Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Tools for Identification and Analysis of Neuronal Population Activity

Recently-developed technologies for monitoring activity in populations of neurons make it possible for the first time, in principle, to ask many basic questions in neuroscience. However, computational tools for analyzing newly available data need to be developed. The goal of this thesis is to contribute to this effort by focusing on two specific problems. First, we used a point-process regression framework to provide a methodology for statistical assessment of the link between neural spike synchrony and network-wide oscillations. In simulations, we showed that our method can recover ground-truth relationships, and in two types of spike train data we illustrated the kinds of results the method can produce. The approach improves on methods in the literature and may be adapted to many different experimental settings. Second, we considered the problem of source extraction in calcium imaging data, i.e., the detection of neurons within a field of view and the extraction of each neuron’s activity. The data we mainly focus on are recorded with a microendoscope, which has the unique advantage of imaging deep brain regions in freely behaving animals. These data suffer from high levels of background fluorescence, as well as the potential for overlapping neuronal signals. Based on the existing constrained nonnegative matrix factorization (CNMF) framework, we developed an efficient method to process microendoscopic data. Our method utilizes a novel algorithm to initialize the spatial shapes and temporal activity of the neurons from the raw video data independently from the strong fluctuating background. This step ensures the efficiency and accuracy of solving a nonconvex CNMF problem. Our method also models the complicated background by including its low-spatial frequency structure and the locally-low-rank feature to avoid absorbing cellular signals into the background term. We developed a tractable solution to estimate the background activity using this new model. After subtracting the approximated background, we followed the CNMF framework to demix neural signals and recover denoised and deconvolved temporal activity. We optimized several algorithms in solving the CNMF problems to get accurate results. In practice, our method outperforms all existing methods and has been adopted by many experimental labs.
Learning Reliable Policies in the Bandit Setting with Application to Adaptive Clinical Trials

The stochastic multi-armed bandit problem is a well-known model for studying the explorationexploitation trade-off. It has significant possible applications in adaptive clinical trials, which allow for a dynamic change of patient allocation ratios. However, most bandit learning algorithms are designed with the goal of minimizing the expected regret. While this approach is useful in many areas, in clinical trials, it can be sensitive to outlier data especially when the sample size is small. In this article, we propose a modification of the BESA algorithm [Baransi, Maillard, and Mannor, 2014] which takes into account the variance in the action outcomes in addition to the mean. We present a regret bound for our approach and evaluate it empirically both on synthetic problems as well as on a dataset form the clinical trial literature. Our approach compares favorably to a suite of standard bandit algorithms.
A visual tracking method based on deep learning without online model updating

The paper proposes a visual tracking method based on deep learning without online model updating. In consideration of the advantages of deep learning in feature representation, deep model SSD (Single Shot Multibox Detector) is used as the object extractor in the tracking model. Simultaneously, the color histogram feature and HOG (Histogram of Oriented Gradient) feature are combined to select the tracking object. In the process of tracking, multi-scale object searching map is built to improve the detection performance of deep detection model and the tracking efficiency. In the experiment of eight respective tracking video sequences in the baseline dataset, compared with six state-of-the-art methods, the method in the paper has better robustness in the tracking challenging factors, such as deformation, scale variation, rotation variation, illumination variation, and background clutters, moreover, its general performance is better than other six tracking methods.
A Multi-Source Linked Open Data Fusion Method for Gene Disorder Drug Relationship Querying

Biomedical data are gradually increasing. More and more research focused on Relationship querying between genes, drugs, and disorders. Generally, genes, drugs, and disorders information were stored in different heterogeneous datasets. These datasets were stored in different places and different formats, such as RDF/XML, SQL relational databases, and text, etc. The challenge is the fusion of multi-source and cross-platform biomedical datasets for most application based on gene disorder drug Relationship querying. To tackle these problems, we propose a novel multi-source linked open data fusion method for gene disorder drug Relationship querying. In this method, a variety of biomedical datasets are converted into RDF triple data; and then multi-source datasets are formed into a storage system with data fusion method. After fusion, the sys-tem can query the relationships among various entities from different datasets. The experiment results demonstrate that our method significantly has advantages in integrating multi-source heterogeneous biomedical datasets with high efficiency and reliability. The SPARQL query experiment is carried on 4 different datasets by using 9 kinds of common query questions proposed in this paper. The results show that most of the query results came from different datasets. the method can be used to fusion more other different biomedical datasets.
Framework for Observing the Maintenance Needs, Runtime Metrics and the Overall Quality-in-Use

The post-release maintenance is usually the most expensive phase in the software product lifecycle from the first design concepts to the end of product support. To reduce the costs related to post-release maintenance, we propose a run-time framework for measuring software quality characteristics applying the ISO/IEC 25000 software quality and software quality in use models as the starting point. Measurement probes are linked into the software during the development phase and used to collect quality information during the run time. As a proof-of-concept, we implemented measurements in an open-source software project to demonstrate the utility of the framework. As a result, this paper presents a framework for collecting runtime metrics and measuring software quality-in-use with a systematic interface. Additionally, examples of measurement scenarios are presented.
Listing Induced Steiner Subgraphs as a Compact Way to Discover Steiner Trees in Graphs

This paper investigates induced Steiner subgraphs as a variant of the classical Steiner trees, so as to compactly represent the (exponentially many) Steiner trees sharing the same underlying induced subgraph. We prove that the enumeration of all (inclusion-minimal) induced Steiner subgraphs is harder than the well-known Hypergraph Transversal enumeration problem if the number of terminals is not ﬁxed. When the number of terminals is ﬁxed, we propose a polynomial delay algorithm for listing all induced Steiner subgraphs of minimum size. We also propose a polynomial delay algorithm for listing the set of minimal induced Steiner subgraphs when the number of terminals is 3
Global accounting with the workflow management system TIM at Deutsche Bahn Group

The accounting of the Deutsche Bahn Group, a public German railway company with 320,000 employees, went through a centralization process to transfer the entire company’s accounting activities from 350 locations to three shared service centers. In the course of the transformation, processes had to be changed on a global level and a workflow management system was needed in order to automate these processes. The project, Global Accounting Shared Service Center (GASSC) started in 2015, choosing TIM, a workflow management system specialized on human workflows, as one main pillar out of several digital tools that were introduced simultaneously to make this transition possible. A workflowdriven approach was required to efficiently handle all accounting requests and communication efforts, while considering legal and compliance requirements at the same time. The subproject of the GASSC project, for which the TIM product was chosen, was named SeMa – Service Management. The organizational desire for further automation led to efforts to combine the workflow with existing thirdparty systems to meet regulatory requirements, faster turnaround time goals and to realize performance monitoring at a global scale. This was realized by partly using robotic process automation in combination with human workflows. The SeMa project was honored with the “Process Solution Award 2019” in the category of workflow management by the renowned gfo organization in Germany.
Context-Regularized Neural Collaborative Filtering for Game App Recommendation

People spend a substantial amount of time playing games on their smartphones. Owing to growth in the number of newly released games, it is getting more difficult for people to identify which of the broad selection of games they want to play. In this paper, we introduce context-aware recommendation for game apps that combines neural collaborative filtering and item embedding. We find that some contexts special to games are effective in representing item embeddings in implicit feedback situations. Experimental results show that our proposed method outperforms conventional methods.
Semantic Similarity of Side Effect and Indication Relations of Drugs Inferred from Neural Embedding

Patient-reported information on medication-effect experience can contribute to pharmacovigilance, and nowadays patients share their experience on social media which have been investigated for an alternative data source. Extracting the relations between pairs of medication-effect terms from social media data is a challenging task, but inferring the medication-effect relations from known (base) relations using the neural embedding technique seems to be a promising solution. This study aimed at understanding how the similar semantics is carried over from the base relations to inferred relations in the neural embedding of Twitter data. From a set of 99 randomly chosen inferred medicationeffect relations whose associated tweets were manually annotated, we observed that the accuracies of having the inferred relations with the similar semantics to the base relations are 0.586 for medication-side effect relations and 0.688 for medication-indication relations. This demonstrated the utility of inference through relational similarity based upon neural embedding technique.
Generating Knowledge Graphs from Scientific Literature of Degenerative Diseases

Degenerative diseases, such as the Alzheimer’s Disease, can be very serious and life-threatening. As the scientific community strives to fully understand their exact root causes and advance their research on the domain, a massive amount of knowledge is generated. To represent and link all this knowledge, we propose the generation of knowledge graphs from the scientific literature. We aim to provide researchers the ability to relate their new discoveries with the current knowledge and possibly formulate new hypotheses to further advance the research. In this paper, we describe a method to extract information from scientific literature for generating a knowledge graph reusing existing domain ontologies. We demonstrate the effectiveness of our method by generating knowledge graphs from a set of abstracts of scientific papers on Alzheimer’s Disease.
ENHANCEMENT OF PIXEL VALUE ORDERING BASED DATA HIDING BY ROW BLOCK PARTITION

The development of information and communication technology that support digital data transmission such as text, image, audio and video gives several effects. One of them is data security that becomes the main priority during the transmission process. Pixel-Value-Ordering (PVO) which one of data hiding methods can be implemented to achieve the requirement. It embeds data on maximum pixel and minimum pixel in a blok which is a part of the carrier image. However, PVO has capacity a problem, that only 2 bits per block can be hidden. To handle this problem, we propose a new approach by dividing blocks dinamically based on its complexity. These blocks are grouped into 4: smooth block, semi-smooth block, normal block and rough block . Using this approach, the stego capacity can be improved up to 2.6 times in average of previous method by keeping the quality stego more than 65 dB for all testing image.
Digital system for spiking neural network emulation

The present project is about the design, simulation and an experimentational test of a digital system in a single chip able to emulate the behavior of spiking neural networks, which is possible thanks to the use of mathematical models that emulate the behavior of these networks in the brain. A modular system has been proposed in order to provide the necessary flexibility and scalability for the simulation of different neural networks. At the same time the most flexible, simple and efficient option has been chosen in order to have a good performance without losing or reducing the necessary accuracy and exactitude for the emulation of the neural networks. The solution has been implemented by making use of different combinational blocks and totally synchronous flip-flops from a 100 MHz clock signal, besides, the description of the system was performed by using the high-level hardware description language VHDL. Finally, a neural network for pattern recognition has been implemented on a programmable logical device FPGA in order to demonstrate the correct operation of the digital system.
Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph

Deep-learning based techniques are increasingly being used for different machine learning tasks on knowledge graphs. While it has been shown empirically that these techniques often achieve better predictive performances than their classical counterparts, where features are extracted from the graph, they lack interpretability. Interpretability is a vital aspect in critical domains such as the health and financial sector. In this paper, we present a technique that builds a decision tree of class-specific substructures in order to classify different entities within the knowledge graph. We show how our proposed technique is competitive to current state-of-the-art deep-learning techniques on four benchmark datasets, while being fully interpretable.
Combining Context Features in Sequence-Aware Recommender Systems

There are several important design choices that machine learning practitioners need to make when incorporating predictors into RNN-based contextual recommender systems. A great deal of currently reported findings about these decisions focus on the setting where predicted items take on values from the space of sequence items. This work provides an empirical evaluation of some straightforward approaches of dealing with such problems on a real-world large scale prediction problem from the travel domain, where predicted entities do not live in the space of sequence items.
The Complexity of Homomorphism Indistinguishability

For every graph class F , let HomInd(F) be the problem of deciding whether two given graphs are homomorphism-indistinguishable over F , i.e., for every graph F in F , the number hom(F, G) of homomorphisms from F to G equals the corresponding number hom(F, H) for H. For several natural graph classes (such as paths, trees, bounded treewidth graphs), homomorphism-indistinguishability over the class has an efficient structural characterization, resulting in polynomial time solvability [6]. In particular, it is known that two non-isomorphic graphs are homomorphism-indistinguishable over the class Tk of graphs of treewidth k if and only if they are not distinguished by k-dimensional Weisfeiler-Leman algorithm, a central heuristic for isomorphism testing: this characterization implies a polynomial time algorithm for HomInd(Tk), for every fixed k ∈ N. In this paper, we show that there is a polynomial-time-decidable class F of undirected graphs of bounded treewidth such that HomInd(F) is undecidable. Our second hardness result concerns the class K of complete graphs. We show that HomInd(K) is co-NP-hard, and in fact, we show completeness for the class C=P (under P-time Turing reductions). On the algorithmic side, we show that HomInd(P) can be solved in polynomial time for the class P of directed paths. We end with a brief study of two variants of the HomInd(F) problem: (a) the problem of lexographic-comparison of homomorphism numbers of two graphs, and (b) the problem of computing certain distance-measures (defined via homomorphism numbers) between two graphs. 2012 ACM Subject Classification Theory of computation → Graph algorithms analysis; Mathematics of computing → Graph theory
Reading as active sensing : a computational model of gaze planning in word recognition

Word processing has recently been conceptualized as the outcome of simultaneously activating patterns of cortical connectivity, reflecting (possibly redundant) distributional regularities in the input at the graphemic, morpho-syntactic and morpho-semantic levels (Burzio, 2004; Baayen, 2007; Post et al., 2008). This view argues for a more complex and differentiated neurobiologi-cal substrate for human language than both classical dual-route 2007) can posit. Brain areas devoted to word processing appear to maximize the opportunity of using both general and specific information simultaneously (Libben, 2006), rather than maximize processing efficiency and economy of storage. Topological models of lexical self-organization can shed light on such a dynamic view of word processing from a computational perspective (Pirrelli, 2007; Pirrelli et al., in press). In these models , lexical storage and learning is based on the concurrent self-organization of " spatial " word-based information (e.g. segmental or graphemic patterns) and temporal (i.e. sequential) information, accounting for concomitant effects of redundant morphological structure and predictive parsing, as well as for short-term and long-term memory effects in the encoding and processing of symbolic sequences. This makes spatio-temporal self-organizing networks of this kind ideally suitable for investigating anticipatory processes in word recognition and reading. Experimental studies based on ERP (event-related potentials) and eye-movement evidence show that people use prior (lexical and semantic) contextual knowledge to anticipate upcoming words (Altmann and Kamide, 1999; Federmeier, 2007). DeLong et al. (2005) demonstrate that expected words are pre-activated in subjects' brain in a graded fashion, reflecting their expected probability. This body of evidence provides a solid empirical ground to the probabilistic approach to lexical prediction and gaze planning
The role of vitamin D replacement therapy in serum FGF23 concentration in children with myelomeningocele compared with healthy children – a preliminary study

Abstract Background Fibroblast growth factor 23 (FGF23) is a recently discovered bone-derived regulator of vitamin D metabolism and phosphate homeostasis. It inhibits phosphate reabsorption and calcitriol production by the kidney. Myelomeningocele (MMC) remains the most severe form of neural tube defects involving serious locomotor disability, osteoporosis and pathologic fractures. We aimed to investigate the influence of vitamin D replacement therapy on serum FGF23 concentration in children with MMC and compare the results with healthy participants. Methods This prospective analysis was conducted on 16 children with MMC and 20 healthy children. Serum FGF23 levels were measured; for the studied group, before and after vitamin D replacement therapy with cholecalciferol (vitamin D3). The children’s medical charts were analyzed to determine age, sex, anthropometric measurements, calcium and phosphate, cholecalciferol and renal function parameters. Results There were significant differences in vitamin D and FGF23 serum concentrations between the studied groups. The median vitamin D levels in the MMC group increased during replacement therapy (7 vs. 18.5 ng/mL, p = 0.29) in comparison to the median of 25.5 ng/mL in the control group. In MMC children we found a significant decrease in median serum FGF23 after vitamin D replacement therapy (from 42.1 to 0 RU/mL, p < 0.001). FGF23 correlated positively with albumin, serum and urine phosphate levels and negatively with alkaline phosphatase. Conclusions 1. Serum concentration of FGF-23 is increased in MMC children in comparison to a healthy control group. 2. Vitamin D replacement therapy decreases FGF23 concentrations in MMC children, although further studies are still warranted to gain detailed insight on the FGF23 in the MMC population. 3. Children with MMC present vitamin D deficiency. Nutrition supplemented with low doses of cholecalciferol (vitamin D3) (intakes reaching recommended daily allowances) was insufficient to correct 25(OH)-D level in that population of patients.
Mitochondrial Genomes of Two Thaparocleidus Species (Platyhelminthes: Monogenea) Reveal the First rRNA Gene Rearrangement among the Neodermata

Phylogenetic framework for the closely related Ancylodiscoidinae and Ancyrocephalinae subfamilies remains contentious. As this issue was never studied using a large molecular marker, we sequenced the first two Ancylodiscoidinae mitogenomes: Thaparocleidus asoti and Thaparocleidus varicus. Both mitogenomes had two non-coding regions (NCRs) that contained a number of repetitive hairpin-forming elements (RHE). Due to these, the mitogenome of T. asoti (16,074 bp) is the longest among the Monogenea; especially large is its major NCR, with 3500 bp, approximately 1500 bp of which could not be sequenced (thus, the total mitogenome size is ≈ 17,600 bp). Although RHEs have been identified in other monopisthocotyleans, they appear to be independently derived in different taxa. The presence of RHEs may have contributed to the high gene order rearrangement rate observed in the two mitogenomes, including the first report of a transposition of rRNA genes within the Neodermata. Phylogenetic analyses using mitogenomic dataset produced Dactylogyrinae embedded within the Ancyrocephalinae (paraphyly), whereas Ancylodiscoidinae formed a sister-group with them. This was also supported by the gene order analysis. 28S rDNA dataset produced polyphyletic Dactylogyridae and Ancyrocephalinae. The phylogeny of the two subfamilies shall have to be further evaluated with more data.
Comparison of lixisenatide in combination with basal insulin vs other insulin regimens for the treatment of patients with type 2 diabetes inadequately controlled by basal insulin: Systematic review, network meta‐analysis and cost‐effectiveness analysis

To evaluate the comparative efficacy and safety of lixisenatide combined with basal insulin (BI) vs intensive premix insulin (premix), BI plus prandial insulin with the main meal (basal‐plus) or progressively covering all meals (basal‐bolus) in patients with type 2 diabetes mellitus (T2DM) inadequately controlled by BI, and the long‐term cost‐effectiveness of lixisenatide from a Chinese healthcare system perspective.
Application of biophysical and skin imaging techniques to evaluate the film‐forming effect of cosmetic formulations

Products with film‐forming effect, or ‘second skin’, which guarantees an immediate protective effect after application, is a highlight, especially when composed of natural ingredients. Thus, the objective of this study was to evaluate the immediate film‐forming effect on skin of a gel and emulsion formulations added with Kappaphycus alvarezii and Caesalpinia spinosa extracts through biophysical and skin imaging techniques, especially with the Reflectance Confocal Microscopy (RCM).