Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling Up Markov Logic Probabilistic Inference for Social Graphs

Link prediction is a fundamental problem in social network analysis. Although the link prediction problem is not new, the challenge of how to exploit various existing network information, such as network structure data and node attribute data, to enable AI-style knowledge inference for large social networks still remains unsolved. In this paper, we design and implement a scalable framework that treats link prediction as knowledge reasoning using Markov Logic Networks (MLNs). Differing from other probabilistic graphical models, MLNs allow undirected relationships with cycles and long-range (non-adjacent) dependency, which are essential and abound in social networks. In our framework, the prior knowledge is captured as the structure dependency (such as friendship) and the attribute dependency (such as social communities) in terms of inference rules, associated with uncertainty represented as probabilities. Next, we employ the random walk to discover the inference subgraph, on which probabilistic inference is performed, so that the required computation and storage cost can be significantly reduced without much sacrifice of the inference accuracy. Our extensive experiments with real-world datasets verify the superiority of our proposed approaches over two baseline methods and show that our approaches are able to provide a tunable tradeoff between inference accuracy and efficiency.
Molecular Epidemiology and Evolution of Influenza Viruses Circulating within European Swine between 2009 and 2013

ABSTRACT The emergence in humans of the A(H1N1)pdm09 influenza virus, a complex reassortant virus of swine origin, highlighted the importance of worldwide influenza virus surveillance in swine. To date, large-scale surveillance studies have been reported for southern China and North America, but such data have not yet been described for Europe. We report the first large-scale genomic characterization of 290 swine influenza viruses collected from 14 European countries between 2009 and 2013. A total of 23 distinct genotypes were identified, with the 7 most common comprising 82% of the incidence. Contrasting epidemiological dynamics were observed for two of these genotypes, H1huN2 and H3N2, with the former showing multiple long-lived geographically isolated lineages, while the latter had short-lived geographically diffuse lineages. At least 32 human-swine transmission events have resulted in A(H1N1)pdm09 becoming established at a mean frequency of 8% across European countries. Notably, swine in the United Kingdom have largely had a replacement of the endemic Eurasian avian virus-like (“avian-like”) genotypes with A(H1N1)pdm09-derived genotypes. The high number of reassortant genotypes observed in European swine, combined with the identification of a genotype similar to the A(H3N2)v genotype in North America, underlines the importance of continued swine surveillance in Europe for the purposes of maintaining public health. This report further reveals that the emergences and drivers of virus evolution in swine differ at the global level. IMPORTANCE The influenza A(H1N1)pdm09 virus contains a reassortant genome with segments derived from separate virus lineages that evolved in different regions of the world. In particular, its neuraminidase and matrix segments were derived from the Eurasian avian virus-like (“avian-like”) lineage that emerged in European swine in the 1970s. However, while large-scale genomic characterization of swine has been reported for southern China and North America, no equivalent study has yet been reported for Europe. Surveillance of swine herds across Europe between 2009 and 2013 revealed that the A(H1N1)pdm09 virus is established in European swine, increasing the number of circulating lineages in the region and increasing the possibility of the emergence of a genotype with human pandemic potential. It also has implications for veterinary health, making prevention through vaccination more challenging. The identification of a genotype similar to the A(H3N2)v genotype, causing zoonoses at North American agricultural fairs, underlines the importance of continued genomic characterization in European swine.
The Concept of Comonotonicity in Actuarial Science and Finance: Theory

In an insurance context, one is often interested in the distribution function of a sum of random variables. Such a sum appears when considering the aggregate claims of an insurance portfolio over a certain reference period. It also appears when considering discounted payments related to a single policy or a portfolio at different future points in time. The assumption of mutual independence between the components of the sum is very convenient from a computational point of view, but sometimes not realistic. We will determine approximations for sums of random variables, when the distributions of the terms are known, but the stochastic dependence structure between them is unknown or too cumbersome to work with. In this paper, the theoretical aspects are considered. Applications of this theory are considered in a subsequent paper. Both papers are to a large extent an overview of recent research results obtained by the authors, but also new theoretical and practical results are presented.
Spatial Hearing and Speech Intelligibility in Bilateral Cochlear Implant Users

Objective: The abilities to localize sounds and segregate speech from interfering sounds in a complex auditory environment were studied in a group of adults who use bilateral cochlear implants. The first aim of the study was to investigate the change in speech intelligibility under bilateral and unilateral listening modes as a function of bilateral experience during the first 6 mo of activation. The second aim was to look at whether localization and speech intelligibility in the presence of interfering speech are correlated and if the relationship is specific to the bilateral listening mode. The third aim was to examine whether sound lateralization (right versus left) emerges before sound localization within a hemifield. Design: Participants were 17 native English speaking adults with postlingual deafness. All subjects received the Nucleus 24 Contour implant in both ears, either during the same surgery or during two separate surgeries that were no more than 1 mo apart. Both devices for each subject were activated at the same time, regardless of surgical approach. Speech intelligibility was measured at 3 and 6 mo after activation. Target speech was presented at 0° in front. Testing was conducted in quiet and in the presence of four-talker babble. The babble was located on the right, on the left, or in front (colocated with the target). Sound localization abilities were measured at the 3 mo interval. All testing was conducted under three listening modes: left ear alone, right ear alone, or bilateral. Results: On the speech-in-babble task, benefit of listening with two ears compared with one was greater when going from 3 to 6 mo of experience. This was evident when the target speech and interfering speech were spatially separated, but not when they were presented from the same location. At 3 mo postactivation of bilateral hearing, 82% of subjects demonstrated bilateral benefit when right/left discrimination was evaluated. In contrast, 47% of subjects showed a bilateral benefit when sound localization was evaluated, suggesting that directional hearing might emerge in a two-step process beginning with discrimination and converging on more fine-grained localization. The bilateral speech intelligibility scores were positively correlated with sound localization abilities, so that listeners who were better able to hear speech in babble were generally better able to identify source locations. Conclusions: During the early stages of bilateral hearing through cochlear implants in postlingually deafened adults, there is an early emergence of spatial hearing skills. Although nearly all subjects can discriminate source locations to the right versus left, less than half are able to perform the more difficult task of identifying source locations in a multispeaker array. Benefits for speech intelligibility with one versus two implants improve with time, in particular when spatial cues are used to segregate speech and competing noise. Localization and speech-in-noise abilities in this group of patients are somewhat correlated.
Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents.
Using natural vibrations to guide control for locomotion

Control for physically based characters presents a challenging task because it requires not only the management of the functional aspects that lead to the successful completion of the desired task, but also the resulting movement must be visually appealing and meet the quality requirements of the application. Crafting controllers to generate desirable behaviors is difficult because the specification of the final outcome is indirect and often at odds with the functional control of the task. This paper presents a method which exploits the natural modal vibrations of a physically based character in order to provide a palette of basis coordinations that animators can use to assemble their desired motion. A visual user interface allows an animator to guide the final outcome by selecting and inhibiting the use of specific modes. Then, an optimization routine applies the user-chosen modes in the tuning of parameters for a fixed locomotion control structure. The result is an animation system that is easy for an animator to drive and is able to produce a wide variety of locomotion styles for varying character morphologies.
Secure Multicast Communications using a Key Distribution Center

In this paper, we treat the entity authentication and subsequent key distribution problem in order to enhance multiple-entity (group of entities) communications with confidentiality and integrity services. We first give a summary of the existing methods to solve the key distribution problem. After this introduction of some known methods, we propose a different one, which detailed description will show its benefits over the known mechanisms. A key management scheme, based in the new method, is also fully described. Finally, the conclusions give a summary of the main advantages introduced by this new method.
2016 Ieee International Conference on Big Data (big Data) Predicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-side Platforms

—Online advertising has been all the rage these years. Budget control and traffic prediction turn out to be important issues for the demand-side platforms (DSPs). However, DSPs cannot easily grab the information of audiences and media platforms. Although DSPs might have the information immediately, it is still hard to response the request of advertisements in real-time due to the high volume of features. Therefore, we propose a method predicting traffic of requests from perspective of DSPs. The features we used are simple to be extracted from historical data. The prediction model we chose is regression model with closed-form solution. Both the features and regression model make our prediction adaptive in real-time systems. Our method can detect traffic anomalies and prevent it from overwhelming prediction. Moreover, our method can also keep pace of the trend. Experiment results show that our method's error rate of prediction is about 0.9% in total, and 10% per time unit.
Vision-Based Lane Detection for Autonomous Artificial Intelligent Vehicles

Intelligent vehicles are one of the enlightening ideas that will shape our future by providing enhanced safety and improved mobility. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. However, lane detection is a challenging task because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle. A developed preprocessing phase including a grayscale conversion, noise removal, edge detection with automatic thresholding, lines extraction using Hough transform, lines or boundaries of the road fitted by hyperbolas are simulated. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.
Making the Case for Elliptic Curves in DNSSEC

The Domain Name System Security Extensions (DNSSEC) add authenticity and integrity to the DNS, improving its security. Unfortunately, DNSSEC is not without problems. DNSSEC adds digital signatures to the DNS, significantly increasing the size of DNS responses. This means DNSSEC is more susceptible to packet fragmentation and makes DNSSEC an attractive vector to abuse in amplification-based denial-of-service attacks. Additionally, key management policies are often complex. This makes DNSSEC fragile and leads to operational failures. In this paper, we argue that the choice for RSA as default cryptosystem in DNSSEC is a major factor in these three problems. Alternative cryptosystems, based on elliptic curve cryptography (ECDSA and EdDSA), exist but are rarely used in DNSSEC. We show that these are highly attractive for use in DNSSEC, although they also have disadvantages. To address these, we have initiated research that aims to investigate the viability of deploying ECC at a large scale in DNSSEC.
Characterizing information transmission in cerebellar granule neuron

At the cellular scale, single-neurons process information mainly through spikes or action potentials [1]. Although the types of synaptic plasticity and the range of times cales over which they operate suggest that synapses have a more active role in information processing, the parameter space still remains unexplored. We used a mathematical model of cerebellar granule cell to explore information transmission in mossy fibre - granule cell synapse of the cerebellum. The impact of plasticity changes in excitatory synaptic release probability and variation in intrinsic excitability of granule cell was studied combining the modulatory effects of inhibition. We explore the changes in pre and post synaptic factors and report their influence on first spike latency and spike amplitude, revealing the indicators of information encoding in individual neurons [2].
Condition-invariant, top-down visual place recognition

In this paper we present a novel, condition-invariant place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images, alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We conduct an exhaustive set of experiments evaluating the relationship between place recognition performance and computational resources using part of the challenging Alderley sunny day - rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. We achieve recall rates of up to 51% at 100% precision, matching places that have undergone drastic perceptual change while rejecting match hypotheses between highly aliased images of different places. Human trials demonstrate the performance is approaching human capability. The results provide a new benchmark for single image, condition-invariant place recognition.
High accuracy traffic monitoring using road-side line-scan cameras

This paper presents a new technique for simultaneous traffic monitoring and law enforcement using a roadside rig head based on two line-scan cameras. As the vehicle travels across the vertical scanning lines, two images are formed and can be correlated with each other to determine the vehicle speed. Our first tests showed that the resulting videos are easily interpreted by computers in real-time and that the measures are both accurate (less than 1% error) and stable. In addition to speed, the system is able to extract a broad range of traffic information, among others the vehicle size, acceleration and inter-vehicle distances
The Mind as a Weapon: A Phenomenological Exploration of how Elementary Teachers Use Mental Imagery to Prepare for Active Shooter Events

The purpose of this transcendental, phenomenological study was to understand how teachers use aspects of mental imagery as a preparatory stress inoculation tool for potentially confronting active shooters at Truman Elementary School (pseudonym). The theory guiding this study was self-efficacy theory as theorized by Albert Bandura; self-efficacy theory rationalizes the way one views oneself will be the source of motivation in subsequent performance. In the context of this study, if teachers saw themselves as successful, then they were successful. Mental imagery was generally defined as visualization techniques that prepared teachers to mentally condition themselves to deal with active shooter events. The study took place at Truman Elementary School, and the participants consisted of 10 certified teachers. Prior to conducting interviews, site documents pertaining to current training for active shooter events were reviewed. Primary data were collected by interviewing 10 teachers, followed by a focus group session with the same participants. The collected data were analyzed using Moustakas’ seven steps of phenomenological research, which included identifying key statements from the participants, clustering responses into themes, and the construction of textural and structural descriptions for each participant. Results from the study included the desire of participants to preserve life, participants desire for additional training, the need for participants to always be prepared, collaboration with other participants, and the outside influences of each participant, which aided in mental image formation. The study determined teachers used mental imagery as a stress inoculation tool for potentially confronting active shooters.
Multimodal interaction with a virtual character in interactive storytelling

A number of interactive storytelling (IS) systems offer the user the possibility to input natural language input which determines how a story progresses. We introduce a framework for real-time signal processing (SSI) to analyze the users' state which then can influence the feelings of the story characters and their actions in the story. SSI is not only used to enable more natural character responses that are sensitive to the user's state at runtime. In addition SSI offers the possibility to collect a large variety of synchronized user data which can be used to analyze the user's experience in offline mode. The underlying narrative in which the approach was tested is based on a classical XIXth century psychological novel: Madame Bovary, by Flaubert.
Dramatic Reduction of PrPC Level and Glycosylation in Peripheral Nerves following PrP Knock-Out from Schwann Cells Does Not Prevent Transmissible Spongiform Encephalopathy Neuroinvasion

Expression of the prion protein (PrPC) is a requirement for host susceptibility to the transmissible spongiform encephalopathies (TSEs) and thought to be necessary for the replication and transport of the infectious agent. The mechanism of TSE neuroinvasion is not fully understood, although the routing of infection has been mapped through the peripheral nervous system (PNS) and Schwann cells have been implicated as a potential conduit for transport of the TSE infectious agent. To address whether Schwann cells are a requirement for spread of the TSE agent from the site of infection to the CNS, PrPC expression was selectively removed from Schwann cells in vivo. This dramatically reduced total PrPC within peripheral nerves by 90%, resulting in the selective loss of glycosylated PrPC species. Despite this, 139A and ME7 mouse-passaged scrapie agent strains were efficiently replicated and transported to the CNS following oral and intraperitoneal exposure. Thus, the myelinating glial cells within the PNS do not appear to play a significant role in TSE neuroinvasion.
Markov Chain Monte Carlo Estimation of a Multiparameter Decision Model: Consistency of Evidence and the Accurate Assessment of Uncertainty

Decision models are usually populated 1 parameter at a time, with 1 item of information informing each parameter. Often, however, data may not be available on the parameters themselves but on several functions of parameters, and there may be more items of information than there are parameters to be estimated. The authors show how in these circumstances all the model parameters can be estimated simultaneously using Bayesian Markov chain Monte Carlo methods. Consistency of the information and/or the adequacy of the model can also be assessed within this framework. Statistical evidence synthesis using all available data should result in more precise estimates of parameters and functions of parameters, and is compatible with the emphasis currently placed on systematic use of evidence. To illustrate this, WinBUGS software is used to estimate a simple 9-parameter model of the epidemiology of HIV in women attending prenatal clinics, using information on 12 functions of parameters, and to thereby compute the expected net benefit of 2 alternative prenatal testing strategies, universal testing and targeted testing of high-risk groups. The authors demonstrate improved precision of estimates, and lower estimates of the expected value of perfect information, resulting from the use of all available data.