Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergent Behaviors from Folksonomy Driven Interactions

To reflect the evolving knowledge on the Web this paper considers ontologies based on folksonomies according to a new concept structure called "Folksodriven" to represent folksonomies. This paper describes a research program for studying Folksodriven tags interactions leading to Folksodriven cluster behavior. The goal of the research is to understand the type of simple local interactions which produce complex and purposive group behaviors on Folksodriven tags. We describe a synthetic, bottom-up approach to studying group behavior, consisting of designing and testing a variety of social interactions and cultural scenarios with Folksodriven tags. We propose a set of basic interactions which can be used to structure and simplify the process of both designing and analyzing emergent group behaviors. The presented behavior repertories was developed and tested on a folksonomy environment.
Mixture Gaussian Signal Estimation with L_infty Error Metric

We consider the problem of estimating an input signal from noisy measurements in both parallel scalar Gaussian channels and linear mixing systems. The performance of the estimation process is quantified by the $\ell_\infty$ norm error metric. We first study the minimum mean $\ell_\infty$ error estimator in parallel scalar Gaussian channels, and verify that, when the input is independent and identically distributed (i.i.d.) mixture Gaussian, the Wiener filter is asymptotically optimal with probability 1. For linear mixing systems with i.i. d. sparse Gaussian or mixture Gaussian inputs, under the assumption that the relaxed belief propagation (BP) algorithm matches Tanaka's fixed point equation, applying the Wiener filter to the output of relaxed BP is also asymptotically optimal with probability 1. However, in order to solve the practical problem where the signal dimension is finite, we apply an estimation algorithm that has been proposed in our previous work, and illustrate that an $\ell_\infty$ error minimizer can be approximated by an $\ell_p$ error minimizer provided the value of $p$ is properly chosen.
The adaptive and the thresholded Lasso for potentially misspecified models

We revisit the adaptive Lasso as well as the thresholded Lasso with refitting, in a high-dimensional linear model, and study prediction error, $\ell_q$-error ($q \in \{1, 2 \} $), and number of false positive selections. Our theoretical results for the two methods are, at a rather fine scale, comparable. The differences only show up in terms of the (minimal) restricted and sparse eigenvalues, favoring thresholding over the adaptive Lasso. As regards prediction and estimation, the difference is virtually negligible, but our bound for the number of false positives is larger for the adaptive Lasso than for thresholding. Moreover, both these two-stage methods add value to the one-stage Lasso in the sense that, under appropriate restricted and sparse eigenvalue conditions, they have similar prediction and estimation error as the one-stage Lasso, but substantially less false positives.
Depression Symptoms Modelling from Social Media Text: An Active Learning Approach

A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD dataset that reﬂects both the clinical insights and the distribution of depression symptoms from the samples of self-disclosed depressed population. In our work, we describe an Active Learning (AL) framework which uses an initial supervised learning model that leverages 1) a state-of-the-art large mental health forum text pre-trained language model further ﬁne-tuned on a clinician annotated DSD dataset, 2) a Zero-Shot learning model for DSD, and couples them together to harvest depression symptoms related samples from our large self-curated Depression Tweets Repository (DTR). Our clinician annotated dataset is the largest of its kind. Furthermore, DTR is created from the samples of tweets in self-disclosed depressed users Twitter timeline from two datasets, including one of the largest benchmark datasets for user-level depression detection from Twitter. This further helps preserve the depression symptoms distribution of self-disclosed Twitter users tweets. Subsequently, we iteratively retrain our initial DSD model with the harvested data. We discuss the stopping criteria and limitations of this AL process, and elaborate the underlying constructs which play a vital role in the overall AL process. We show
THE KOHA CODE

This paper introduces the Koha model, a new theory that aims to explain two unresolved phenomena within biological neural networks: How information is processed and stored within neural circuits, and how neurons learn to become pattern detectors. In the Koha model, the dendritic spines of a neuron serve as computational units that scan for precise spike patterns in their synaptic inputs. The model proposes the existence of a temporal code within each dendritic spine, which is used for the dampening or amplification of signals, depending on the temporal information of incoming spike trains. Compelling evidence is provided and a concrete process is described for how signal filtration occurs within spine necks. A competitive learning algorithm is then proposed that describes how neurons use their internal temporal codes to become pattern detectors.
PATTERN RECOVERY BY SLOPE

LASSO and SLOPE are two popular methods for dimensionality reduction in the high-dimensional regression. LASSO can eliminate redundant predictors by setting the corresponding regression coefﬁcients to zero, while SLOPE can additionally identify clusters of variables with the same absolute values of regression coefﬁcients. It is well known that LASSO Irrepresentability Condition is sufﬁcient and necessary for the proper estimation of the sign of sufﬁciently large regression coefﬁcients. In this article we formulate an analogous Irrepresentability Condition for SLOPE, which is sufﬁcient and necessary for the proper identiﬁcation of the SLOPE pattern, i.e. of the proper sign as well as of the proper ranking of the absolute values of individ-ual regression coefﬁcients, while proper ranking guarantees a proper clustering. We also provide asymptotic results on the strong consistency of pattern recovery by SLOPE when the number of columns in the design matrix is ﬁxed while the sample size diverges to inﬁnity.
Content delivery in erasure broadcast channels with cache and feedback

We study a content delivery problem in the context of a K-user erasure broadcast channel such that a content providing server wishes to deliver requested files to users, each equipped with a cache of a finite memory. Assuming that the transmitter has state feedback and user caches can be filled during off-peak hours reliably by decentralized cache placement, we characterize the achievable rate region as a function of the memory sizes and the erasure probabilities. The proposed delivery scheme, based on the broadcasting scheme proposed by Wang and Gatzianas et al., exploits the receiver side information established during the placement phase. Our results can be extended to centralized cache placement as well as multi-antenna broadcast channels with state feedback.
Norges Teknisk-naturvitenskapelige Universitet Extending Inla to a Class of Near-gaussian Latent Models

This work extends the Integrated Nested Laplace Approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent field can have a near-Gaussian distribution. Two important class of models that can be addressed with our proposed method are non-Gaussian random effects models and dynamic models with non-Gaussian error term for the observation and/or system equation. Our approach is applied to two examples and the results are compared with that obtained by Markov Chain Monte Carlo (MCMC), showing similar accuracy with only a small fraction of computational time. Implementation of the proposed extension is available in the R package INLA.
Detection and Mitigation of Algorithmic Bias via Predictive Parity

Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected outcomes regardless of group. Testing and satisfying PP is especially important in many settings where model scores are interpreted by humans or directly provide access to opportunity, such as healthcare or banking. Solutions for PP violations have primarily been studied through the lens of model calibration. However, we find that existing calibration-based tests and mitigation methods are designed for independent data, which is often not assumable in large-scale applications such as social media or medical testing. In this work, we address this issue by developing a statistically rigorous non-parametric regression based test for PP with dependent observations. We then apply our test to illustrate that PP testing can significantly vary under the two assumptions. Lastly, we provide a mitigation solution to provide a minimally-biased post-processing transformation function to achieve PP.
Multiple-Access Channel With Partial and Controlled Cribbing Encoders

In this paper, we consider a multiple-access channel (MAC) with partial cribbing encoders. This means that each of the two encoders obtains a deterministic function of the output of the other encoder with or without delay. The partial cribbing scheme is especially motivated by the additive noise Gaussian MAC, where perfect cribbing results in the degenerated case of full cooperation between the encoders and requires an infinite entropy link. We derive a single-letter characterization of the capacity of the MAC with partial cribbing for the cases of causal and strictly causal cribbing. Several numerical examples, such as those of quantized cribbing, are presented. We further consider and derive the capacity region where the cribbing depends on actions that are functions of the previous cribbed observations. In particular, we consider a scenario where the action is taken to decide “to crib or not to crib” and show that a naive time-sharing strategy is not optimal.
Corrections to D1d5p Black Hole Entropy from Entropy Function Formalism

We show that in IIB string theory and for D1D5p black holes in ten dimensions the method of entropy function works. Despite the more complicated Wald formula for the entropy of D1D5p black holes in ten dimensions, their entropy is given by entropy function at its extremum point. We use this method for computing the entropy of the system both at the level of supergravity and for its higher order α′3R4 corrections.
19 F eb 2 01 6 Real Time Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming-the Case of Safari

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth r equirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). However, Google and c ertain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shapi ng their encrypted video traffic via DPI. This highlights the need fornew traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representati on layer and playout buffer. We present a new method and show for the first time that video quality representation classificat ion for (YouTube) encrypted HTTP adaptive streaming is possible. We analyze the performance of this classification method with S afari over HTTPS. Based on a large number of offline and online traffic classification experiments, we demonstrate that it c an independently classify, in real time, every video segment i nto one of the quality representation layers with 97.18% averag e accuracy.
Learning Body Shape and Pose from Dense Correspondences

The overview of our deform-and-learn strategy is depicted in Fig. 1. It alternates deformable surface registration that fits a 3D model to 2D images and training of deep neural network that predicts 3D body shape and pose from a single image. As the first step of an iteration, we train a conditional generative adversarial networks (cGANs) that predicts 3D joint positions from 2D joint positions (Section 2), which will guide the registration process. Given image-surface dense correspondences, the registration step fits a template model to images (Section 3). After registration, we obtain a collection of body parameters θfit which is then used as supervisional signals θanno in order to train deep ConvNets that predicts body parameters θconv (Section 4). Also, the joint positions obtained using registration are used for supervising cGANs. The body parameter estimation results are used as initial solutions of surface registration in the next round. This training process is iterated for several times to get better results. Note that in the very beginning the initial pose of registration is in the T-pose, θ0.
A Hierarchical Approach to Neural Context-Aware Modeling

In the research field of natural language processing, many approaches for translation and transcription tasks are based exclusively on the local context of a sentence. This restriction is reasonable when working with techniques such as n-gram models. Modern machine-learning approaches, for example neural networks, have become far more powerful and thus lifted the restriction of the models to a local context. Therefore, the potential of employing an extended context to enhance natural language processing systems is examined by evaluating the systems on a newly introduced text-understanding task. In the context of text understanding problems, a novel task to detect semantic errors in contextual text passages is established. The definition of the task does thereby not disclose any information about the position of the modifications, nor give insight about the number of out-of-context substitutions on the data. To evaluate the computational models on the task, an existing large-scale data source is adopted through a newly introduced automated modification process. Altering the data on a semantic level enables the machine-learning algorithms to be tested against a challenging set of modified text passages that require comprising a broader narrative discourse. The automated modification process is applied to the 2016 TEDTalk corpus containing over 2,000 TEDTalks distributed over various fields and topics. Automating the process enables the cost-effective modification of the complete dataset. To create a baseline setup, a standard language model and a supervised binary classification model are employed. Both approaches are only referring to one sentence at a time and thus are not capable to infer meaning out of the greater context. In order to overcome this limitation, a new model topology is developed to assess the potentials of employing an extended context to improve existing systems. The new model-design hierarchically encodes the narrative history and employs the most salient features of the long-term context to support the intra-sentence out-of-context error detection. The result of the evaluation shows the limitation of the baseline models, indicating that the contextual errors cannot be identified when limiting the computational models to a single sentence. Utilizing the context-aware topology with hierarchical context representations increases the performance, showing the efficiency of hierarchical context models for the semantic error detection task.
D S ] 3 1 O ct 2 00 3 Maintaining Information in Fully-Dynamic Trees with Top Trees ∗

We introduce top trees as a new data structure that makes it simpler to maintain many kinds of information in a fully-dynamic forest. As prime examples, we show how to maintain the diameter, center, and median of each tree in the forest. The forest can be updated by insertion and deletion of edges and by changes to vertex and edge weights. Each update is supported in O(log n) time, where n is the size of the tree(s) involved in the update. Also, we show how to support nearest common ancestor queries and level ancestor queries with respect to arbitrary roots in O(log n) time. Finally, with marked and unmarked vertices, we show how to compute distances to a nearest marked vertex. The later has applications to approximate nearest marked vertex in general graphs, and thereby to static optimization problems over shortest path metrics. Technically speaking, top trees can easily be derived from either Frederickson’s topology trees [Ambivalent Data Structures for Dynamic 2-Edge-Connectivity and k Smallest Spanning Trees, SIAM J. Comput. 26 (2) pp. 484–538, 1997] or Sleator and Tarjan’s dynamic trees [A Data Structure for Dynamic Trees. J. Comput. Syst. Sc. 26 This paper includes work presented at ICALP’97 [1] and SWAT’00 [2]. The IT University of Copenhagen, Glentevej 67, DK-2400, Denmark. E-Mail: stephen@it-c.dk The IT University of Copenhagen, Glentevej 67, DK-2400, Denmark. E-Mail: jholm@it-c.dk Fogedmarken 12, 2tv, DK-2200 Copenhagen, Denmark. E-Mail: kdl@k2.dk Work done while master’s student at University of Copenhagen. AT&T Labs–Research, E-Mail: mthorup@research.att. com
On the Monotonicity of the Generalized Marcum and Nuttall Q-Functions †

Monotonicity criteria are established for the generalized Marcum Q-function, QM (α, β), and the standard NuttallQ-function, QM,N(α, β). Specifically, we present that QM (α, β) is monotonically increasing with regard to its order M , for all ranges of the parameters α, β, whereasQM,N (α, β) possesses analogous monotonicity behavior with respect to M + N , under the assumption of a ≥ 1. For the normalized NuttallQ-function,QM,N(α, β), we also state the same monotonicity criterion without the necessity of restricting the range of the parameter α. By exploiting these results, we propose closed-form upper and lower bounds for the standard and normalized Nutta ll Q-functions. Furthermore, specific tight upper and lower bounds for QM (α, β), that have already been proposed in the literature for the ca s of integerM , are appropriately utilized in order to extend its validity over real values of M . The offered theoretical results can be efficiently applied in the study o f digital communications over fading channels, the capacity analysis of multiple-input multiple-output ( MIMO) channels and the decoding of turbo or low-density parity-check (LDPC) codes. Index Terms Digital communications over fading channels, generalized Marcum Q-function, NuttallQ-function, MIMO channels, LDPC codes. S. K. Mihos, V. M. Kapinas, and G. K. Karagiannidis are with th e Electrical and Computer Engineering Department, Aristot le University of Thessaloniki, 54124 Thessaloniki, Greece (e -mail: sotmihos@ee.auth.gr, {kapinas, geokarag }@auth.gr). †This work is dedicated to the memory of Marvin K. Simon. August 28, 2009 DRAFT SUBMITTED TO IEEE TRANSACTIONS ON INFORMATION THEORY 1
Efficiency in Quantum Key Distribution Protocols using entangled Gaussian states

Efficiency is a key issue in any real implementation of a cryptographic protocol since the physical resources are not unlimited. We will first show that Quantum Key Distribution is possible with an"Entanglement based"scheme with NPPT symmetric Gaussian states in spite of the fact that these systems cannot be distilled with Gaussian operations (they are all bound entangled). In this work we analyze the secrecy properties of Gaussian states under Gaussian operations. Although such operations are useless for quantum distillation, we prove that it is possible to distill efficiently a secret key secure against finite coherent attacks from sufficiently entangled Gaussian states with non-positive partial transposition. Moreover, all such states allow for efficient key distillation, when the eavesdropper is assumed to perform individual attacks before in an efficient way.
The International Journal of Digital Curation

The Chronopolis Digital Preservation Initiative, one of the Library of Congress’ latest efforts to collect and preserve at-risk digital information, has completed its first year of service as a multi-member partnership to meet the archival needs of a wide range of domains. Chronopolis is a digital preservation data grid framework developed by the San Diego Supercomputer Center (SDSC) at UC San Diego, the UC San Diego Libraries (UCSDL), and their partners at the National Center for Atmospheric Research (NCAR) in Colorado and the University of Maryland's Institute for Advanced Computer Studies (UMIACS). Chronopolis addresses a critical problem by providing a comprehensive model for the cyberinfrastructure of collection management, in which preserved intellectual capital is easily accessible, and research results, education material, and new knowledge can be incorporated smoothly over the long term. Integrating digital library, data grid, and persistent archive technologies, Chronopolis has created trusted environments that span academic institutions and research projects, with the goal of long-term digital preservation. A key goal of the Chronopolis project is to provide cross-domain collection sharing for long-term preservation. Using existing high-speed educational and research networks and mass-scale storage infrastructure investments, the partnership is leveraging the data storage capabilities at SDSC, NCAR, and UMIACS to provide a preservation data grid that emphasizes heterogeneous and highly redundant data storage systems. In this paper we will explore the major themes within Chronopolis, including:
Semidefinite Programming Relaxations of the Travelling Salesman Problem ( Replaced by DP 2008-96 )

We consider a new semidefinite programming (SDP) relaxation of the symmetric traveling salesman problem (TSP), that may be obtained via an SDP relaxation of the more general quadratic assignment problem (QAP). We show that the new relaxation dominates the one in the paper: [D. Cvetković, M. Cangalović and V. Kovačević-Vujčić. Semidefinite Programming Methods for the Symmetric Traveling Salesman Problem. In Proceedings of the 7th International IPCO Conference on Integer Programming and Combinatorial Optimization, 1999, 126–136, Springer-Verlag, London, UK. ] Unlike the bound of Cvetković et al., the new SDP bound is not dominated by the Held-Karp linear programming bound, or vice versa.
Cell model for efficient simulation of wave propagation in human ventricular tissue under normal and pathological conditions

In this paper, we formulate a model for human ventricular cells that is efficient enough for whole organ arrhythmia simulations yet detailed enough to capture the effects of cell level processes such as current blocks and channelopathies. The model is obtained from our detailed human ventricular cell model by using mathematical techniques to reduce the number of variables from 19 to nine. We carefully compare our full and reduced model at the single cell, cable and 2D tissue level and show that the reduced model has a very similar behaviour. Importantly, the new model correctly produces the effects of current blocks and channelopathies on AP and spiral wave behaviour, processes at the core of current day arrhythmia research. The new model is well over four times more efficient than the full model. We conclude that the new model can be used for efficient simulations of the effects of current changes on arrhythmias in the human heart.