Miracle-dz/UniFAR-FTUs huggingface dataset:
A simple rule for tuning Event-Based PID controllers with Symmetric Send-On-Delta sampling strategy

In this paper we analyse the conditions that produce oscillations in event based PID controllers. A Symmetric Send-On-Delta scheme (SSOD) is assumed, and the describing function is the tool used to address the problem. Once the conditions for oscillations are established, a new robustness to oscillations performance measure is introduced. Then a very simple and intuitive rule of thumb is given about how to tune PID controllers to avoid the limit cycles. The rule presented here entails with the classical concept of phase margin, providing the key to design SSOD based PID by using continuous PID tuning methods. The proposed ideas are tested in simulations with a batch of models widely used in PID design methods testing.
A low-swing clock double-edge triggered flip-flop

A low-swing clock double-edge triggered flip-flop (LSDBF) is developed to reduce power consumption significantly compared to conventional FFs. LSDFF avoids unnecessary internal node transition and reduces conflicting currents. The overall power saving in flip-flop operation is estimated to be 30.2 to 50.8% with additional 78% power savings in a clock network.
A Linear Physical Programming Approach to Power Flow and Energy Storage Optimization in Smart Grids Models

The Optimal Power Flow problem (OPF) plays a crucial role in the successful energy management of modern smart grids. The diffusion of renewable energy sources poses new challenges to the power grid in which integrated energy storage combined with green generation solutions can help to address challenges associated with both power supply and demand variability. This work refers to a smart grid context and proposes a time indexed OPF model considering storage dynamics, adopting a preference-based optimization method with chance constraints to provide a suitable service level.
Factor Graphs for Region-based Whole-scene Classification

Semantic scene classification is still a challenging problem in computer vision. In contrast to the common approach of using low-level features computed from the scene, our approach uses explicit semantic object detectors and scene configuration models. To overcome faulty semantic detectors, it is critical to develop a region-based, generative model of outdoor scenes based on characteristic objects in the scene and spatial relationships between them. Since a fully connected scene configuration model is intractable, we chose to model pairwise relationships between regions and estimate scene probabilities using loopy belief propagation on a factor graph. We demonstrate the promise of this approach on a set of over 2000 outdoor photographs, comparing it with existing discriminative approaches and those using low-level features.
IoT enabled Insurance Ecosystem - Possibilities Challenges and Risks

Internet of Thing (IoT) is looking over to overhaul the business processes of many industries including insurance domain. The current line of business such as Property and Casualty, Health, and Life Insurance can avail tremendous benefits from the contextual and relevant data being generated from billions of connected devices; Smartphone's, wearable gadget and other electronic smart sensors. For P&C insurer's the biggest challenges is not the rapidly changing environment but tackling these challenges with strategies linked to the past. This paper presents the key opportunities, challenges, potential applications and risks of IoT in Insurance domain. It crystallomancy on the efficient use of data analytics from data generated in IoT ecosystem and highlights business model changes that Insurance industries may face in near future.
Factors affecting reverse knowledge transfer from subsidiaries to multinational companies: Focusing

Although research on reverse knowledge transfer (RKT) from subsidiaries to headquarters is increasingly prominent, the debate concerning the primary determinants influencing RKT has not reached an academic consensus. Therefore, we have attempted to draw an overall picture for RKT by using both knowledge transfer capacity and relational capital as overarching theoretical lenses. We find, in the context of South Korea, that knowledge development capability, subsidiary willingness and autonomy are critical factors affecting the reverse transfer of local market information within MNC networks. Furthermore, both the knowledge integration mechanism and trust are facilitators for improving relational capital between subsidiaries and MNCs. This factor facilitates RKT from the former to the latter.
Secure and Efficient Retrieval of Data in Cloud

Cloud computing is becoming more interesting day by day. As the use of cloud services increases it’s now important to do something for improving efficiency and security of cloud computing. Cloud storage contains huge amount of data, in such case to search that data efficiently becomes a challenging task. Also security vulnerability of such online storage systems is always non trustable. The recent researches are trying to solve this problem by the method of keyword search. But these methods solves this problem to some extend but some methods increases the computational burden on the cloud server or makes the retrieval of files the costly by means of bandwidth efficiency by sending all similar files to the requesting user. This paper discuses this problem and later gives the solution to solve this problem. To solve this problem the method of keyword search has been used. This paper tries to solve the problem of searching files through the huge amount of files securely and efficiently. The previous methods makes the search non efficient by means of time and computational cost, but the method discussed in this paper makes the searching very efficient and secure. It also reduces the burden on the cloud server by sharing it to the user or owner side. Thus makes it very efficient technique in the era of mobile devices where the cloud services can be used on any devices with the expectation of lesser computational cost which finally results into improved performance.
An index-based approach for similarity search supporting time warping in large sequence databases

This paper proposes a new novel method for similarity search that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. Previous methods for processing similarity search that supports time warping fail to employ multi-dimensional indexes without false dismissal since the time warping distance does not satisfy the triangular inequality. Our primary goal is to innovate on search performance without permitting any false dismissal. To attain this goal, we devise a new distance function D/sub tw-lb/ that consistently underestimates the time warping distance and also satisfies the triangular inequality D/sub tw-lb/ uses a 4-tuple feature vector that is extracted from each sequence and is invariant to time warping. For efficient processing of similarity search, we employ a multi-dimensional index that uses the 4-tuple feature vector as indexing attributes and D/sub tw-lb/ as a distance function. The extensive experimental results reveal that our method achieves significant speedup up to 43 times with real-world S&P 500 stock data and up to 720 times with very large synthetic data.
Thresholding estimators for linear inverse problems and deconvolutions

Thresholding algorithms in an orthonormal basis are studied to estimate noisy discrete signals degraded by a linear operator whose inverse is not bounded. For signals in a set Θ), sufficient conditions are established on the basis to obtain a maximum risk with minimax rates of convergence. Deconvolutions with kernels having a Fourier transform which vanishes at high frequencies are examples of unstable inverse problems, where a thresholding in a wavelet basis is a suboptimal estimator. A new mirror wavelet basis is constructed to obtain a deconvolution risk which is proved to be asymptotically equivalent to the minimax risk over bounded variation signals. This thresholding estimator is used to restore blurred satellite images.
Cafeteria, commissary and cooking: Foodways and negotiations of power and identity in a women's prison

CAFETERIA, COMMISSARY AND COOKING: FOODWAYS AND NEGOTIATIONS OF POWER AND IDENTITY IN A WOMEN’S PRISON by Amy B. Smoyer Adviser: Professor Deborah L. Tolman This study uses foodways theory to build knowledge about the lived experience of incarceration by analyzing women’s narratives about prison food and eating. Semi-structured interviews were conducted with 30 formerly incarcerated women in New Haven, CT. The interviews were digitally recorded and transcribed. Findings explain the different ways that inmates collect, prepare, distribute and consume food, and the centrality of these activities to incarcerated life. By shedding light on these daily routines, the world of prison life comes into greater focus. Thematic analysis of the data further illuminates the prison experience by suggesting the positive and negative ways that food impacts inmate’s perceptions of themselves, their social networks and the State. Negative foodways humiliated the women, accentuated their powerlessness, and reinforced their perceptions of the State as nonsensical and apathetic towards their needs. Positive foodways illustrated the inmates’ capacity to resist State power, build/maintain relationships and construct positive self-narratives. Racialized foodways narratives began to reveal how food stories may be deployed to reinforce prison’s racial character and construct the identities of self and other. Foodways interventions to support the rehabilitative goals of correctional facilities are proposed. These data suggest that inmates want to build positive relationships and identities and that prison food systems could do more to help women realize these intentions.
Shader space navigator: a turbo for an intuitive and effective shading process

In this paper, we first point out difficulties faced by CG artists in the shading process: (1) a lot of technical details on shaders required, (2) long rendering time, and (3) repeated cumbersome trial-and-errors. To make them overcome such difficulties, we propose Shader Space Navigator, a system that efficiently searches for shaders similar to a given query shader. With Shader Space Navigator, CG artists find quality shaders from the database that are very close to the final result shader, and thus complete the shading process easily by slightly tuning some attributes of those shaders. As a result, the CG artists can create their final shaders in an intuitive and efficient way thereby avoiding a large number of time-consuming rendering processes.
Investigating explanatory theories on laypeople’s risk perception of personal economic collapse in a bank crisis – the Cyprus case

We investigate the explanatory power of decision, psychometric, and trust theory to describe laypeople’s risk perception of personal economic collapse in a bank crisis. The aim of this investigation is to improve the understanding of the effects of national initiatives for crisis fighting taken to prevent systemic risk. Using a stratified sample of 738 Cypriote citizens, we conducted an investigation in Cyprus in the spring of 2013 when the country was facing a bank crisis. At that point in time, the Cypriote Government had imposed capital controls to prevent a bank run. We find that decision theory variables alone have low explanatory value on laypeople’s risk perception, and that laypeople’s risk perception in this situation is affected primarily by psychometric variables. Further, confidence in one’s own bank also explains risk perception. Our findings contribute novel knowledge about risk perceptions in a financial crisis, with practical crisis management implications for regulators.
Optimization-based trajectory generation with linear temporal logic specifications

We present a mathematical programming-based method for optimal control of discrete-time dynamical systems subject to temporal logic task specifications. We use linear temporal logic (LTL) to specify a wide range of properties and tasks, such as safety, progress, response, surveillance, repeated assembly, and environmental monitoring. Our method directly encodes an LTL formula as mixed-integer linear constraints on the continuous system variables, avoiding the computationally expensive processes of creating a finite abstraction of the system and a Büchi automaton for the specification. In numerical experiments, we solve temporal logic motion planning tasks for high-dimensional (10+ continuous state) dynamical systems.
Transparency maximization methodology for haptic devices

In this paper, a design methodology is presented aimed at maximizing haptic device transparency, as seen from the user side. The methodology developed focuses on endpoint side fidelity, and optimizes not only mechanism dimensions, but also all relevant design parameters including relative position of endpoint desired path to device location, motor transmission ratios, and rotor inertias or motor sizes. The methodology is applied to a 5-degree-of-freedom (5-DOF) haptic device, part of a training medical urological simulator, and is applicable to any haptic mechanism. The transparency maximization is achieved using a multivariable optimization approach and an objective function including mechanism-induced parasitic torques/forces and motor and transmission parameters, as seen from the user side, under several constraints. The objective function and the kinematical and operational constraints are described and discussed. A new 5-DOF haptic mechanism is constructed according to the developed procedure, resulting in a substantially improved device with respect to an existing one, developed with a standard optimization method.
Approximate availability analysis of VAXcluster systems

The availability of an n-processor VAX cluster system is determined using a hierarchical approach that allows: (1) a closed-form answer to an apparently difficult problem to be obtained, and (2) the determination of the optimal number of processors in the cluster for a given set of cluster parameters. The approach is a two-level hierarchical model in which the lower-level model is a nine-state Markov chain that is solved in a closed form. The nine-state Markov chain is then aggregated into a three-state device analogous to a diode. Subsequently, the system availability is computed by analyzing a simple network. >
Eriksson OO Writing Process AN OBJECT ORIENTED MODEL OF THE WRITING PROCESS

ABSTRACT: The writing process has been described in several ways, where the central parts are planning, text generation and revision (Hayes, 1996) in some way (Carey and Flower, 1989). Solving problems can be viewed as representing the problem space and finding a path in that space (Hayes and Nash, 1996). That representation can be done with object orientation (OO). OO is a paradigm in software engineering where all parts of a system are regarded as objects with a particular responsibility. This paper will attempt to construct a writing model that, by using the OO paradigms with the extensions of design patterns and roles, make the process of representing the problem space easier. The paper will begin with a background to the writing processes and object oriented concepts used to create the object oriented model and then present the new version of the model.
Two-step supervised confidence measure for automatic face recognition

This paper deals with automatic face recognition in the context of a real application for the Czech News Agency. This system will be used to annotate people in photographs during insertion into the database. Unfortunately, the accuracy of the current face recognition approaches is limited and therefore another task to process the recognition results is very important. The main contribution of this work thus consists in proposing and evaluating a novel supervised confidence measure method as the post-processing step in order to detect incorrectly classified face images from the classifier's output. We experimentally show that the proposed confidence measure is beneficial for our application.
Snake Genome Sequencing: Results and Future Prospects

Snake genome sequencing is in its infancy—very much behind the progress made in sequencing the genomes of humans, model organisms and pathogens relevant to biomedical research, and agricultural species. We provide here an overview of some of the snake genome projects in progress, and discuss the biological findings, with special emphasis on toxinology, from the small number of draft snake genomes already published. We discuss the future of snake genomics, pointing out that new sequencing technologies will help overcome the problem of repetitive sequences in assembling snake genomes. Genome sequences are also likely to be valuable in examining the clustering of toxin genes on the chromosomes, in designing recombinant antivenoms and in studying the epigenetic regulation of toxin gene expression.
Low Power Electronics and Applications Multi-threshold Null Convention Logic (mtncl): an Ultra-low Power Asynchronous Circuit Design Methodology

This paper develops an ultra-low power asynchronous circuit design methodology, called Multi-Threshold NULL Convention Logic (MTNCL), also known as Sleep Convention Logic (SCL), which combines Multi-Threshold CMOS (MTCMOS) with NULL Convention Logic (NCL), to yield significant power reduction without any of the drawbacks of applying MTCMOS to synchronous circuits. In contrast to other power reduction techniques that usually result in large area overhead, MTNCL circuits are actually smaller than their original NCL versions. MTNCL utilizes high-Vt transistors to gate power and ground of a low-Vt logic block to provide for both fast switching and very low leakage power when idle. To demonstrate the advantages of MTNCL, a number of 32-bit IEEE single-precision floating-point co-processors were designed for comparison using the 1.2 V IBM 8RF-LM 130 nm CMOS process: original NCL, MTNCL with just combinational logic (C/L) slept, 82 energy, and idle power, showing the tradeoffs between the various MTNCL architectures, and that the best MTNCL design is much better than the original NCL design in all aspects, and much better than the synchronous MTCMOS design in terms of area, energy per operation, and idle power, although the synchronous design can operate faster.
Instruction-Cycle-Based Dynamic Voltage Scaling Power Management for Low-Power Digital Signal Processor With 53% Power Savings

This paper presents and analyzes a fully digital instruction-cycle-based dynamic voltage scaling (iDVS) power management strategy for low-power processor designs. The proposed iDVS technique is fully compatible with conventional DVS scheduler algorithms. An additional computer aided design-based design flow was embedded in a standard cell library to implement the iDVS-based processor in highly integrated system-on-a-chip applications. The lattice asynchronous self-timed control digital low-dropout regulator with swift response and low quiescent current was also utilized to improve iDVS voltage transition response. Results show that the iDVS-based processor with the proposed adaptive instruction cycle control scheme can efficiently perform millions of instructions per second during iDVS transition. The iDVS-based digital signal processor chip was implemented in a HH-NEC 0.18-μm standard complementary metal-oxide semiconductor. Measurement results show that the voltage tracking speed with 11.6 V/μs saved 53% power.
Shape recognition and grasping by robotic hands with soft fingers and omnidirectional camera

The purpose of this paper is to establish a method of shape recognition and grasping of unknown objects using a robotic hand with two soft fingers and one omnidirectional camera. For shape recognition, we propose to use a simple 2D version of the visual volume intersection method which takes advantage of a special feature of the omnidiretional camera attached to the hand. For grasping we propose a simple grasp quality criterion to obtain the best grasping position for the two soft fingers based on a visual hull in a horizontal grasp plane. We conducted several experiments and the results show the validity of the proposed method.
Service deployment on high performance active network nodes

In the context of realizing service deployment on high performance active nodes, we address the problem of installing and configuring software components in complex, heterogeneous node environments. Service deployment is difficult on high-performance nodes due to their complex architectures. They are often based on multiprocessors and run service components in multiple concurrent execution environments. Many types of execution environments have been developed each of which is usually optimized for a certain type of tasks. Advanced active nodes provide more than one to support the whole spectrum of services. Also, different types of active nodes usually support different sets of execution environments. All this motivates us to develop a service deployment scheme that can cope with heterogeneous active nodes. This paper presents our approach to this problem, called Chameleon. It has two important aspects. First, the service model we propose is based on components with two types of interfaces - a data flow interface for programming packet flows and a control interface for controlling and managing the service components. A service is structured as an arbitrary tree of such components. Second, the service specification is independent of any particular node architecture. During the service deployment phase, the service specification is resolved recursively on each node offering the service and is driven by node-specific parameters. The result of this resolution is a tree of service components, which can differ among different types of nodes. Our solution allows a service to take full advantage of specific node features, such as those related to performance or security. This paper advocates a service model that is specialized for active networking. We base the model on two basic abstractions, namely composable containers and connectors.
Sparse Reinforcement Learning via Convex Optimization

We propose two new algorithms for the sparse reinforcement learning problem based on different formulations. The first algorithm is an off-line method based on the alternating direction method of multipliers for solving a constrained formulation that explicitly controls the projected Bellman residual. The second algorithm is an online stochastic approximation algorithm that employs the regularized dual averaging technique, using the Lagrangian formulation. The convergence of both algorithms are established. We demonstrate the performance of these algorithms through several classical examples.
Modeling of Nonlinear Active Circuits Using FDTD Approach

-This paper, describes a voltagesource formulation of the extended finite-difference time-domain algorithm for the purpose of modeling nonlinear microwave devices. Based on this approach, the device-wave interaction is characterized and incorporated into FDTD algorithm. Analysis of nonlinear properties, including harmonic generation and intermodulation, can be accomplished by using a large signal device circuit model. This technique is applied to the analysis of a typical nonlinear microwave amplifier, which includes a threeterminal active MESFET device. Simulation results are in good agreement with MICROWAVE OFFICE results.
Sliding Mode Variable Structure Observer for PMSM Position Sensorless

In this paper, a novel sliding mode variable structure observer is presented for estimating the rotor position angle of permanent magnetic synchronous motors to achieve position sensorless vector control. Compared to other traditional observers, the proposed observer can overcome the defect of parameter dependence based on motor mode. Simulation results show that the proposed observer can greatly enhance the stability of the system regardless of parameter variation and load disturbance.