Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hyperspectral Survey of New York City Lighting Technology

Using side-facing observations of the New York City (NYC) skyline, we identify lighting technologies via spectral signatures measured with Visible and Near Infrared (VNIR) hyperspectral imaging. The instrument is a scanning, single slit spectrograph with 872 spectral channels from 0.4–1.0 μm. With a single scan, we are able to clearly match the detected spectral signatures of 13 templates of known lighting types. However, many of the observed lighting spectra do not match those that have been measured in the laboratory. We identify unknown spectra by segmenting our observations and using Template-Activated Partition (TAP) clustering with a variety of underlying unsupervised clustering methods to generate the first empirically-determined spectral catalog of roughly 40 urban lighting types. We show that, given our vantage point, we are able to determine lighting technology use for both interior and exterior lighting. Finally, we find that the total brightness of our scene shows strong peaks at the 570 nm Na-II, 595 nm Na-II and 818 nm Na-I lines that are common in high pressure sodium lamps, which dominate our observations.
Distinct Roles of SOM and VIP Interneurons during Cortical Up States

During cortical network activity, recurrent synaptic excitation among pyramidal neurons is approximately balanced by synaptic inhibition, which is provided by a vast diversity of inhibitory interneurons. The relative contributions of different interneuron subtypes to inhibitory tone during cortical network activity is not well-understood. We previously showed that many of the major interneuron subtypes in mouse barrel cortex are highly active during Up states (Neske et al., 2015); while fast-spiking (FS), parvalbumin (PV)-positive cells were the most active interneuron subtype, many non-fast-spiking (NFS), PV-negative interneurons were as active or more active than neighboring pyramidal cells. This suggests that the NFS cells could play a role in maintaining or modulating Up states. Here, using optogenetic techniques, we further dissected the functional roles during Up states of two major NFS, PV-negative interneuron subtypes: somatostatin (SOM)-positive cells and vasoactive intestinal peptide (VIP)-positive cells. We found that while pyramidal cell excitability during Up states significantly increased when SOM cells were optogenetically silenced, VIP cells did not influence pyramidal cell excitability either upon optogenetic silencing or activation. VIP cells failed to contribute to Up states despite their ability to inhibit SOM cells strongly. We suggest that the contribution of VIP cells to the excitability of pyramidal cells may vary with cortical state.
Statistical consistency and asymptotic normality for high-dimensional robust M-estimators

We study theoretical properties of regularized robust M-estimators, applicable when data are drawn from a sparse high-dimensional linear model and contaminated by heavy-tailed distributions and/or outliers in the additive errors and covariates. We first establish a form of local statistical consistency for the penalized regression estimators under fairly mild conditions on the error distribution: When the derivative of the loss function is bounded and satisfies a local restricted curvature condition, all stationary points within a constant radius of the true regression vector converge at the minimax rate enjoyed by the Lasso with sub-Gaussian errors. When an appropriate nonconvex regularizer is used in place of an l_1-penalty, we show that such stationary points are in fact unique and equal to the local oracle solution with the correct support---hence, results on asymptotic normality in the low-dimensional case carry over immediately to the high-dimensional setting. This has important implications for the efficiency of regularized nonconvex M-estimators when the errors are heavy-tailed. Our analysis of the local curvature of the loss function also has useful consequences for optimization when the robust regression function and/or regularizer is nonconvex and the objective function possesses stationary points outside the local region. We show that as long as a composite gradient descent algorithm is initialized within a constant radius of the true regression vector, successive iterates will converge at a linear rate to a stationary point within the local region. Furthermore, the global optimum of a convex regularized robust regression function may be used to obtain a suitable initialization. The result is a novel two-step procedure that uses a convex M-estimator to achieve consistency and a nonconvex M-estimator to increase efficiency.
(Approximate) Conic Nearest Neighbors and the induced Voronoi Diagram

For a given point set in Euclidean space we consider the problem of finding (approximate) nearest neighbors of a query point but restricting only to points that lie within a fixed cone with apex at the query point. We investigate the structure of the Voronoi diagram induced by this notion of proximity and present approximate and exact data structures for answering cone-restricted nearest neighbor queries. In particular we develop an approximate Voronoi diagram of size O((n/! d )log(1/! )) that can be used to answer cone-restricted nearest neighbor queries in O(log(n/! )) time.
Server-assisted speech recognition over the Internet

We propose a new architecture for deploying speech recognition over the Internet. The client performs the recognition, but is assisted by the server who computes the speech parameters. To demonstrate the architecture, we developed a Java-based Web-navigation system where the precomputed HMM models of the hyperlinked words are stored on the Web page and downloaded by the client. We tested the system on a digit-recognition example. The results show that with quantization and compression of the speech parameters, good recognition can be achieved in acceptable download and calculation time even on clients with modest connection speeds and computational powers.
Coupling finite difference method with finite particle method for modeling viscous incompressible flows

A hybrid approach to couple finite difference method (FDM) with finite particle method (FPM) (ie, FDM‐FPM) is developed to simulate viscous incompressible flows. FDM is a grid‐based method that is convenient for implementing multiple or adaptive resolutions and is computationally efficient. FPM is an improved smoothed particle hydrodynamics (SPH), which is widely used in modeling fluid flows with free surfaces and complex boundaries. The proposed FDM‐FPM leverages their advantages and is appealing in modeling viscous incompressible flows to balance accuracy and efficiency. In order to exchange the interface information between FDM and FPM for achieving consistency, stability, and convergence, a transition region is created in the particle region to maintain the stability of the interface between two methods. The mass flux algorithm is defined to control the particle creation and deletion. The mass is updated by N‐S equations instead of the interpolation. In order to allow information exchange, an overlapping zone is defined near the interface. The information of overlapping zone is obtained by an FPM‐type interpolation. Taylor‐Green vortices and lid‐driven shear cavity flows are simulated to test the accuracy and the conservation of the FDM‐FPM hybrid approach. The standing waves and flows around NACA airfoils are further simulated to test the ability to deal with free surfaces and complex boundaries. The results show that FDM‐FPM retains not only the high efficiency of FDM with multiple resolutions but also the ability of FPM in modeling free surfaces and complex boundaries.
Dynamic Dazzle Distorts Speed Perception

Static high contrast (‘dazzle’) patterns, such as zigzags, have been shown to reduce the perceived speed of an object. It has not escaped our notice that this effect has possible military applications and here we report a series of experiments on humans, designed to establish whether dynamic dazzle patterns can cause distortions of perceived speed sufficient to provide effective defence in the field, and the extent to which these effects are robust to a battery of manipulations. Dynamic stripe patterns moving in the same direction as the target are found to increase the perceived speed of that target, whilst dynamic stripes moving in the opposite direction to the target reduce the perceived speed. We establish the optimum position for such dazzle patches; confirm that reduced contrast and the addition of colour do not affect the performance of the dynamic dazzle, and finally, using the CO2 challenge, show that the effect is robust to stressful conditions.
A systematic method of developing information sharing systems based on activity theory

Information sharing in policing enables proactive and preventative work to enhance welfare, security and safety for the public; however, achieving these goals requires having an effective and efficient mechanism for sharing information within and between organisations. There is currently no systematic approach to specifically analyse the requirements of an information sharing system, which motivates the need to develop such an approach. In this research, the Activity Theory was taken as a basis to identify the requirements of the approach and design a suitable framework, which was then evaluated through the use of two case studies in the public sector in the United Arab Emirates (UAE). This research aims to design an information sharing framework that takes proper account of the actors and their activities to improve the information sharing system. To this end, it presents the development of the Activity Theory-based Information Sharing Analysis (AcTIShA) Framework, proposing a mechanism for analysing the information need and supporting the information sharing. This framework incorporates the concept of information analysis adopted from the Activity Theory, which is used for understanding the information. The Activity Theory plays a crucial role in analysing the elements concerned with the information, such as actors, actions, information artefacts and purposes within the organisation’s activities. To illustrate the application of the AcTIShAFramework, a system design based on its use is demonstrated. The design science research paradigm is adopted to evaluate the outcomes of the framework through two case studies. These were carried out in two different public sectors of the UAE, namely the Ministry of Interior and the Ministry of Health, to evaluate the AcTIShA-Framework. The focus group method was conducted with a variety of participants from both sectors to discuss and evaluate the information sharing mechanism. It is demonstrated that the framework provides a systematic and versatile approach to be utilised to improve the sharing of the information among organisations, and that it will benefit the development of information sharing systems. The conclusions, contributions and suggestions drawn from this study are used to extend the activities of information sharing. Finally, the research provides guidance for developing information sharing systems.
Detecting the number of clusters of individuals using the software structure: a simulation study

The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software structure allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual‐based model. We found that in most cases the estimated ‘log probability of data’ does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic ΔK based on the rate of change in the log probability of data between successive K values, we found that structure accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
DAMN: A Debugging and Manipulation Tool for Android Applications

Mobile developers tend to use source code obfuscation to protect their code against reverse engineering. Unfortunately, some developers rely on the idea that obfuscated applications also provide additional security. But that is not the case since mistakes in design are still present and can be used for arbitrary attacks. However, manually analyzing such obfuscated applications is time consuming for researchers due to the complexity of the generated code. Our debugging and manipulation tool (DAMN) offers a new way of investigating Android applications, including obfuscated ones. It combines static source code reversing with dynamic manipulation techniques to get rid of obfuscation penalties and supports the investigator during the analyzing process. DAMN can display the reversed source code, pause any application at any given time and allows to manipulate its state. All those features make DAMN a powerful reversing and analyzing tool for manual investigations of obfuscated Android applications.
Preserve Available Sensing And Interactivity Capabilities Of Mobile Devices

Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. Despite increasing usage of mobile computing; using its full potential is difficult due to its inherent problems such as limited resource. Cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. The foundation of cloud computing is the delivery of services, software and processing capacity over the Internet, reducing cost, increasing storage, automating systems, decoupling of service delivery from underlying technology, and providing flexibility and mobility of information In this paper, we developed an architecture that uses cloud to do computations that consume resources badly on mobiles. It aims at finding the right spots in an application automatically where the execution can be partitioned and migrated to the cloud. Thus, an elastic application can augment the capabilities of a mobile device including computation power, storage, and network bandwidth, with the light of dynamic execution configuration according to device"s status memory, and battery level. We demonstrate results of the proposed application model using data collected from one of our elastic application.
QoS Multimedia Multicast Routing

—14632—X Summary of Approximation Factors for the QoSMT Problem 71.3 Primal–Dual Approach to the QoSMT Problem. 71-000 A Simpler ILP Formulation † Two Primal–Dual Methods for the Simple ILP Formulation † Primal–Dual 4.311-Approximation Algorithm 71.4 Experimental Study . 71-000 References. 71-000
Energy Efficient Data Transmission Mechanism in Wireless Sensor Networks

In wireless sensor networks, data transmission is an essential step for various applications, and an energy efficient data transmission mechanism is needed since the energy resource of WSN is restricted. This paper investigates the coalition-based cooperative data transmission mechanism (CC-DTM), analyzes its entire energy consumption, and finds out the optimal number of sensor nodes needed in the cooperative data transmission. The condition under which CC-DTM is more energy-saving than the cluster approach is also deduced. Furthermore, an energy-load balanced node selection strategy is proposed. The results of simulation experiment show the validity of this energy efficient data transmission mechanism.
Removal of adherent waterdrops from images acquired with stereo camera

In this paper, we propose a new method that can remove view-disturbing noises from stereo images. One of the thorny problems in outdoor surveillance by a camera is that adherent noises such as waterdrops on the protecting glass surface lens disturb the view from the camera. Therefore, we propose a method for removing adherent noises from stereo images taken with a stereo camera system. Our method is based on the stereo measurement and utilizes disparities between stereo image pair. Positions of noises in images can be detected by comparing disparities measured from stereo images with the distance between the stereo camera system and the glass surface. True disparities of image regions hidden by noises can be estimated from the property that disparities are generally similar with those around noises. Finally, we can remove noises from images by replacing the above regions with textures of corresponding image regions obtained by the disparity referring. Experimental results show the effectiveness of the proposed method.
Evaluation of linear mixture modelling of urban areas in Landsat TM satellite imagery

In this paper, the author presents the experiences made by applying the linear mixture modelling technique to an urban area located in the north of San Diego, Ca. Landsat TM satellite imagery was chosen to perform the analysis on. The paper presents the results of linear unmixing applied to raw and first-order corrected imagery. To evaluate the accuracy of the unmixing method, the results of the sub-pixel classification were compared to a reference, which is represented by high-resolution imagery, called ADAR. images were acquired at the same day time. In both scenes, noise removal and geometric registration with terrain correction were performed. Topographic effects were also corrected. Conspicuous are the big reflectance differences of vegetation and soil in the images. This is caused by different climatic backgrounds. 1996 was a year with less precipitation and higher temperatures than usually and 1998, a year dominated by El Niño, was relatively cool and wet. The resulted reflectance variations of especially vegetation might effect the classification results. Reference values for the sub-pixel classification were obtained by an interpretation of digital airborne photographs taken by an ADAR 5500 system. The ADAR data were acquired in June 1998. The images have a ground solution distance of 1m. 3 LINEAR MIXTURE MODELLING Linear Mixture Modelling has been discussed often in articles. Therefore here will be only a very brief explanation of the technique. LMM computes the spectral constituents of a pixel by analysing its spectral signature. It assumes a single reflectance of a photon at the object within the given instantaneous field of view. In many cases, it approximates reality. Whenever there is radiation transmission through one of the materials or if there are multiple reflections within a material or between two objects a nonlinear mixing occurs. In the LMM, the DN of a pixel in each spectral band is expressed as a linear combination of the characteristic relative radiance of its component endmembers weighted by their respective areal proportions within the pixel. [Ichoku 1996] Thus, the DNi of a pixel in the i-th band is given by:
System Combination of RBMT plus SPE and Preordering plus SMT

System architecture, experimental settings and evaluation results of EHR group in the en-ja, zh-ja, JPCzh-ja and JPCko-ja tasks are described. Our system concept is combination of a rule based method and a statistical method. System combination of rule-based machine translation (RBMT), RBMT plus statistical post-editing (SPE) and preordering plus statistical machine translation (SMT) is conducted. From the multiple outputs of three systems, candidate selection part selects the best output by language model score. For JPCzh-ja task devtest data translation, SPE improves BLEU score by 17.81, preordering improves BLEU score by 1.89 and system combination improves BLEU score by 0.26.
Constructive Negation in Extensional Higher-Order Logic Programming

Extensional higher-order logic programming has been recently proposed as an interesting extension of classical logic programming. An important characteristic of the new paradigm is that it preserves all the well-known properties of traditional logic programming. In this paper we enhance ex-tensional higher-order logic programming with constructive negation. We argue that the main ideas underlying constructive negation are quite close to the existing proof procedure for extensional higher-order logic programming and for this reason the two notions amalgamate quite conveniently. We demonstrate the soundness of the resulting proof procedure and describe an actual implementation of a language that embodies the above ideas. In this way we obtain the first (to our knowledge) higher-order logic programming language supporting constructive negation and offering a new style of programming that genuinely extends that of traditional logic programming.
Pattern Mining and Querying in Business Intelligence

Business intelligence is currently practiced in many organizations through the data mining tools. The most applicable data mining tasks are classification and association rule discovery. Even though existing data mining tools can successfully discover rules that represent relationships among operational stored data, we observe that the number of discovered rules is somewhat exceeding users’ need. We therefore propose the extensions of conventional classification and association mining by incorporating search constraints over the discovered rules. The framework of the proposed business intelligence system, as well as its implementation and applications are presented in this paper.
Fuzzy Sliding Mode Observer with Grey Prediction for the Estimation of the State-of-Charge of a Lithium-Ion Battery

We propose a state-of-charge (SOC) estimation method for Li-ion batteries that combines a fuzzy sliding mode observer (FSMO) with grey prediction. Unlike the existing methods based on a conventional first-order sliding mode observer (SMO) and an adaptive gain SMO, the proposed method eliminates chattering in SOC estimation. In this method, which uses a fuzzy inference system, the gains of the SMO are adjusted according to the predicted future error and present estimation error of the terminal voltage. To forecast the future error value, a one-step-ahead terminal voltage prediction is obtained using a grey predictor. The proposed estimation method is validated through two types of discharge tests (a pulse discharge test and a random discharge test). The SOC estimation results are compared to the results of the conventional first-order SMO-based and the adaptive gain SMO-based methods. The experimental results show that the proposed method not only reduces chattering, but also improves estimation accuracy.
Learning decision trees to determine turn-taking by spoken dialogue systems

This paper presents a method for deciding the timing of turn-taking in spoken dialogue systems. This method uses a decision tree learned from the corpus of dialogues between human users and systems in which desirable turn-taking behaviors are annotated by hand. It utilizes a variety of attributes, such as recognition and understanding results and prosodic information. Unlike most of the existing systems it enables spoken dialogue systems to decide the timing of turn-taking based on not only pauses but also other features, so that users can speak to the system even if they put pauses in the middle of their utterances. The result of a preliminary exper-iment shows that the learned decision tree outperforms the baseline strategy, which takes turn at every user pauses.