Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.
Genome-wide analysis of YB-1-RNA interactions reveals a novel role of YB-1 in miRNA processing in glioblastoma multiforme

Altered miRNA expression is believed to play a crucial role in a variety of human cancers; however, the mechanisms leading to the dysregulation of miRNA expression remain elusive. In this study, we report that the human Y box-binding protein (YB-1), a major mRNA packaging protein, is a novel modulator of miRNA processing in glioblastoma multiforme (GBM). Using individual nucleotide-resolution crosslinking immunoprecipitation coupled to deep sequencing (iCLIP-seq), we performed the first genome-wide analysis of the in vivo YB-1-RNA interactions and found that YB-1 preferentially recognizes a UYAUC consensus motif and binds to the majority of coding gene transcripts including pre-mRNAs and mature mRNAs. Remarkably, our data show that YB-1 also binds extensively to the terminal loop region of pri-/pre-miR-29b-2 and regulates the biogenesis of miR-29b-2 by blocking the recruitment of microprocessor and Dicer to its precursors. Furthermore, we show that down-regulation of miR-29b by YB-1, which is up-regulated in GBM, is important for cell proliferation. Together, our findings reveal a novel function of YB-1 in regulating non-coding RNA expression, which has important implications in tumorigenesis.
Targeting S100B in Cerebral Ischemia and in Alzheimer's Disease

S100B is an EF-hand calcium-binding protein that exerts both intracellular and extracellular effects on a variety of cellular processes. The protein is predominantly expressed in the central nervous system by astrocytes, both physiologically and during the course of neurological disease. In the healthy adult brain and during development, constitutive S100B expression acts as a trophic factor to drive neurite extension and to referee neuroplasticity. Yet, when induced during central nervous system disease, the protein can take on maladaptive roles and thereby exacerbate brain pathology. Based on genetic and pharmacological lines of evidence, we consider such deleterious roles of S100B in two common brain pathologies: ischemic stroke and Alzheimer's disease (AD). In rodent models of ischemic brain damage, S100B is induced early on during the subacute phase, where it exacerbates gliosis and delayed infarct expansion and thereby worsens functional recovery. In mouse models of AD, S100B drives brain inflammation and gliosis that accelerate cerebral amyloidosis. Pharmacological inhibition of S100B synthesis mitigates hallmark pathologies of both brain diseases, opening the door for translational approaches to treat these devastating neurological disorders.
Modelling Output and Inflation Using Direct Measures of Expectations

This thesis provides an applied macroeconometric study of the determination of output and inflation and of stabilization policy. Its focus is on the time series analysis of output and inflation, concentrating on the use of direct measures of expectations and data obtained in real time, and modelled in multivariate settings. Direct measures of expectations are useful in this study as they allow a better understanding of the expectations formation processes as well as the role played by expectations in the output and inflation dynamics and in the conduct of monetary policy. The use of real time data is important as it focuses on information sets available when decisions are made. This study considers three empirical exercises using actual and expected output and inflation series for the UK and US for the last forty years and making use of different sources of survey data. They are presented to build an increasingly sophisticated picture of the interactions between growth, inflation and stabilization policy. The first chapter concentrates on the measurement of actual and expected inflation and output from different sources of survey data, eventually developing a multivariate framework with which to analyse expectations at different time horizons. The second chapter concentrates on the dynamics between actual and expected inflation and output, developing a simple growth model that distinguishes between the longrun trend in output and short and medium term fluctuation around that trend, with inflation assumed to be driven by a model of time-consistent monetary policy. The last chapter builds on the previous ones by considering a small macro model of the economy based on three main behavioural relationships: a Phillips curve, an IS curve and a monetary policy rule; its focus is on the role of expectations in the conduct of monetary policy.
Relationship between Lower Limb Muscle Structure and Function in Cerebral Palsy

[Purpose] The purpose of the study was to provide information for intervention by comparing lower limb muscle thickness, gross motor function and functional level of activity daily living between cerebral palsy (CP) and mental retardation (MR). [Subjects] Sixty subjects participated: 38 CP and 9 MR subjects and 13 normally developing infants. [Methods] Ultrasonography and a manual muscle tester were used for measuring the thickness and strength of knee extensor and ankle plantar flexor muscles. The Gross Motor Function Measure (GMFM) and Wee Functional Independence Measure (WeeFIM) were used to evaluate level of gross motor and independence level. [Results] Knee extensor thicknesses of CP and MR subjects were thinner than those of normally developing infants. Strengths of knee extensor and ankle plantar flexor showed differences being strongest in normally developing infants, followed by MR, and CP. Subjects in the examination of GMFM, there were no significant differences between CP and MR. A decline in social cognition of MR subjects was found in the examination of WeeFIM. [Conclusion] CP and MR subjects had smaller muscle thicknesses and strengths than those of normally developing infants, and lower gross motor function and functional independent level.
Inequality Aversion in a Variety of Games - an Indirect Evolutionary Analysis

The indirect evolutionary approach integrates forward-looking evaluation of opportunities and adaptation in the light of the past. Subjective motivation determines behaviour, but long-run evolutionary success of motivational types depends on objective factors only. This can justify intrinsic aversion to inequality in reward allocation games. Whereas earlier analysis was restricted to specific games, this article considers a more complex environment comprising different games which - studied in isolation - yield opposite implications. Persistent divergence between intrinsic motivation and true material success is possible depending on the definition of inequality aversion as well as on agents' ability to discriminate between games. Copyright 2006 The Author(s). Journal compilation Royal Economic Society 2006.
On the use of dynamic information for speaker identification

This paper describes a language independent method for speaker identification. This method is based on a novel characterization of the speech signal that captures the dynamic information contained in the cepstral coefficients. The proposed method was evaluated through several experiments on a corpus of Mexican speakers. The achieved results demonstrated the relevance of the signal characterization, reaching an identification accuracy as high as 97% under a multi-class scheme.
SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least Squares Algorithm

We develop a Recursive $\mathcal{L}_1$-Regularized Least Squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an Expectation-Maximization type algorithm. Simulation studies in the context of channel estimation, employing multi-path wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm, in terms of both mean squared error (MSE) and computational complexity.
Proprietary Information Spillovers and Auditor Choice

Using exogenous shocks to the auditing industry, including large auditor mergers and the collapse of Arthur Andersen, I document a reluctance of rivals to engage the same auditor due to information spillover concerns. This reluctance is more evident in concentrated industries where barriers to mobility, proxied by differentiation and capital expenditure levels, are low. More secretive manufacturing firms are also more reluctant to share their auditor with a rival. I also find weak evidence that the concern for information spillovers is lessened when rivals are dissimilar in terms of sales or when they are headquartered in the same state, where other conduits for information spillover, including employee turnover, are present. Last, I find some evidence that auditors extract rents from clients concerned about information spillovers.
A Multi-resolution Framework for Information Extraction from Free Text

Extraction of relations between entities is an important part of Information Extraction on free text. Previous methods are mostly based on statistical correlation and dependency relations between entities. This paper re-examines the problem at the multiresolution layers of phrase, clause and sentence using dependency and discourse relations. Our multi-resolution framework ARE (Anchor and Relation) uses clausal relations in 2 ways: 1) to filter noisy dependency paths; and 2) to increase reliability of dependency path extraction. The resulting system outperforms the previous approaches by 3%, 7%, 4% on MUC4, MUC6 and ACE RDC domains respectively.
Robot development in cooperation with patients: Application of Hyper-Tether to support Home Oxygen Therapy patients

Hyper-Tether is a highly functional system which uses tethers to connect a wide range of robots, vehicles and humans. Home Oxygen Therapy (H.O.T.) is a medical treatment for severe lung diseases in which the patients are supplied concentrated oxygen through a cannula. The need to carry a portable oxygen supply device limits the quality of life of the patients, and so we have designed a robot follower using Hyper-Tether. The robot, which can carry an oxygen tank and follow behind the patient, was tested by H.O. T. users and evaluated using questionnaires. This paper describes the development and testing of the robot follower, with particular focus on iterative design based on patients' feedback.
Transient Neural Activation in Human Amygdala Involved in Aversive Conditioning of Face and Voice

Elucidating the neural mechanisms involved in aversive conditioning helps find effective treatments for psychiatric disorders such as anxiety disorder and phobia. Previous studies using fMRI and human subjects have reported that the amygdala plays a role in this phenomenon. However, the noxious stimuli that were used as unconditioned stimuli in previous studies (e.g., electric shock) might have been ecologically invalid because we seldom encounter such stimuli in daily life. Therefore, we investigated whether a face stimulus could be conditioned by using a voice that had negative emotional valence and was collected from a real-life environment. A skin conductance response showed that healthy subjects were conditioned by using these stimuli. In an fMRI study, there was greater amygdala activation in response to the faces that had been paired with the voice than to those that had not. The right amygdala showed transient activity in the early stage of acquisition. A psychophysiological interaction analysis indicated that the subcortical pathway from the medial geniculate body to the amygdala played a role in conditioning. Modulation of the subcortical pathway by voice stimuli preceded the transient activity in the amygdala. The finding that an ecologically valid stimulus elicited the conditioning and amygdala response suggests that our brain is automatically processing unpleasant stimuli in daily life.
Label Embedding Approach for Transfer Learning

Automatically tagging textual mentions with the concepts, types and entities that they represent are important tasks for which supervised learning has been found to be very effective. In this paper, we consider the problem of exploiting multiple sources of training data with variant ontologies. We present a new transfer learning approach based on embedding multiple label sets in a shared space, and using it to augment the training data.
Intestinal Autophagy and Its Pharmacological Control in Inflammatory Bowel Disease

Intestinal mucosal barrier, mainly composed of the intestinal mucus layer and the epithelium, plays a critical role in nutrient absorption as well as protection from pathogenic microorganisms. It is widely acknowledged that the damage of intestinal mucosal barrier or the disturbance of microorganism balance in the intestinal tract contributes greatly to the pathogenesis and progression of inflammatory bowel disease (IBD), which mainly includes Crohn’s disease and ulcerative colitis. Autophagy is an evolutionarily conserved catabolic process that involves degradation of protein aggregates and damaged organelles for recycling. The roles of autophagy in the pathogenesis and progression of IBD have been increasingly studied. This present review mainly describes the roles of autophagy of Paneth cells, macrophages, and goblet cells in IBD, and finally, several potential therapeutic strategies for IBD taking advantage of autophagy.
GPU-Based Multi-resolution Volume Rendering for Large Seismic Data

This paper presents a parallel multi-resolution volume rendering algorithm based on graphics processing unit (GPU). The algorithm is based on several important criteria, rendering is done adaptively by selecting high-resolution cells close to a center of attention and low-resolution cells away from this area. We employ the 3D texture mapping capability commonly available in modern GPU as a core rendering engine and take advantage of visibility tests and adaptive level of detail (LOD) selection to accelerate the whole rendering process. Experimental results show that our method achieve real-time performance for very large seismic data (10GB) on standard PC hardware.
International Journal of Innovative Research in Computer and Communication Engineering Design of Traffic Redundancy and Elimination Approach for Reducing Cloud Bandwidth and Costs

In last couple of years there huge increase in the usage cloud computing because cloud computing is emerging style of IT-delivery in which applications, data and resources are rapidly provisioned provided as standardized offerings to users with a flexible price. But it is important to provide the convenient pricing model for the users of cloud. Hence we design a new traffic redundancy and elimination scheme for reducing the cloud bandwidth and costs. I. INTRODUCTION The cloud computing paradigm has achieved widespread adoption in recent years. Its success is due largely to customers' ability to use services on demand with a pay-as-you go [2] pricing model, which has proved convenient in many respects. Low costs and high flexibility make migrating to the cloud compelling. Cloud computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. Traffic redundancy and elimination approach is used for minimizing the cost. Our new traffic redundancy elimination approach also called as novel-TRE or receiver based TRE, which detects redundancy at the client side and there is no need of server to continuously. However for server specific TRE approach it is difficult to handle the traffic efficiently and it doesn't suites for the cloud environment because of high processing costs. Novel-TRE matches incoming chunks with a previously received chunk chain or local file and sending to the server for predicting the future data. Packet level redundant content elimination [3] as a universal primitive on all internet routers, such a universal deployment would immediately reduce link loads everywhere. However, we argue that far more significant network-wide benefits can be derived by redesigning network routing protocols to leverage the universal deployment. The " redundancy-aware " intra-and inter-domain routing algorithms show that they enable better traffic engineering, reduce link usage costs, and enhance ISPs' responsiveness to traffic variations. Disadvantage Of course, deploying redundancy elimination mechanisms on multiple network routers is likely to be expensive to start with. However, we believe that the significant long term benefits of our approaches offer great incentives for networks to adopt them. End-system redundancy elimination [4] provides fast, adaptive and parsimonious in memory usage in order to opportunistically leverage resources …
Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia

Following the suggestions of a recent National Research Council report [NRC (National Research Council) (2006) Surface Temperature Reconstructions for the Last 2,000 Years (Natl Acad Press, Washington, DC). ], we reconstruct surface temperature at hemispheric and global scale for much of the last 2,000 years using a greatly expanded set of proxy data for decadal-to-centennial climate changes, recently updated instrumental data, and complementary methods that have been thoroughly tested and validated with model simulation experiments. Our results extend previous conclusions that recent Northern Hemisphere surface temperature increases are likely anomalous in a long-term context. Recent warmth appears anomalous for at least the past 1,300 years whether or not tree-ring data are used. If tree-ring data are used, the conclusion can be extended to at least the past 1,700 years, but with additional strong caveats. The reconstructed amplitude of change over past centuries is greater than hitherto reported, with somewhat greater Medieval warmth in the Northern Hemisphere, albeit still not reaching recent levels.
Analysis of the Isgur-Wise function of the $\Lambda_b \to \Lambda_c$ transition with light-cone QCD sum rules

In this article, we use the light-cone QCD sum rules to relate the $\Lambda_b$ baryon light-cone distribution amplitudes to the Isgur-Wise function $\xi(\omega)$ of the $\Lambda_b \to \Lambda_c$ transition, and obtain a simple relation. The numerical value of the Isgur-Wise function $\xi(\omega)$ is consistent with the prediction of the QCD sum rules.
The contribution of renewable energy technologies to sustainable community development in Rusitu Valley, Zimbabwe

This exploratory study is based on the case of the Rusitu Valley, a low income rural community in Zimbabwe. Data was collected using largely qualitative methods and quantitative methods were used to obtain supportive descriptive statistics. Information elicited from focus group discussions conducted with members of the Rusitu Valley community as well as responses obtained from a brief structured questionnaire were used to abstract the Rusitu Valley as a complex adaptive system. Input from in-depth interviews with government representatives in energy policy, local government and non-governmental organisations as well as a review of secondary sources was used to support the analysis and confirm the contextual validity of the study. This study revealed that there is intimate connection between renewable energy technologies and sustainable community development. A key finding was that the contribution of renewable energy technologies in Rusitu Valley is mostly towards the economic dimensions of the community and is relatively limited with regard to social and environmental dimensions. Therefore, this study concluded that renewable energy technologies have not sufficiently contributed towards sustainable community development in the Rusitu Valley. This study also found that the contribution of renewable energy technologies is constrained not only by internal limitations but also external factors. A conclusion drawn from this study was that effective contribution of renewable energy technologies towards social, economic and environmental facets can be enhanced through mainstreaming of renewable energy in policy and planning, as well strengthening institutions and local capacity which would have the overall effect of sustainable community development in low income communities
The dynamics of innovation and investment, with application to Australia 1984 - 1998

Ever since the start of the Industrial Revolution in Britain in the 1760s, innovation and investment have been crucial elements in economic explanations of the dynamics of capitalism. Classical economics recognises that innovation embodied in the form of new machines through fixed capital investment is the essential process for realising economic development. This study sets up a theoretical linkage between innovation and investment in historical time, without reference to any static equilibrium model. In this way, the relationship between instability of cycles and trend growth can be clearly identified. A theoretical framework and specific model of innovation and investment are developed. This is followed by an empirical investigation in support of this analysis to show plausibility in the important linkages between innovation and investment that have been missed when examined through static analysis of these relations. The statistical analysis is based on recent Australian industry sector data (1984-98) on R&D and capital expenditure in panel data form and in evolutionary industry life-cycle form. Conclusions from this work indicate the need to re-examine the way strategies are formed and developed in both the private and public sectors for more effective appropriation of innovation into the investment planning process.