Miracle-dz/UniFAR-FTUs huggingface dataset:
Micro active guide wire catheter using ICPF actuator

We have proposed a new prototype model of micro-catheter with active guide wire that has two bending degrees of freedom using ICPF (ionic conducting polymer film) actuator on its front end as the servo actuator. In this paper, the bending characteristics of the micro-active catheter (MAC) have been measured by application of electricity in physiological saline solution. By using simulators (whose conditions are similar to those of a body cavity), we also carried out simulation experiments "in vitro" and the contrast experiments of operability between the proposed catheter and conventional guide wire catheter. The experimental results indicate that the proposed MAC is applicable to intracavity operations.
Optimization of Multiuser MIMO Networks with Interference

Maximizing the total mutual information of a mul- tiuser multiple-input multiple-output (MIMO) system with inter- ference is a well-known and challenging problem. In this paper, we consider the power control problem of finding the maximum sum of mutual information for multiuser MIMO systems with equal power allocation at each link. A new and powerful global optimization method using a branch-and-bound framework cou- pled with the reformulation-linearization technique (BB/RLT) is introduced. The proposed BB/RLT is the first such method that guarantees finding a global optimum for multiuser MIMO systems with interference. In addition, we propose a modified branch-and-bound (BB) variable selection strategy to accelerate the convergence process, and apply the proposed technique to several MIMO systems in order to demonstrate its efficacy. I. INTRODUCTION
A Parallel Computational Model for Heterogeneous Clusters

This paper addresses the maximal lifetime scheduling for sensor surveillance systems with K sensors to 1 target. Given a set of sensors and targets in an Euclidean plane, a sensor can watch only one target at a time and a target should be watched by k, kges1, sensors at any time. Our task is to schedule sensors to watch targets and pass data to the base station, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration up to the time when there exists one target that cannot be watched by k sensors or data cannot be forwarded to the base station due to the depletion of energy of the sensor nodes. We propose an optimal solution to find the target watching schedule for sensors that achieves the maximal lifetime. Our solution consists of three steps: 1) computing the maximal lifetime of the surveillance system and a workload matrix by using linear programming techniques, 2) decomposing the workload matrix into a sequence of schedule matrices that can achieve the maximal lifetime, and 3) determining the sensor surveillance trees based on the above obtained schedule matrices, which specify the active sensors and the routes to pass sensed data to the base station. This is the first time in the literature that this scheduling problem of sensor surveillance systems has been formulated and the optimal solution has been found. We illustrate our optimal method by a numeric example and experiments in the end
An overview of JB (Jade Bird) component library system JBCL

The article introduces the JB (Jade Bird) Component Library system-JBCL. The goal of JBCL is to describe, manage, store and retrieve components. Based on the JB component model, the JB Component Library Data Model is developed. JBCL provides three sets of tools to assist users and managers to make best use of the JB component library, and uses a faceted method as the main classification strategy, and uses some other methods as auxiliary. Accordingly, it provides multiple retrieval means.
Objective Evaluation of the Neutral Tone in Putonghua Proficiency Testing

Correct pronunciation of the neutral tone (T0) is an important factor in assessing Putonghua (PTH) proficiency levels. The T0 objective evaluation system is an important subsystem of the Putonghua Proficiency Test (PPT) objective evaluation system. After analyzing the T0 features, this paper presents a pitch normalizing algorithm which reflects auditory sensitivity. Seven features are selected as evaluation parameters. Using the Gaussian Mixture Models (GMM) or the Multi Layer Perceptron (MLP) Models, data drawn from 43 individuals' utterances is tested. The accuracy of the objective evaluations described above is 89% identical to that usually achieved in subjective evaluation.
A Dynamic and Self-study Language Model Oriented to Chinese Characters Input

In this paper, a statistic language model is put forward to predict the next inputting word to improve the performance of the input method. So this paper constructs a general language model and a user language model, and then combines them into a new language model which was called as dynamic and self-study language model. Using the general language model in our experiment, the average length of input codes (ALIC) is reduced from 2.557 to 2.479 and the hit rate of first characters (HRFC) is also improved from 78.704% to 96.202%. Using the dynamic and self-study language model in our experiment, when the number of inputted Chinese characters is less then 20 thousand, the HRFC increases rapidly, while the ALIC reduces rapidly. And when the number is greater than 20 thousand, the HRFC and ALIC become steady. Thus its clear that dynamic and self-study language model performs well in input method. Otherwise, we provide a modified Church-Gale smoothing method to reduce the size of general language model. This method can reduce the size to 5 percent in order to fit the request of handheld device.
Missile Aim-Point Choice Based on Analytic Hierarchy Process

For the same target, the combat effect is varied while missile hits different part of target. How to choose rational aim-point is an effective method of improving the missile attack efficiency. Analytic hierarchy process (AHP for short) combines qualitative and quantitative analysis method. Through analyses the factors and their correlations in complex system, the system can be decompounded into different elements. Then these elements are classified into different levels, so multilayer analysis structure model is constructed objectively. Importance of varied factors in reality is difficult to quantify can be solved by this method. This paper takes communication building as example. According to a vast amount of data of experts and analyzing the experts¿ opinions, the priority sequence of aim-points is determined when a missile targets on communication building with AHP. Satellite receiving set is the first aim-point to be attacked through analysis. In actual combat, satellite receiving set is placed on the top of a building. It can be detected, identified and attacked easily. Once the satellite receiving set is destroyed, communication building will lose its combat significance. The analysis result is accurate and objective, and it has feasibility.
Bayesian Networks for Knowledge-Based Authentication

We consider adaptive index utilization as a fine-grained problem in autonomic databases in which an existing index is dynamically determined to be used or not in query processing. As a special case, we study this problem for structural joins, the core operator in XML query processing, in the main memory. We find that index utilization is beneficial for structural joins only under certain join selectivity and distribution of matching elements. Therefore, we propose adaptive algorithms to decide whether to use an index probe or a data scan for each step of matching during the processing of a structural join operator. Our adaptive algorithms are based on the history, the look-ahead information, or both. We have developed a cost model to facilitate this adaptation and have conducted experiments with both synthetic and real-world data sets. Our results show that adaptively utilizing indexes in a structural join improves the performance by taking advantage of both sequential scans and index probes
Numerical prediction of severe convection: comparison with operational forecasts

The prediction of severe convection is a major forecasting problem in Australia during the summer months. In particular, severe convection in the Sydney basin frequently produces heavy rain or hail, flash flooding, and destructive winds. Convective activity is a forecasting challenge for the Sydney basin, mainly from October to April. Currently, there is a need for improved numerical model guidance to supplement the official probabilistic convective outlooks, issued by the operational forecasters. In this study we assess the performance of a very high resolution (2 km) numerical weather prediction (NWP) model in terms of how well it performed in providing guidance on heavy rainfall and hail, as well as other mesoscale features such as low level convergence lines. Two cases are described in which the operational forecasts were incorrect on both occasions. Non‐severe thunderstorms were predicted on 1 December 2000 but severe convection occurred. Severe convection was predicted on 8 December 2000, but no convection was reported. In contrast, the numerical model performed well, accurately predicting severe convection on 1 December and no convection on 8 December. These results have encouraged a program aimed at providing an enhanced numerical modelling capability to the operational forecasters for the Sydney basin. Copyright © 2003 Royal Meteorological Society
Temporal Match Analysis and Recommending Substitutions in Live Soccer Games

Soccer is one of the most complex and dynamic games. It is challenging to figure out the game’s pattern in real-time. We propose a novel network metric and entropy-based live soccer analytic framework (NMELSA) that identifies the opponent team’s tactics in a live soccer match by observing all the events until the specified minute of the game. We design a live game replacement model which recommends substitute players based on the on-field players’ live game ratings. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.
Understanding the Far-Field Properties of Orbital Angular Momentum Beams through the Antenna Aperture Field Method

This work aims to provide insight into the far-field properties of orbital angular momentum (OAM) beams. Semi-analytical expressions for the far field of a general cylindrically-symmetric OAM aperture field are derived using the antenna aperture field method. The far-field properties of a circularly-polarized OAM carrying fields are considered. In particular, we derive analytical expression for the minimum theoretically achievable AR of a circularly-polarized OAM beam. For a common OAM-carrier aperture field distribution, i.e., the Laguerre-Gaussian distribution, analytical expressions of the fields are obtained in closed forms. The equations obtained in this paper show excellent agreement with predictions obtained from full-wave simulations. The derived analytical and semi-analytical formulas can potentially guide the design of OAM communication systems.
Message passing algorithms for dirichlet diffusion trees

We demonstrate efficient approximate inference for the Dirichlet Diffusion Tree (Neal, 2003), a Bayesian nonparametric prior over tree structures. Although DDTs provide a powerful and elegant approach for modeling hierarchies they haven't seen much use to date. One problem is the computational cost of MCMC inference. We provide the first deterministic approximate inference methods for DDT models and show excellent performance compared to the MCMC alternative. We present message passing algorithms to approximate the Bayesian model evidence for a specific tree. This is used to drive sequential tree building and greedy search to find optimal tree structures, corresponding to hierarchical clusterings of the data. We demonstrate appropriate observation models for continuous and binary data. The empirical performance of our method is very close to the computationally expensive MCMC alternative on a density estimation problem, and significantly outperforms kernel density estimators.
Using genetic algorithms to solve optimization problems in construction

Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the solution space in an intelligent manner to evolve better solutions. The algorithm does not need any specific programming efforts but requires encoding the solution as strings of parameters. The field of application of genetic algorithms has increased dramatically in the last few years. A large variety of possible GA application tools now exist for non‐computer specialists. Complicated problems in a specific optimization domain can be tackled effectively with a very modest knowledge of the theory behind genetic algorithms. This paper reviews the technique briefly and applies it to solve some of the optimization problems addressed in construction management literature. The lessons learned from the application of GA to these problems are discussed. The result of this review is an indication of how the GA can contribute in solving construction‐related optimization problems. A summary of general guidelines to develop solutions using this optimization technique concludes the paper.
Prior Precision Modulates the Minimization of Auditory Prediction Error

The predictive coding model of perception proposes that successful representation of the perceptual world depends upon canceling out the discrepancy between prediction and sensory input (i.e., prediction error). Recent studies further suggest a distinction to be made between prediction error triggered by non-predicted stimuli of different prior precision (i.e., inverse variance). However, it is not fully understood how prediction error with different precision levels is minimized in the predictive process. Here, we conducted a magnetoencephalography (MEG) experiment which orthogonally manipulated prime-probe relation (for contextual precision) and stimulus repetition (for perceptual learning which decreases prediction error). We presented participants with cycles of tone quartets which consisted of three prime tones and one probe tone of randomly selected frequencies. Within each cycle, the three prime tones remained identical while the probe tones changed once at some point (e.g., from repetition of 123X to repetition of 123Y). Therefore, the repetition of probe tones can reveal the development of perceptual inferences in low and high precision contexts depending on their position within the cycle. We found that the two conditions resemble each other in terms of N1m modulation (as both were associated with N1m suppression) but differ in terms of N2m modulation. While repeated probe tones in low precision context did not exhibit any modulatory effect, repeated probe tones in high precision context elicited a suppression and rebound of the N2m source power. The differentiation suggested that the minimization of prediction error in low and high precision contexts likely involves distinct mechanisms.
Haptic Interaction Design for Everyday Interfaces

This chapter sets about to provide the background and orientation needed to set a novice designer on his or her way to bringing haptics successfully into an interactive product. To define appropriate roles for haptic interaction, it is necessary to integrate a basic awareness of human capabilities on one hand and current device technology on the other. Here, I explore this integration by first summarizing the most salient constraints imposed by both humans and hardware. I then proceed to relate perceptual, motor, and attentional capabilities to a selection of emerging application contexts chosen to be relevant to contemporary design trends and opportunities. These include abstract communication and notification, augmentation of graphical user interfaces, expressive control, affective communication, and mobile and handheld computing. Our touch (haptic) sense is such an integral part of our everyday experience that few of us really notice it. Notice it now, as you go about your business. Within and beneath our skin lie layers of ingenious and diverse tactile receptors comprising our tactile sensing subsystem. These receptors enable us to parse textures, assess temperature and material, guide dexterous manipulations, find a page’s edge to turn it, and deduce a friend’s mood from a touch of his hand. Intermingled with our muscle fibers and within our joints are load cells and position transducers making up our proprioceptive sense, which tell our nervous systems of a limb’s position and motion and the resistance it encounters. Without these and their close integration with our body’s motor control, it would be exceedingly difficult to break an egg neatly into a bowl, play a piano, walk without tripping, stroke a pet, write, draw, or even type. Touch is our earliest sense to develop (Montagu, 1986). It has evolved to work in a tight partnership with vision and hearing in many ways we are only beginning to understand, as we study processes (such as hand-eye coordination) and how we process conflicting or competing information from different senses. In stark contrast to the importance of touch in our everyday experience, the use of touch is marginalized in contemporary computer interfaces, overlooked in the rush to accommodate graphical capability in desktop-based systems. The primary advances have been in feel-focused improvements in nonactuated pointing tools for both function and aesthetics. Scroll wheels have been designed for the user to click with just the right resistance and frequency; and most cell phones now come with vibrators that indicate incoming calls. Meanwhile, the use of haptic feedback in the consumer sphere is largely limited to gaming, and tactile feedback to simple cell phone alerts.
Scalable management of enterprise and data-center networks

The networks in campuses, companies, and data centers are growing larger and becoming more complicated to manage. Today, network operators devote tremendous time and effort to three key management tasks — routing, access control, and troubleshooting. Rather than trying to make today’s brittle networks easier to manage, we focus on new network designs that are inherently easier to manage and scale to many hosts, switches, and applications. We design and develop a new management system that scales the routing, access control, and performance diagnosis in enterprise and data center networks. The key challenges are the large number of hosts, switches, and applications in these networks and the need for flexible policies, while faced with strict memory and power constraints in the switches. To address these challenges, we propose three key ideas: (1) designing new data structures and algorithms that make effective use of limited memory in switches; (2) redirecting traffic when simple switches do not have enough memory to handle packets; (3) rethinking the division of labor among switches, hosts, and a centralized management system to make the network both flexible and scalable. Based on the key ideas, we propose a new management system that addresses the scalability challenges of routing, supporting flexible policies, and performance diagnosis with three key components: (i) BUFFALO: A scalable packet forwarding architecture that reduces the switch memory usage for storing the forwarding table using Bloom filters — a compact way of representing a set of elements. To gracefully handle the false positives caused by Bloom filters, BUFFALO sends packets through a slightly longer path. (ii) DIFANE: A scalable way to enforce flexible management policies from the centralized management system to the switches. DIFANE rethinks the division of labor between the centralized management system and the switches, by pulling some rule processing functions back to the switches, to achieve better scalability. (iii) SNAP: A scalable network performance diagnosis architecture that exposes the interactions between the network and applications in data centers. SNAP passively logs traffic statistics in the end-host network stack and pinpoints problems that occur at the network device, network stack and the application software. Our systems can be easily implemented with small modifications in today’s switches and end hosts, as demonstrated by our prototypes built using the OpenFlow switches and Microsoft Windows servers, and our evaluation using configuration data from AT&T networks and a deployment in a production data center.
A Time-constrained Network Voronoi Construction and Accessibility Analysis in Location-based Service Technology

Accessibility analysis usually requires special models of spatial location analysis based on some geometric constructions, such as Voronoi diagram (abbreviated to VD). There are many achievements in classic Voronoi model research, however suffering from the following limitations for location-based services (LBS) applications. (1) It is difficult to objectively reflect the actual service areas of facilities by using traditional planar VDs, because human activities in LBS are usually constrained only to the network portion of the planar space. (2) Although some researchers have adopted network distance to construct VDs, their approaches are used in a static environment, where unrealistic measures of shortest path distance based on assumptions about constant travel speeds through the network were often used. (3) Due to the computational complexity of the shortest-path distance calculating, previous researches tend to be very time consuming, especially for large datasets and if multiple runs are required. To solve the above problems, a novel algorithm is developed in this paper. We apply network-based quadrat system and 1-D sequential expansion to find the corresponding subnetwork for each focus. The idea is inspired by the natural phenomenon that water flow extends along certain linear channels until meets others or arrives at the end of route. In order to accommodate the changes in traffic conditions, the length of network-quadrat is set upon the traffic condition of the corresponding street. The method has the advantage over Dijkstra’s algorithm in that the time cost is avoided, and replaced with a linear time operation.
Informedia @ TRECVID 2011

The Informedia group participated in three tasks this year, including: Multimedia Event Detection (MED), Semantic Indexing (SIN) and Surveillance Event Detection. Generally, all of these tasks consist of three main steps: extracting feature, training detector and fusing. In the feature extraction part, we extracted a lot of low-level features, high-level features and text features. Especially, we used the Spatial-Pyramid Matching technique to represent the low-level visual local features, such as SIFT and MoSIFT, which describe the location information of feature points. In the detector training part, besides the traditional SVM, we proposed a Sequential Boosting SVM classifier to deal with the large-scale unbalance classification problem. In the fusion part, to take the advantages from different features, we tried three different fusion methods: early fusion, late fusion and double fusion. Double fusion is a combination of early fusion and late fusion. The experimental results demonstrated that double fusion is consistently better, or at least comparable than early fusion and late fusion.