Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational prediction of SARS-CoV-2 encoded miRNAs and their putative host targets

Over the past two decades, there has been a continued research on the role of small non-coding RNAs including microRNAs (miRNAs) in various diseases. Studies have shown that viruses modulate the host cellular machinery and hijack its metabolic and immune signalling pathways by miRNA mediated gene silencing. Given the immensity of coronavirus disease 19 (COVID-19) pandemic and the strong association of viral encoded miRNAs with their pathogenesis, it is important to study Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) miRNAs. To address this unexplored area, we identified 8 putative novel miRNAs from SARS-CoV-2 genome and explored their possible human gene targets. A significant proportion of these targets populated key immune and metabolic pathways such as MAPK signalling pathway, maturity-onset diabetes, Insulin signalling pathway, endocytosis, RNA transport, TGF-β signalling pathway, to name a few. The data from this work is backed up by recently reported high-throughput transcriptomics datasets obtains from SARS-CoV-2 infected samples. Analysis of these datasets reveal that a significant proportion of the target human genes were down-regulated upon SARS-CoV-2 infection. The current study brings to light probable host metabolic and immune pathways susceptible to viral miRNA mediated silencing in a SARS-CoV-2 infection, and discusses its effects on the host pathophysiology.
Orbitofrontal cortex neurons code utility changes during natural reward consumption as correlates of relative reward-specific satiety

Natural, on-going reward consumption can differentially reduce the subjective value (‘utility’) of specific rewards, which indicates relative, reward-specific satiety. Two-dimensional choice indifference curves (IC) represent the utility of choice options with two distinct reward components (‘bundles’) according to Revealed Preference Theory. We estimated two-dimensional ICs from stochastic choices and found that natural on-going consumption of two bundle rewards induced specific IC distortions that indicated differential reduction of reward utility indicative of relative reward-specific satiety. Licking changes confirmed satiety in a mechanism-independent manner. Neuronal signals in orbitofrontal cortex (OFC) that coded the value of the chosen option followed closely the consumption-induced IC distortions within recording periods of individual neurons. A neuronal classifier predicted well the changed utility inferred from the altered behavioral choices. Neuronal signals for more conventional single-reward choice options showed similar relationships to utility alterations from on-going consumption. These results demonstrate a neuronal substrate for the differential, reward-specific alteration of utility by on-going reward consumption reflecting reward-specific satiety. Significance Repeated delivery reduces the subjective value (‘utility’) of rewards to different degrees depending on their individual properties, a phenomenon commonly referred to as sensory-specific satiety. We tested monkeys during economic choice of two-component options. On-going consumption differentially reduced reward utility in a way that suggested relative reward-specific satiety between the two components. Neurons in the orbitofrontal cortex (OFC) changed their responses in close correspondence to the differential utility reduction, thus representing a neuronal correlate of relative reward-specific satiety. Control experiments with conventional single-component choice showed similar satiety-induced differential response reductions. These results are compatible with the notion of OFC neurons coding crucial decision variables robustly across different satiety levels.
DSAT: Ontology-based Information Extraction on Technical Data Sheets

Current engineering design processes oftentimes involve transferring information from manufacturer-provided data sheets into domain-speciﬁc design tools. While most data sheets are provided only as PDF ﬁles, this remains a tedious and manual task. This paper presents the Data Sheets Annotation Tool (DSAT), which assists engineers in gathering the information required in the design process. Using an Ontology-Based Information Extraction (OBIE) method, the properties of components are extracted from data sheets and subsequently presented in an integrated, web-based interface. Engineers can now review and correct these automatic annotations, before exporting them for further use. In the demonstration, we employ a real-world use case rooted in model-based space-system engineering. We show how the automated process can extract relevant component-attributes from technical data sheets and how users can redact the results. We further highlight the impact of content and quality of the underlying ontologies.
A Framework for Resource Dependent EDSLs in a Dependently Typed Language (Artifact)

Idris’ Eﬀects library demonstrates how to embed resource dependent algebraic eﬀect handlers into a dependently typed host language, providing run-time and compile-time based reasoning on type-level resources. Building upon this work, Resources is a framework for realising Embedded Domain Speciﬁc Languages (EDSLs) with type systems that contain domain speciﬁc substructural properties. Diﬀering from Eﬀects , Resources allows a language’s substructural properties to be encoded within type-level resources that are associated with language variables. Such an association allows for multiple eﬀect instances to be reasoned about autonomically and without explicit type-level declaration. Type-level predicates are used as proof that the language’s substructural properties hold. Several exemplar EDSLs are presented that illustrates our framework’s operation and how dependent types provide correctness-by-construction guarantees that substructural properties of written programs hold.
The Dynamic Spectrum Access Policy Framework in Action

Because radio spectrum is a ﬁnite resource, its usage and sharing is regulated by government agencies through policies that manage spectrum allocation. With more portions of the spectrum being licensed for commercial use, the importance of providing an increased level of automation when evaluating such policies becomes crucial for the eﬃciency and eﬃcacy of spectrum management. This poster show-cases the Dynamic Spectrum Access Policy Framework, which acts as a machine-readable policy repository providing policy management features and spectrum access request evaluation. It includes the use of the framework’s policy management capabilities to create and modify policies in a novel policy representation using two recommended web standards (OWL and PROV-O), and the request evaluation engine to verify the assignment of permit/deny eﬀects to spectrum requests.
Towards Multiple Ontology Merging with CoMerger

To obtain a knowledge graph representing a domain of interest, it is often necessary to combine several, independently developed ontologies. Existing approaches are mostly limited to binary merge and lack scalability. This paper presents the development of an e ﬃ cient multiple ontologies merging method, C o M erger . For e ﬃ cient processing, rather than directly merging a large number of ontologies, we group related concepts across ontologies into partitions and merge ﬁrst within and then across those partitions. Experiments on real-life datasets conﬁrm the feasibility of our approach and demonstrate its superiority over binary strategies. Our implementation is available through a live web portal.
Anu Question Answering System

AnuQA is a question answering system built on top of a search index and an enterprise knowledge graph. In this work, we describe five semantic technologies that have helped us address real world challenges in deploying this system. These challenges include bias in knowledge base population, entity re-resolution on streaming data, ontology alignment across data sources, explaining relationships, and providing a single unified query interface for business analytics.
A Demonstration of CodeBreaker: A Machine Interpretable Knowledge Graph for Code

Knowledge graphs have been extremely useful in powering diverse applications like natural language understanding. CodeBreaker attempts to construct machine interpretable knowledge graphs about program code to similarly power diverse applications such as code search, code understanding, and code automation. We have built such a 1.98 billion edges knowledge graph by a detailed analysis of function usage in 1.3 million Python programs in GitHub, documentation about the functions in 2300+ modules, forum discussions with more than 47 million posts, class hierarchy information, etc. In this work, we will demonstrate one application of this knowledge graph, which is a code recommendation engine for programmers within an IDE. All user interactions within the application get translated into SPARQL queries, which have quite different characteristics than queries against traditional knowledge graphs such as DBpedia or Wikidata. Aspects of code such as data ﬂow are inherently transitive, hence the SPARQL is complex and requires property paths. One of our goals is to provide these queries as a basis for graph querying benchmarks, while allowing users the ability to interact with a real application built on top of a large graph database.
Serving Bosch Production Data as Virtual KGs

Analyses of manufacturing processes is vital for eﬀective and eﬃcient manufacturing. In complex industrial settings, such analyses should account for data that comes from many diﬀerent and highly heterogeneous machines, and thus are aﬀected by the data integration challenge. In this work, we show how this challenge can be addressed with semantics using Virtual Knowledge Graphs. For this purpose, we propose the SIB Framework, in which we semantically integrate Bosch manufacturing data. In this demo we we present SIB in action on 2 scenarios for the analysis of the Surface Mounting Process (SMT) pipeline.
Wikipedia Category Ontology: A Framework for Utilization of the Wikipedia Category Structure by Knowledge Engineers

Wikipedia categories are intended to group together pages on similar topics and are organized in a hierarchical structure. Since the editorial policy for Wikipedia categories diﬀers from policies used by knowledge engineers, various types of relationship exist in its category structure. In this paper, we propose a novel framework called “Wikipedia category ontology” (WCO) that aims to act as a basis for interpreting the Wikipedia category structure and is based on a classiﬁcation of category and relationship types. WCO enables particular Wikipedia category substructures to be extracted, including class–subclass hierarchies, class–instance, and sets of diﬀused categories for a particular category. WCO is available online in the form of linked open data at http://wcontology.org/ .
A High-Throughput Screen of a Library of Therapeutics Identifies Substrates of P-glycoprotein

The ATP-binding cassette transporter P-glycoprotein (P-gp) is known to limit brain penetration of many chemotherapy drugs. Although Food and Drug Administration guidelines require that potential interactions of investigational drugs with P-gp be explored, often this information does not enter into the literature. As such, we developed a high-throughput screen (HTS) to identify substrates of P-gp from a series of chemical libraries, testing a total of 10,804 compounds, most of which have known mechanisms of action. We used the CellTiter-Glo viability assay to test library compounds against parental KB-3-1 human cervical adenocarcinoma cells and the colchicine-selected sub-line KB-8-5-11 that over-expresses P-gp. KB-8-5-11 cells were also tested in the presence of a P-gp inhibitor (tariquidar) to assess reversability of transporter-mediated resistance. Of the tested compounds, a total of 90 P-gp substrates were identified including 55 newly identified P-gp substrates. Substrates were confirmed using an orthogonal killing assay against HEK-293 cells transfected with P-gp. We confirmed that AT7159 (cyclin-dependent kinase inhibitor); AT9283, (Janus kinase 2/3 inhibitor); ispinesib (kinesin spindle protein inhibitor); gedatolisib (PKI-587, phosphoinositide 3-kinase/mammalian target of rampamycin inhibitor); GSK-690693 (AKT inhibitor); and KW-2478 (heat shock protein 90 inhibitor) were substrates, and direct ATPase stimulation was assessed. ABCG2 was also found to confer high levels of resistance to AT9283, GSK-690693 and gedatolisib, while ispinesib, AT7519 and KW-2478 were weaker substrates. Combinations of P-gp substrates and inhibitors were assessed to demonstrate on-target synergistic cell killing. This data will be of use in determining understanding how chemotherapeutic agents will cross the blood-brain barrier.
A Time-domain Monaural Speech Enhancement with Feedback Learning

In this paper, we propose a type of neural network with feedback learning in the time domain called FTNet for monaural speech enhancement, where the proposed network consists of three principal components. The first part is called stage recurrent neural network, which is introduced to effectively aggregate the deep feature dependencies across different stages with a memory mechanism and also remove the interference stage by stage. The second part is the convolutional auto-encoder. The third part consists of a series of concatenated gated linear units, which are capable of facilitating the information flow and gradually increasing the receptive fields. Feedback learning is adopted to improve the parameter efficiency and therefore, the number of trainable parameters is effectively reduced without sacrificing its performance. Numerous experiments are conducted on TIMIT corpus and experimental results demonstrate that the proposed network can achieve consistently better performance in terms of both PESQ and STOI scores than two state-of-the-art time domain-based baselines in different conditions.
COVID-19 lockdown induces disease-mitigating structural changes in mobility networks

Significance During the COVID-19 pandemic, mobility restrictions have proved to be an effective mitigation strategy in many countries. To apply these measures more efficiently in the future, it is important to understand their effects in detail. In this study, we use mobile phone data to uncover profound structural changes in mobility in Germany during the pandemic. We find that a strong reduction of long-distance travel rendered mobility to be more local, such that distant parts of the country became less connected. We demonstrate that due to this loss of connectivity, infectious diseases can be slowed down in their spatial spread. Our study provides important insights into the complex effects of mobility restrictions for policymakers and future research. In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the “small-world” effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions.
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch

We examine the problem of transferring a policy learned in a source environment to a target environment with different dynamics, particularly in the case where it is critical to reduce the amount of interaction with the target environment during learning. This problem is particularly important in sim-to-real transfer because simulators inevitably model real-world dynamics imperfectly. In this paper, we show that one existing solution to this transfer problem - grounded action transformation - is closely related to the problem of imitation from observation (IfO): learning behaviors that mimic the observations of behavior demonstrations. After establishing this relationship, we hypothesize that recent state-of-the-art approaches from the IfO literature can be effectively repurposed for grounded transfer learning.To validate our hypothesis we derive a new algorithm - generative adversarial reinforced action transformation (GARAT) - based on adversarial imitation from observation techniques. We run experiments in several domains with mismatched dynamics, and find that agents trained with GARAT achieve higher returns in the target environment compared to existing black-box transfer methods
Balancing exploration and exploitation with information and randomization

Explore-exploit decisions require us to trade off the benefits of exploring unknown options to learn more about them, with exploiting known options, for immediate reward. Such decisions are ubiquitous in nature, but from a computational perspective, they are notoriously hard. There is therefore much interest in how humans and animals make these decisions and recently there has been an explosion of research in this area. Here we provide a biased and incomplete snapshot of this field focusing on the major finding that many organisms use two distinct strategies to solve the explore-exploit dilemma: a bias for information ('directed exploration') and the randomization of choice ('random exploration'). We review evidence for the existence of these strategies, their computational properties, their neural implementations, as well as how directed and random exploration vary over the lifespan. We conclude by highlighting open questions in this field that are ripe to both explore and exploit.
First-principles study of dopant trap level and concentration in Si(110)/a-SiO2 interface

We investigate the dopant trap level and equilibrium concentration of Si(110)/a-SiO2 2 interface with a wide variety of dopants (B, C, N, Br, Cl, F and H). The electronic and atomic properties of intrinsic and extrinsic defects are analyzed using First-principles calculation. It is shown that the average trap levels for hole and electron deepen as the electronegativity of the dopant increases. Also, we applied a simple thermodynamic model to evaluate the equilibrium concentration of active trap as a function of dopant concentration at the interface. From the model it turns out that H and F completely passivate the intrinsic Pb center of Si and reduce the trap concentration, while other elements, especially N, Br and Cl, induces new trap states which amounts to several times more than the pre-existing Pb center.
Correlation Matrix Bordering for Optimal Massive MIMO Power Allocation Algorithms

The dramatic increase in the amount of communicating antenna elements in Massive and Ultra-Massive MIMO systems on the one hand necessitates the application of the optimized (in case of multipath fading) power allocation strategies and on the other exploiting fast and efficient algorithms of channel correlation matrix estimation. The research addresses both problems by proposing a bordering procedure applied to the mercury/water-filling power allocation algorithm. The performed analysis demonstrated its sufficient dominance (in terms of overall system capacity) over the equal gain algorithm and insignificant loss, compared to the classic water-filling method. It was shown that bordering of the correlation matrix of Massive MIMO system up to pentadiagonal structures introduces insignificant capacity deviation, hence can be successfully exploited in practice for its estimation via fast recursive procedures.
ITO/ZnO/CH3NH3PbI3/Ag Hetro-Structure based Photodetector

This paper reports ZnO/CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> heterostructure based Photodetector fabricated on an ITO coated glass substrate. The CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> and ZnO nanoparticles are prepared using a low-cost sol-gel chemical route. The synthesized ZnO and CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> characterized using XRD, HRSEM, AFM, UV-visible (UV) Spectroscopy and Photo luminance (PL) Spectroscopy. XRD result confirms the growth of the Hexagonal structure of metal oxide ZnO nanoparticles. And HRSEM image of ZnO and CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> shows the even uniform growth and distribution of nanoparticles. An efficient Photoluminance band is shown at ∼796 nm in perovskite CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> thin and ∼447 nm in ZnO thin film at room temperature when excited with light of wavelength 430 nm and 365 nm respectively. Finally, ZnO/CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> based heterojunction photodiode is characterized for optoelectronics properties using ITO/ZnO/CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub>/Ag fabricated heterojunction device.
An Enhanced 0.8$\text{V}_\text{OC}$-Model-Based Global Maximum Power Point Tracking Method for Photovoltaic Systems

Under partial shading conditions (PSC), the power-voltage (<italic>P–V</italic>) characteristic curve of a photovoltaic string exhibits multiple peaks, posing a big challenge to the problem of global maximum power point tracking (GMPPT). The traditional 0.8<inline-formula><tex-math notation="LaTeX">$\text{V}_\text{OC}$</tex-math></inline-formula>-model-based GMPPT method locates the global maximum power point (GMPP) locus by comparing the power at each local power peak. However, a considerable amount of time is required for iteratively scanning each 0.8<inline-formula><tex-math notation="LaTeX">$\text{V}_\text{OC}$</tex-math></inline-formula> vicinity. To address this problem, an improved 0.8<inline-formula><tex-math notation="LaTeX">$\text{V}_\text{OC}$</tex-math></inline-formula>-model-based GMPPT method is proposed in this article. A shading vector is introduced to characterize the PSC. The proposed GMPPT method estimates the 0.8<inline-formula><tex-math notation="LaTeX">$\text{V}_\text{OC}$</tex-math></inline-formula> region with the GMPP directly from the measured shading vector by the <italic>k</italic>-nearest neighbors approach and saves the time consumed in the comparison process used in the conventional method. Simulation and experimental results demonstrate that the proposed method is capable of tracking the GMPP efficiently and accurately under various shading patterns.