Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Control via Gröbner Bases and Variable Elimination

We consider the problem of optimal decentralized controller synthesis. There are several classes of such problems for which effective solution methods are known, including the quadratically invariant one. In this technical note, we use Gröbner bases and elimination theory to characterize all closed-loop maps achievable by forming a feedback loop with decentralized controllers. We show that this approach allows solution of a wide class of optimal decentralized control problems; it includes not only quadratically invariant problems under a technical condition but also some other problems which are not quadratically invariant.
Sliding mode control and optimal GPC algorithm for coupled tanks

In this paper we have applied two types of control on two coupled water tanks, where the dynamic model is nonlinear system. The first control is the sliding mode with linear surface, the advantages of this type of control is the discontinuity behavior which can be applied to variable structure systems (VSS) and its good robustness against the injected disturbances, and/or the noise, and finally unaffected control to the parameter variation due to its nature, the second control is the generalized predictive control (GPC), this control is widely used in the industry, for the application of this control we should linearize the coupled model Around desired operating point, and stabilize the system by using the LQR method, and finally we apply the GPC algorithm on the discrete system.
Multiplatform analysis of the radiative effects and heating rates for an intense dust storm on 21 June 2007

Dust radiative effects and atmospheric heating rates are investigated for a Saharan dust storm on 21 June 2007 using a combination of multiple satellite data sets and ground and aircraft observations as input into a delta‐four stream radiative transfer model (RTM). This combines the strengths of the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations and CloudSat satellites and in situ aircraft data to characterize the vertical structure of the dust layers (5 km in height with optical depths between 1.5 and 2.0) and underlying low‐level water clouds. These observations were used, along with Aerosol Robotic Network retrievals of aerosol optical properties, as input to the RTM to assess the surface, atmosphere, and top of atmosphere (TOA) shortwave aerosol radiative effects (SWAREs). Our results show that the dust TOA SWARE per unit aerosol optical depth was −56 W m−2 in cloud‐free conditions over ocean and +74 W m−2 where the dust overlay low‐level clouds, and show heating rates greater than 10 K/d. Additional case studies also confirm the results of the 21 June case. This study shows the importance of identifying clouds beneath dust as they can have a significant impact on the radiative effects of dust, and hence assessments of the role of dust aerosol on the energy budget and climate.
Electrowetting-based actuation of liquid droplets for microfluidic applications

A microactuator for rapid manipulation of discrete microdroplets is presented. Microactuation is accomplished by direct electrical control of the surface tension through two sets of opposing planar electrodes fabricated on glass. A prototype device consisting of a linear array of seven electrodes at 1.5 mm pitch was fabricated and tested. Droplets (0.7–1.0 μl) of 100 mM KCl solution were successfully transferred between adjacent electrodes at voltages of 40–80 V. Repeatable transport of droplets at electrode switching rates of up to 20 Hz and average velocities of 30 mm/s have been demonstrated. This speed represents a nearly 100-fold increase over previously demonstrated electrical methods for the transport of droplets on solid surfaces.
The kinaesthetic senses

This review of kinaesthesia, the senses of limb position and limb movement, has been prompted by recent new observations on the role of motor commands in position sense. They make it necessary to reassess the present‐day views of the underlying neural mechanisms. Peripheral receptors which contribute to kinaesthesia are muscle spindles and skin stretch receptors. Joint receptors do not appear to play a major role at most joints. The evidence supports the existence of two separate senses, the sense of limb position and the sense of limb movement. Receptors such as muscle spindle primary endings are able to contribute to both senses. While limb position and movement can be signalled by both skin and muscle receptors, new evidence has shown that if limb muscles are contracting, an additional cue is provided by centrally generated motor command signals. Observations using neuroimaging techniques indicate the involvement of both the cerebellum and parietal cortex in a multi‐sensory comparison, involving operation of a forward model between the feedback during a movement and its expected profile, based on past experience. Involvement of motor command signals in kinaesthesia has implications for interpretations of certain clinical conditions.
Meteorological Prediction Using Support Vector Regression with Genetic Algorithms

The theory of support vector regression (SVR) is introduced in this paper. And genetic algorithms (GAs) are adopted to optimize free parameters of support vector regression. Then we develop an optimal meteorological prediction model based on support vector Regression with genetic algorithms (SVRG). In this study, SVRG is applied to predict meteorology. The experimental results indicate that SVRG model proposed in this paper overcomes some shortcomings of the traditional SVR, and can achieve better forecasting accuracy and performance than traditional SVR and BP neural network (BPNN) prediction models. Consequently, the meteorological prediction model based on SVRG is an effective method.
A conceptual model and experiments on how people classify and retrieve documents

The question arises regarding the effectiveness of the o~rrcnt automated document storage and retrieval systems for offh:cs. A survey performed by Safayeni et aL 1 showed that only 22% of offices tha t have installed office automation ctluipment use any kind of software for filing documents; even when used, the percentage of documents on-line is very small. Furthermore, none of the respondents identified the att tomated filing of documents as an example of a "successful technology. " The level of satisfaction with document retrieval software was lower than that for word processing arm electronic mail systems.
From Transformation-Based Dimensionality Reduction to Feature Selection

Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce dimensionality: feature selection and feature transformation. When one wishes to keep the original meaning of the features, feature selection is desired. Feature selection and transformation are typically presented separately. In this paper, we introduce a general approach for converting transformation-based methods to feature selection methods through l1/l∞ regularization. Instead of solving feature selection as a discrete optimization, we relax and formulate the problem as a continuous optimization problem. An additional advantage of our formulation is that our optimization criterion optimizes for feature relevance and redundancy removal automatically. Here, we illustrate how our approach can be utilized to convert linear discriminant analysis (LDA) and the dimensionality reduction version of the Hilbert-Schmidt Independence Criterion (HSIC) to two new feature selection algorithms. Experiments show that our new feature selection methods out-perform related state-of-the-art feature selection approaches.
Network Monitoring and Forensics

Distributed system fault detection and analysis has, until recently, focused on building passive monitoring tools without any system level knowledge. It is only now that new approaches, such as instrumenting the software used in the system to collect data about request path, have been used. In this paper we present a new monitoring and fault detection tool for distributed systems. It uses a combination of existing tools and a new method of building dynamic dependency chains. It works on multi tiered services to localize the faults and sufficiently provide detailed information for the system administrator to correctly diagnose the service failure. We evaluate our system by injecting faults deterministically and demonstrate that our system gives relevant and sufficient information.
Deletion of the Chemokine Binding Protein Gene from the Parapoxvirus Orf Virus Reduces Virulence and Pathogenesis in Sheep

Orf virus (ORFV) is the type species of the Parapoxvirus genus of the family Poxviridae and infects sheep and goats, often around the mouth, resulting in acute pustular skin lesions. ORFV encodes several secreted immunomodulators including a broad-spectrum chemokine binding protein (CBP). Chemokines are a large family of secreted chemotactic proteins that activate and regulate inflammation induced leukocyte recruitment to sites of infection. In this study we investigated the role of CBP in vivo in the context of ORFV infection of sheep. The CBP gene was deleted from ORFV strain NZ7 and infections of sheep used to investigate the effect of CBP on pathogenesis. Animals were either infected with the wild type (wt) virus, CBP-knockout virus or revertant strains. Sheep were infected by scarification on the wool-less area of the hind legs at various doses of virus. The deletion of the CBP gene severely attenuated the virus, as only few papules formed when animals were infected with the CBP-knock-out virus at the highest dose (107 p.f.u). In contrast, large pustular lesions formed on almost all animals infected with the wt and revertant strains at 107 p.f. u. The lesions for the CBP-knock-out virus resolved approximately 5–6 days p.i, at a dose of 107 pfu whereas in animals infected with the wt and revertants at this dose, lesions began to resolve at approximately 10 days p.i. Few pustules developed at the lowest dose of 103 p.f. u. for all viruses. Immunohistochemistry of biopsy skin-tissue from pustules showed that the CBP-knockout virus replicated in all animals at the highest dose and was localized to the skin epithelium while haematoxylin and eosin staining showed histological features of the CBP-knockout virus typical of the parent virus with acanthosis, elongated rete ridges and orthokeratotic hyperkeratosis. MHC-II immunohistochemistry analysis for monocytes and dendritic cells showed greater staining within the papillary dermis of the CBP-knock-out virus compared with the revertant viruses, however this was not the case with the wt where staining was similar. Our results show that the CBP gene encodes a secreted immunodulator that has a critical role in virulence and pathogenesis.
Insight to AMP and ADMM based Sparse signal reconstruction

Compressive sensing (CS) is a rising field which have already overcome Nyquist rate. It enables sparse signal recovery with the help of efficient algorithms. It reduces memory requirements and cost of computation. Signal recovery is an important phase in reconstruction of sparse signal. In this paper a comparison is made between two reconstruction algorithms approximate message passing algorithm (AMP) and ADMM (Alternate direction method of multipliers) for solving the basis pursuit problem.
Credit Risk Measurement of Chinese Listed Corporations Based on the KMV Model

Credit risk management has become a fundamental and crucial work for commercial banks. This paper studies the credit risk measurement of listed corporations by using various types of credit risk models, and analyzes their applicability in China. This paper also makes an empirical analysis to the Chinese listed corporations’ credit risk on the basis of the KMV model. Finally, several proposals on how to enhance credit risk management ability of Chinese listed corporations are put forward.
StackArmor: Comprehensive Protection From Stack-based Memory Error Vulnerabilities for Binaries

StackArmor is a comprehensive protection technique for stack-based memory error vulnerabilities in binaries. It relies on binary analysis and rewriting strategies to drastically reduce the uniquely high spatial and temporal memory predictability of traditional call stack organizations. Unlike prior solutions, StackArmor can protect against arbitrary stack-based attacks, requires no access to the source code, and offers a policy-driven protection strategy that allows end users to tune the securityperformance tradeoff according to their needs. We present an implementation of StackArmor for x86 64 Linux and provide a detailed experimental analysis of our prototype on popular server programs and standard benchmarks (SPEC CPU2006). Our results demonstrate that StackArmor offers better security than prior binaryand source-level approaches, at the cost of only modest performance and memory overhead even with full protection.
Novel robust decision support tool assisting early diagnosis of pathological voices using acoustic analysis of sustained vowels

Effective vocal communication is critical in daily life, and 30% of the general population may suffer from a voice disorder at some point in their lives. Early diagnosis of voice pathologies facilitates mitigating symptoms and optimizing treatment for expedient recovery. Here, we studied the potential of an automated clinical decision support tool to differentiate subjects with early onset voice disorders from healthy controls simply on the basis of a single sustained vowel phonation. We characterized 200 phonations from 200 subjects with 445 speech signal processing algorithms, extracting clinically useful properties of the phonations in order to differentiate healthy and pathological cases. We selected parsimonious gender-dependent feature subsets and demonstrated that we can automatically differentiate healthy and pathological subject cohorts with approximately 91% overall accuracy. These compelling findings endorse the use of the proposed methodology towards assisting speech experts in vocal performance assessment and diagnosis of early onset voice disorders.
20 00 Generalized Riffle Shuffles and Quasisymmetric functions

Let xi be a probability distribution on the positive integers P, i.e., the probability of i ∈ P is xi. (Hence xi ≥ 0 and ∑ xi = 1.) Fix n ∈ P, and define a random permutation w ∈ Sn as follows. For each 1 ≤ j ≤ n, choose independently an integer ij (from the distribution xi). Then standardize the sequence i = i1 · · · in in the sense of [28, p. 322], i.e., suppose that k appears ak times in the sequence i, and replace the 1’s in i by 1, 2, . , a1 from leftto-right, then the 2’s in i by a1 + 1, a1 + 2, . , a1 + a2 from left-to-right, etc. For instance, if i = 311431, then w = 412653. This defines a probability distribution on the symmetric group Sn, which we call the QS-distribution (because of the close connection with quasisymmetric functions explained below). If we need to be explicit about the parameters x = (x1, x2, . . .), then we will refer to the QS(x)-distribution.
Static and dynamic software metrics complexity analysis in regression testing

In maintenance, assuring code quality and operation, software metrics is widely used by the various software organizations. Software metrics quantify different types of software complexity like size metrics, control flow metrics and data flow metrics. These software complexities must be continuously calculated, followed and controlled. One of the main objectives of software metrics is that measures static and dynamic metrics analysis. It is always considered that high degree of complexity in a fragment is bad in comparison to a low degree of complexity in a fragment. Software metrics can be used in different phases of the software lifecycle. In this paper we will discuss the different metrics and comparison between both static and dynamic metrics. We try to evaluate and analyze different aspects of software static and dynamic metrics in regression testing which offers of estimating the effort needed for testing.
B-HIVE : A CELL PLANNING TOOL FOR URBAN WIRELESS NETWORKS

The paper presents a demand-based engineering method for designing urban wireless networks. We present an integrated approach to include the effects of both tele-traffic and radio signal coverage for cellular network planning. The novelty of our scheme is an application of clustering technique to determine the cell and base-station locations. We model the cell planning problem as a clustering problem and apply standard clustering techniques, with modifications to suit our needs, for designing the wireless network. We present the planning tool prototype B-Hive, which is based on these ideas and demonstrate initial results for cellular network planning of Mumbai.
Spatio-Temporal Pattern of Landscape Dynamics in Shimoga, Tier II City, Karnataka State, India

Urbanisation and associated growth patterns (urban sprawl) are characteristic of spatial temporal land use changes taking place at regional levels. Unplanned urbanization and consequent impacts on natural resources including basic amenities has necessitated the investigation and understanding of mechanisms and dynamics of land use and land-use change on a range of spatial scales and evaluate the environmental consequences of these changes at the landscape scale. Rapid urbanization subsequent to globalization in Karnataka state show dominant changes in land use during the last two decades. Urban regions are getting urbanized at much faster rates while peri-urban areas are experiencing sprawl. These processes have negative impacts on natural resources, economic health, and community characteristics. Quantitative estimations of urbanisations patterns are required to help local and regional land use planners to better identify, understand and provide appropriate infrastructure and basic amenities. Multi-temporal remote sensing data would help in understanding land cover and land use changes. A combination of land cover, land use dynamics with gradient analysis and spatial metrics, would help in characterizing spatiotemporal patterns of landscape dynamics. This communication is based on the analysis of urbanization process and landscape fragmentation of a tier II city in Karnataka. Supplementary data including historical maps, qualitative data have also been used to understand the urbanization process and fragmentation patterns. Spatial metrics aided in characterizing long-term trends and patterns of urban growth. Quantitative and qualitative analyses of spatial results helped in visualizing the pattern of growth, in order to highlight future land use trends. The results of the analysis shows of dispersed growth initially causing increased fragmentation. Gradually the growth filled in vacant non-urban area, resulting in more compact or aggregated urban pattern. Improved understanding of urban growth, helps in effective and suitable regional planning.
A Fast Randomized Algorithm for Computing a Hierarchically Semiseparable Representation of a Matrix

Randomized sampling has recently been proven a highly efficient technique for computing approximate factorizations of matrices that have low numerical rank. This paper describes an extension of such techniques to a wider class of matrices that are not themselves rank-deficient but have off-diagonal blocks that are; specifically, the class of so-called hierarchically semiseparable (HSS) matrices. HSS matrices arise frequently in numerical analysis and signal processing, particularly in the construction of fast methods for solving differential and integral equations numerically. The HSS structure admits algebraic operations (matrix-vector multiplications, matrix factorizations, matrix inversion, etc.) to be performed very rapidly, but only once the HSS representation of the matrix has been constructed. How to rapidly compute this representation in the first place is much less well understood. The present paper demonstrates that if an $N\times N$ matrix can be applied to a vector in $O(N)$ time, and if individual entries of the matrix can be computed rapidly, then provided that an HSS representation of the matrix exists, it can be constructed in $O(N\,k^{2})$ operations, where $k$ is an upper bound for the numerical rank of the off-diagonal blocks. The point is that when legacy codes (based on, e.g., the fast multipole method) can be used for the fast matrix-vector multiply, the proposed algorithm can be used to obtain the HSS representation of the matrix, and then well-established techniques for HSS matrices can be used to invert or factor the matrix.
Vertebrate bHLH genes and the determination of neuronal fates.

The assembly of neuronal circuits requires that neurons with the appropriate phenotype are generated at defined positions and in correct number. Rapid progress is being made in deciphering the cellular and molecular mechanisms regulating neurogenesis in the vertebrate nervous system [1, 2]. In vivo cell lineage tracing studies and in vitro cultures have demonstrated the existence of various types of neural progenitor cells, including multipotent stem cells that can generate neurons, astrocytes, and oligodendrocytes, and precursors with more restricted proliferation and differentiation potentials [3, 4]. A number of secreted molecules regulate the proliferation, lineage commitment, and/or differentiation of these different types of progenitors in culture [5], and Notch signaling has been shown to influence whether progenitors enter a differentiation pathway or remain undifferentiated [6]. A large number of transcription factors are sequentially and transiently expressed in neural precursor cells as neurogenesis proceeds. Gain-of-function studies performed in Xenopus or chick embryos and loss-of-function studies carried out in mice have implicated different transcription factor families in distinct operations. Proteins of the basic helix-loop-helix (bHLH) class have a central role in the determination of neuronal lineages in the peripheral and central nervous system and in the acquisition of pan-neuronal traits by differentiating neurons. Many phenotypic characteristics of neurons are regulated by homeodomain proteins, but bHLH factors are also involved in the specification of certain aspects of the phenotype of neurons, in particular their neurotransmitter identity, thereby coupling generic and subtype-specific subprograms of differentiation.
Uncertainty detection in natural language: a probabilistic model

Designing approaches able to automatically detect uncertain expressions within natural language is central to design efficient models based on text analysis, in particular in domains such as question-answering, approximate reasoning, knowledge-based population. This article proposes an overview of several contributions and classifications defining the concept of uncertainty expressions in natural language, and the related detection methods that have been proposed so far. A new supervised and generic approach is next introduced for this specific task; it is based on the statistical analysis of multiple lexical and syntactic features used to characterize sentences through vector-based representations that can be analyzed by proven classification methods. The global performance of our approach is demonstrated and discussed with regard to various dimensions of uncertainty and text specificities. This method is available for download at https://github.com/pajean/uncertaintyDetection.