Miracle-dz/UniFAR-FTUs huggingface dataset:
Extended Kalman filtering for flexible joint space robot control

In this paper, an extended Kalman filter (EKF) strategy to estimate state variables from noisy measurements in flexible joint space manipulators is presented. First, an EKF that estimates the link and motor positions/velocities using only measurements from motor sensors is developed for space robots modeled with a classical linear joint dynamics model. Second, an extension for a novel nonlinear joint dynamics formulation is provided. The state estimates are coupled to a flexible joint adaptive controller in order to provide a complete closed-loop solution for real-time estimation and control. In numerical simulations, the EKF-adaptive controller combination demonstrates, for both dynamics representations, good performance when tracking a 12.6 × 12.6 m square trajectory.
A Novel Signaling Network Essential for Regulating Pseudomonas aeruginosa Biofilm Development

The important human pathogen Pseudomonas aeruginosa has been linked to numerous biofilm-related chronic infections. Here, we demonstrate that biofilm formation following the transition to the surface attached lifestyle is regulated by three previously undescribed two-component systems: BfiSR (PA4196-4197) harboring an RpoD-like domain, an OmpR-like BfmSR (PA4101-4102), and MifSR (PA5511-5512) belonging to the family of NtrC-like transcriptional regulators. These two-component systems become sequentially phosphorylated during biofilm formation. Inactivation of bfiS, bfmR, and mifR arrested biofilm formation at the transition to the irreversible attachment, maturation-1 and -2 stages, respectively, as indicated by analyses of biofilm architecture, and protein and phosphoprotein patterns. Moreover, discontinuation of bfiS, bfmR, and mifR expression in established biofilms resulted in the collapse of biofilms to an earlier developmental stage, indicating a requirement for these regulatory systems for the development and maintenance of normal biofilm architecture. Interestingly, inactivation did not affect planktonic growth, motility, polysaccharide production, or initial attachment. Further, we demonstrate the interdependency of this two-component systems network with GacS (PA0928), which was found to play a dual role in biofilm formation. This work describes a novel signal transduction network regulating committed biofilm developmental steps following attachment, in which phosphorelays and two sigma factor-dependent response regulators appear to be key components of the regulatory machinery that coordinates gene expression during P. aeruginosa biofilm development in response to environmental cues.
An Efficient Association Mining Method via Matrix Compression

With the development of computer network technology and popularization, and the technology of data acquisition, data management and data query develop rapidly. People who is in the surging "data ocean", need an intelligent technology badly, to "explore" more valuable "oil "from the "data ocean",so the data mining technology which has been successfully used in commercial, medical, financial, and other fields, emerges. The primary technology of data mining is classification analysis, cluster analysis, Association Rule analysis, and etc. Association Rule analysis is an important branch of data mining, which can extract the valuable associations or rules people want to know from data warehouse. Based on the background of "data platform for public petition", it aims to study how to combine the Association Rule analysis technology with the current massive government-data, extracting association information from the massive hidden government-data, which can provide userful and valuable information according to the practical significance of the associations for system managers and decision makers. This paper study the classic Association Rule algorithm Apriori, propose the improved Apriori algorithm based on matrix com-pression. Based on this improved Apriori algorithm, develop the module of the analysis of cases evaluation, which can associate the cases and case handlers according to the information of cases and case handlers fistly, and then get the practical significance from the association result analysis, which can provide help to the managers and decision makers.
A General Approach to Policy-Based Management of the Performance Impact of Administrative Utilities

Administrative utilities (e.g., filesystem and database backups, garbage collection in the Java Virtual Machines) are an essential part of the operation of production systems. Since production work can be severely degraded by the execution of such utilities, it is desirable to have policies of the form “There should be no more than an x% degradation of production work due to utility execution. ” Two challenges arise in providing such policies: (1) providing an effective mechanism for throttling the resource consumption of utilities and (2) translating from policy expressions of “degradation units” into the units used by the throttling mechanism. We address (1) by using self-imposed sleep, a technique that forces utilities to slow down their processing by a configurable amount. We address (2) by employing a feedback loop that estimates the performance that would have been achieved by the production work if no utility were present. We demonstrate the effectiveness of these approaches in a prototype system that incorporates these capabilities into IBM’s DB2 Universal Database server.
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State

The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligences. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward this goal: developing a machine's ability to recognize the human affective state given four physiological signals. We describe difficult issues unique to obtaining reliable affective data and collect a large set of data from a subject trying to elicit and experience each of eight emotional states, daily, over multiple weeks. This paper presents and compares multiple algorithms for feature-based recognition of emotional state from this data. We analyze four physiological signals that exhibit problematic day-to-day variations: The features of different emotions on the same day tend to cluster more tightly than do the features of the same emotion on different days. To handle the daily variations, we propose new features and algorithms and compare their performance. We find that the technique of seeding a Fisher Projection with the results of sequential floating forward search improves the performance of the Fisher Projection and provides the highest recognition rates reported to date for classification of affect from physiology: 81 percent recognition accuracy on eight classes of emotion, including neutral.
Ambiguous Arabic Words Disambiguation

In this paper we put forward an unsupervised system WSD-AL for Arabic word disambiguation. We apply some pre-processing steps to texts containing the ambiguous word in the corpus and we extract the most relevant words. Then, we put to use the Context-Matching algorithm that returns a semantic coherence score corresponding to the context of use that is semantically closest to the original sentence. These Contexts are generated using the glosses of the ambiguous word and the corpus. The results found by the proposed ‎system are satisfactory, as the rate of disambiguation obtained ‎equals 78.
Object detection with sector scanning sonar

The object detection subsystem is the foundation to object avoidance and path planning. It plays an active role in supporting the operations of an autonomous underwater vehicle (AUV). The first challenge with the implementation of an object detection subsystem for an AUV is in identification of a detection system. The considerations for implementing the sector scanning sonar over other acoustic and video imaging alternatives were based on the factors of its small size, lower power consumption, data rate and visibility. The next challenge is on achieving a reliable object detection subsystem. We review the Otsu thresholding and static thresholding for object detection using the sector scanning sonar. The proposed static thresholding is based on the methodology of an adaptive thresholding with constant false alarm rate (CFAR). Several modifications to the detection methodologies are proposed along with their advantages and disadvantages are also be presented. Experimental dataset collected at Republic of Singapore Yacht Club (RSYC) is used to evaluate the detection methodologies.
Examples of moderate deviation principle for diffusion processes

Taking into account some likeness of moderate deviations (MD) and central limit theorems (CLT), we develop an approach, which made a good showing in CLT, for MD analysis of a family $S^\kappa_t=\frac{1}{t^\kappa}\int_0^tH(X_s)ds, t\to\infty$ for an ergodic diffusion process $X_t$, provided that $0.5<\kappa<1$, and appropriate $H$. We use a well known decomposition with "corrector'': $\frac{1}{t^\kappa}\int_0^tH(X_s)ds=$corrector$+\frac{1}{t^\kappa}$ ${M_t}$martingale. and show that, as in the CLT analysis, the corrector is negligible, and the main contribution in the MD brings the family "$ \frac{1}{t^\kappa}M_t, \ t\to\infty. $'' Starting from Freidlin, [7], and finishing by Wu's papers [33]-[37], in the MD study Laplace's transform dominates. In the paper, we replace this technique by "Stochastic exponential'' one, enabling to formulate the MDP conditions in terms of "drift-diffusion'' parameters and $H$. However, a verification of these conditions heavily depends on a specificity of a diffusion model. That is why the paper is named "Examples . ''.
Inferring weak references for fixing Java memory leaks

Lots of efforts have yet been devoted to the detection of memory leaks. However, very few work concerns on the problem of memory leak fixing. In this paper, we propose a dynamic approach for the weak reference based memory leak fixing. The approach can automatically infer the weakenable references in a program, rank them and report them to the user. The user can then weaken references according to the reports to fix the memory leaks.
Investigation of Anti-diabetic and Hypocholesterolemic Potential of Psyllium Husk Fiber (Plantago psyllium) in Diabetic and Hypercholesterolemic Albino Rats

The present study was conducted to observe the effect of Plantago psyllium on blood glucose and cholesterol levels in normal and alloxan induced diabetic rats. To investigate the effect of Plantago psyllium 40 rats were included in this study divided into four groups of ten rats in each group. One group A was normal, second group B was diabetic, third group C was non diabetic and hypercholesterolemic and fourth group D was diabetic and hypercholesterolemic. Two groups B and D were made diabetic by intraperitonial injection of alloxan dissolved in 1mL distilled water at a dose of 125mg/Kg of body weight. Two groups C and D were made hypercholesterolemic by oral administration of powder cholesterol (1g/Kg of body weight). The blood samples from all the rats were collected from coccygial vein on 1 day, then on 21 and 42 day respectively. All the samples were analyzed for blood glucose and cholesterol level by using enzymatic kits. The blood glucose and cholesterol levels of treated groups of rats showed significant reduction after 7 weeks of treatment with Plantago psyllium. By statistical analysis of results it was found that Plantago psyllium has anti-diabetic and hypocholesterolemic activity in diabetic and hypercholesterolemic albino rats. Keywords—Albino rats, alloxan, Plantago psyllium, statistical analysis
Quality of Service of Video Streaming in Vehicular Adhoc Networks: Performance Analysis

Vehicular ad hoc network (VANET) is an emerging network technology with the goal of providing public safety, traffic coordination and infotainment for the comfort of passengers in moving vehicles. However, it faces the challenge of not providing the expected quality of service (QoS) when streaming videos online. This challenge emanate from the variable topology infrastructure caused by intrinsic high mobility nodes and dynamic environments of VANETs. A better QoS in video applications can possibly be achieved if suitable routing protocols (RPs) is/are used. Hence, this paper compared and analyzed four existing routing protocols in VANETs. The objective was to identify an efficient RPs that can deliver better QoS for video streaming applications with respect to number of nodes used. A simulation was conducted using OPNET 14.5 Modeler and results analyzed with respect to throughput, delay and routing traffic received. Results shows that OLSR performance is better when few nodes are used and AODV shows better service when many nodes are used, thereby guaranteeing the user high quality of video when streaming online. Based on the results, it would be recommended that OLSR and AODV be used as hybrid protocol to provide better QoS for video streaming in VANETs.
Caged black holes: Black holes in compactified spacetimes. II. 5D numerical implementation

We describe the first convergent numerical method to determine static black hole solutions (with S^3 horizon) in 5d compactified spacetime. We obtain a family of solutions parametrized by the ratio of the black hole size and the size of the compact extra dimension. The solutions satisfy the demanding integrated first law. For small black holes our solutions approach the 5d Schwarzschild solution and agree very well with new theoretical predictions for the small corrections to thermodynamics and geometry. The existence of such black holes is thus established. We report on thermodynamical (temperature, entropy, mass and tension along the compact dimension) and geometrical measurements. Most interestingly, for large masses (close to the Gregory-Laflamme critical mass) the scheme destabilizes. We interpret this as evidence for an approach to a physical tachyonic instability. Using extrapolation we speculate that the system undergoes a first order phase transition.
A Candidate of a Type 2 Quasi-stellar Object at z = 0.9: Large X-Ray Absorption with a Strong Broad Hα Emission Line

Deep hard X-ray and near-infrared observations of a type 2 radio-quiet quasi-stellar object (QSO) candidate at z = 0.9, AX J08494+4454, are reported. The 0.5-10 keV Chandra X-Ray Observatory spectrum of AX J08494+4454 is hard and is explained well with a power-law continuum absorbed by a hydrogen column density of (2.3 ± 1.1) × 1023 cm-2. The 2-10 keV luminosity of the object is estimated to be 7.2 × 1044 ergs s-1, after correcting the absorption, and reaches hard X-ray luminosities of QSOs. The large X-ray absorption and the large intrinsic luminosity support the original identification of AX J08494+4454 as a type 2 radio-quiet QSO. Nevertheless, the deep Subaru/IRCS J-band spectroscopic observation suggests the presence of a strong broad Hα emission line from AX J08494+4454. If real, the broad Hα emission line has a velocity width of 9400 ± 1000 km s-1, which corresponds to a typical broad Balmer line velocity width of a luminous QSO. The existence of the strong broad Hα line means that the object is not a type 2 QSO but a luminous cousin of a Seyfert 1.9 galaxy in the source-frame optical spectrum. The Balmer decrement of broad lines and the broad Hα emission to the hard X-ray luminosity ratio suggest that the nucleus is affected by dust extinction with an AV of 1-3 mag in the optical wavelength. Optical colors and faint optical magnitudes of AX J08494+4454 are consistent with the model. The estimated amount of dust extinction is much smaller than that expected from the X-ray column density (AV = 130 ± 60 mag).
A multiscale technique for optical flow computation using piecewise affine approximation

We present a technique to estimate the optical flow in an image sequence, based on a piecewise affine model. In this piecewise approach, the area of interest in each image frame is divided into a set of small triangular patches. These triangular meshes are established over a set of feature points, which are extracted from the images and tracked from one frame to another. The velocity field within each triangular patch is parameterized by an affine transform. A multiscale coarse-to-fine approach is employed to increase the robustness of the method as well as the accuracy of the optical flow resulted from piecewise affine approximations. Finally, an adaptive filter is used to refine the estimated flow field. The filter is designed in such a way that not only can it reduce noises caused by errors of the process described above, but it can also avoid smoothing the discontinuities in the motion field. The method has been implemented and some experimental results are presented in this paper. The method takes advantage of widely used MPEG-4 encoding hardware/software tools
The 2 nd International Conference on Ambient Systems, Networks and Technologies (ANT) A Data Fusion Approach to Context-Aware Service Delivery in Heterogeneous Network Environments

Context-awareness is a key ingredient in any ubiquitous and pervasive system and provides intelligence to the system, allowing computing devices make appropriate and timely decisions on behalf of users. Context-awareness in mobile computing refers to internal and external adaptation of the environment and applications to the context state of each other. Such systems should adapt to the changes and variations of user’s context such as location, device status, connectivity and etc. In this paper we present our perspective of a context-aware service platform which is based on the idea of utilizing network information as services that is delivered via application programming interfaces and propose a fuzzy MADM method and a context similarity measure. We take into account the quality of contextual information in aggregating contextual information from di ﬀ erent sources
A Simple and Fast Distributed Algorithm to Compute a Minimum Spanning Tree in the Internet

A central problem in wide area networks is to eeciently multicast a message to all members (hosts) of a target group. One of the most eeective methods to multicast a message is to send the message along the edges of a spanning tree connecting all the members of the group. In this paper we propose a new fully distributed algorithm to build a minimum spanning tree (MST) in a generic communication network. During the execution, the algorithm maintains a collection of disjoint trees spanning all the group members. Every tree, which initially consists of only one node, independently expands by joining the closest tree, until all the nodes are connected in a single tree. The resulting communication topology is both robust (there are no singularities subject to failures) and scalable (every node stores a limited amount of local information that is independent of the size of the network).
What Do Frictions Mean for Q-theory Testing?

This paper develops and empirically tests a model designed to distinguish the role of real and financing frictions on firms investment, debt financing and equity financing policies. Real frictions include fixed costs of investment and adjustment costs. Financing frictions include taxes, collateral constraints, flotation costs of equity and dividend constraints. Because of financing frictions, all corporate policies are interrelated and depend on average Q. Due to fixed costs of investment and binding financing constraints,the sensitivity of corporate policies to Q is non-linear. The empirical tests demonstrate that both the endogeneity and non-linearities created by real and financing frictions are economically significant. The paper provides a rationale for the documented poor performance of Q-theory in explaining investment, and for the differential performance of the neoclassical investment model in explaining investment and stock returns. The paper extends Q-theory to explain debt and equity issues, and shows that market to book sorts control for non-linearities in investment policies.
Likelihood analysis of non-Gaussian measurement time series

SUMMARY In this paper we provide methods for estimating non-Gaussian time series models. These techniques rely on Markov chain Monte Carlo to carry out simulation smoothing and Bayesian posterior analysis of parameters, and on importance sampling to estimate the likelihood function for classical inference. The time series structure of the models is used to ensure that our simulation algorithms are efficient.
A Novel Full Comparator Design Based on Quantum-Dot Cellular Automata

— A lot of research has been done for implementing digital systems at nano-scale level. A quantum-dot cellular automaton (QCA) is a promising as well as emerging technology for implementing digital systems at nano-scale. QCA have attracted a lot of attention because of its extremely small feature size (at the molecular or even atomic scale) and its ultra-low power consumption, making it one candidate for replacing CMOS technology. This technology has been studied from a variety of physical and chemical aspects and in this paper, a novel full comparator design is introduced as a digital logic application for QCA-based circuits. Comparator is one of the important components in digital logic design and it widely used in central processing units (CPUs). The proposed design is compared with previous works in terms of area and delay. Comparisons show that the proposed design has improvement in area and delay.
Self-Organization with Lateral Connections

A self-organizing neural network model for the development of afferent and lateral input connections in cortical feature maps is presented. The weight adaptation process is purely activity-dependent, unsupervised, and local. The afferent input weights self-organize into a topological map of the input space. At the same time, the lateral interaction weights develop a smooth "Mexican hat" shaped distribution. Weak lateral connections die off, leaving a pattern of connections that represents the significant long-term correlations of activity on the feature map. The model demonstrates how self-organization can bootstrap itself based on input information only, without global supervision or predetermined lateral interaction. The model can potentially account for experimental observation such as critical periods for self-organization in cortical maps and development of horizontal connections in the primary visual cortex.
Gaze cueing elicited by emotional faces is influenced by affective context

When we observe someone shift their gaze to a peripheral event or object, a corresponding shift in our own attention often follows. This social orienting response, joint attention, has been studied in the laboratory using the gaze cueing paradigm. Here, we investigate the combined influence of the emotional content displayed in two critical components of a joint attention episode: The facial expression of the cue face, and the affective nature of the to-be-localized target object. Hence, we presented participants with happy and disgusted faces as cueing stimuli, and neutral (Experiment 1), pleasant and unpleasant (Experiment 2) pictures as target stimuli. The findings demonstrate an effect of ‘emotional context’ confined to participants viewing pleasant pictures. Specifically, gaze cueing was boosted when the emotion of the gazing face (i.e., happy) matched that of the targets (pleasant). Demonstrating modulation by emotional context highlights the vital flexibility that a successful joint attention system requires in order to assist our navigation of the social world.