Miracle-dz/UniFAR-FTUs huggingface dataset:
Insect vision inspired object detection in the video with a moving cluttered background

Motion information is an important cue for insects to perceive the surrounding environment. Flying insects, demonstrate extraordinary capability in locating and detecting visual objects in a cluttered moving background. Utilizing insect vision in computational models is an intriguing challenge. We inherit the two-dimensional elementary motion detector model and improve the small-field model for video applications, which effectively suppresses the disturbance of the moving background directly in front of cameras. And then we propose an object detection method using peak, thresholding and direction voting, which locates the uncertain targets by detecting peaks in the modulus matrix, coupled with thresholding and then removing false targets according to the consistency of the argument matrix within the region of uncertain targets. We use the ObjectVideo Virtual Video visual-surveillance-simulation test bed to evaluate our detection results. Our methods produce robust target discrimination against the moving cluttered background.
A survey of the cell-growth problem and some its variations 1

A very brief survey of the main results concerning the cell–growth problem and its variations is given. The name stems from an analogy with an animal which, starting from a single cell of some specified basic polygonal shape, grows step by step in the plane by adding at each step a cell of the same shape to its periphery. The fundamental combinatorial problem concerning these animals is ”How many animals with n cells are there? ” This problem was included in the list of unsolved problems in the enumeration of graphs by Frank Harary in 1960. Despite serious efforts over the last 40 years, this problem is completely open. However, a few asymptotic results are known. For example, let p(n) denote the number of polyominoes (square animals) having n cells. It was proved that (p(n)) tends to a limit Θ, which satisfies the following inequality: 3.87 < Θ < 4.65 . The situation could hardly be worse, since the first digit of Θ is not even known. The difficulty of the classical cell–growth problem has led to the study of various restricted classes of polyominoes. Some variations of this problem are considered. Unsolved problems are stated. Chemical applications of this problem are mentioned too. 1. Classical cell–growth problem Combinatorial problem known as cell-growth problem is stated as follows [1–7]. The name stems from an analogy with an animal which, starting from a single cell of some specified basic polygonal shape, grows step by step in the plane by adding at each step a cell of the same shape to its periphery. Thus if the basic shape is a square, the animals are the polyominoes (Fig.1a). If the basic shape is an equilateral triangle or a regular hexagon, we obtain triangular and hexagonal animals looking like those in Fig.1b and Fig.1c. Animals are defined as simply–connected ones if they have no holes and as multiply–connected ones otherwise. All animals presented in Fig.1 are simply–connected ones. The smallest multiply–connected polyomino is shown in Fig.2. The fundamental combinatorial problem concerning these animals is ”How many animals with n cells are there? ” This problem was included in the list of unsolved problems in the enumeration of graphs by Harary in 1960 [8]. Polyominoes have the most long history, going to the start of the 20th century, but 1Supported by Com2MaC-KOSEF, The Republic of Korea.
A Novel Feature Selection for Gene Expression Data

The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in an acceptable classification accuracy. Therefore, a good feature selection method based on the number of features investigated for sample classification is needed in order to speed up the processing rate, predictive accuracy, and to avoid incomprehensibility. In this paper, particle swarm optimization (PSO) is used to implement a feature selection, and the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as an evaluator of PSO. The support vector machines (SVMs) with the one-versus-rest method serve as a classifier for the classification problem. Experimental results show that our method simplifies features effectively and obtains a higher classification accuracy compared to the other classification methods from the literature.
A wide-band RF front-end with linear active notch filter for mobile TV applications

This paper presents a wide-band RF front-end with linear active notch filter covering both T-DMB and DVB-H. A single to differential converter with the low amplitude/phase error and 6dB step RF VGA using the capacitor are implemented. Also, highly linear and Q-enhanced tunable active inductor is proposed. The linear active notch filter rejects GSM band up to 23dB and achieves 20dB linearity improvement. The RF front-end is fabricated on 90nm CMOS technology and consumes 29.7mW.
Power Control in Cellular Radio Systems

The primary goal of cellular radio systems is to provide communications services to a large number of mobile users. Due to the dramatic increase of this market, the available resources have to be utilized efficiently. Closed-loop power control is considered as an important component in these resource management algorithms. Here, relevant aspects on power control are discussed, using an automatic control framework. Generally, power control strategies are implemented as cascade control, with an inner loop to compensate for fast variations and an outer loop focusing on longer term statistics. Issues as capacity, load and stability are discussed and related to whether it is possible to accommodate the requirements of all users or not. The operation of such algorithms are illustrated by simulations.
An Exact Algorithm for the Resource-Constrained Project Scheduling Problem Based on a New Mathematical Formulation

In this paper we consider the Project Scheduling Problem with resource constraints, where the objective is to minimize the project makespan. We present a new 0-1 linear programming formulation of the problem that requires an exponential number of variables, corresponding to all feasible subsets of activities that can be simultaneously executed without violating resource or precedence constraints. Different relaxations of the above formulation are used to derive new lower bounds, which dominate the value of the longest path on the precedence graph and are tighter than the bound proposed by Stinson et al. (1978). A tree search algorithm, based on the above formulation, that uses new lower bounds and dominance criteria is also presented. Computational results indicate that the exact algorithm can solve hard instances that cannot be solved by the best algorithms reported in the literature.
Centre for the Analysis of Risk and Optimisation Modelling Applications Combinatorial Optimization and Metaheuristics Combinatorial Optimization and Metaheuristics

A multidisciplinary research centre focussed on understanding, modelling, quantification, management and control of RISK ABSTRACT: Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (" efficient ") algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find " quickly " (reasonable run-times), with " high " probability, provable " good " solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods.
Improving Location Accuracy by Combining WLAN Positioning and Sensor Technology

This paper presents a heterogeneous location-aware system that combines two technologies so that their properties can be aggregated to produce more accurate location data. This paper proposes and demonstrates a location system that uses a sensor mounted on a robot to determine its location acoustically and complement the data with location from the existing Ekahau Positioning Engine (EPE). It is shown that the acoustic localization is capable of verifying errors that exist in the location information provided by the EPE. The heterogeneous system resulted in location information that gives 100% accuracy in spatial space information which the EPE was unable to provide all the time. The sensor fusion by combining the acoustic location system with the EPE helped improve the accuracy of location sensing by verifying the location information and increases location data availability.
Modeling and Design of Planar Integrated Magnetic Components

(Abstract) Recently planar magnetic technologies have been widely used in power electronics, due to good cooling and ease of fabrication. High frequency operation of magnetic components is a key to achieve high power density and miniaturization. However, at high frequencies, skin and proximity effect losses in the planar windings become significant, and parasitics cannot be ignored. This piece of work deals with the modeling and design of planar integrated magnetic component for power electronics applications. First, one-dimensional eddy current analysis in some simple winding strategies is discussed. Two factors are defined in order to quantify the skin and proximity effect contributions as a function of frequency. For complicated structures, 2D and 3D finite element analysis (FEA) is adopted and the accuracy of the simulation results is evaluated against exact analytical solutions. Then, a planar litz structure is presented. Some definitions and guidelines are established, which form the basis to design a planar litz conductor. It can be constructed by dividing the wide planar conductor into multiple lengthwise strands and weaving these strands in much the same manner as one would use to construct a conventional round litz wire. Each strand is subjected to the magnetic field everywhere in the winding window, thereby equalizing the flux linkage. 3D FEA is utilized to investigate the impact of the parameters on the litz performance. The experimental results verify that the planar litz structure can reduce the AC resistance of the planar windings in a specific frequency range. After that, some important issues related to the planar boost inductor design are described, including core selection, winding configuration, losses estimation, and thermal modeling. Two complete design examples targeting at volume optimization and winding parasitic capacitance minimization are provided, respectively. This work demonstrates that planar litz conductors are very promising for high frequency planar magnetic components. The optimization of a planar inductor involves a tradeoff between volumetric efficiency and low value of winding capacitance.
New Directions in Adaptive Disk Reorganization

While processor clock rates continue to track Moore’s law, the physical mechanisms which contribute to disk access time are improving at a much slower pace. As a result, disk access time is getting longer relative to CPU cycles. Caching can alleviate congestion by keeping frequently used data in main memory, but these data must initially read from disk at some point. Furthermore, the entire working set will not always fit in main memory. In such a scenario, not only can the file buffer cache fail to be effective, but the contents of main memory themselves may be swapped to disk. Accordingly, it pays to reduce the cost of disk operations. One technique to mitigate the problem is efficient data arrangement: strategically placing data on the disk in order to minimize expected access time. Since this strategy relies on knowledge about access patterns, the arrangement cannot generally be determined when the data are first written. Instead, a common approach is to gather statistics and reorganize the data to reduce expected access time given the observed pattern [3, 16, 11, 13, 2]. Statistics gathering and data relocation are not free, however. It is important to view data arrangement in the proper context of other mechanisms. File caching is the most important of these; it has smaller overhead cost and a much larger potential payoff than data reorganization, because a cache hit bypasses disk access entirely. File caches cannot handle all requests, however, so a good reorganization scheme should work in conjunction with file caching to reduce access latency. Other considerations for the design of a reorganization system are the way in which it affects the effectiveness of the disk prefetch cache and the way it interacts with disk scheduling algorithms.
Federal Reserve Bank of Philadelphia the Impact of Unemployment on Alternative Poverty Measures the Impact of Unemployment on Alternative Poverty Measures the Impact of Unemployment on Alternative Poverty Measures the Impact of Unemployment on Alternative Poverty Measures

The views expressed here are those of the author and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. Abstract The analysis uses data from the March Current Population Survey to estimate state-level cross-section/time-series models of the effects of unemployment on alternative poverty indexes. The indexes include the official headcount rate and alternatives based on improved identification and aggregation procedures. The estimated effects turn critically on the measurement approaches, both for the total sample population and for selected subgroups. For some broader, distribution-sensitive indexes, the declines in unemployment of the last decade had no significant impact on poverty. The findings thus provide important lessons for researchers exploring the links between economic conditions and poverty and for policymakers developing strategies to reduce poverty.
Lymphotoxin Signaling Is Initiated by the Viral Polymerase in HCV-linked Tumorigenesis

Exposure to hepatitis C virus (HCV) typically results in chronic infection that leads to progressive liver disease ranging from mild inflammation to severe fibrosis and cirrhosis as well as primary liver cancer. HCV triggers innate immune signaling within the infected hepatocyte, a first step in mounting of the adaptive response against HCV infection. Persistent inflammation is strongly associated with liver tumorigenesis. The goal of our work was to investigate the initiation of the inflammatory processes triggered by HCV viral proteins in their host cell and their possible link with HCV-related liver cancer. We report a dramatic upregulation of the lymphotoxin signaling pathway and more specifically of lymphotoxin-β in tumors of the FL-N/35 HCV-transgenic mice. Lymphotoxin expression is accompanied by activation of NF-κB, neosynthesis of chemokines and intra-tumoral recruitment of mononuclear cells. Spectacularly, IKKβ inactivation in FL-N/35 mice drastically reduces tumor incidence. Activation of lymphotoxin-β pathway can be reproduced in several cellular models, including the full length replicon and HCV-infected primary human hepatocytes. We have identified NS5B, the HCV RNA dependent RNA polymerase, as the viral protein responsible for this phenotype and shown that pharmacological inhibition of its activity alleviates activation of the pro-inflammatory pathway. These results open new perspectives in understanding the inflammatory mechanisms linked to HCV infection and tumorigenesis.
Load optimization in a planar network.

We analyze the asymptotic properties of a Euclidean optimization problem on the plane. Specifically, we consider a network with three bins and $n$ objects spatially uniformly distributed, each object being allocated to a bin at a cost depending on its position. Two allocations are considered: the allocation minimizing the bin loads and the allocation allocating each object to its less costly bin. We analyze the asymptotic properties of these allocations as the number of objects grows to infinity. Using the symmetries of the problem, we derive a law of large numbers, a central limit theorem and a large deviation principle for both loads with explicit expressions. In particular, we prove that the two allocations satisfy the same law of large numbers, but they do not have the same asymptotic fluctuations and rate functions.
INVERSION TRANSDUCTION GRAMMAR COVERAGE OF ARABIC-ENGLISH WORD ALIGNMENT FOR TREE-STRUCTURED STATISTICAL MACHINE TRANSLATION

We present the first known direct measurement of word alignment coverage on an Arabic-English parallel corpus using inversion transduction grammar constraints. While direct measurements have been reported for several European and Asian languages, to date no results have been available for Arabic or any Semitic language despite much recent activity on Arabic- English spoken language and text translation. Many recent syntax based statistical MT models operate within the domain of ITG expressiveness, often for efficiency reasons, so it has become important to determine the extent to which the ITG constraint assumption holds. Our results on Arabic provide further evidence that ITG expressiveness appears largely sufficient for core MT models.
Household Income Diversification into Rural Nonfarm Activities

The traditional vision of rural economies as purely agricultural is clearly obsolete. Farm households across the developing world earn an increasing share of their income from nonfarm sources (see Table 4.1). And the specialized nonfarm households that emerge often diversify into multiple business activities. Decisions affecting non-farm enterprise creation, closure and growth are, thus, commonly taken within the confines of a multi-activity rural household. Thus, the nonfarm enterprise dynamics, discussed in Chapter 5, fit into a household decision-making environment that helps to shape labor and capital allocations across activities, as well as outcomes. Consequently, a fuller understanding of nonfarm enterprise dynamics requires an assessment of the household decision-making environment within which these decisions are frequently made.
Modeling Common Volatility and Dynamic Risk Premia in European Equity Markets

This paper tests the hypothesis that the market portfolio in European equity returns is a dynamic factor in the sense that individual stock return volatilities and risk premia are driven by the dynamics of a common dynamic factor namely, the market portfolio. Support for the hypothesis would suggest that the dynamic Arbitrage Pricing Theory with observed dynamic factors (APT) provides a satisfactory description of the dynamics of European stock return. In addition, this paper examines the role of portfolio size, in the behavior of first and second moment dynamics.
Smooth pursuit adaptation (SPA) exhibits features useful to compensate changes in the properties of the smooth pursuit eye movement system due to usage

Smooth-pursuit adaptation (SPA) refers to the fact that pursuit gain in the early, still open-loop response phase of the pursuit eye movement can be adjusted based on experience. For instance, if the target moves initially at a constant velocity for ~100–200 ms and then steps to a higher velocity, subjects learn to up-regulate the pursuit gain associated with the initial target velocity (gain-increase SPA) in order to reduce the retinal error resulting from the velocity step. Correspondingly, a step to a lower target velocity leads to a decrease in gain (gain-decrease SPA). In this study we demonstrate that the increase in peak eye velocity during gain-increase SPA is a consequence of expanding the duration of the eye acceleration profile while the decrease in peak velocity during gain-decrease SPA results from reduced peak eye acceleration but unaltered duration. Furthermore, we show that carrying out stereotypical smooth pursuit eye movements elicited by constant velocity target ramps for several hundred trials (=test of pursuit resilience) leads to a clear drop in initial peak acceleration, a reflection of oculomotor and/or cognitive fatigue. However, this drop in acceleration gets compensated by an increase in the duration of the acceleration profile, thereby keeping initial pursuit gain constant. The compensatory expansion of the acceleration profile in the pursuit resilience experiment is reminiscent of the one leading to gain-increase SPA, suggesting that both processes tap one and the same neuronal mechanism warranting a precise acceleration-duration trade-off. Finally, we show that the ability to adjust acceleration duration during pursuit resilience depends on the integrity of the oculomotor vermis (OMV) as indicated by the complete loss of the duration adjustment following a surgical lesion of the OMV in one rhesus monkey we could study.
Scattering Theory for Orthogonal Wavelets

We apply the Lax-Phillips wave equation scattering theory to multiresolutions associated with wavelets: For the wavelet scattering, the translation symmetry, the scaling operator, and the scaling function are identiied in the scattering theoretic spectral transform; the scaling function is shown to be analytic; and an analytic spectral function is identiied as an invariant for multiresolutions, normalized so that the Haar wavelet corresponds to the constant function. For the study of the functional equation, we introduce almost periodic spaces and establish a general convergence for the innnite product formula with the limit in the L 2-space of the corresponding Bohr group.