Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein Backbone Angle Prediction in Multidimensional - Space

A significant step towards establishing the structure and function of a protein is the prediction of the local conformation of the polypeptide chain. In this article we present systems for the prediction of 3 new alphabets of local structural motifs. The motifs are built by applying multidimensional scaling (MDS) and clustering to pair-wise angular distances for multiple - angle values collected from high-resolution protein structures. The predictive systems, based on ensembles of bidirectional recurrent neural network architectures, and trained on a large non-redundant set of protein structures, achieve 72%, 66% and 60% correct structural motif prediction on an independent test set for di-peptides (6 classes), tripeptides (8 classes) and tetra-peptides (14 classes), respectively, 28-30% above base-line statistical predictors. To demonstrate that structural motif predictions contain relevant structural information, we build a further system, based on ensembles of two-layered bidirectional recurrent neural networks, to map structural motif predictions into traditional 3-class (helix, strand, coil) secondary structure. This system achieves 79.5% correct prediction using the “hard” CASP 3-class assignment, and 81.4% with a more lenient assignment, outperforming a sophisticated state-of-the-art predictor (Porter) trained in the same experimental conditions. All the predictive systems will be provided free of charge to academic users and made publicly available at the address http://distill.ucd. ie/.
diffussion in Social Media

The proliferation of social networks in our daily lives has given rise to a plethora of opportunities to explore the flow of information and ideas throughout a network. In a network, information flows in the form of cascades. Analyzing cascade virality is an important, yet challenging, problem in social network analysis. An understanding of the properties of a viral cascade is crucial in order to understand exactly how information and ideas spread, as well as the differences in things that spread vs. things that don’t. It can be used for tasks such as determining the most influential individuals within a network, which translates directly to various practical applications, such as maximizing marketing effectiveness or influencing public opinion. However, cascade virality is a complex phenomenon with many different aspects, making it difficult to model and analyze. When analyzing the propagation of information throughout a network, there are many features of the network one must take into account. For example, consider structural and temporal network features: these two network features alone have a wide-reaching and profound impact on how viral a cascade is or is not, and influence how viral it can or can not be. Structural features of a network include its number of nodes and edges, the amount of clustering, the length of various paths, and its underlying distribution. Temporal features include the speed at which information propagates, the time to end for cascades, and the lifetime of cascades. Other aspects one must consider include the probability of the information spreading from one node to the next, the amount by which it spreads at each level of propagation, the amount of nodes reached in the network, and what kind of environments stop or assist in the spread of cascades. Thus, depending on the context of the underlying network, a cascade’s virality can be defined in many ways. Acquiring a full, coherent understanding and analysis of cascade virality appears daunting when faced with the task of accounting for these many intricacies. With this problem in mind, our goal is to understand and analyze cascade virality and how information spreads in a social network. Specifically, we are interested in identifying the features that are most common for viral cascades and performing various analyses on them. In order to accomplish this, we have chosen to analyze the Twitter network. Twitter was a natural choice for the network to analyze because its sole purpose is spreading information, and the user-follower and retweet relationships give the potential for viral cascades over a massive number of nodes in the network.
Lagrange Programming Neural Network Approach for Target Localization in Distributed MIMO Radar

In this paper, the problem of source localization in distributed multiple-input multiple-output (MIMO) radar using bistatic range measurements, which correspond to the sum of transmitter-to-target and target-to-receiver distances, is addressed. Our solution is based on the Lagrange programming neural network (LPNN), which is an analog neural computational technique for solving nonlinear constrained optimization problems according to the Lagrange multiplier theory. The local stability of the proposed positioning algorithm is also investigated. Furthermore, we have extended the LPNN based approach to more challenging scenarios, namely, when time synchronization among all antennas cannot be fulfilled, and there are position uncertainties in the MIMO radar transmit and receive elements. The optimality of the developed algorithms is demonstrated by comparing with the Cramér-Rao lower bound via computer simulations.
On Network Coding for Stable Multicast Communication

We address the problem of transmitting packets to multiple receiver nodes over single-hop channels with probabilistic reception. The objective is to evaluate the stability properties of different transmission policies with and without network coding. We consider a dynamic network coding scheme based on the instantaneous queue content and we show that network coding increases the stable throughput and reduces the transmission energy costs (at the expense of additional processing energy costs). The comparison of (deterministic and random) linear network coding schemes with plain retransmission policies opens up several new questions regarding the tradeoffs of stable throughput, energy efficiency, packet overhead and complexity.
Construction of a Single Lentiviral Vector Containing Tetracycline-Inducible Alb-uPA for Transduction of uPA Expression in Murine Hepatocytes

The SCID-beige/Alb-uPA mouse model is currently the best small animal model available for viral hepatitis infection studies [1]. But the construction procedure is often costly and time-consuming due to logistic and technical difficulties. Thus, the widespread application of these chimeric mice has been hampered [2]. In order to optimize the procedure, we constructed a single lentiviral vector containing modified tetracycline-regulated system to control Alb-uPA gene expression in the cultured hepatocytes. The modified albumin promoter controlled by tetracycline (Tet)-dependent transactivator rtTA2S-M2 was integrated into a lentiviral vector. The full-length uPA cDNA was inserted into another lentiviral vector containing PTight, a modified Tet-responsive promoter. Two vectors were then digested by specific enzymes and ligated by DNA ligase 4. The ligated DNA fragment was inserted into a modified pLKO.1 cloning vector and the final lentiviral vector was then successfully constructed. H2.35 cell, Lewis lung carcinoma, primary kidney, primary hepatic interstitial and CT26 cells were infected with recombinant lentivirus at selected MOI. The expression of uPA induced by DOX was detectable only in the infected H2.35 cells, which was confirmed by real-time PCR and Western blot analysis. Moreover, DOX induced uPA expression on the infected H2.35 cells in a dose-dependent manner. The constructed single lentiviral vector has many biological advantages, including that the interested gene expression under “Tet-on/off” system is controlled by DOX in a dose-depending fashion only in murine liver cells, which provides an advantage for simplifying generation of conditional transgenic animals.
Massive Query Expansion by Exploiting Graph Knowledge Bases for Image Retrieval

Annotation-based techniques for image retrieval suffer from sparse and short image textual descriptions. Moreover, users are often not able to describe their needs with the most appropriate keywords. This situation is a breeding ground for a vocabulary mismatch problem resulting in poor results in terms of retrieval precision. In this paper, we propose a query expansion technique for queries expressed as keywords and short natural language descriptions. We present a new massive query expansion strategy that enriches queries using a graph knowledge base by identifying the query concepts, and adding relevant synonyms and semantically related terms. We propose a topological graph enrichment technique that analyzes the network of relations among the concepts, and suggests semantically related terms by path and community detection analysis of the knowledge graph. We perform our expansions by using two versions of Wikipedia as knowledge base achieving improvements of the system's precision up to more than 27%.
Environmental Marketplace Advocacy: Influences and Implications of U.S. Public Response

Environmental marketplace advocacy represents a form of corporate issue advocacy and a significant source of environmental communication for the U.S. public. This study tested a model of environmental marketplace advocacy, conceptually grounded in the persuasion knowledge model, with a national U.S. audience. The model predicted 78% of variance in attitudes toward the advertiser and its environmental impact. Contrary to expectations, participants’ environmental concern was positively associated with persuasion; this effect was reversed among those with a graduate degree, a background in science, or membership in an environmental organization. Important implications for both corporate and environmental advocacy are discussed.
Molecular Genetic Analysis of Ethanol Intoxication in Drosophila melanogaster1

Abstract Recently, the fruit fly Drosophila melanogaster has been introduced as a model system to study the molecular bases of a variety of ethanol-induced behaviors. It became immediately apparent that the behavioral changes elicited by acute ethanol exposure are remarkably similar in flies and mammals. Flies show signs of acute intoxication, which range from locomotor stimulation at low doses to complete sedation at higher doses and they develop tolerance upon intermittent ethanol exposure. Genetic screens for mutants with altered responsiveness to ethanol have been carried out and a few of the disrupted genes have been identified. This analysis, while still in its early stages, has already revealed some surprising molecular parallels with mammals. The availability of powerful tools for genetic manipulation in Drosophila, together with the high degree of conservation at the genomic level, make Drosophila a promising model organism to study the mechanism by which ethanol regulates behavior and the mechanisms underlying the organism's adaptation to long-term ethanol exposure.
3-D Map Merging on Pose Graphs

In this letter, we propose an approach for merging three-dimensional maps represented as pose graphs of point clouds. Our method can effectively deal with typical distortions affecting simultaneous localization and mapping-generated maps. Traditional map merging techniques that use a single rigid body transformation to relate the reference frames of different maps. Instead, our approach achieves more accurate results by eliminating the inconsistencies resulting from distortions affecting the inputs, and can succeed in those situations where traditional approaches fail for substantial deformations. The core idea behind our solution is to localize the robot in a reference map by using the data from another map as observations. We validated our approach on publicly available datasets, and provide quantitative results that confirm its effectiveness on challenging instances of the merging problem.
Rank-Me: A Java Tool for Ranking Team Members in Software Bug Repositories

In this paper a team member ranking technique is presented for software bug repositories. Member ranking is performed using numbers of attributes available in software bug repositories, and a ranked list of developers is generated who are participating in development of software project. This ranking is generated from the contribution made by the individual developers in terms of bugs fixed, severity and priority of bugs, reporting newer problems and comments made by the developers. The top ranked developers are the best contributors for the software projects. The proposed algorithm can also be used for classifying and rating the software bugs using the ratings of members participating in the software bug repository.
An Improved Weighted Feature Abstracting Algorithm

In non-supervised data set, the importance of each feature is different. If the feature is setted with a proper weight, which can fully considers the lever of the influence on the cluster effect, then the clustering result will be improved. A feature evaluate function is proposed to obtain a set of feature weight vectors by minimizing the function, which is a multi-objective problem. So a fast and elitist multi-objective genetic algorithm is used to solve the problem and obtain the weight of feature. Finally, the weight of feature is introduced into the standard K-Means algorithm and the experiments on the UCI dataset show the validity of the algorithm.
Axiomatization of General Concept Inclusions from Finite Interpretations

Description logic knowledge bases can be used to represent knowledge about a particular domain in a formal and unambiguous manner. Their practical relevance has been shown in many research areas, especially in biology and the semantic web. However, the tasks of constructing knowledge bases itself, often performed by human experts, is difficult, time-consuming and expensive. In particular the synthesis of terminological knowledge is a challenge every expert has to face. Because human experts cannot be omitted completely from the construction of knowledge bases, it would therefore be desirable to at least get some support from machines during this process. To this end, we shall investigate in this work an approach which shall allow us to extract terminological knowledge in the form of general concept inclusions from factual data, where the data is given in the form of vertex and edge labeled graphs. As such graphs appear naturally within the scope of the Semantic Web in the form of sets of RDF triples, the presented approach opens up the possibility to extract terminological knowledge from the Linked Open Data Cloud. We shall also present first experimental results showing that our approach has the potential to be useful for practical applications.
Object identification with Pay-As-You-Go crowdsourcing

The conventional crowdsourcing paradigm requires an explicit task description and payment scheme. Requesters can then easily determine whether the crowdsourced results are satisfactory, and workers will have a fairly clear expectation of the monetary reward once the task is accomplished. However, such a paradigm becomes problematic when it is applied to Object Identification (OI) tasks. First, for OI tasks, it is difficult for requesters to evaluate whether sufficient numbers of objects have been found by an individual worker, warranting payment. Second, the same objects can be detected by many workers and ending up being unnecessary workload and inefficient performance. In this paper, we design a new crowdsourcing paradigm for OI tasks. Designing such a paradigm is challenging. Firstly, an easily-detected object can be found by multiple workers, which leads to an unfair situation that the requester has to make extra payments for the duplication. Secondly, there is usually a time limit to finish the overall crowdsourcing process, which demands efficient assignment strategy. To address these challenges, we propose solutions to achieve fairness by a Pay-As-You-Go (PAYG) mechanism and efficiency by a new worker-assignment scheme, Adaptive Worker Assignment (AWA). Extensive experiments are conducted to demonstrate the advantages of this new paradigm.
DNA from KI, WU and Merkel Cell Polyomaviruses Is Not Detected in Childhood Central Nervous System Tumours or Neuroblastomas

Background BK and JC polyomaviruses (BKV and JCV) are potentially oncogenic and have in the past inconclusively been associated with tumours of the central nervous system (CNS), while BKV has been hinted, but not confirmed to be associated with neuroblastomas. Recently three new polyomaviruses (KIPyV, WUPyV and MCPyV) were identified in humans. So far KIPyV and WUPyV have not been associated to human diseases, while MCPyV was discovered in Merkel Cell carcinomas and may have neuroepithelial cell tropism. However, all three viruses can be potentially oncogenic and this compelled us to investigate for their presence in childhood CNS and neuroblastomas. Methodology The presence of KI, WU and MCPyV DNA was analysed, by a joint WU and KI specific PCR (covering part of VP1) and by a MCPyV specific regular and real time quantitative PCR (covering part of Large T) in 25 CNS tumour biopsies and 31 neuroblastoma biopsies from the Karolinska University Hospital, Sweden. None of the three new human polyomaviruses were found to be associated with any of the tumours, despite the presence of PCR amplifiable DNA assayed by a S14 housekeeping gene PCR. Conclusion In this pilot study, the presence of MCPyV, KI and WU was not observed in childhood CNS tumours and neuroblastomas. Nonetheless, we suggest that additional data are warranted in tumours of the central and peripheral nervous systems and we do not exclude that other still not yet detected polyomaviruses could be present in these tumours.
The effects of emotional and rational news appeals on political attitudes

The present study investigates how emotional and rational appeals in political news media stories interact with preexisting attitudes in changing citizens’ political attitudes. It is hypothesized that news media appeals that match predispositions are more likely to affect attitudes than mismatching media appeals. That is, people holding attitudes primarily based on affect should be more susceptible to emotionally arousing media content than to rational appeals. For people holding attitudes primarily based on cognition, rational appeals in news media are expected to be more persuasive than emotional appeals. These assumptions were tested in a real-world setting in the run-up of a popular vote on immigration. In order to test the hypotheses, the data of a three-wave panel survey were matched with data from a content analysis of the news coverage on the issue. Results indicate the expected matching effects: Voters whose attitudes are primarily based on affect were more likely to change their attitude when confronted with emotional appeals as compared to rational appeals. In contrast, voters holding cognitively based attitudes were more likely to change their attitude when they received rational appeals as compared to emotional appeals.
Joint frequency assignment and optimal association of stations to access points in IEEE 802.11 WLAN

Frequency channels are allocated to the access-points (APs) subject to an acceptable co-channel interference level in an IEEE 802.11 WLAN. Due to limited number of the non-overlapping frequency channels, all APs in a given area may not be activated simultaneously. Therefore which subset of the APs to select for activation is a major concern. Given a set of selected APs, often a station (STA) can potentially associate with more than one AP. The association of an STA to an inappropriate AP will not only lead to degraded service for the concern STA but also may pull down the throughput of the other STAs associated with that AP. Thus finding the optimal association between STAs and APs is another important concern. We argue that treating these two issues in succession may lead to suboptimal solutions, whereas, ignoring the co-channel interference may result over estimation of the throughput. In this paper, an integrated model based on integer programming and an efficient greedy algorithm are proposed that address both aspects simultaneously and maximizes the overall throughput while taking care of load balancing across different APs. Computational results show that indeed the integrated approach is superior to both two-step approach and methods that ignore the interference.
A Hybrid Model for Chinese Word Segmentation

This paper describes a hybrid model that combines machine learning with linguistic and statistical heuristics for integrating unknown word identification with Chinese word segmentation. The model consists of two major components: a tagging component that annotates each character in a Chinese sentence with a position-of-character (POC) tag that indicates its position in a word, and a merging component that transforms a POC-tagged character sequence into a word-segmented sentence. The tagging component uses a support vector machine (Vapnik, 1995) based tagger to produce an initial tagging of the text and a transformation-based tagger (Brill, 1995) to improve the initial tagging. In addition to the POC tags assigned to the characters, the merging component incorporates a number of linguistic and statistical heuristics to detect words with regular internal structures, recognize long words, and filter non-words. Experiments show that, without resorting to a separate unknown word identification mechanism, the model achieves an F-score of 95.0% for word segmentation and a competitive recall of 74.8% for unknown word identification.
Adaptive Resource Allocation for QoE-Aware Mobile Communication Networks

A real-time adaptive resource allocation algorithm considering the end user's Quality of Experience (QoE) in the context of video streaming service is presented in this work. An objective no-reference quality metric, namely Pause Intensity (PI), is used to control the priority of resource allocation to users during the scheduling process. An online adjustment has been introduced to adaptively set the scheduler's parameter and maintain a desired trade-off between fairness and efficiency. The correlation between the data rates (i.e. Video code rates) demanded by users and the data rates allocated by the scheduler is taken into account as well. The final allocated rates are determined based on the channel status, the distribution of PI values among users, and the scheduling policy adopted. Furthermore, since the user's capability varies as the environment conditions change, the rate adaptation mechanism for video streaming is considered and its interaction with the scheduling process under the same PI metric is studied. The feasibility of implementing this algorithm is examined and the result is compared with the most commonly existing scheduling methods.
Generalized Thermostatistical Description of Intermittency and Non-extensivity in Turbulence and Financial Markets

We describe a simple and accurate framework for modeling the statistical behavior of both fully developed turbulence and short-term dynamics of financial markets based on the formalism of Tsallis' generalized non-extensive thermostatistics. Within this framework, we show that intermittency and non-extensivity are naturally linked by the entropic parameter q. Our results, concerning both probability density functions and structure functions exponents are in very good agreement with experimental data.