Miracle-dz/UniFAR-FTUs huggingface dataset:
A General and Efficient Multiple Kernel Learning Algorithm

While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lankriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constraint quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental results show that the proposed algorithm helps for automatic model selection, improving the interpretability of the learning result and works for hundred thousands of examples or hundreds of kernels to be combined.
A Green Time-Bounded Routing Protocol in Solar-Based Vehicular Networks

Energy saving is always an important issue. Most of the researches pay attention to save the energy but lacking in harvest energy especially the solar energy. The solar panel is an efficient way to harvest more energy. In this works, a solar vehicle equips the solar panel that can charge the battery via the sunlight. The ECR (energy consumption ratio) is used to measure the efficiency of the solar vehicle which is denoted as arrival ratio divided by energy consumption. This work aims to achieve a high ECR of solar vehicles, therefore, the solar vehicle should achieve the time-bounded routing request with high energy efficiency. The solar vehicle should speed up to satisfy time bounded routing request and slow down to charge the battery when the solar illumination is high. To achieve that, a green time-bounded routing protocol is proposed to make the solar vehicle can satisfy the time-bounded routing request and charge the battery simultaneously. Finally, simulation results illustrate the performance improvement in remaining battery energy, arrival ratio, and ECR.
Times-to-Default : Life Cycle, Global and Industry Cycle Impacts

This paper studies times-to-default of individual firms across various risk classes. Using Standard & Poor’s ratings database we explore the behaviour of instantaneous default probabilities conditioning on microand macro-economic variables and identify their impacts. We show that both financial markets and the global business cycle matter for explaining times-to-default. More importantly past macro-economic and financial information is also instrumental in modelling large variations in intensities. Short term determinants and long term trends are therefore relevant for capturing patterns of default probabilities. In a semiparametric setup we document limitations of traditional credit risk models. We also show how parsimonious covariate choices or lagged information can improve the performance of simple models, and introduce industry contagion factors. ∗FAME & University of Geneva. †Associate Fellow, FERC, Warwick Business School, Coventry CV4 7AL, UK. ‡The first author gratefully acknowledges support by Standard & Poor’s. In addition he thanks financial support from the Swiss National Science Foundation through the National Center of Competence: Financial Valuation and Risk Management (NCCR FINRISK) and from the Geneva Research Collaboration Foundation (GRC). We thank Arnaud de Servigny, Olivier Scaillet, René Stulz, FAME colleagues, anonymous participants to the 2004 GRETA Credit conference and members of Standard & Poor’s European academic panel for useful comments. The views expressed therein are those of the authors but not necessarily those of Standard & Poor’s or any other institution. All remaining errors are ours.
Visualizing Paths in Context

Data about movement through a space is increasingly becoming available for capture and analysis. In many applications, this data is captured or modeled as transitions between a small number of areas of interests, or a finite set of states, and these transitions constitute paths in the space. Similarities and differences between paths are of great importance to such analyses, but can be difficult to assess. In this work we present a visualization approach for representing paths in context, where individual paths can be compared to other paths or to a group of paths. Our approach summarizes path behavior using a simple circular layout, including information about state and transition likelihood using Markov random models, together with information about specific path and state behavior. The layout avoids line crossovers entirely, making it easy to observe patterns while reducing visual clutter. In our tool, paths can either be compared in their natural sequence or by aligning multiple paths using Multiple Sequence Alignment, which can better highlight path similarities. We applied our technique to eye tracking data and cell phone tower data used to capture human movement.
COMPACT ATTITUDE SENSOR SYSTEM USING SR-UKF NSF Summer Undergraduate Fellowship in Sensor Technologies

This paper describes the selection and integration of attitude sensors for a novel flying device. The overall goal of the experimental flyer project is to create a small, cheap, and lightweight device that is capable of controlled flight. This project focuses on the development of a state estimation system for the flyer that allows for precise and rapid attitude measurement. A sensor package consisting of a three-axis magnetometer, a three-axis accelerometer, and three one-axis rate gyroscopes is presented. The sensor measurements are filtered and fused together using a Square-Root Unscented Kalman Filter (SR-UKF) developed in MATLAB. The feasibility of porting the filter to a Microchip dsPIC30F microcontroller is discussed, and the performance of the filter is evaluated.
Multiview Intrinsic Images of Outdoors Scenes with an Application to Relighting

We introduce a method to compute intrinsic images for a multiview set of outdoor photos with cast shadows, taken under the same lighting. We use an automatic 3D reconstruction from these photos and the sun direction as input and decompose each image into reflectance and shading layers, despite the inaccuracies and missing data of the 3D model. Our approach is based on two key ideas. First, we progressively improve the accuracy of the parameters of our image formation model by performing iterative estimation and combining 3D lighting simulation with 2D image optimization methods. Second, we use the image formation model to express reflectance as a function of discrete visibility values for shadow and light, which allows to introduce a robust visibility classifier for pairs of points in a scene. This classifier is used for shadow labeling, allowing to compute high-quality reflectance and shading layers. Our multiview intrinsic decomposition is of sufficient quality to allow relighting of the input images. We create shadow-caster geometry which preserves shadow silhouettes and, using the intrinsic layers, we can perform multiview relighting with moving cast shadows. We present results on several multiview datasets, and show how it is now possible to perform image-based rendering with changing illumination conditions.
OpenSAN

In recent years, with the rapid development of satellite technology including On Board Processing (OBP) and Inter Satellite Link (ISL), satellite network devices such as space IP routers have been experimentally carried in space. However, there are many difficulties to build a future satellite network with current terrestrial Internet technologies due to the distinguished space features, such as the severely limited resources, remote hardware/software upgrade in space. In this paper, we propose OpenSAN, a novel architecture of software-defined satellite network. By decoupling the data plane and control plane, OpenSAN provides satellite network with high efficiency, fine-grained control, as well as flexibility to support future advanced network technology. Moreover, we also discuss some practical challenges in the deployment of OpenSAN.
The Digital Office: Overview

This paper describes our efforts to develop a "Digital Ofrice" in which we augment a physical office setting with cameras and other electronic devices. Our goal is to bring the worlds of electronic and physical documents closer together and to facilitate the interaction of humans with all kinds of documents. In the "Digital Office" we extend the traditional notion of "scanning" documents to include the capture of whiteboards, books, desktops, and the human office workers themselves. In particular, we give an overview of three systems in which video cameras unobtrusively observe and capture whiteboards and human gestures, papers on the desktop, and the motion of a user’s face which is used to control the display of electronic documents in a browser. Each of these systems combines the features and affordances of both physical and electronic documents, and together they begin to illuminate the intelligent office environment of the future.
Word alignment based recognizing textural entailment

In this paper we focus on finding a best semantic alignment between two sentences. We model it as a bipartite weighted matching, and it can be solved in polynomial time by using combinational optimization method. Base on this alignment we integrated several methods to perform recognizing textural entailment (RTE). Finally, we implement our method on to RTE1 Test Data. The result shows that word alignment based RTE has a good performance.
A Decision Support Tool to Locate Shelters in Emergency Logistics

The objective of this research is to develop a systematic methodology to locate shelters considering both transportation and social factors in the aftermath of disasters. When anticipated demands for hurricane evacuation shelter spaces exceed existing capacity as defined by the preceding standards, there is a need to utilize less preferred facilities. It is critical that shelter selection decisions be made carefully considering both accessibility and facility conditions, and in consultation with local emergency management and public safety officials. While Red Cross and other relief agencies propose strategies to locate shelters, they currently do not consider how evacuees choose facilities based on accessibility to shelters and the in-facility congestion. This was evident in recent disasters such as Hurricane Katrina and Rita where some of the smaller shelters turned out to be inaccessible or unsafe for evacuees to use. This report provides a mathematical model to consider traveler routing and in facility delays in the location of facilities; considers the use of social cost functions to optimize the location of facilities in a network; and provides conclusions and directions for future research.
Background segmentation with feedback: The Pixel-Based Adaptive Segmenter

In this paper we present a novel method for foreground segmentation. Our proposed approach follows a non-parametric background modeling paradigm, thus the background is modeled by a history of recently observed pixel values. The foreground decision depends on a decision threshold. The background update is based on a learning parameter. We extend both of these parameters to dynamic per-pixel state variables and introduce dynamic controllers for each of them. Furthermore, both controllers are steered by an estimate of the background dynamics. In our experiments, the proposed Pixel-Based Adaptive Segmenter (PBAS) outperforms most state-of-the-art methods.
Detecting selfish carrier-sense behavior in WiFi networks by passive monitoring

With the advent of programmability in radios, it is becoming easier for wireless network nodes to cheat to obtain an unfair share of the bandwidth. In this work we study the widely used 802.11 protocol and present a solution to detect selfish carrier-sensing behavior where a node raises the CCA (clear channel assessment) threshold for carrier-sensing, or simply does not sense carrier (possibly randomly to avoid detection). Our approach is based on detecting any asymmetry in carrier-sense behavior between node pairs and finding multiple such witnesses to raise confidence. The approach is completely passive. It requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed by using a machine learning approach to infer carrier-sense relationships between network nodes. Evaluations using a real testbed as well as ns2 simulation studies demonstrate excellent detection ability. The metric of selfishness used to estimate selfish behaviormatches closely with actual degree of selfishness observed.
Motion planning with constraints using configuration space approximations

Robots executing practical tasks in real environments are often subject to multiple constraints. These constraints include orientation constraints: e.g., keeping a glass of water upright, torque constraints: e.g., not exceeding the torque limits for an arm lifting heavy objects, visibility constraints: e.g., keeping an object in view while moving a robot arm, etc. Rejection sampling, Jacobian projection techniques and optimization-based approaches are just some of the methods that have been used to address such constraints while computing motion plans for robots performing manipulation tasks. In this work, we present an approach to handling certain types of constraints in a manner that significantly increases the efficiency of existing methods. Our approach focuses on the sampling step of a motion planner. We implement this step as the drawing of samples from a set that has been computed in advance instead of the direct sampling of constraints. We show how our approach can be applied to different constraints: orientation constraints on the end-effector of an arm, visibility constraints and dual-arm constraints. We present simulated results to validate our method, comparing it to approaches that use direct sampling of constraints.
Direct writing of photonic devices using femtosecond laser pulses

We report on our capabilities to fabricate photonic devices in a large variety of different transparent dielectrics including highly nonlinear and active glasses as well as crystalline materials using tightly focused femtosecond laser pulses. We contrast the inscription of optical waveguides via thermal diffusion and cumulative heating and also discuss the realisation of depressed-cladding structures as well as stress-field induced guides. The fabrication of devices ranging from low loss optical waveguides, splitters, interferometers and resonant structures like waveguide Bragg gratings to more complex elements like integrated lasers and quantum circuits is demonstrated.
Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification

In most classification problems, a classifier assigns a single class to each instance and the classes form a flat non-hierarchical structure, without superclasses or subclasses. In hierarchical multilabel classification problems, the classes are hierarchically structured, with superclasses and subclasses, and instances can be simultaneously assigned to two or more classes at the same hierarchical level. This article proposes two new hierarchical multilabel classification methods based on the well-known local approach for hierarchical classification. The methods are compared with two global methods and one well-known local binary classification method from the literature. The proposed methods presented promising results in experiments performed with bioinformatics datasets.
Energy efficiency in wireless sensor networks: A game-theoretic approach based on coalition formation

A coalitional game theoretic scheme is proposed that aims at maximizing wireless sensor network lifetime under specified QoS. Employing a small number of nodes of increased computing power and lifetime called representatives, an adaptive clustering scheme is proposed where neighboring nodes form coalitions in order to increase energy efficiency at the cost of controllable data-accuracy reduction. The coalition formation is globally optimized by the representatives. The spatial correlation of the sensed phenomenon measurements is exploited to formulate a cooperation scheme that reduces drastically the number of node transmissions. The specifications regarding the accuracy of the collected data determine the extent of coalition formation. The efficiency and stability of the proposed coalitional scheme are studied through simulations.
A Finite Element Analysis Rupture Index (FEARI) as an Additional Tool for Abdominal Aortic Aneurysm Rupture Prediction

Currently, abdominal aortic aneurysms (AAAs), which are a permanent dilation of the aorta, are treated surgi- cally when the maximum transverse diameter surpasses 5.5cm. AAA rupture occurs when the locally acting wall stress exceeds the locally acting wall strength. There is a need to review the current diameter-based criterion, and so it may be clinically useful to develop an additional tool to aid the surgical decision-making process. A Finite Element Analysis Rup- ture Index (FEARI) was developed. Ten patient-specific AAAs were reconstructed, and the corresponding wall stress computed. Previous experimental work on determination of ultimate tensile strengths (UTS) from AAA tissue samples was implemented in this study. By com- bining peak wall stress along with average regional UTS, a new approach to the estimation of patient-specific rupture risk has been developed. Ten cases were studied, all of which were awaiting or had previously undergone surgical AAA repair. A detailed exami- nation of these ten cases utilising the FEARI analysis suggested that there was a possibility that some of the AAAs may have been less prone to rupture than previously considered. It is proposed that FEARI, used alongside other rupture risk factors, may improve the current surgical decision-making process. The use of FEARI as an additional tool for rupture prediction may provide a useful adjunct to the diameter-based approach in surgical decision-making.
Entropic Graphs for Registration

In many applications, fusion of images acquired via two or more sensors requires image alignment to an identical pose, a process called image registration. Image registration methods select a sequence of transformations to maximize an image similarity measure. Recently a new class of entropic-graph similarity measures was introduced for image registration, feature clustering and classification. This chapter provides an overview of entropic graphs in image registration and demonstrates their performance advantages relative to conventional similarity measures. In this chapter we introduce : techniques to extend
A 60µW 10Mb/s fully digital FSK demodulator for power-jitter efficient medical BAN

A 60µW power-jitter efficient direct conversion FSK demodulator is implemented in 0.18µm CMOS technology for 10Mb/s data rate Medical BAN (MBAN) application. To be tolerable to FSK data demodulation with Modulation Index (MI) of 1 and in the presence of large amount of jitter, a Jitter Cancelation Buffer (JCB) is proposed so as to adjust data transition time to cancel jitter down. In addition, the size of JCB is also optimized to achieve maximum power-jitter efficiency of 60µW power dissipation and 0.1 Unit Interval (UI) jitter.
Interaction of mobile camera devices with physical maps

Traditional paper-based maps are still superior to their digital counterparts used on mobile devices in several ways. They provide high-resolution, large-scale information with zero power consumption. On the other hand digital maps provide personalized and dynamic information on request, but suffer from small outer scales and low resolutions. In this work we try to combine the advantages of both by using mobile camera devices (such as smartphones ore PDA) as a map-referenced magic lens that displays geo-referenced information on top of the physical map. We will mainly focus on the interaction schemes that arise from using mobile camera devices with physical maps and briefly explain how the device tracking over existing physical maps can be realized.
Conjugal Power and Adolescent Identification with Parents

The relation of conjugal power to adolescent identification with parents is examined in a secondary analysis of survey data obtained from 18,664 adolescents. The data indicate that adolescents who perceive conjugal relations as equalitarian are highest in identification with parents while those who perceive the mother as dominant are lowest. Sex (lifferences in identification with a parent are not less in equalitarian families than other families. Some implications of the findings with regard to socialization and marital satisfaction are briefly discussed.
Residual Noise Control for Coherence Based Dual Microphone Speech Enhancement

Among various enhancement methods, dual microphone methods are utilized for their low cost implementation and use of spatial noise reduction. In this paper, we present a double microphone noise reduction method for nearby microphones. Our method is based on a modified coherence based filter which reduces correlated noises using cross spectral subtraction. In our proposed method the amount of noise reduction is controlled in order to achieve an acceptable compromise between residual noise and speech distortion. The performance of the enhancement technique is carried out using PESQ measure. Obtained results confirm the ability of the proposed approach to reduce distortion of signal.
Streptomyces alboflavus RPS and Its Novel and High Algicidal Activity against Harmful Algal Bloom Species Phaeocystis globosa

Phaeocystis globosa blooms have frequently occurred along coastal waters and exerted serious impacts on ecological environments by releasing toxic hemolytic substances, forming nuisance foam, and causing oxygen depletion. An actinomycete strain RPS with high algicidal activity against P. globosa was isolated and identified as Streptomyces alboflavus, based on morphology, physiological and biochemical characteristics, and 16S rDNA sequence analysis. RPS lysed 95% of P. globosa within 48 h by releasing an extracellular active substance into the growth medium. The activity of RPS supernatant was sensitive to temperature at and above 50°C and insensitive to pH from 3 to 11. The molecular weight of the active substance was between 100 Da and 1000 Da, and approximately 90% of it was extracted by ethyl acetate. It was presumed that the active component efficiently inhibited the movement of P. globosa, caused the flagella to fall off the algae, and finally lysed the algal cells. RPS showed a wide target range against harmful algae. S. alboflavus RPS with high algicidal activity and such novel features of temperature and pH sensitivity, low molecular weight, algicidal process, and target range possesses great potential in the biological control of P. globosa blooms.
The Importance of Including Dependencies in Trace Based Performance Analysis of On-chip Networks

With the advent of large scale chip-level multiprocesssors, there is renewed interest in the design and analysis of on-chip networks. The use of full system simulation is the most accurate way to perform such an analysis, but unfortunately it is very slow and thus limits the design space exploration. In order to overcome this problem researchers frequently use trace based simulation to study different network topologies and properties, which can be done much faster. Unfortunately, unless the traces used include information about dependencies between messages (packets), trace based simulation can lead one to draw incorrect conclusions about things like overall execution time. In this paper we will demonstrate the importance of including dependency information in traces, and show that doing so results in much better simulation accuracy without excessively extending simulation time. We will also show that this approach can be used to evaluate whether or not networks are being over-designed.