Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost-efficiency in multivariate Lévy models

Abstract In this paper we determine lowest cost strategies for given payoff distributions called cost-efficient strategies in multivariate exponential Lévy models where the pricing is based on the multivariate Esscher martingale measure. This multivariate framework allows to deal with dependent price processes as arising in typical applications. Dependence of the components of the Lévy Process implies an influence even on the pricing of efficient versions of univariate payoffs.We state various relevant existence and uniqueness results for the Esscher parameter and determine cost efficient strategies in particular in the case of price processes driven by multivariate NIG- and VG-processes. From a monotonicity characterization of efficient payoffs we obtain that basket options are generally inefficient in Lévy markets when pricing is based on the Esscher measure.We determine efficient versions of the basket options in real market data and show that the proposed cost efficient strategies are also feasible from a numerical viewpoint. As a result we find that a considerable efficiency loss may arise when using the inefficient payoffs.
Semantic Frame-Based Approach for Reader-Emotion Detection

Previous studies on emotion classification mainly focus on the writer's emotional state. By contrast, this research emphasizes emotion detection from the readers' perspective. The classification of documents into reader-emotion categories can be applied in several ways, and one of the applications is to retain only the documents that trigger desired emotions to enable users to retrieve documents that contain relevant contents and at the same time instill proper emotions. However, current IR systems lack the ability to discern emotions within texts, and the detection of reader-emotion has yet to achieve a comparable performance. Moreover, previous machine learning-based approaches are generally not human understandable. Thereby, it is difficult to pinpoint the reason for recognition failures and understand the types of emotions articles inspire in their readers. In this paper, we propose a flexible semantic frame-based approach (FBA) for reader-emotion detection that simulates such process in a human perceptive manner. FBA is a highly automated process that incorporates various knowledge sources to learn semantic frames from raw text that characterize an emotion and are comprehensible for humans. Generated frames are adopted to predict reader-emotion through an alignment-based matching algorithm that allows a semantic frame to be partially matched through a statistical scoring scheme. Experimental results demonstrate that our approach can effectively detect reader-emotions by exploiting the syntactic structures and semantic associations in the context, while outperforming currently well-known statistical text classification method sand the stat-of-the-art reader-emotion detection method.
Syntactic theories and the algebra of record terms

Recently, many type systems for records have been proposed. For most of them, the types cannot be describes as the terms of an algebra. In this case, type checking or type inference in the case of first order type systems, cannot be derived from existing algorithms. We define record terms as the terms of an equational algebra. We prove decidability of the unification problem for records terms by showing that its equational theory is syntactic. We derive a complete algorithm and prove its determination. We define a notion of canonical terms and approximations of record terms by canonical terms, and show that approximations commute with unification. We also study generic record terms, which extend record terms to model a form os sharing between terms. We prove that the equational theory of generic record terms and that the corresponding unification algorithm always terminates.
Hierarchical segmentation via a diffusion scheme in color/texture feature space

This paper presents a segmentation scheme for images containing both smooth regions and textures. It is based on a vector-valued anisotropic diffusion on a combined color/Gabor feature space, followed by a hierarchical segmentation using dynamics of multiscale 'generalized gradient' watersheds. The proposed method gives good segmentation results and is shown to be more effective than its counterpart, which uses only multiscale color information.
Seven Layers of Strength in a Model Early Childhood Teacher Preparation Program

ABSTRACT The purpose of this qualitative study was to propose a framework for the types of practices and ideologies that the most successful clinical-based, early childhood teacher education programs use to prepare their preservice teachers for the profession. Grounded theory methods guided the collection and analysis of data. Participants included preservice teachers and faculty members from three model universities. The researcher collected data from semistructured interviews, open-ended surveys, and university documents. Data analysis resulted in a framework that explains clinical practice across model early childhood teacher preparation programs. Findings suggest that seven layers of strength contribute to the programs’ expertise in preparing their preservice teachers.
Interference Robust Transmission for the Downlink of an OFDM-Based Mobile Communications System

Radio networks for future mobile communications systems, for example, 3GPP Long-Term Evolution (LTE), are likely to use an orthogonal frequency division multiplexing- (OFDM-) based air interface in the downlink with a frequency reuse factor of one to avoid frequency planning. Therefore, system capacity is limited by interference, which is particularly crucial for mobile terminals with a single receive antenna. Nevertheless, next generation mobile communications systems aim at increasing downlink throughput. In this paper, a single antenna interference cancellation (SAIC) algorithm is introduced for amplitude-shift keying (ASK) modulation schemes in combination with bit-interleaved coded OFDM. By using such a transmission strategy, high gains in comparison to a conventional OFDM transmission with quadrature amplitude modulation (QAM) can be achieved. The superior performance of the novel scheme is confirmed by an analytical bit-error probability (BEP) analysis of the SAIC receiver for a single interferer, Rayleigh fading, and uncoded transmission. For the practically more relevant multiple interferer case we present an adaptive least-mean-square (LMS) and an adaptive recursive least-squares (RLS) SAIC algorithm. We show that in particular the RLS approach enables a good tradeoff between performance and complexity and is robust even to multiple interferers.
Fast 3-D Capacitance Extraction by Inexact Factorization and Reduction

Capacitance-extraction algorithms based on the boundary element method (BEM) have to solve large linear systems. The number of unknowns equals the number of discretization panels n, which is much greater than the number of conductors m. The authors present a capacitance-extraction algorithm RedCap that first reduces the BEM system of size n into a small system of size O(m) and then solves the small system to compute the capacitances. RedCap uses a number of techniques, including the hierarchical-refinement technique of HiCap [Shi, 2002], the dense-to-sparse transformation of PHiCap [Yan, 2005], a reordering of the sparse linear system, and an incomplete LU factorization, to obtain the reduced system. RedCap achieves a significant speed improvement over previous methods. On benchmark problems with conductors in uniform and multilayer dielectrics, RedCap is up to 100 times faster than FastCap [Nabors and White, 1991] and up to four times faster than PHiCap [Yan, 2005], while restricting error to within 2% of FastCap
Distributed Shared Memory for New Generation Networks

Shared memory is widely believed to provide an easier programming model than message passing for expressing parallel algorithms. Distributed Shared Memory (DSM) systems provide the illusion of shared memory on top of standard message passing hardware at very low implementation cost, but provide acceptable performance on only a limited class of applications. In this paper we study the main sources of overhead found in software-coherent, distributed shared-memory systems and argue that recent revolutionary changes in network technology now allow us to design protocols that minimize such overheads and that approach the performance of full hardware coherence. Specifically, we claim that memory-mapped network interfaces that support a global physical address space can greatly improve the performance of DSM systems. To support this claim we study a variety of coherence protocols that can take advantage of the global physical address space and compare their performance with the best known protocol for pure message passing hardware. For the programs in our application suite, protocols taking advantage of the new hardware features improve performance by at least 50\% and by as much as an order of magnitude.
Music retrieval based on rhythm content and dynamic time warping method

Rhythm information, which plays an important role in music features, still has a long way to go. Most current researches on this field are based on single feature, which is unstable. In this paper, we proposed a novel method to change this by fusing rhythm feature with gammatone frequency cepstral coefficients(GFCC) feature. After the pre-processing including detecting the beginning of songs, removing the silence part using Energy and Zero crossing rate, our training and testing features are generated by fusing rhythm feature including pitch, tempo etc. with GFCC feature. Furthermore, we present several ways to measure the rhythmic similarity between two or more songs. This allows similar songs to be retrieved from a large collection. For recognition, we choose the Dynamic Time Warping(DTW) algorithm calculating the distance between test music and music database, and then we get a ranking list based on distance. It is demonstrated that we can improve the recognition rates by 21.3% on average based on our music database by using rhythm features fused with GFCC features comparing with Predominant melody, MFCC and GFCC features. Our music database has 500 songs and we choose 100 songs as testing music.
Alarming features: birds use specific acoustic properties to identify heterospecific alarm calls

Vertebrates that eavesdrop on heterospecific alarm calls must distinguish alarms from sounds that can safely be ignored, but the mechanisms for identifying heterospecific alarm calls are poorly understood. While vertebrates learn to identify heterospecific alarms through experience, some can also respond to unfamiliar alarm calls that are acoustically similar to conspecific alarm calls. We used synthetic calls to test the role of specific acoustic properties in alarm call identification by superb fairy-wrens, Malurus cyaneus. Individuals fled more often in response to synthetic calls with peak frequencies closer to those of conspecific calls, even if other acoustic features were dissimilar to that of fairy-wren calls. Further, they then spent more time in cover following calls that had both peak frequencies and frequency modulation rates closer to natural fairy-wren means. Thus, fairy-wrens use similarity in specific acoustic properties to identify alarms and adjust a two-stage antipredator response. Our study reveals how birds respond to heterospecific alarm calls without experience, and, together with previous work using playback of natural calls, shows that both acoustic similarity and learning are important for interspecific eavesdropping. More generally, this study reconciles contrasting views on the importance of alarm signal structure and learning in recognition of heterospecific alarms.
Using simulation to build inspection efficiency benchmarks for development projects

It is difficult for organizations introducing and using software inspections to evaluate how efficient they are. However, it is of practical importance to determine whether they have been efficiently implemented or whether further corrective actions are necessary to bring them up to standard. We present in this paper a procedure for building inspection efficiency benchmarks based on simulation and typical inspection data. Based on most of the data published in the literature, we build an industry-wide benchmark which intends to capture the current practice regarding inspection efficiency. This benchmark construction procedure can also be used to build enterprise specific benchmarks. Last, we assess how robust we can expect them to be by distorting their input distributions to reflect violations of the assumptions made.
Design and constraints of the Drosophila segment polarity module: robust spatial patterning emerges from intertwined cell state switches.

The Drosophila segment polarity genes constitute the last tier in the segmentation cascade; their job is to maintain the boundaries between parasegments and provide positional "read-outs" within each parasegment for the entire developmental history of the animal. These genes constitute a relatively well-defined network with a relatively well-understood patterning task. In a previous publication (von Dassow et al. 2000. Nature 406:188-192) we showed that a computer model predicts the segment polarity network to be a robust boundary-making device. Here we elaborate those findings. First, we explore the constraints among parameters that govern the network model. Second, we test architectural variants of the core network, and show that the network tolerates a wide variety of adjustments in design. Third, we evaluate several topologically identical models that incorporate more or less molecular detail, finding that more-complex models perform noticeably better than simplified ones. Fourth, we discuss two instances in which the failure of the network model to behave in a life-like fashion highlights mechanistic details that need further experimental investigation. We conclude with an explanation of how the segment polarity network can be understood as an interwoven conspiracy of simple dynamical elements, several bistable switches and a homeostat. The robustness with which the network as a whole maintains a spatial regime of stable cell state emerges from generic dynamical properties of these simple elements.
Materials and Design Strategies of Stretchable Electrodes for Electronic Skin and its Applications

The human skin is an essential organ of the human body for sensing various changes in the external environment, including pressure, temperature, and humidity, as well as for interacting with the outside world. Therefore, in the field of soft electronics, the development of the electronic skin (e-skin) that mimics the functions and properties of the human skin is an important research topic. The potential applications of the e-skin technology include wearable electronics, soft robotics, human–machine interfaces (HMIs), and prosthetic devices. Toward such research goals, various technologies are required. Among these, the stretchable electrode technology is a key component for achieving soft, stretchable e-skin and its applications. Therefore, various materials and design strategies for stretchable electrodes have been developed by many research groups. The successful development of stretchable electrodes has resulted in the rapid progress of high-performance stretchable device components such as stretchable sensors and actuators. Moreover, the integration of these device components with stretchable interconnects has yielded an integrated e-skin system, through which interactive human–machine interfacing can be achieved. This paper presents an overview of state-of-the-art technological advances in materials and design strategies for the development of stretchable electrodes. In addition, e-skin systems and their HMI applications based on such stretchable electrodes are briefly reviewed.
Gradually Learning Programming Supported by a Growable Programming Language

Learning programming is a difficult task. The learning process is particularly disorienting when you are approaching programming for the first time. As a student you are exposed to several new concepts (control flow, variable, etc. but also coding, compiling, etc.) and new ways to think (algorithms). Teachers try to expose the students gradually to the new concepts by presenting them one-by-one but the tools at student's disposal do not help: they provide support, suggestion, and documentation for the full programming language of choice hampering the teacher's efforts. On the other side, students need to learn real languages and not didactic languages. In this paper, we propose an approach to gradually teach programming supported by a programming language that grows-together with its implementation-along with the number of concepts presented to the students. The proposed approach can be applied to the teaching of any programming language, and some experiments with Javascript are reported.
Comments on non-supersymmetric orientifolds at strong coupling

We consider several properties of a set of anti-Dp-branes in the presence of orientifold p-planes in type-II theory. This system breaks all the supersymmetries of the theory, but is free of tachyons. In particular, we center on the case of a single anti-Dp-brane stuck at a negatively charged orientifold p-plane, and study its strong coupling behaviour for p = 2,3,4. Interestingly enough, as the coupling increases the system undergoes a phase transition where an additional antibrane is created. We conclude with some remarks on the limit of large number of antibranes on top of orientifold planes.
Active Cache Based Defense against DoS Attacks in Wireless Mesh Network

Wireless mesh network (WMN) is evolving to be a new paradigm for wireless Internet connectivity as it obviates the need for wired infrastructure at every access point (AP) a.k. a mesh router (MR). The MRs collaboratively forward the traffic towards the Internet gateway (IGW). The self-configurable architecture of MRs paves way for malicious intruders to conduct a denial-of-service attack (DoS) on the MRs by flooding the network with a large volume of traffic; thus rendering the system inaccessible to the real users. In this paper, we present a cache based defense at the MRs to identify flooding style DoS attacks. We use a most frequently used cache mechanism to identify such flows and raise an early alert to curb them. We effectively avert any performance degradation by dropping the identified attack flows along the forwarding routers. Simulation results indicate that our scheme offers an active line of defense against DoS attacks
Ubiquitous access to home appliance control system using infrared ray and power line communication

Home appliance control system is a legitimate need of people nowadays. Digital home providers like to install their own network and provide home network enabled appliances. It implies customers need to replace existing appliances with new expensive home network enabled appliance. In this paper, a simple but reliable approach to control home appliances is presented. In this approach, we use infrared ray and power line communication integration in our home appliances control system. This system supports the ubiquitous access control mechanism which enables users to check their appliances' statuses and control them remotely through their cellular phone or Internet.
A unified framework to verify the effect of Optimal Power Flow's dispatching interval to the system operational cost

This paper proposed a unified framework to verify the effect of dispatching interval of an Optimal Power Flow (OPF) program to the operational cost under a normal operation of an Vertically Integrated Electric Supply Industry (VIESI). A system load profile is assumed to be constant during a 5-minute interval. For any demand changed, it is balanced by adjusting generated power provided from load following units. The allocation of mismatched power among these units is performed based on their droop characteristics. A Unit Commitment (UC) is taken into account. The operated cost is compared among five intervals of dispatching OPF i.e., 5, 15, 30, 60 minutes and 24 hrs. The proposed framework is tested with a modified 39-bus of New England's system.
Bayesian Nonparametric Estimation of a Unimodal Density via Two S-paths 1

A Bayesian nonparametric method for unimodal densities on the real line is provided by considering a class of species sampling mixture models containing random densities that are unimodal and not necessarily symmetric. This class of densities generalize the model considered by Brunner (1992), in which the Dirichlet process is replaced by a more general class of species sampling models. A novel and explicit characterization of the posterior distribution via a finite mixture of two dependent S-paths is derived. This results in a closed-form and tractable Bayes estimator for any unimodal density in terms of a finite sum over two S-paths. To approximate this class of estimates, we propose a sequential importance sampling algorithm that exploits the idea of the accelerated path sampler, an efficient path-sampling Markov chain Monte Carlo method. Numerical simulations are given to demonstrate the practicality and the effectiveness of our methodology. A Bayesian nonparametric method for unimodal densities on the real line is provided by considering a class of species sampling mixture models containing random densities that are unimodal and not necessarily symmetric. This class of densities generalize the model considered by Brunner (1992), in which the Dirichlet process is replaced by a more general class of species sampling models. A novel and explicit characterization of the posterior distribution via a finite mixture of two dependent 1 AMS 2000 subject classifications. Primary 62G05; secondary 62F15.
Bank loans and borrower value during the recent financial crisis Empirical evidence from France

We investigate the impact of bank loan announcements on borrower value during the recent boom and bust cycle of the 2000’s using a sample of 253 large loans to French borrowers. We find no significant stock market reaction to bank loan announcements during the boom period but a significant and negative one during the financial crisis. Hence, although we document significant changes in bank behavior during the crisis with conservative contractual and organizational modifications, we cannot provide empirical support for the certification value of bank loans during a period of increased informational asymmetries. However, bank loan announcements for larger firms receiving large loans funded by international pools of lenders contribute to borrower value even during the crisis.
Artificial bee colony optimizer for historical Arabic manuscript images binarization

Historical manuscript image binarization is a very important step towards full word spotting system. In this paper, we present a novel binarization algorithm based on artificial bee colony optimizer. The proposed approach contains two phases. The first phase is stretching the intensity level of the image by contrast stretching filter and removing the noise by image cleaning algorithm, the second phase is determining the number of clusters, number of colony and iterations for starting Artificial Bee Colony (ABC) algorithm. The proposed approach is tested on a set of images collected from the electronic Arabic manuscripts database and compared against three famous binarization methods such as Niblack's, Otsu's and Savoul's. The Experimental results show that the proposed approach is a promising approach and can obtain the desired results better than the other compared methods.