Miracle-dz/UniFAR-FTUs huggingface dataset:
DOOMRED: A New Optimization Technique for Boosted Cascade Detectors on Enforced Training Set

We propose a new method to optimize the completely-trained boosted cascade detector on an enforced training set. Recently, due to the accuracy and real-time characteristics of boosted cascade detectors like the Adaboost, a lot of variant algorithms have been proposed to enhance the performance given a fixed number of training data. And, most of algorithms assume that a given training set well exhibits the real world distributions of the target and non-target instances. However, this is seldom true in real situations, and thus often causes higher false-classification ratio. In this paper, to solve the optimization problem of completely trained boosted cascade detector on false-classified instances, we propose a new base hypothesis weight optimization algorithm called DOOMRED (Direct Optimization Of Margin for Rare Event Detection) using a mathematically derived error upper bound of boosting algorithms. We apply the proposed algorithm to a cascade structured frontal face detector trained by AdaBoost algorithm. Experimental results demonstrate that the proposed algorithm has competitive ability to maintain accuracy and real-time characteristic of the boosted cascade detector compared to those of other heuristic approaches while requiring reasonably small amount of optimization time.
The relation between memory and power-law exponent

The inter-event time of many real systems are empirically found to be power-law distributed, and memory (the first-order autocorrelation), as a quantity ranging from -1 to +1, has been proposed to characterize the short-range correlation of time series. While series from real systems are usually found to be positively or negatively correlated, the memories for empirical power-law series are predominantly positive. In this paper, we study the mechanism for such memory bias. We analyze the bounds of memory for a family of random series with a specified marginal, which are generated by rearranging the orders of i.i. d. samples drawn from the marginal while preserving their values. While such rearrangement can produce uniform or Gaussian distributed series with arbitrary memory from -1 to +1, we found in this paper that power-law distribution imposes a special constraint on the possible memory of such series, which depends on the power-law exponent. Probabilistic and approximation methods are developed respectively to analyze the bounds when corresponding population moments converge and diverge, accompanied by validation from experimental results.
Competition between charge and spin order in the t-U-V extended Hubbard model on the triangular lattice

Several new classes of compounds can be modeled in first approximation by electrons on the triangular lattice that interact through on-site repulsion U as well as nearest-neighbor repulsion V. This extended Hubbard model on a triangular lattice has been studied mostly in the strong coupling limit for only a few types of instabilities. Using the extended two-particle self-consistent approach ETPSC, that is valid at weak to intermediate coupling, we present an unbiased study of the density and interaction dependent crossover diagram for spin- and charge-density wave instabilities of the normal state at arbitrary wave vector. When U dominates over V and electron filling is large, instabilities are chiefly in the spin sector and are controlled mostly by Fermi surface properties. Increasing V eventually leads to charge instabilities. In the latter case, it is mostly the wave vector dependence of the vertex that determines the wave vector of the instability rather than Fermi surface properties. At small filling, nontrivial instabilities appear only beyond the weak coupling limit. There again, charge-density wave instabilities are favored over a wide range of dopings by large V at wave vectors corresponding to 33 superlattice in real space. Commensurate fillings do not play a special role for this instability. Increasing U leads to competition with ferromagnetism. At negative values of U or V, neglecting superconducting fluctuations, one finds that charge instabilities are favored. In general, the crossover diagram presents a rich variety of instabilities. We also show that thermal charge-density wave fluctuations in the renormalized-classical regime can open a pseudogap in the single-particle spectral weight, just as spin or superconducting fluctuations.
A modified hamming distance measure for quick detection of dissimilar binary images

Given two dissimilar binary images we are interested in detecting their dissimilarity quickly. In our previous work, we developed a Probabilistic Model for Matching (PMM) that can be used to detect distinct-dissimilar binary images quickly with high confidence; e.g., by checking as few as five points with 93% confidence. In this paper we develop a similarity measure to be used with PMM. The similarity measure is a modification of the Hamming distance that measures the concurrence between two images. We show using real images, that by using this similarity measure, PMM can be extended to quasi-similar (non-distinct-dissimilar) images.
Test data augmentation : generating new test data from existing test data Technical Report : TR-0804

Existing automated test data generation techniques tend to start from scratch, implicitly assuming no pre-existing test data are available. However, this assumption may not always hold, and where it does not, there may be a missed opportunity; perhaps the pre-existing test cases could be used to assist the automated generation of additional test cases. This paper introduces search-based test data augmentation, a technique that can generate additional test data from existing test data using a meta-heuristic search algorithm. The proposed technique is compared to a widely studied test data generation approach in terms of both efficiency and effectiveness. The empirical evaluation shows that test data augmentation can be up to two orders of magnitude more efficient than existing test data generation techniques, while achieving comparable effectiveness in terms of structural coverage and mutation score.
Educational interventions for improving control of blood pressure in patients with hypertension: a systematic review protocol

Introduction The aim of this review is to evaluate the effectiveness of educational interventions on improving the control of blood pressure in patients with hypertension. Methods Randomised controlled trials including patients over 18 years of age, regardless of sex and ethnicity, with a diagnosis of hypertension (either treated or not treated with antihypertensive medications) will be assessed in our analysis. We will electronically search four databases: MEDLINE, CINAHL, PEDro and ScienceDirect. There will be no language restrictions in the search for studies. The data will be extracted independently by two authors using predefined criteria. Disagreements will be resolved between the authors. The risk of bias will be assessed using the Cochrane risk of bias tool. After searching and screening of the studies, we will run a meta-analysis of the included randomised controlled trials. We will summarise the results as risk ratio for dichotomous data and mean differences for continuous data. Ethics and dissemination The review will be published in a journal. The findings from the review will also be disseminated electronically and at conference presentations. Trial registration number PROSPERO CRD4201401071.
Reinforcement learning for a snake-like robot controlled by a central pattern generator

A snake has large potential to move in various environments by drastically changing its 'gait' pattern, in spite of its simple body. We configured a control scheme for a snakelike robot, using a central pattern generator, and developed a learning algorithm to acquire a good control rule in a changing environment. Although the snake-like robot has a large degree of freedom, a computer simulation showed that a control rule that allows the robot to move to a target direction on an inclined ground or in a changing environment is obtained by our scheme.
Design and implementation of a virtual reconfigurable architecture for different applications of intrinsic evolvable hardware

The authors present a novel virtual reconfigurable architecture (VRA) for realising real-world applications of intrinsic evolvable hardware (EHW) on field programmable gate arrays (FPGAs). The phenotype representation of the proposed evolvable system is based on a two-dimensional function element (FE) network. Compared with the traditional Cartesian genetic programming, the proposed approach includes more connection restrictions in the FE network to reduce genotype length. Another innovative feature of the VRA is that the whole evolvable system, which consists of an evolutionary algorithm unit, a fitness value calculation unit and an FE array unit, can be realised on a single FPGA. On this work, a custom Xilinx Virtex xcv2000E FPGA, which is fitted in the Celoxica RC1000 Peripheral Component Interconnect (PCI) board is utilised as the hardware platform. The main motive of the research is to design a general, flexible evolvable system with powerful computation ability to achieve intrinsic evolution. As examples, the proposed evolvable system is devoted to evolve two real-world applications: a character recogniser and an image operator by using gate level evolution and function level evolution, respectively. The experimental results show that the VRA can bring higher computational ability and more flexibility than traditional approach to intrinsic EHW.
Building virtual 802.11 testbeds towards open 5G experimentation

Together with recent advancements in Radio Access Network (RAN) technologies, Wi-Fi is expected to be at the center of research on the subject of ubiquitous wireless connectivity, towards building the new 5G ecosystem. Nevertheless, the necessary testbed infrastructure to support large scale experimentally driven research seems to be missing. In this work we present the design and implementation of a novel Virtual Wi-Fi Testbed. We present how a traditional Wireless Testbed can support hundreds of virtual Wi-Fi nodes that are open to experimenters. We discuss the design and implementation of the virtualization tools. We demonstrate the accuracy and the overhead analysis of the approach in the face of actual testbed conditions. Implementation experience is also reported on the benefits of using the proposed virtualization approach for a simple association algorithm.
ResearchIsolation and characterization of microparticles in sputum from cystic fibrosis patients

Background: Microparticles (MPs) are membrane vesicles released during cell activation and apoptosis. MPs have different biological effects depending on the cell from they originate. Cystic fibrosis (CF) lung disease is characterized by massive neutrophil granulocyte influx in the airways, their activation and eventually apoptosis. We investigated on the presence and phenotype of MPs in the sputum, a rich non-invasive source of inflammation biomarkers, of acute and stable CF adult patients. Methods: Spontaneous sputum, obtained from 21 CF patients (10 acute and 11 stable) and 7 patients with primary ciliary dyskinesia (PCD), was liquefied with Sputasol. MPs were counted, visualized by electron microscopy, and identified in the supernatants of treated sputum by cytofluorimetry and immunolabelling for leukocyte (CD11a), granulocyte (CD66b), and monocyte-macrophage (CD11b) antigens. Results: Electron microscopy revealed that sputum MPs were in the 100-500 nm range and did not contain bacteria, confirming microbiological tests. CF sputa contained higher number of MPs in comparison with PCD sputa. Levels of CD11a+-and CD66b+-, but not CD11b+-MPs were significantly higher in CF than in PCD, without differences between acute and stable patients. Conclusions: In summary, MPs are detectable in sputa obtained from CF patients and are predominantly of granulocyte origin. This novel isolation method for MPs from sputum opens a new opportunity for the study of lung
The Plant U1 Small Nuclear Ribonucleoprotein Particle 70K Protein Interacts with Two Novel Serine/Arginine–Rich Proteins

The U1 small nuclear ribonucleoprotein particle (U1 snRNP) 70K protein (U1-70K), one of the three U1 snRNP–specific proteins, is implicated in basic and alternative splicing of nuclear pre-mRNAs. We have used the Arabidopsis U1-70K in the yeast two-hybrid system to isolate cDNAs encoding proteins that interact with it. This screening has resulted in the isolation of two novel plant serine/arginine–rich (SR) proteins, SRZ-22 and SRZ-21 (SRZ proteins). Neither the N-terminal region nor the arginine-rich C-terminal region of U1-70K alone interact with the SRZ proteins. The interaction of U1-70K with the SRZ proteins is confirmed further in vitro using a blot overlay assay. The plant SRZ proteins are highly similar to each other and contain conserved modular domains unique to different groups of splicing factors in the SR family of proteins. SRZ proteins are similar to human 9G8 splicing factor because they contain a zinc knuckle, precipitate with 65% ammonium sulfate, and cross-react with the 9G8 monoclonal antibody. However, unlike the 9G8 splicing factor, SRZ proteins contain a glycine hinge, a unique feature in other splicing factors (SC35 and ASF/SF2), located between the RNA binding domain and the zinc knuckle. SRZ-22 and SRZ-21 are encoded by two distinct genes and are expressed in all tissues tested with varied levels of expression. Our results suggest that the plant SRZ proteins represent a new group of SR proteins. The interaction of plant U1-70K with the SRZ proteins may account for some differences in pre-mRNA splicing between plants and animals.
Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition

Issues in the quadratic discriminant functions (QDF) are discussed and two types of modified quadratic disriminant functions (MQDF1, MQDF2) which are less sensitive to the estimation error of the covariance matrices are proposed. The MQDF1 is a function which employs a kind of a (pseudo) Bayesian estimate of the covariance matrix instead of the maximum likelihood estimate ordinarily used in the QDF. The MQDF2 is a variation of the MQDF1 to save the required computation time and storage. Two discriminant functions were applied to Chinese character recognition to evaluate their effectiveness, and remarkable improvement was observed in their performance.
Variable Selection Issues in Tree-Based Regression Models

Recently, there has been increasing interest in the use of classification and regression tree (CART) analysis. A tree-based regression model can be constructed by recursively partitioning the data with such criteria as to yield the maximum reduction in the variability of the response. Unfortunately, the exhaustive search may yield a bias in variable selection, and it tends to choose a categorical variable as a splitter that has many distinct values. In this study, an unbiased tree-based regression generalized unbiased interaction detection and estimation (GUIDE) model is introduced for its robustness against the variable selection bias. Not only are the underlying theoretical differences behind CART and GUIDE in variable selection presented, but also the outcomes of the two different tree-based regression models are compared and analyzed by utilizing intersection inventory and crash data. The results underscore GUIDE's strength in selecting variables equally. A simulation shed additional light on the resulting negative impact when an algorithm was inappropriately applied to the data. This paper concludes by addressing the strengths and weaknesses of—and, more important, the differences between—the two hierarchical tree-based regression models, CART and GUIDE, and advises on the appropriate application. It is anticipated that the GUIDE model will provide a new perspective for users of tree-based models and will offer an advantage over existing methods. Users in transportation should choose the appropriate method and utilize it to their advantage.
A preliminary analysis of theorizing effort in ERP research

This paper looks at a fundamental issue in Information Systems (IS) research methodology, that of the use and construction of theories. Theory usage and construction are required for developing science, however they are poorly understood and pose difficult challenges to the researcher. To explore this issue, we propose a set of criteria and apply it to a sample of papers in the field of ERP research. Our analysis highlights the preponderance of descriptive and explanatory theoretical proposals, which do not attempt to make predictions. Furthermore, as most of these studies are impossible to replicate, theory confirmation is a challenge. This work is a contribution to better understand knowledge building in IS research.
Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations

Influence maximization is a problem to find small sets of highly influential individuals in a social network to maximize the spread of influence under stochastic cascade models of propagation. Although the problem has been well-studied, it is still highly challenging to find solutions of high quality in large-scale networks of the day. While Monte-Carlo-simulation-based methods produce near-optimal solutions with a theoretical guarantee, they are prohibitively slow for large graphs. As a result, many heuristic methods without any theoretical guarantee have been developed, but all of them substantially compromise solution quality. To address this issue, we propose a new method for the influence maximization problem. Unlike other recent heuristic methods, the proposed method is a Monte-Carlo-simulation-based method, and thus it consistently produces solutions of high quality with the theoretical guarantee. On the other hand, unlike other previous Monte-Carlo-simulation-based methods, it runs as fast as other state-of-the-art methods, and can be applied to large networks of the day. Through our extensive experiments, we demonstrate the scalability and the solution quality of the proposed method.
New design of a variable-temperature ultrahigh vacuum scanning tunneling microscope

We present the design of a variable-temperature ultrahigh vacuum (UHV) scanning tunneling microscope which can be operated between 20 and 400 K. The microscope is mounted directly onto the heat exchanger of a He continuous flow cryostat without vibration isolation inside the UHV chamber. The coarse approach is performed with an inertial slider driven by the same piezo tube that is also used for scanning. The performance of the instrument is demonstrated by two different kinds of measurements: First we show topographic images of close packed metal surfaces with atomic resolution. Then, we present results from scanning tunneling spectroscopy measurements on WSe2 under illumination at variable temperatures
Generating stationary random graphs on Z with prescribed i

Let F be a probability distribution with support on the non-negative integers. Two algorithms are described for generating a stationary random graph, with vertex set Z, so that the degrees of the vertices are i.i. d. random variables with distribution F . Focus is on an algorithm where, initially, a random number of “stubs” with distribution F is attached to each vertex. Each stub is then randomly assigned a direction, left or right, and the edge configuration is obtained by pairing stubs pointing to each other, first exhausting all possible connections between nearest neighbors, then linking second nearest neighbors, and so on. Under the assumption that F has finite mean, it is shown that this algorithm leads to a welldefined configuration, but that the expected length of the shortest edge of a vertex is infinite. It is also shown that any stationary algorithm for pairing stubs with random, independent directions gives infinite mean for the total length of the edges of a given vertex. Connections to the problem of constructing finitary isomorphisms between Bernoulli shifts are discussed.
Radio Resource Management for Fast Fading Environments

Adaptive resource allocation based on perfect Channel State Information (CSI) can significantly improve the performance of Orthogonal Frequency Division Multiple Access (OFDMA) systems. However, in real systems, accurate CSI is impossible because of noisy channel estimates, channel feedback delays, and processing delays. Therefore, only imperfect CSI can be used for resource allocation purposes. In this paper, we minimize the impact of errors caused by imperfect CSI and evaluate the significance of periodic CSI feedback in a fast fading environment. In a fast fading environment, periodic CSI feedback requires a high overhead load; and the uplink resources reserved for feedback purposes are limited. Thus, we present a strategy that optimizes the usage of those uplink resources. Simulation results show that this strategy leads to a higher overall fairness and system throughput.
Comparing State- and Operation-Based Change Tracking on Models

In recent years, models are increasingly used throughout the entire lifecycle in software development projects. In effect, the need for collaborating on these models emerged, requiring change tracking and versioning. However, many researchers have shown that existing methods and tools for Version Control (VC) do not work well on graph-like models, such as UML, SysML or domain-specific modeling languages. To alleviate this, alternative techniques and methods have been proposed which can be classified into state-based and operation-based approaches. Existing research shows advantages of operation-based over state-based approaches in selected use cases, such as conflict detection or merging. However, there are only few results available on the advantages of operation-based approaches in the most common use case of a VC system: review and understand change. In this paper, we present and discuss both approaches and their use cases. Moreover, we present the results of an empirical study to compare a state-based with an operation-based approach for the use case of reviewing and understanding change. For this study, we have mined an operation-based model repository and interviewed users to assess their understanding of randomly selected changes. Our results indicate that users better understand complex changes in the operation-based representation.