a set of synthetically generated points in order to reach a given level of classification complexity, which is assessed by measures that estimate the difficulty of a classification problem based on the geometrical distribution of the data.
Material Classification from Imprecise Chemical Composition : Probabilistic vs Possibilistic Approach

In this paper we propose a method of explainable material classification from imprecise chemical compositions. The problem of classification from imprecise data is addressed with a fuzzy decision tree whose terms are learned by a clustering algorithm. We deduce fuzzy rules from the tree, which will provide a justification of the result of the classification. Two opposed approaches are compared : the probabilistic approach and the possibilistic approach.
Overview of Deep Learning Architectures for EEG-based Brain Imaging

Despite numerous successful applications of Deep Learning (DL) to large-scale image, video, speech and text data, they remain relatively unexplored in brain imaging field. In this paper, we make an overview of recent DL architectures for recognizing cognitive brain activities from Electroencephalogram (EEG) data with particular emphasis on Brain Computer Interface(BCI) technologies and Affective Neurocomputing. We discuss the use of convolutional, recurrent neural nets, as well as deep belief networks, echo-state networks, reservoir computing, and denoising auto encoder models. A major challenge in modeling brain cognitive activity from EEG data is finding representations that are invariant to inter- and intra-subject differences, as well as the inherent noise in the EEG recordings. The reviewed studies reveal the great potential of DL to decode human intentions in BCI applications and to find the invariant descriptors of human emotions across subjects in Affective Neurocomputing applications. Many of the DL models prove to be more accurate and efficient than traditional machine learning models.
Integrated Balancing of an RTS Game: Case Study and Toolbox Refinement

A recent publication suggested an integrated process for game balancing by means of a combination of manual and automated balancing approaches. In this work, we refine the approach and show how to apply it to a "real game", not a simple prototype, namely to an open source clone of the real-time strategy game "Red Alert". Specifically, we are interested in how to utilize the outcomes of automated balancing to inform and direct our iterated approach. This enables us to follow and explore several diverse balancing solutions at the same time, which should in turn improve performance and reliability of the balancing process. We are not primarily interested in the determined solutions, but in the process that can easily be adapted to other games and balancing goals.
Therapie der Dupuytren’schen Kontraktur mit Kollagenase

Zusammenfassung Die Dupuytren’sche Kontraktur (DK) oder Morbus Dupuytren ist eine progressive fibroproliferative Erkrankung des palmoplantaren Bindegewebes, bei der es zu einer charakteristischen Knoten- und/oder Strangbildung aus Kollagen kommt. Bei fortschreitender Erkrankung führt die Strangverdickung zu einer Beugekontraktur des betroffenen Fingers, die in einer deutlichen Behinderung der Betroffenen münden kann, insbesondere bei beidseitigem Befall. Die Erkrankung ist in Europa relativ verbreitet, wobei die Prävalenz in nordischen Ländern am höchsten ist. In Österreich geht man von rund 200 000 Betroffenen aus. Die Inzidenz steigt mit zunehmendem Alter, wobei Männer häufiger und früher erkranken als Frauen. Die Ätiologie der DK ist nicht vollständig geklärt, scheint jedoch multifaktoriell zu sein, wobei eine genetische Prädisposition als gesichert gilt. Der natürliche Verlauf der Erkrankung kann zwischen relativ gutartig und massiver Progredienz und Rezidivhäufigkeit variieren. Zumeist findet sich ein schubweiser Verlauf. Die DK ist nicht heilbar; die Behandlungsmethoden reichen von minimalinvasiven bis zu offenen chirurgischen Verfahren. Collagenase Clostridium histolyticum (CCH) ist eine nichtchirurgische, enzymatische Injektionsbehandlung für erwachsene Patienten (≥ 18 Jahre), die in Europa seit 2011 zur Behandlung der DK mit einem tastbaren Strang zugelassen ist. Klinische Studien und praktische Erfahrungen einzelner Zentren bestätigen die Wirksamkeit und Sicherheit der CCH-Behandlung von Beugekontrakturen. Das vorliegende Konsensus Statement wurde unter der Ägide der Österreichischen Gesellschaft für Handchirurgie und der Mitwirkung der Österreichischen Gesellschaften für Unfallchirurgie, Orthopädie und orthopädische Chirurgie sowie für Plastische, Ästhetische und Rekonstruktive Chirurgie erstellt. Es beschreibt die verschiedenen chirurgischen Verfahren unter besonderer Berücksichtigung der Kollagenase-Behandlung und stellt eine Orientierungshilfe für deren Einsatz dar. Das Statement soll nicht nur den Stand des Wissens der DK-Behandlung abbilden, sondern auch als Richtschnur dienen und zu einem einheitlich hohen Qualitätsstandard der Kontraktur-Behandlung in chirurgischen Zentren und spezialisierten Arztpraxen in ganz Österreich beitragen.
Verbal and nonverbal predictors of executive function in early childhood

ABSTRACT The study of executive function (EF) has become increasingly popular in multiple areas of research. A wealth of evidence has supported the value of EF in shaping notable outcomes across typical and atypical development; however, little evidence has supported the cognitive contributors to early EF development. The current study used data from a large longitudinal sample of healthy children to investigate the differential influence of verbal and nonverbal cognition on later EF. Participants were assessed at 2 years of age using the Mullen Scales of Early Learning, and Mullen scores were used to calculate nonverbal and verbal developmental quotients. Executive function was measured at 6 years using assessments from the Stanford-Binet, Cambridge Neuropsychological Test Automated Battery, and the Behavior Rating Inventory of Executive Function. Results suggested that early nonverbal cognition was a better predictor of 6-year EF as measured by task-based laboratory assessments, whereas verbal cognition was a better predictor of parent-reported EF. Findings are discussed in regard to EF development and characteristics of EF measurement.