Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymmetric relationships between El Niño–Southern Oscillation and entrance tropical cyclones in the South China Sea during fall

Tropical cyclones (TCs) initially forming in the western North Pacific (WNP) and later moving into the South China Sea (SCS) are defined as entrance TCs. El Niño–Southern Oscillation (ENSO) and inter‐annual variability of entrance TCs during fall exhibit four types of asymmetric relationships: La Niña‐more, La Niña‐less, El Niño‐more, and El Niño‐less. Large‐scale modulation of the La Niña‐more (La Niña‐less) type features negative sea surface temperature (SST) anomalies in the tropical central‐eastern Pacific with a maximum centre in the eastern (central) Pacific. The responding circulation pair extends northwestwards (northwards) with a cyclonic anomaly in the SCS and an anticyclonic anomaly in the North Pacific (NP). Above‐normal TCs forming to the east/northeast of the Philippines are steered by anomalous southeasterly (southerly) flows to enter the SCS (NP), leading to more (less) entrance TCs. For the El Niño‐less (El Niño‐more) type, positive SST anomalies over the tropical Pacific exhibit a centre in the eastern (central) Pacific. A circulation pair characterizes an anticyclonic anomaly over the entire (southwestern) SCS and a cyclonic anomaly within the NP (displacing westwards into the northern SCS) inducing anomalous flows from the northern (central and southern) SCS into the tropical WNP. TC entrance in both El Niño‐less and El Niño‐more types is hindered by inter‐annual flows. More entrance TCs in the El Niño‐more type are facilitated by the intra‐seasonal oscillation (ISO). Entrance TCs move into the SCS along a favourable environment provided by a northwest–southeasterly elongated 30–60‐day cyclonic anomaly. Regarding the relative role of the inter‐annual and intra‐seasonal modes, ISO (ENSO) exerts major modulatory effects on entrance TCs in the El Niño‐more (El Niño‐less) type. Both ISO and ENSO jointly facilitate (suppress) entrance TCs in the La Niña‐more (La Niña‐less) type.
MammoSVD : a Content-Based Image Retrieval System Using a Reference Database of Mammographies VERSION FOR BLIND REVIEW

In this paper, we present a content-based image retrieval (CBIR) system called MammoSVD. This CBIR system is developed based on breast density – fatty or dense, and the database used, from the IRMA project, provides images with the ground truth already set. Singular value decomposition (SVD) is proposed for the breast density characterization by the selection of the first singular values, in order to represent texture along with the dimensionality reduction. Support-vector machine (SVM) is used to perform the retrieval operation. Considering the first 25% of the retrieved images, the precision rate is 83%; the precision rate is 78% considering the 50% of the retrieved images. The results obtained indicate the potential of the implemented CBIR system.
Universal Denoising of Discrete-Time Continuous-Amplitude Signals

We consider the problem of reconstructing a discrete-time signal (sequence) with continuous-valued components corrupted by a known memoryless channel. When performance is measured using a per-symbol loss function satisfying mild regularity conditions, we develop a sequence of denoisers that, although independent of the distribution of the underlying ldquocleanrdquo sequence, is universally optimal in the limit of large sequence length. This sequence of denoisers is universal in the sense of performing as well as any sliding-window denoising scheme which may be optimized for the underlying clean signal. Our results are initially developed in a ldquosemi-stochasticrdquo setting, where the noiseless signal is an unknown individual sequence, and the only source of randomness is due to the channel noise. It is subsequently shown that in the fully stochastic setting, where the noiseless sequence is a stationary stochastic process, our schemes universally attain optimum performance. The proposed schemes draw from nonparametric density estimation techniques and are practically implementable. We demonstrate efficacy of the proposed schemes in denoising Gray-scale images in the conventional additive white Gaussian noise (AWGN) setting, with additional promising results for less conventional noise distributions.
A comparative study of Gaussian selection methods in large vocabulary continuous speech recognition

Gaussian mixture models are the most popular probability density used in automatic speech recognition. During decoding, often many Gaussians are evaluated. Only a small number of Gaussians contributes significantly to probability. Several promising methods to select relevant Gaussians are known. These methods have different properties in terms of required memory, overhead and quality of selected Gaussians. Projection search, bucket box intersection, and Gaussian clustering are investigated in a broadcast news system with focus on adaptation (MLLR). Index Terms: speech recognition, LVCSR, Gaussian selection, speaker adaptation, MLLR.
Business Model: A Perquisite for Success in the Network Economy

The contemporary network economy is built on powerful fixed and mobile network infrastructures. These infrastructures provide the solid ground for the continuous and rapid introduction of innovative both telecommunication services and business application servicesOne of the most critical preconditions for the success of these services is to be based on sound business models. Especially today, in the dawn of the networked economy, the concept of business model is not a theoretical tool but a prerequisite for success. Business model is one of the most common factors encountered for, when Internet firms succeed in business. Furthermore, the rapid introduction of innovative applications necessitates the rational design of their business model. However, despite the extensive use, and sometimes misuse, of the business model concept, there is not extensive the scientific research that has been conducted in this area. Moreover, there is still some ambiguity concerning the exact meaning of this concept, which results in a diversity of definitions and a confusion in terminology. In the present paper, initially we present a literature review on the theoretical foundations of business model presenting its definitionsand its components. Then we focus on the research that has been conducted so far concerning methodologies for designing new business models, we identify its shortcomings and we propose a new framework for ‘digital’ business model design, by implementing it in a real life business case.
Asymptotic resolution bounds of generalized modularity and statistically significant community detection

The maximization of generalized modularity performs well on networks in which the members of all communities are statistically indistinguishable from each other. However, there is no theory defining the maximization performance in more realistic networks where edges are heterogeneously distributed within and between communities. We establish the asymptotic theoretical bounds on the resolution parameter of generalized modularity using the random graph properties. From this new perspective on random graph model, we find the resolution limit of modularity maximization can be explained in a surprisingly simple and straightforward way. Given a network produced by the stochastic block models, the communities for which the resolution parameter is larger than their densities are likely to be spread among multiple clusters; while communities for which the resolution parameter is smaller than their background inter-community edge density get merged into one large component. Therefore, no suitable resolution parameter exits when the intra-community edge density in a subgraph is lower than the inter-community edge density in some other subgraph. For such networks, we propose a progressive agglomerative heuristic algorithm to detect statistically significant communities at multiple scales.
Automated English-Korean Translation for Enhanced Coalition Communications

■￻This article describes our progress on automated, two-way English-Korean translation of text and speech for enhanced military coalition communications. Our goal is to improve multilingual communications by producing accurate translations across a number of languages. Therefore, we have chosen an interlingua-based approach to machine translation that readily extends to multiple languages. In this approach, a natural-language-understanding system transforms the input into an intermediate-meaning representation called a semantic frame, which serves as the basis for generating output in multiple languages. To produce useful, accurate, and effective translation systems in the short term, we have focused on limited military-task domains, and have configured our system as a translator's aid so that the human translator can confirm or edit the machine translation. We have obtained promising results in translation of telegraphic military messages in a naval domain, and have successfully extended the system to additional military domains. The system has been demonstrated in a coalition exercise and at Combined Forces Command in the Republic of Korea. From these demonstrations we learned that the system must be robust enough to handle new inputs, which is why we have developed a multistage robust translation strategy, including a part-of-speech tagging technique to handle new words, and a fragmentation strategy for handling complex sentences. Our current work emphasizes ongoing development of these robust translation techniques and extending the translation system to application domains of interest to users in the military coalition environment in the Republic of Korea.
Generalization for Multidimensional Playfair Cipher

: Playfair cipher is a multi letter, poly alphabetic, symmetric cipher having a 2 dimensional key matrix supporting the security of 26 English alphabets. From the survey it is found that there are other variants which have key matrices with 3 and 4 dimensions. The main aim of this research is to provide the generalization for multidimensional Playfair cipher which includes choosing the dimension based on the number of values/characters supported by the Playfair cipher variant and the corresponding encryption and decryption processes. It is found from the dimensional analysis that more is the dimension for the fixed number of values/characters supported, stronger is the cipher against brute force attack with respect to possible number of groups.
Deep Spatial-Spectral Subspace Clustering for Hyperspectral Image

Hyperspectral image (HSI) clustering is a challenging task due to the complex characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large spectral variability. In this paper, we propose a novel deep spatial-spectral subspace clustering network (DS3C-Net), which explores spatial-spectral information via the multi-scale auto-encoder and collaborative constraint. Considering the structure correlations of HSI, the multi-scale auto-encoder is first designed to extract spatial-spectral features with different-scale pixel blocks which are selected as the inputs. Then, the collaborative constrained self-expressive layers are introduced between the encoder and decoder, to capture the self-expressive subspace structures. By designing a self-expressiveness similarity constraint, the proposed network is trained collaboratively, and the affinity matrices of the feature representation are learned in an end-to-end manner. Based on the affinity matrices, the spectral clustering algorithm is utilized to obtain the final HSI clustering result. Experimental results on three widely used hyperspectral image datasets demonstrate that the proposed method outperforms state-of-the-art methods.
Direct Adaptive Control of Unknown Nonlinear Systems Using a New Neuro-Fuzzy Method Together with a Novel Approach of Parameter Hopping

The direct adaptive regulation for affine in the control nonlinear dynamical systems possessing unknown nonlinearities, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Dynamical Systems (FDS) operating in conjunction with High Order Neural Network Functions (F-HONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of a fuzzy dynamical system (FDS) and in the sequel the fuzzy rules are approximated by appropriate HONNFs. The fuzzy-recurrent high order neural networks (F-RHONN) are used as models of the unknown plant, practically transforming the original unknown system into a F-RHONN model which is of known structure, but contains a number of unknown constant value parameters. The proposed scheme does not require a-priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions, is extremely fast and, hence, can be applied in several difficult and very demanding real-time engineering applications. When the F-RHONN model matches the unknown plant, we provide a comprehensive and rigorous analysis of the stability properties of the closed loop system. Convergence of the state to zero plus boundedness of all other signals in the closed loop is guaranteed without the need of parameter (weights) convergence, which is assured only if a sufficiency-of-excitation condition is satisfied. The existence of the control signal is always assured by introducing a novel method of parameter hopping and incorporating it in weight updating law. Simulations illustrate the approximation superiority of the proposed scheme in comparison to other well established approaches. The applicability of the method is also tested on well known simulated nonlinear plants where it is shown that by following the proposed procedure one can obtain asymptotic regulation. Comparison is also made to simple RHONN controllers, showing that our approach is superior to the case of simple RHONN's.
Nearly optimal computations with structured matrices

We propose a nearly optimal algorithm that uses 2n 2 random parameters, O(n) memory space and O((nlog2n)loglogn) operations in a fixed arbitrary field in order to compute the rank and a basis for the null space of a structured n x n matrix X represented with O(n) parameters of its short generator, as well as to solve a linear system Xy = b or to determine its inconsistency. If rank X = n, the algorithm also computes det X and a short generator of X -I. The cost bounds cover correctness verification for the output but not the cost of the generation of random parameters. The algorithm gives a unified t reatment of various classes of structured matrices including ones of Toeplitz, Hankel, Vandermonde and Cauchy types. 1 I n t r o d u c t i o n 1.1 C o m p u t a t i o n s w i t h s t r u c t u r e d m a t r i c e s Computations with structured matrices is a classical subject with long history and is a major subject of computer algebra and applied linear algebra (cf. [4], [11], [17]-[19]). Such matrices are omnipresent in signal and image processing, sciences and engineering. The next table displays some best known structured matrices.
Improved Versions of Tardos Fingerprinting Scheme

We examine Tardos’ optimal probabilistic fingerprinting scheme and improve the performance of the paper significantly. In addition we improve Tardos’ proofs, that applied for coalitions of size at least four, to include coalitions of any size. The examination is done by investigating constant parameters that were used for the scheme creation in Tardos’ paper. We formulate the constraints that those parameters have to satisfy and analytically find the values that yield a minimal codeword length. This analysis enables to shorten the codeword length by a factor of approximately 4. The last part of our work finds out how good Tardos’ scheme is in practice. Simulations of codeword creation, pirate collusion and accusation process are used to determine the actual codeword length that provides a required level of security. The thesis begins with an overview of traitor tracing schemes and fingerprinting codes and summarizes the works of Chor, Fiat and Naor [2], Boneh and Shaw [1], Cox, Kilian, Leighton and Shamoon [3] and the work of Gabor Tardos [6, 7].
Retrievals of Riming and Snow Density From Vertically Pointing Doppler Radars

Retrievals of ice and snow are made from Ka‐ and W‐band zenith‐pointing Doppler radars at Hyytiälä, Finland, during the snow experiment component of the Biogenic Aerosols: Effects on Clouds and Climate (2014) field campaign. In a novel optimal estimation retrieval, mean Doppler velocity is exploited to retrieve a density factor parameter, which modulates the mass, shape, terminal velocity, and backscatter cross sections of ice particles. In a case study including aggregate snow and graupel we find that snow rate and ensemble mean ice density can be retrieved to within 50% of in situ measurements at the surface using dual‐frequency Doppler radar retrievals. While Doppler measurements are essential to the retrieval of particle density, the dual‐frequency ratio provides a strong constraint on particle size. The retrieved density factor is strongly correlated with liquid water path, indicating that riming is the primary process by which the density factor is modulated. Using liquid water path as a proxy for riming, profiles classified as unrimed snow, rimed snow, and graupel exhibit distinct features characteristic of aggregation and riming processes, suggesting the potential to make estimates of process rates from these retrievals. We discuss the potential application of the technique to future satellite missions.
Multiple-symbol differential sphere decoding

In multiple-symbol differential detection (MSDD) for power-efficient transmission over fading channels without channel state information, blocks of N received symbols are jointly processed to decide on N - 1 data symbols. The search space for the maximum-likelihood (ML) estimate is therefore (complex) (N - 1)-dimensional, and MSDD quickly becomes computationally intractable as N grows. Sphere decoding (SD) is a widely used approach to find the ML estimate in such high-dimensional spaces. In this paper, we devise the application of SD to accomplish MSDD, and we refer to the resulting technique as multiple-symbol differential sphere decoding (MSDSD). We present an efficient algorithm for MSDSD, whose excellent performance versus complexity trade-off is verified by various simulation results and by comparisons with other, suboptimum approaches known from the literature.
Water as a coolant of cities

Since more than half of the world population already lives in cities, it is important to understand the urban climate and its particularities. One of the typical aspects of urban areas is that cities are generally warmer than their rural surrounding. This phenomenon was first time described 200 years ago in the case of London and is commonly referred to as the Urban Heat Island (UHI). Urban heat island roots from a wide scale of factors typical for cities. Increased heat storage caused by higher specific heat capacities of construction materials together with darker color of urban surfaces, lack of vegetation and open water bodies, anthropogenic heat added by industry, traffic or air-conditioning, or the specific geometry of the street canyons are just few examples of how cities become warmer than rural areas. One of the often suggested ways to mitigate UHI is increasing evaporation by reintroducing vegetation and open water back to urban areas.
Towards an efficient single system image cluster operating system

The lack of a single system image operating system (OS) for clusters restricts their use for parallel processing. We propose an approach for building an efficient single system image (SSI) cluster operating system. The proposed system implements distributed services performing global and dynamic resource management to offer high performance, high availability and ease of programming. The programming API of the OS running on each cluster node is kept unmodified but the high level OS services can benefit from all cluster resources. Our approach has been validated by a prototype based on Linux.
Smooth constraints for spline variational modeling

This article introduces a new class of constraints for spline variational modeling, which allows more flexible user specification, as a constrained point can "slide" along a spline curve. Such constraints can, for example, be used to preserve correct parameterization of the spline curve. The spline surface case is also studied. Efficient numerical schemes are discussed for real-time solving, as well as interactive visualization during the energy minimization process. Examples are shown, and numerical results discussed.
No Java without Caffeine A Tool for Dynamic Analysis of Java Programs

To understand the behavior of a program, a maintainer reads some code, asks a question about this code, conjectures an answer, and searches the code and the documentation for confirmation of her conjecture. However, the confirmation of the conjecture can be error-prone and time-consuming because the maintainer has only static information at her disposal. She would benefit from dynamic information. In this paper, we present Caffeine, an assistant that helps the maintainer in checking her conjecture about the behavior of a Java program. Our assistant is a dynamic analysis tool that uses the Java platform debug architecture to generate a trace, i.e., an execution history, and a Prolog engine to perform queries over the trace. We present a usage scenario based on the n-queens problem, and two real-life examples based on the Singleton design pattern and on the composition relationship.
Multicopy cache: A highly energy-efficient cache architecture

Caches are known to consume a large part of total microprocessor energy. Traditionally, voltage scaling has been used to reduce both dynamic and leakage power in caches. However, aggressive voltage reduction causes process-variation-induced failures in cache SRAM arrays, thus compromising cache reliability. We present MultiCopy Cache (MC 2 ), a new cache architecture that achieves signiﬁcant reduction in energy consumption through aggressive voltage scaling while maintaining high error resilience (reliability) by exploiting multiple copies of each data item in the cache. Unlike many previous approaches, MC 2 does not require any error map characterization and therefore is responsive to changing operating conditions (e.g., Vdd noise, temperature, and leakage) of the cache. MC 2 also incurs signiﬁcantly lower overheads compared to other ECC-based caches. Our experimental results on embedded benchmarks demonstrate that MC 2 achieves up to 60% reduction in energy and energy-delay product (EDP) with only 3.5% reduction in IPC and no appreciable area overhead.
The thinning problem

The main problem addressed in this work is how to confine the set of sequential processes of a disjunctive/ conjunctive resource allocation system (D/C-RAS) to a subset of their feasible behaviours while optimizing some performance criterion. A canonical characterization of this problem is provided in the form of a mixed integer programming formulation, for the case that the optimized performance criterion is the maximization of the system throughput. However, an important additional development of this effort is a complete analytical characterization of the performance control problem of D/C-RAS. This characterization is based on an effective integration of recent results on the liveness-enforcing supervision of D/C-RAS with the theory of Markov decision processes, and it concretizes the interaction between the D/C-RAS logical and performance control. Throughout the development of the article, examples are used to highlight the concepts and formulations involved.