Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Estimation Using Higher Order Statistics [Correspondence] - Image Processing, IEEE Transactions on

The objective of this paper is to introduce a fourth-order cost function of the displaced frame difference (DFD) capable of estimating motion even for small regions or blocks. Using higher than second-order statistics is appropriate in case the image sequence is severely corrupted by additive Gaussian noise. Some results are presented and compared to those obtained from the mean kurtosis and the mean square error of the DFD. Manuscript received December 15, 1994; revised October 3, 1995. This work was supported by the Spanish Ministry of Education under Contract TIC 95-1022-CO5-04. Portions of this paper were presented at EUSIPCO-94, Edinburgh, Scotland, September 1994. The authors are with the Universitat Politkcnica de Catalunya, Department of Signal Theory and Communications, Barcelona, Spain (e-mail: elisam gps.tsc.upc.es). Publisher Item Identifier S 1057-7149(96)04173-5.
Evolving Good Recommendations

Recommender systems are new types of internet-based software tools, designed to help users find their way through today’s complex on-line shops and entertainment websites. This paper focuses on the use of evolutionary search to fine-tune a profile-matching algorithm within a recommender system, to find profiles similar to the current user (or active user, A). Selected data from those profiles are then used to build recommendations. By evolving profile-matching, we tailor it to the preferences of individual users. This enables the recommender system to make more accurate predictions of users' likes and dislikes, and hence better recommendations to users.
Deep Learning for steganalysis is better than a Rich Model with an Ensemble Classifier, and is natively robust to the cover source-mismatch

Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, is very promising for the steganalysis. In this paper, we follow-up the study of Qian et al., and show that, due to intrinsic joint minimization, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments were conducted in order to find the best " shape " of the CNN. Second, experiments were carried out in the clairvoyant scenario in order to compare the CNN and FNN to an RM with an EC. The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. The results show that the CNN and FNN are naturally robust to the mismatch problem. In Addition to the experiments, we provide discussions on the internal mechanisms of a CNN, and weave links with some previously stated ideas, in order to understand the impressive results we obtained.
The circadian clock regulates inflammatory arthritis

There is strong diurnal variation in the symptoms and severity of chronic inflammatory diseases, such as rheumatoid arthritis. In addition, disruption of the circadian clock is an aggravating factor associated with a range of humaninflammatory diseases. To investigate mechanistic links between the biological clock and pathways underlying inflammatory arthritis, mice were administered collagen (or saline as a control) to induce arthritis. The treatment provoked an inflammatory response within the limbs, which showed robust daily variation in paw swelling and inflammatory cytokine expression. Inflammatory markers were significantly repressed during the dark phase. Further work demonstrated an active molecular clock within the inflamed limbs and highlighted the resident inflammatory cells, fibroblast‐like synoviocytes (FLSs), as apotential source of the rhythmic inflammatory signal. Exposure of mice to constant light disrupted the clock in peripheral tissues, causing loss of the nighttime repression of local inflammation. Finally, the results show that the core clock proteins cryptochrome (CRY) 1 and 2 repressed inflammation within the FLSs, and provide novel evidence that a CRY activator has anti‐inflammatory properties in human cells. We conclude that under chronic inflammatory conditions, the clock actively represses inflammatory pathways during the dark phase. This interaction has exciting potential as a therapeutic avenue for treatment of inflammatory disease. —Hand, L. E. , Hopwood, T.W. , Dickson, S. H. , Walker, A. L. , Loudon, A. S. I. , Ray, D.W. , Bechtold, D.A. , Gibbs, J. E. The circadian clock regulates inflammatory arthritis. FASEB J. 30, 3759–3770 (2016) www.fasebj. org
Generating network trajectories using gradient descent in state space

A local and simple learning algorithm is introduced that gradually minimizes an error function for neural states of a general network. Unlike standard backpropagation algorithms, it is based on linearizing the neurodynamics which are interpreted as constraints for the different network variables. From the resulting equations, the weight update is deduced which has a minimal norm and produces state changes directed precisely towards target values. As an application, it is shown how to generate desired neural state space curves on recurrent Hopfield-type networks.
A Locally Group Key Management with Revocation and Self-healing Capability for Sensor Networks

In this paper, we present an efficient key management scheme for resource limited sensor networks. Motivated by the fact that group key distribution from remote base station is costly in term of communication, we introduce the scheme of locally group key management scheme with revocation and self-healing capability for sensor network. The idea behind the scheme is use secret sharing to distribute group key and manage group member as well as group header.
Synchronous frame current control of multi-phase synchronous motor - part II asymmetric fault condition due to open phases

In this paper, the synchronous frame current controller of a multi-phase synchronous motor with open phases is proposed. The proposed method can not only possess the superiority of a linear controller over the conventional hysteresis-type current controller but it also enables the current to be regulated without a steady state error. The key idea is that all the harmonics of the current and voltage, which can still contribute to the torque positively, can be equivalently represented as dc components even under asymmetric fault conditions. Through out this paper, a five-phase synchronous motor with one open phase is used as a practical example and the basic concepts can be easily extended into an n-phase ac motor with multiple open phases, which has the arbitrary number or configuration of the remaining healthy phases. Experimental results prove the validity of the proposed modeling and control algorithm.
DSL/Model Co-Evolution in Industrial EMF-Based MDSE Ecosystems

Model Driven Engineering and Domain Specific Languages (DSLs) are being used in industry to increase productivity, and enable novel techniques like virtual prototyping. Using domain specific languages, engineers can model a systems in terms of their domain, rather than encoding it in general purpose concepts, like those offered by UML. However, domain specific languages evolve over time, often in a non-backwards-compatible way with respect to their models. When this happens, for models to remain usable they need to be co-evolved. Because the number of models in an industrial setting grows so large, manual co-evolution is becoming unfeasible calling for an automated approach. Many approaches for automated co-evolution of models exist in literature, each operating in a highly specialized context. In this paper, we present a high-level architecture that tries to capture the general process needed for automated co-evolution of models in response to DSL evolution. We then assess to what extent existing approaches already implement this architecture, and conclude which challenges are still open.
Optimization of synthetic aperture microwave radiometer antenna array distributed on two-dimensional grid points

The principle of antenna array distribution of two-dimensional synthetic aperture microwave radiometer is presented. The optimization technique based on differential evolution algorithm (DE) is presented for the design of antenna array. The object function is designed by minimizing redundant array and maximizing the distance between spatial frequency coverage samples, the stochastic array distributed on two-dimensional grid points are optimized, and the optimal results are obtained. Comparing with two-dimensional regular antenna array, the results show that the optimal array can obtain more spatial frequency samples and larger spatial frequency coverage.
Planar F-Deletion: Approximation and Optimal FPT Algorithms

Let F be a finite set of graphs. In the F-Deletion problem, we are given an n-vertex, m-edge graph G and an integer k as input, and asked whether at most k vertices can be deleted from G such that the resulting graph does not contain a graph from F as a minor. F-Deletion is a generic problem and by selecting different sets of forbidden minors F, one can obtain various fundamental problems such as Vertex Cover, Feedback Vertex Set or Treewidth t-Deletion. In this paper we obtain a number of generic algorithmic results about Planar F-Deletion, when F contains at least one planar graph. The highlights of our work are - A randomized O(nm) time constant factor approximation algorithm for the optimization version of Planar F-Deletion. - A randomized O(2^{O(k)} n) time parameterized algorithm for Planar F-Deletion when F is connected. Here a family F is called connected if every graph in F is connected. The algorithm can be made deterministic at the cost of making the polynomial factor in the running time n\log^2 n rather than linear. These algorithms unify, generalize, and improve over a multitude of results in the literature. Our main results have several direct applications, but also the methods we develop on the way have applicability beyond the scope of this paper. Our results -- constant factor approximation and FPT algorithms -- are stringed together by a common theme of polynomial time preprocessing.
Coherent channel based subband multichannel dereverberation

This paper presents a multichannel dereverberation algorithm that only uses coherent acoustic channels. In the framework of multi-input/output inverse theorem (MINT), the equalization performance varies depending on the length of the input acoustic channels. However, only the portion of observed channel that resemble the true acoustic channel contributes to performance enhancement when measurement error is accounted. Hence, the proposed algorithm derives the frequency dependent viable channel length (VCL) from the coherence analysis of Monte Carlo observations of a single acoustic channel. The VCL of the room impulse response (RIR) is determined by the portion where the stochastic characteristic of multiple observations is highly coherent. Experiments are conducted to compare the equalization performance of the subband MINT algorithm depending on the length of the input RIR. The equalization performance using frequency dependent VCL is as good as the one obtained using the maximum length of the measured channel, while its complexity is significantly reduced.
Real-Time Lettering on 3D Signs with a 2D Font Engine

Standard texturing shows a number of problems with 3D objects such as road signs, labels, or books. If letters are displayed at too large a scale, textures show blurred instead of hard edges; if letters are displayed at tiny sizes, textures appear either too pixelated or too blurry, but seldom well readable. In 2D as opposed to 3D, letters are created on demand in the required size by a sophisticated font rendering engine, a standard component of today’s operating systems. A number of specific improvements such as hinting and RGB subpixel rendering are available. This paper demonstrates how these partially proprietary and patented 2D functions can be leveraged for 3D rendering. The price to pay is a loss in geometric precision, since typical 2D font rendering engines only handle affine transformations, which can merely approximate perspective projection. However, in most situations this is outweighed easily by the gain in clarity.
Implementation of Personalized User Model Based on Ontology : A Review Study

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for web users to find specific documents that are relevant to their particular needs. Therefore, a new web search personalization approach has been proposed that captures the user's interests and preferences in the form of concepts by mining search results and their clickthroughs. Since the user’s location information play a very important role in web search, it is needed to separate the concepts into content concepts and location concepts, and then organizes them into ontologies to create an ontology-based profile to precisely capture the user's content and location interests and hence improve the search accuracy. In personalized search systems, the search results are ranked according to user’s interest or the searchable documents are arranged according to user-defined concepts for obtaining the desired information faster. This paper will make an extensive review of the personalization consideration; it proposes a personalized user model to provide more user-oriented information considering context information such as a personal profile with preferences and location. The system architecture is designed to support an effective execution usage on Web services and client applications. Keywords— Web search, Clickthrough data, concept, location search, ontology, personalization, user profile.
10 Gbit line rate packet-to-disk using n2disk

Capturing packets to disk at line rate and with high precision packet timestamping is required whenever an evidence of network communications has to be provided. Typical applications of long-term network traffic repositories are network troubleshooting, analysis of security violations, and analysis of high-frequency trading communications. Appliances for 10 Gbit packet capture to disk are often based on dedicated network adapters, and therefore very expensive, making them usable only in specific domains. This paper covers the design and implementation of n2disk, a packet capture to disk application, capable of dumping 10 Gbit traffic to disk using commodity hardware and open-source software. In addition to packet capture, n2disk is able to index the traffic at line-rate during capture, enabling users to efficiently search specific packets in network traffic dump files.
Disciplinary reporting affects the interpretation of climate change impacts in global oceans

Climate change is affecting marine ecosystems, but different investigative approaches in physical, chemical, and biological disciplines may influence interpretations of climate‐driven changes in the ocean. Here, we review the ocean change literature from 2007 to 2012 based on 461 of the most highly cited studies in physical and chemical oceanography and three biological subdisciplines. Using highly cited studies, we focus on research that has shaped recent discourse on climate‐driven ocean change. Our review identified significant differences in spatial and temporal scales of investigation among disciplines. Physical/chemical studies had a median duration of 29 years (n = 150) and covered the greatest study areas (median 1.41 × 107 km2, n = 148). Few biological studies were conducted over similar spatial and temporal scales (median 8 years, n = 215; median 302 km2, n = 196), suggesting a more limited ability to separate climate‐related responses from natural variability. We linked physical/chemical and biological disciplines by tracking studies examining biological responses to changing ocean conditions. Of the 545 biological responses recorded, a single physical or chemical stressor was usually implicated as the cause (59%), with temperature as the most common primary stressor (44%). The most frequently studied biological responses were changes in physiology (31%) and population abundance (30%). Differences in disciplinary studies, as identified in this review, can ultimately influence how researchers interpret climate‐related impacts in marine systems. We identified research gaps and the need for more discourse in (1) the Indian and other Southern Hemisphere ocean basins; (2) research themes such as archaea, bacteria, viruses, mangroves, turtles, and ocean acidification; (3) physical and chemical stressors such as dissolved oxygen, salinity, and upwelling; and (4) adaptive responses of marine organisms to climate‐driven ocean change. Our findings reveal that highly cited biological studies are rarely conducted on scales that match those of physical and chemical studies. Rather, we suggest a need for measuring responses at biologically relevant scales.
Safe and automatic live update for operating systems

Increasingly many systems have to run all the time with no downtime allowed. Consider, for example, systems controlling electric power plants and e-banking servers. Nevertheless, security patches and a constant stream of new operating system versions need to be deployed without stopping running programs. These factors naturally lead to a pressing demand for live update---upgrading all or parts of the operating system without rebooting. Unfortunately, existing solutions require significant manual intervention and thus work reliably only for small operating system patches. In this paper, we describe an automated system for live update that can safely and automatically handle major upgrades without rebooting. We have implemented our ideas in Proteos, a new research OS designed with live update in mind. Proteos relies on system support and nonintrusive instrumentation to handle even very complex updates with minimal manual effort. The key novelty is the idea of state quiescence, which allows updates to happen only in safe and predictable system states. A second novelty is the ability to automatically perform transactional live updates at the process level, ensuring a safe and stable update process. Unlike prior solutions, Proteos supports automated state transfer, state checking, and hot rollback. We have evaluated Proteos on 50 real updates and on novel live update scenarios. The results show that our techniques can effectively support both simple and complex updates, while outperforming prior solutions in terms of flexibility, security, reliability, and stability of the update process.
Algorithms of task-allocation and cooperation in multi mobile robot system

Until now, many methods are used to solve the problems of task-allocation and path planning in multi mobile robot system. There are two new algorithms presented in this paper. The task-allocation algorithm resolves the puzzle of task getting and accomplishing with the least cost based on the optimal notion. The environment of the system is constructed with the topological graphics. Path-planning algorithm programs an optimal path from the initial point to the destination that satisfies some performances based on the idea of depth-first search. And we designed an emulational test bed based on the multi-robot material flow system of the storages and docks. Then we studied the results of the emulational experiments about the presented algorithms.
Brassica napus L. cultivars show a broad variability in their morphology, physiology and metabolite levels in response to sulfur limitations and to pathogen attack

Under adequate sulfur supply, plants accumulate sulfate in the vacuoles and use sulfur-containing metabolites as storage compounds. Under sulfur-limiting conditions, these pools of stored sulfur-compounds are depleted in order to balance the nitrogen to sulfur ratio for protein synthesis. Stress conditions like sulfur limitation and/or pathogen attack induce changes in the sulfate pool and the levels of sulfur-containing metabolites, which often depend on the ecotypes or cultivars. We are interested in investigating the influence of the genetic background of canola (Brassica napus) cultivars in sulfur-limiting conditions on the resistance against Verticillium longisporum. Therefore, four commercially available B. napus cultivars were analyzed. These high-performing cultivars differ in some characteristics described in their cultivar pass, such as several agronomic traits, differences in the size of the root system, and resistance to certain pathogens, such as Phoma and Verticillium. The objectives of the study were to examine and explore the patterns of morphological, physiological and metabolic diversity in these B. napus cultivars at different sulfur concentrations and in the context of plant defense. Results indicate that the root systems are influenced differently by sulfur deficiency in the cultivars. Total root dry mass and length of root hairs differ not only among the cultivars but also vary in their reaction to sulfur limitation and pathogen attack. As a sensitive indicator of stress, several parameters of photosynthetic activity determined by PAM imaging showed a broad variability among the treatments. These results were supported by thermographic analysis. Levels of sulfur-containing metabolites also showed large variations. The data were interrelated to predict the specific behavior during sulfur limitation and/or pathogen attack. Advice for farming are discussed.
FlashLite: A High Performance Machine for Data Intensive Science

Data is predicted to transform the 21st century, fuelled by an exponential growth in the amount of data captured, generated and archived. Traditional high performance machines are optimized for numerical computing rather than IO performance or for supporting large memory applications. This paper discusses a new machine, called FlashLite, which addresses these challenges. The paper describes the motivation for the design, and discusses some driving application themes.
Endogenous Public Policy and Long-Run Growth

We study the determinants of voting outcomes on the provision of public consumption through marginal income taxes in the context of the simple linear growth model. We focus on how the dynamic politicoeconomic equilibrium maps the economic fundamentals to policies and long-run growth. We find that in a deterministic growth environment voters internalize, although imperfectly, the deadweight losses of taxation and vote for lower taxes when the productivity of capital is higher. Therefore, the politicoeconomic channel reinforces the positive role of productivity for growth. In a stochastic environment, we find that if business cycles are driven by productivity shocks in the endogenous growth framework, equilibrium policies imply that taxes should fall in high growth periods and rise in low-growth periods. In line with existing evidence, our model predicts procyclical public consumption and countercyclical public consumption GDP shares.