Miracle-dz/UniFAR-FTUs huggingface dataset:
Noise Reduction for Experimental Data

We introduce an algorithm for nonlinear noise reduction which is based on locally linear ts to the nonlinear dynamics. We claim that it is both robust and exible enough to apply it to a variety of experimental time series. In the second part of the paper we show its performance on data set A. The correlation dimension, Liapunov exponent and the Poincar e map are computed before and after the noise reduction.
Predicting movement and laterality from Deep Brain Local Field Potentials

The use of Deep Brain Local Field Potentials (LFP) in the process of connecting the human brain with artificial devices is one of the most promising fields in neural engineering. Inner mechanisms of our the central nervous system (CNS) can be understood through the study of LFPs. Of special importance are the the LFPs that come from subthalamic nucleus (STN) as they are related to the preparation, execution and imaging of movements. While researchers have focused on decoding movements and its laterality, left or right sided visually cued movements from STN LFPs, there is scope for using the same information for prediction of movements and laterality. In this paper, an algorithm is proposed that can be used to predict movement and laterality using STN LFPs. For this, wavelet packet transform (WPT) is used to generate separated frequency components of the LFPs. Then a selection of time and frequency domain features are used, namely time window based power features, causality features computed using granger causality and cross correlation, and frequency domain features computed using discrete cosine transform (DCT). Utilizing a weighted sequential feature selection process (WSFS), promising results are obtained from a Bayesian classifier along with cross validation procedure.
2 2 0 Ju l 2 00 0 Self-gravitating bosons at nonzero temperature

A system of charged bosons at finite temperature and chemical potential is studied in a general-relativistic framework. We assume that the boson fields interact only gravitationally. At sufficiently low temperature the system exists in two phases: the gas and the condensate. By studying the condensation process numerically we determine the critical temperature T c at which the condensate emerges. As the temperature decreases, the system eventually settles down in the ground state of a cold boson star.
Development of an Inflammation-Associated Colorectal Cancer Model and Its Application for Research on Carcinogenesis and Chemoprevention

Chronic inflammation is a well-recognized risk factor for development of human cancer in several tissues, including large bowel. Inflammatory bowel disease, including ulcerative colitis and Crohn's disease, is a longstanding inflammatory disease of intestine with increased risk for colorectal cancer development. Several molecular events involved in chronic inflammatory process may contribute to multistep carcinogenesis of human colorectal cancer in the inflamed colon. They include overproduction of reactive oxygen and nitrogen species, overproduction and upregulation of productions and enzymes of arachidonic acid biosynthesis pathway and cytokines, and intestinal immune system dysfunction. In this paper, I will describe several methods to induce colorectal neoplasm in the inflamed colon. First, I will introduce a protocol of a novel inflammation-associated colon carcinogenesis in mice. In addition, powerful tumor-promotion/progression activity of dextran sodium sulfate in the large bowel of Apc Min/+ mice will be described. Finally, chemoprevention of inflammation-associated colon carcinogenesis will be mentioned.
Financial Applications of Human Perception of Fractal Time Series

The purpose of this thesis is to explore the interaction between people’s financial behaviour and the market’s fractal characteristics. In particular, I have been interested in the Hurst exponent, a measure of a series’ fractal dimension and autocorrelation. In Chapter 2 I show that people exhibit a high level of sensitivity to the Hurst exponent of visually presented graphs representing price series. I explain this sensitivity using two types of cues: the illuminance of the graphs, and the characteristic of the price change series. I further show that people can learn how to identify the Hurst exponents of fractal graphs when feedback about the correct values of the Hurst exponent is given. In Chapter 3 I investigate the relationship between risk perception and Hurst exponent. I show that people assess risk of investment in an asset according to the Hurst exponent of its price graph if it is presented along with its price change series. Analysis reveals that buy/sell decisions also depend on the Hurst exponent of the graphs. In Chapter 4 I study forecasts from financial graphs. I show that to produce forecasts, people imitate perceived noise and signals of data series. People’s forecasts depend on certain personality traits and dispositions. Similar results were obtained for experts. In Chapter 5 I explore the way people integrate visually presented price series with news. I find that people’s financial decisions are influenced by news more than the average trend of the graphs. In the case of positive trend, there is a correlation between financial forecasts and decisions. Finally, in Chapter 6 I show that the way people perceive fractal time series is correlated with the Hurst exponent of the graphs. I use the findings of the thesis to describe a possible mechanism which preserves the fractal nature of price series.
Frame-Error-Rate-wise Optimal Code-Aided Hypothesis Testing

This paper addresses the issue of code-aided hypothesis testing in communication systems, i.e., the estimation of discrete-valued nuisance parameters. A hypothesis testing procedure optimal in the sense of the minimization of the frame-error rate (FER) is derived. Its complexity is shown to be comparable to other recently-proposed code-aided hypothesis procedures. Moreover, when a conditional maximum a posteriori decision rule is used to make the decisions about the transmitted sequence, it is shown that the proposed hypothesis testing procedure combined with sequence detection reduces, in a good approximation, to a joint maximum-likelihood problem. Finally, as an illustrative example, we show the case of phase ambiguity resolution for a convolutionally-coded transmission.
Counting Cycles

We obtain sharp bounds for the number of n-cycles in a finite graph G as a function of the number of edges, and prove that the complete graph is optimal in more ways than could be imagined. En route, we prove sharp estimates on both ∑n i=1 x k i and ∑n i=1 |xi| , subject to the constraints that ∑n i=1 x 2 i = C and ∑n i=1 xi = 0. This note has been inspired by the following question, which had been asked at the oral entrance exams (see [4]) to the Moscow State University Mathematics Department (MekhMat) to certain applicants: Question 1. Let G be a graph with E edges. Let T be the number of triangles of G. Show that there exists a constant C, such that for all G, T ≤ CE3/2. Before proceeding any further, let us answer the question. We will assume that G is a simple, loopless, undirected graph – that is, there is exactly one edge connecting two vertices v and w of G, and there are no edges whose two endpoints are actually the same vertex. We will need the following Definition 1. The adjacency matrix A(G) is a matrix such that (A(G)ij) = { 1 if ith and jth vertices of G are adjacent, 0 otherwise. We shall also need the following observations: Observation 1. The number of paths of length k between vertices vi and vj of G is equal to A k ij . The proof of this is immediate. It follows that the number of closed paths of length k in G is equal to the trace of Ak. Of course, this statement has to be made with some care, since the trace counts each closed path essentially 2k times (the 2 is for the choice of orientation, the k is for the possible starting points, the “essentially” is because this is not true of paths which Received by the editors February 8, 2008. 1991 Mathematics Subject Classification. Primary 05C35, 05C12, 26D20.
Adaptive genetic algorithm for occupancy grid maps merging

Multi-robot system can improve the efficiency of mapping and exploration. One of the key problems is when and how to merge the partial maps acquired by robots independently to share environmental information between robots. This paper studies the problem of fusing two partial maps without common reference frames and relative position information of robots. On the basis of the similarity metric, the paper applies an adaptive genetic algorithm for finding the overlapping region between the partial occupancy grid maps to realize map merging. The algorithm adjusts the crossover and mutation probability adaptively and nonlinearly with the similarity metric to avoid such disadvantages as premature convergence, low convergence speed and low stability. The experiment results show that the genetic algorithm based map merging does not get stuck at a local optimum, and is robust and can provide fast convergence for the optimal overlapping partial maps.
Reducing Broadcast Overhead Using Clustering Based Broadcast Mechanism in Mobile Ad Hoc Network

Problem statement: Network wide broadcasting is an important function in Mobile Ad Hoc Networks (MANET), which attempts to deliver packets from a source node to all other nodes in the network. Broadcasting is often very useful for route discovery, naming, addressing and helping multicast operations in all kinds of networks. For designing broadcast protocols for ad hoc networks, one of the primary goal is to reduce the overhead (redundancy, contention and collision) while reaching all the nodes in network. Approach: We had discussed many approaches in network wide broadcasting namely flooding, probability based, area based, network knowledge and cluster based broadcasting methods. The implementations and analysis will be made on Linux using the Network Simulator NS2. Results: In this study, cluster based flooding algorithm had been proposed and its metrics were namely routing load and packet delivery ratio was compared with two common flooding algorithms namely simple flooding and probability based flooding. Conclusion/Recommendations: It was concluded that simple flooding required each node to rebroadcast all packets. Probability based methods used some basic understanding of network topology, assigning a probability to node to rebroadcast. Cluster broadcasting algorithm for mobile ad hoc networks guaranteed to deliver messages from a source node to all nodes of network.
Dependence of vortex axisymmetrization on the characteristics of the asymmetry

This study investigates how different characteristics of initial asymmetries, including their positions and profiles, can impact on the vortex axisymmetrization process for barotropic vortices. When an initial disturbance is placed near the core of a vortex, a new asymmetry is generated inside the original asymmetry and grows due to its upshear tilt. Differential basic‐state rotation then shifts the phase to a downshear tilt and the asymmetry weakens. As the initial radius of the imposed asymmetry is increased, the initial upshear tilt of the asymmetry decreases. There is also a decrease in the efficiency with which the differential rotation shifts the phase tilt from upshear to downshear. The latter is related to differential radial propagation of the asymmetry in the form of vortex Rossby waves. These two mechanisms that are position‐dependent act against each other. There is an optimal radius at which the energy exchange between the symmetric and asymmetric flows is maximized. For a range of very different basic‐state profiles examined here, the optimal radius is around 1.5 to 2 times the radius of the maximum wind. The initial growth of asymmetries with higher azimuthal wavenumbers is weaker than their lower‐wavenumber counterparts due to a smaller upshear phase tilt with their smaller azimuthal length‐scales.
Improving Resource Utilization for All-IP Networks By Over Commitment

BBQ, budget-based QoS management infrastructure, is designed to offer end-to-end QoS assurance for All-IP networks. BBQ takes pre-planning approach to forecast incoming traffic based on historic statistics and allocates link resources accordingly. We designed an overbooking mechanism to compensate forecasting errors and to reduce performance degradation accordingly. We also propose an over-admission method to compensate resource under-utilization when some traffics such as telephony are not persistently active during its life time. Simulations show both methods can enhance resource utilization effectively.
Prostaglandins in Cancer Cell Adhesion, Migration, and Invasion

Prostaglandins exert a profound influence over the adhesive, migratory, and invasive behavior of cells during the development and progression of cancer. Cyclooxygenase-2 (COX-2) and microsomal prostaglandin E2 synthase-1 (mPGES-1) are upregulated in inflammation and cancer. This results in the production of prostaglandin E2 (PGE2), which binds to and activates G-protein-coupled prostaglandin E1–4 receptors (EP1–4). Selectively targeting the COX-2/mPGES-1/PGE2/EP1–4 axis of the prostaglandin pathway can reduce the adhesion, migration, invasion, and angiogenesis. Once stimulated by prostaglandins, cadherin adhesive connections between epithelial or endothelial cells are lost. This enables cells to invade through the underlying basement membrane and extracellular matrix (ECM). Interactions with the ECM are mediated by cell surface integrins by “outside-in signaling” through Src and focal adhesion kinase (FAK) and/or “inside-out signaling” through talins and kindlins. Combining the use of COX-2/mPGES-1/PGE2/EP1–4 axis-targeted molecules with those targeting cell surface adhesion receptors or their downstream signaling molecules may enhance cancer therapy.
Abstraction-guided Runtime Checking of Assertions on Lists

ion-guided Runtime Checking of Assertions on Lists Alex Gyori, Pranav Garg, Edgar Pek, P. Madhusudan University of Illinois at Urbana-Champaign, Urbana, IL, USA {gyori, garg11, pek1, madhu}@illinois.edu Abstract. We investigate ways to specify and check, at runtime, assertions that express properties of dynamically manipulated linked-list data structures. Checking an assertion involving whether pointers point to a valid linked list and separation properties of these lists typically requires linear or even quadratic time on the size of the heap. Our main contribution is a way to scale this checking by orders of magnitude, using a novel idea called abstraction-guided runtime checking, whereby we maintain an accurate abstraction of the dynamic heap by utilizing the evolving runtime state, and where the abstraction helps in checking the runtime assertions much faster. We develop this synergistic combination of abstractions and runtime checking for lists, list-segments, and their separation, implement it, and show the tremendous performance gains it yields. In particular, when lists are manipulated using library functions, maintenance of the abstraction is within the libraries and yields constant runtime checking of assertions in the client code. We show that, as the number of assertions get frequent and the data structures get large, abstraction-guided runtime checking, which includes maintenance of the abstraction and the runtime checks, gives close to constant-time per assertion overhead in practice. We investigate ways to specify and check, at runtime, assertions that express properties of dynamically manipulated linked-list data structures. Checking an assertion involving whether pointers point to a valid linked list and separation properties of these lists typically requires linear or even quadratic time on the size of the heap. Our main contribution is a way to scale this checking by orders of magnitude, using a novel idea called abstraction-guided runtime checking, whereby we maintain an accurate abstraction of the dynamic heap by utilizing the evolving runtime state, and where the abstraction helps in checking the runtime assertions much faster. We develop this synergistic combination of abstractions and runtime checking for lists, list-segments, and their separation, implement it, and show the tremendous performance gains it yields. In particular, when lists are manipulated using library functions, maintenance of the abstraction is within the libraries and yields constant runtime checking of assertions in the client code. We show that, as the number of assertions get frequent and the data structures get large, abstraction-guided runtime checking, which includes maintenance of the abstraction and the runtime checks, gives close to constant-time per assertion overhead in practice.
Multi-level governance in regional innovation systems

The paper deals with the regionalisation of technology and innovation policies in a multi-level governance context. It is argued that a number of problems are associated with this regionalisation. Multi-actor policy arenas and multi-level governance structures turn political action into a complex bargaining process between several levels and actor groups. The paper sketches recent theoretical and political developments regarding multi-actor and multi-level governance of innovation at the regional level. Based on the evidence from a German regional innovation system, it aims to analyse whether innovation policy is an appropriate policy for the devolution to regional governments.
LEARNING SELF-ORGANIZING MIXTURE MARKOV MODELS

This paper describes a new algorithm to learn SelfOrganizing map as Markov Mixture Models. Our model realizes an unsupervised learning using unlabelled evolutionary data sets, namely those that describe sequential data. The new formalism that we present is valid for all structure of graphical models. We use EM (Expectation-Maximisation) standard algorithm to maximize the likelihood. The graph structure is integrated in the parameter estimation of Markov model using a neighborhood function to learn a topographic clustering of not i.i. d data set. The new approach provides a self-organizing Markov model using an original learning algorithm. We provide three applications of our model: (1) dealing with continuous data using Gaussian distribution; (2) dealing with categorical data without any coding to encode variables using probability tables; (3) dealing with binary data using Bernoulli distribution.
Performance of Monetary Policy with Internal Central Bank Forecasting

Recent models of monetary policy have analysed the desirability of different optimal and ad hoc interest-rate rules under the restrictive assumption that forecasts of the private sector and central bank are homogeneous. In this paper, we study from a learning perspective the implications of heterogeneity across forecasts by the central bank and private agents for the performance of interest-rate rules.
Decentralized Sparse Signal Recovery for Compressive Sleeping Wireless Sensor Networks

This paper develops an optimal decentralized algorithm for sparse signal recovery and demonstrates its application in monitoring localized phenomena using energy-constrained large-scale wireless sensor networks. Capitalizing on the spatial sparsity of localized phenomena, compressive data collection is enforced by turning off a fraction of sensors using a simple random node sleeping strategy, which conserves sensing energy and prolongs network lifetime. In the absence of a fusion center, sparse signal recovery via decentralized in-network processing is developed, based on a consensus optimization formulation and the alternating direction method of multipliers. In the proposed algorithm, each active sensor monitors and recovers its local region only, collaborates with its neighboring active sensors through low-power one-hop communication, and iteratively improves the local estimates until reaching the global optimum. Because each sensor monitors the local region rather than the entire large field, the iterative algorithm converges fast, in addition to being scalable in terms of transmission and computation costs. Further, through collaboration, the sensing performance is globally optimal and attains a high spatial resolution commensurate with the node density of the original network containing both active and inactive sensors. Simulations demonstrate the performance of the proposed approach.
Power Law Distributions in Information Retrieval

Several properties of information retrieval (IR) data, such as query frequency or document length, are widely considered to be approximately distributed as a power law. This common assumption aims to focus on specific characteristics of the empirical probability distribution of such data (e.g., its scale-free nature or its long/fat tail). This assumption, however, may not be always true. Motivated by recent work in the statistical treatment of power law claims, we investigate two research questions: (i) To what extent do power law approximations hold for term frequency, document length, query frequency, query length, citation frequency, and syntactic unigram frequency? And (ii) what is the computational cost of replacing ad hoc power law approximations with more accurate distribution fitting? We study 23 TREC and 5 non-TREC datasets and compare the fit of power laws to 15 other standard probability distributions. We find that query frequency and 5 out of 24 term frequency distributions are best approximated by a power law. All remaining properties are better approximated by the Inverse Gaussian, Generalized Extreme Value, Negative Binomial, or Yule distribution. We also find the overhead of replacing power law approximations by more informed distribution fitting to be negligible, with potential gains to IR tasks like index compression or test collection generation for IR evaluation.
The Gene Revolution and Global Food Security: Biotechnology Innovation in Latecomers

Introduction Agricultural Biotechnology and Innovation Capacity in Late Development Catching Up in Biotechnology in Malaysia Vietnam's Biotechnology: Building Local Capacity Through State Vision Nigeria as a Lagging Follower in Biotechnology Tanzania's Incipient Innovation Capacity in Biotechnology Comparative Analysis of Innovation Capacity in Latecomer Countries Conclusions and Policy Insights
Portfolio Selection Problems Based on Fuzzy Interval Numbers Under the Minimax Rules

Recently, many researchers pay their attention to portfolio problems under minimax rules. Based on the uncertainty of the expected return of securities, therefore, it is of significant and interesting to study portfolio models based on fuzzy interval numbers under minimax rules(in briefly, PMFM). This PMFM-model can be regarded as a natural generalization of ordinarily portfolio model under the minimax rule. In the present paper, we study the PMFM-model and arrive at some similar and interesting conclusions. These conclusions imply that the optimal solution to PMFM is much better than that of the model without fuzzy interval numbers. Mathematics Subject Classification: 91B28, 91B32, 90C47
Bacillus subtilis spreads by surfing on waves of surfactant

The bacterium Bacillus subtilis produces the molecule surfactin, which is known to enhance the spreading of multicellular colonies on nutrient substrates by lowering the surface tension of the surrounding fluid, and to aid in the formation of aerial structures. Here we present experiments and a mathematical model that demonstrate how the differential accumulation rates induced by the geometry of the bacterial film give rise to surfactant waves. The spreading flux increases with increasing biofilm viscosity. Community associations are known to protect bacterial populations from environmental challenges such as predation, heat, or chemical stresses, and enable digestion of a broader range of nutritive sources. This study provides evidence of enhanced dispersal through cooperative motility, and points to nonintuitive methods for controlling the spread of biofilms.