Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Preservation Algorithm for Service-Oriented Information Search

Personalized information services offer a promising way to improve the accuracy of Web search,but they bring about additional requirements related to data privacy preservation.Nevertheless,current SOA usually have one of the main barriers for deploying personalized search applications,and how to do privacy-preserving personalization is a great challenge.Privacy becomes a more and more serious concern in service-oriented information search,so good algorithms are in need to be designed.In this paper,the authors considered the previous research on k-anonymity involving the influence of Quasi-identifier on sensitive attribute Bottom-up k-anonymity and present a method for calculating the influence of Quasi-identifier on sensitive attribute Bottom-up k-anonymity through microdata directly and improved utility matrix.To better evaluate the information loss of the anonymity data,the authors also present a quality metric,both the two major factors:data utility and privacy guarantee are well preserved,Improved Normalized Certainty Penalty(INCP). To achieve better privacy protection,the authors present a method based on the utility of Quasi-identifier which is L-diversity satisfied.
Security for Wireless Sensor Network withSpin Protocol

In the recent past, Wireless Sensor Networks have been introduced to use in many application. Wireless Sensor Networks provides a way of information sensing, processing, communicating and transfer of information. To designs the networks, the factors needed to be considered are the coverage area, mobility, power consumptions, communication capabilities, etc. Wireless Sensor Networks have limited energy has hence efficient of protocol has a significant impact on network’s lifetime. Security is a main concern everything, whether it is for Wired based networks or wireless based networks. Security is a fundamental requirement of Wireless Sensor Networks. Security in Wireless Sensor Network plays an important role in node communication. For Wireless Sensor Networks has lots of security protocol available but some have limitations. SPIN (Sensor Protocol for Information via Negotiation) has contained three messages which have given security and energy efficiency to Wireless Sensor Network. SPIN protocol is based on data centric approach which efficiently propagates information between sensor nodes in energy constrained mode. SPIN protocol solves the problem of flooding and gossiping. In this documentation we had implement SPIN protocol in NS2 and energy efficient for nodes. We have implement SPIN protocol in Network Simulator. After implementation of SPIN protocol, we had optimized SPIN-I which has given sufficient energy of three stages
Using a generic neural model for lexical substitution (Utiliser un modèle neuronal générique pour la substitution lexicale) [in French]

In this article, we present the participation of the CEA LIST LVIC laboratory to the lexical substitution task of the SemDis 2014 workshop. This work is based on the neural model proposed by (Mikolov et al., 2013b), which has shown good results on various tasks related to semantic similarity in English. We have used this model in a very simple way for performing lexical substitution in French and more particularly for the contextual selection of a lexical substitute among several candidates. The article also investigates the use of various types of resources for generating the substitution candidates. Mots-cles : Substitution lexicale, reseau de neurones, representations lexicales distribuees.
Interactions in Context-Zero: Towards Conceptual Adaptation through the Izbushka Agent

The evolution of technology and its integration into the many facets of modern society has created new forms of communication and interaction. Aspects of the “Information Age” have been infused into our daily routines in a way that has fundamentally altered our modes of thinking about such technology and interaction itself, requiring changes in the way we structure and use information. It is important to understand how we have been impacted by technology and how these new mediums are understood and conceptualized as our perceptions have changed. Technology often creates a sense of immersion during its use as we project conceptualized versions of ourselves into these spaces (Dede, 2009). Many virtual and artificial spaces create environments that bypass the ABSTRACT
Intelligent optimization strategy for ELM-RBF neural networks

A method of intelligent optimization strategy for extreme learning machine-radial basis function (ELM-RBF) neural networks was proposed,in which the centers and impact widths of hidden neural kernels were determined by the intelligent optimization algorithms of differential evolution and particle swarm optimization.Simulation results showed that the ELM-RBF neural networks learning algorithm based on the intelligent optimization strategy could generate much better generalization performance and robustness than other algorithms with the same network architecture.
Adaptive Quantum Simulated Annealing for Bayesian Inference and Estimating Partition Functions

Markov chain Monte Carlo algorithms have important applications in counting problems and in machine learning problems, settings that involve estimating quantities that are difficult to compute exactly. How much can quantum computers speed up classical Markov chain algorithms? In this work we consider the problem of speeding up simulated annealing algorithms, where the stationary distributions of the Markov chains are Gibbs distributions at temperatures specified according to an annealing schedule. We construct a quantum algorithm that both adaptively constructs an annealing schedule and quantum samples at each temperature. Our adaptive annealing schedule roughly matches the length of the best classical adaptive annealing schedules and improves on nonadaptive temperature schedules by roughly a quadratic factor. Our dependence on the Markov chain gap matches other quantum algorithms and is quadratically better than what classical Markov chains achieve. Our algorithm is the first to combine both of these quadratic improvements. Like other quantum walk algorithms, it also improves on classical algorithms by producing "qsamples" instead of classical samples. This means preparing quantum states whose amplitudes are the square roots of the target probability distribution. In constructing the annealing schedule we make use of amplitude estimation, and we introduce a method for making amplitude estimation nondestructive at almost no additional cost, a result that may have independent interest. Finally we demonstrate how this quantum simulated annealing algorithm can be applied to the problems of estimating partition functions and Bayesian inference.
The 4th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2013) Data Programming for Non-Programmers

“Big Data is becoming a standard catch phrase that often means more like “Ubiquitous Data - it’s not necessarily all that big, but it’s everywhere, and its collection rate is accelerating. Citizens may only have a need to spend a few hours per week attempting to make sense of that data. The concomitant worry is that citizens will be left behind as the IT priesthood leverages that data to run our world. But the skills to program in conventional languages are not universal; so, what to do? We are creating a visual programming environment for user-programmers (who are not trained programmers). Because of the sporadic usage pattern of such users, traditional languages and code structuring approaches are both overkill and diffi cult to learn. Therefore, the environment is designed to support very fluid relationships among code chunks, in terms of their relationship and evolution, as well as very fluid interactions among code chunks when processing data. c
Floating Displacement-Force Conversion Mechanism as a Robotic Mechanism

To attach and detach permanent magnets with an operation force smaller than their attractive force, Internally-Balanced Magnetic Unit (IB Magnet) has been developed. The unit utilizes a nonlinear spring with an inverse characteristic of magnetic attraction to produce a balancing force for canceling the internal force applied on the magnet. This paper extends the concept of shifting the equilibrium point of a system with a small operation force to linear systems such as conventional springs. Aligning a linear system and its inverse characteristic spring in series enables a mechanism to convert displacement into force generated by a spring with theoretically zero operation force. To verify the proposed principle, the authors realized a prototype model of inverse characteristic linear spring with an uncircular pulley. Experiments showed that the generating force of a linear spring can be controlled by a small and steady operation force.
Node Attribute Generation on Graphs

Graph structured data provide two-fold information: graph structures and node attributes. Numerous graph-based algorithms rely on both information to achieve success in supervised tasks, such as node classification and link prediction. However, node attributes could be missing or incomplete, which significantly deteriorates the performance. The task of node attribute generation aims to generate attributes for those nodes whose attributes are completely unobserved. This task benefits many real-world problems like profiling, node classification and graph data augmentation. To tackle this task, we propose a deep adversarial learning based method to generate node attributes; called node attribute neural generator (NANG). NANG learns a unifying latent representation which is shared by both node attributes and graph structures and can be translated to different modalities. We thus use this latent representation as a bridge to convert information from one modality to another. We further introduce practical applications to quantify the performance of node attribute generation. Extensive experiments are conducted on four real-world datasets and the empirical results show that node attributes generated by the proposed method are high-qualitative and beneficial to other applications. The datasets and codes are available online.
Ultrafast time-scale Berry-phase gates of atomic clock states

Extremely fast qubit controls can greatly reduce the calculation time in quantum computation, and potentially resolve the finite-time decoherence issues in many physical systems. Here, we propose and experimentally demonstrate pico-second time-scale controls of atomic clock state qubits, using Berry-phase gates implemented with a pair of chirped laser pulses. While conventional methods of microwave or Raman transitions do not allow atomic qubit controls within a time faster than the hyperfine free evolution period, our approach of ultrafast Berry-phase gates accomplishes fast clock-state operations. We also achieves operational robustness against laser parametric noises, since geometric phases are determined by adiabatic evolution pathway only, without being affected by any dynamic details. The experimental implementation is conducted with two linearly polarized, chirped ultrafast optical pulses, interacting with five single rubidium atoms in an array of optical tweezer dipole traps, to demonstrate the proposed ultrafast clock-state gates and their operational robustness.
Energy-Efficient Hybrid Precoding for Massive MIMO mmWave Systems With a Fully-Adaptive-Connected Structure

This paper investigates the hybrid precoding design in millimeter-wave (mmWave) systems with a fully-adaptive-connected precoding structure, where a switch-controlled connection is deployed between every antenna and every radio frequency (RF) chain. To maximally enhance the energy efficiency (EE) of hybrid precoding under this structure, the joint optimization of switch-controlled connections and the hybrid precoders is formulated as a large-scale mixed-integer non-convex problem with high-dimensional power constraints. To efficiently solve such a challenging problem, we first decouple it into a continuous hybrid precoding (CHP) subproblem. Then, with the hybrid precoders obtained from the CHP subproblem, the original problem can be equivalently reformulated as a discrete connection-state (DCS) problem with only 0-1 integer variables. For the CHP subproblem, we propose an alternating hybrid precoding (AHP) algorithm. Then, with the hybrid precoders provided by the AHP algorithm, we develop a matching assisted fully-adaptive hybrid precoding (MA-FAHP) algorithm to solve the DCS problem. It is theoretically shown that the proposed MA-FAHP algorithm always converges to a stable solution with the polynomial complexity. Finally, simulation results demonstrate the superior performance of the proposed MA-FAHP algorithm in terms of EE and beampattern.
Fast Steerable Wireless Backhaul Reconfiguration

Future mobile traffic growth will require 5G cellular networks to densify the deployment of small cell base stations (BS). As it is not feasible to form a backhaul (BH) by wiring all BSs to the core network, directional mmWave links can be an attractive solution to form BH links, due to their large available capacity. When small cells are powered on/off or traffic demands change, the BH may require reconfiguration, leading to topology and traffic routing changes. Ideally, such reconfiguration should be seamless and should not impact existing traffic. However, when using highly directional BH antennas which can be dynamically rotated to form new links, this can become time- consuming, requiring the coordination of BH interface movements, link establishment and traffic routing. In this paper, we propose greedy-based heuristic algorithms to solve the BH reconfiguration problem in real-time. We numerically compare the proposed algorithms with the optimal solution obtained by solving a mixed integer linear program (MILP) for smaller instances, and with a sub- optimal reduced MILP for larger instances. The obtained results indicate that the greedy-based algorithms achieve good quality solutions with significantly decreased execution time.
Gapless Dirac surface states in the antiferromagnetic topological insulator 
MnBi2Te4

A topological insulator can be converted to an axion insulator when time reversal symmetry is broken, which leads to the opening of a gap in the Dirac surface state. Recently, MnBi${}_{2}$Te${}_{4}$ was predicted to host such a state upon antiferromagnetic ordering. Collecting and analyzing very detailed photoemission data, the authors demonstrate that this does not occur and that the Dirac surface state remains gapless well below the N\'eel temperature. Most likely, this is due to the complexity of the magnetic ordering at the surface. The quest for the discovery of an axion insulator is, therefore, still on.
Bayesian Inference with Generative Adversarial Network Priors

Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a physical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large dimension, and/or have prior distributions that are difficult to represent mathematically. In this manuscript we consider the use of Generative Adversarial Networks (GANs) in addressing these challenges. A GAN is a type of deep neural network equipped with the ability to learn the distribution implied by multiple samples of a given field. Once trained on these samples, the generator component of a GAN maps the iid components of a low-dimensional latent vector to an approximation of the distribution of the field of interest. In this work we demonstrate how this approximate distribution may be used as a prior in a Bayesian update, and how it addresses the challenges associated with characterizing complex prior distributions and the large dimension of the inferred field. We demonstrate the efficacy of this approach by applying it to the problem of inferring and quantifying uncertainty in the initial temperature field in a heat conduction problem from a noisy measurement of the temperature at later time.
Limit on the LMC mass from a census of its satellites

We study the orbits of dwarf galaxies in the combined presence of the Milky Way and Large Magellanic Cloud (LMC) and find six dwarfs that were likely accreted with the LMC (Car 2, Car 3, Hor 1, Hyi 1, Phe 2, and Ret 2), in addition to the Small Magellanic Cloud (SMC), representing strong evidence of dwarf galaxy group infall. This procedure depends on the gravitational pull of the LMC, allowing us to place a lower bound on the Cloud’s mass of $M_{\rm LMC} \gt 1.24\times 10^{11} \, \mathrm{M}_\odot$ if we assume that these are LMC satellites. This mass estimate is validated by applying the technique to a cosmological zoom-in simulation of a Milky Way-like galaxy with an LMC analogue where we find that while this lower bound may be overestimated, it will improve in the future with smaller observational errors. We apply this technique to dwarf galaxies lacking radial velocities and find that Eri 3 has a broad range of radial velocities for which it has a significant chance (>0.4) of having been bound to the Cloud. We study the non-Magellanic classical satellites and find that Fornax has an appreciable probability of being an LMC satellite if the LMC is sufficiently massive ($\text{$\sim$} 2.5\times 10^{11} \, \mathrm{M}_\odot$). In addition, we explore how the orbits of Milky Way satellites change in the presence of the LMC and find a significant change for several objects. Finally, we find that the dwarf galaxies likely to be LMC satellites are slightly smaller than Milky Way satellites at a fixed luminosity, possibly due to the different tidal environments they have experienced.
Coherent feedforward loops can be used to approximately compute log-likelihood ratio for detecting persistent signals.

Living cells need to distinguish persistent signals from transient ones. Given that the cellular environment is stochastic, it is necessary to understand how cells make decision from a stochastic point of view. However, there is few work on studying persistence detection, in the context of cell signalling, from a stochastic signal point of view. This paper aims to address this gap. This paper considers a persistence detection problem defined over a reaction pathway consisting of three species: an inducer, a transcription factor (TF) and a gene, where the inducer can activate the TF and an active TF can bind to the gene promoter. We model the pathway using the chemical master equation so the counts of bound promoters over time is a stochastic signal. We consider the problem of using the continuous-time stochastic signal of the counts of bound promoters to infer whether the inducer signal is persistent or not. We use statistical detection theory to derive the solution to this detection problem, which is to compute the log-likelihood ratio of observing a persistent signal to a transient one. We then show, under time-scale separation and other assumptions, that this log-likelihood can be approximately computed by using the continuous-time signals of the number of active TF molecules and the number of bound promoters when the input is persistent. Finally, we show that the coherent feedforward loop can be used to approximately compute the mean of this log-likelihood ratio when the inducer signal is persistent.
Model Unit Exploration for Sequence-to-Sequence Speech Recognition

In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically reduces as the amount of training data is increased. In this work, we examine the impact of the choice of modeling unit for attention-based encoder-decoder models. We conduct experiments on the LibriSpeech 100hr, 460hr, and 960hr tasks, using various target units (phoneme, grapheme, and word-piece); across all tasks, we find that grapheme or word-piece models consistently outperform phoneme-based models, even though they are evaluated without a lexicon or an external language model. We also investigate model complementarity: we find that we can improve WERs by up to 9% relative by rescoring N-best lists generated from a strong word-piece based baseline with either the phoneme or the grapheme model. Rescoring an N-best list generated by the phonemic system, however, provides limited improvements. Further analysis shows that the word-piece-based models produce more diverse N-best hypotheses, and thus lower oracle WERs, than phonemic models.
Free-form Grale reconstruction of Abell 2744: robustness of uncertainties against changes in lensing data

Abell 2744, a massive Hubble Frontier Fields merging galaxy cluster with many multiple images in the core has been the subject of many lens inversions using different methods. While most existing studies compare various inversion methods, we focus on a comparison of reconstructions that use different input lensing data. Since the quantity and quality of lensing data is constantly improving, it makes sense to ask if the estimated uncertainties are robust against changes in the data. We address this question using free-form Grale, which takes only image information as input, and nothing pertaining to cluster galaxies. We reconstruct Abell 2744 using two sets of strong lensing data from the Hubble Frontier Fields community. Our first and second reconstructions use 55 and 91 images, respectively, and only 10 of the 91 images have the same positions and redshifts as in the first reconstruction. Comparison of the two mass maps shows that Grale uncertainties are robust against these changes, as well as small modifications in the inversion routine. Additionally, applying the methods used in Sebesta et al. (2016) for MACS J0416, we conclude that, in a statistical sense, light follows mass in Abell 2744, with brighter galaxies clustering stronger with the recovered mass than the fainter ones. We also show that the faintest galaxies are anti-correlated with mass, which is likely the result of light contamination from bright galaxies, and lensing magnification bias acting on galaxies background to the cluster.
Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast

Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of data describing a restaurant. While earlier work focused solely on the semantic fidelity of outputs, recent work has started to explore methods for controlling the style of the generated text while simultaneously achieving semantic accuracy. Here we experiment with two stylistic benchmark tasks, generating language that exhibits variation in personality, and generating discourse contrast. We report a huge performance improvement in both stylistic control and semantic accuracy over the state of the art on both of these benchmarks. We test several different models and show that putting stylistic conditioning in the decoder and eliminating the semantic re-ranker used in earlier models results in more than 15 points higher BLEU for Personality, with a reduction of semantic error to near zero. We also report an improvement from . 75 to . 81 in controlling contrast and a reduction in semantic error from 16% to 2%.