Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpolation with smoothly nonstationary prediction-error filters

Building on the notions of time-variable filtering and the helix coordinate system, I develop software for filtersthat are smoothly variable in multiple dimensions, but that are quantized into large enough regions to be efficient. Multiscale prediction-error filters (PEFs) can estimate dips from recorded data and use the dip information to fillin unrecorded shot or receiver gathers. The data are typically divided into patches with approximately constant dips, with the requirement that the patches contain enough data samples to provide a sufficientnumber of fittingequations to determine all the coefficients of the filter. Each patch of data represents an independent estimation problem. Instead, I estimate a set of smoothly varying filtersin much smaller patches, as small as one data sample. They are more work to estimate, but the smoothly varying filtersdo give more accurate interpolation results than PEFs in independent patches, particularly on complicated data. To control the smoothness of the filters. I use filters like directional derivatives that Clapp et al. (1998) call “steering filters”. They destroy dips in easily adjusted directions. I use them in residual space to encourage dips in the specified directions. I describe the notion of “radial-steering filters” (Clapp et al. , 1999), i.e., steering filters oriented in the radial direction (lines of constant x t in t x space). Break a common-midpoint gather into pie shaped regions bounded by various values of x t . Such a pie-shaped region tends to have constant dip spectrum throughout the region so it is a natural region for smoothing estimates of dip spectra or of gathering statistics (via 2-D PEFs). In this paper I use smoothly variable PEFs to interpolate missing traces, though they may have many other uses. Finally, since noisy data can produce poor interpolation results, I deal with the separation of signal and noise along with missing data.
Anti-Taint-Analysis : Practical Evasion Techniques Against Information Flow Based Malware Defense

Taint-tracking is emerging as a general technique in softwa re security to complement virtualization and static analysis. It has been applied for accurate detection of a wide ran ge of attacks on benign software, as well as in malware defense. Although it is quite robust for tackling the former problem, application of taint analysis to untrusted (and potentially malicious) software is riddled with several su btle difficulties that lead to gaping holes in the defense tec hniques. These holes arise due to theoretical limitations of inf rmation flow analysis techniques, as well as the nature o f real-world software designs. They can be exploited to devel op anti-taint-analysis techniques that can be incorporate d into malware to evade taint-based defenses. We make two main contributions in this work. First, we show that these evasion attacks are easy-to-construct, provide powerful c apabilities to an attacker, and seem very difficult to mitiga te when applied to malware. Second, we have provided a comprehe nsive collection of such evasion techniques, thereby helping to improve our understanding of the attacker capabi lities in current deployment scenarios, and setting the sta ge for the development of more robust defenses against malware .
Capacity of the multicarrier channel with frequency-selective Nakagami fading

Closed-loop power control providing maximum capacity of the multicarrier channel with frequency selective Nakagami fading is investigated. Use of the famous Gallager channel capacity (water-filling) theorem with the assumption of limited transmitter power and independent fading in partial channels leads to the algorithm for their optimal power loading. Analytical expressions for the capacity of the multicarrier channel as a function of the number of its subchannels and the fading parameters are derived for the cases of Optimal Power Distribution (OPD) and Equal Power Distribution (EPD). The dependence of the capacity gain on the OPD system order, the fading depth and the average SNR due the optimal power control is obtained. Comparison of the power efficiencies of the systems with OPD and EPD is presented. key words: multicarrier modulation, frequency-selective fading,
Brand yourself, design your future: Portfolio-building in the social media age

While the portfolio-building narrative has long been established as central to work in the creative industries, the evolving form of the creative portfolio as a key component of the self-brand and the implications on creative work in the age of social media have been comparatively underexplored. This empirical project draws on a year-long qualitative study composed of in-depth interviews of 56 graphic design professionals about their use of social media platforms that cater to creative professionals. This study identifies the social media logics of the design portfolio as multi-platformed, connected, and temporally dynamic, suggesting a new pace, constancy, and subjectivity of what it means for cultural producers to build, maintain, and distribute their portfolio of projects to sustain their creative careers. As the portfolio becomes digitally distributed across a social media ecology, the labor of portfolio production for creative aspirants becomes never-ending and requires an intensified performative of “always designing. ”
Diversity technique for multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) system using a new subcarrier mapping scheme

The objective of this research is to propose a new subcarrier mapping scheme intercarrier interference self cancellation intercarrier interference self cancellation (ICI-SC) technique using data allocation in space time frequency block codes (STFBC) multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system. It aims to achieve maximum diversity order and to compensate integrated effect of frequency offset (FO) for intercarrier interference (ICI) reduction in the system. Using theoretical derivation of the pairwise error probability (PEP), a simulation model is then being used to compare with adjacent and symmetric subcarrier mapping scheme with FO. The results show that the ICI contained in the received signals can be effectively reduced using a new subcarrier mapping scheme. Thus, the proposed method is using STFBC outperforms existing subcarrier mapping schemes by approximately reducing the ICI with maximum diversity order in MIMO-OFDM system.
SCO-spondin from embryonic cerebrospinal fluid is required for neurogenesis during early brain development

The central nervous system (CNS) develops from the neural tube, a hollow structure filled with embryonic cerebrospinal fluid (eCSF) and surrounded by neuroepithelial cells. Several lines of evidence suggest that the eCSF contains diffusible factors regulating the survival, proliferation, and differentiation of the neuroepithelium, although these factors are only beginning to be uncovered. One possible candidate as eCSF morphogenetic molecule is SCO-spondin, a large glycoprotein whose secretion by the diencephalic roof plate starts at early developmental stages. In vitro, SCO-spondin promotes neuronal survival and differentiation, but its in vivo function still remains to be elucidated. Here we performed in vivo loss of function experiments for SCO-spondin during early brain development by injecting and electroporating a specific shRNA expression vector into the neural tube of chick embryos. We show that SCO-spondin knock down induces an increase in neuroepithelial cells proliferation concomitantly with a decrease in cellular differentiation toward neuronal lineages, leading to hyperplasia in both the diencephalon and the mesencephalon. In addition, SCO-spondin is required for the correct morphogenesis of the posterior commissure and pineal gland. Because SCO-spondin is secreted by the diencephalon, we sought to corroborate the long-range function of this protein in vitro by performing gain and loss of function experiments on mesencephalic explants. We find that culture medium enriched in SCO-spondin causes an increased neurodifferentiation of explanted mesencephalic region. Conversely, inhibitory antibodies against SCO-spondin cause a reduction in neurodifferentiation and an increase of mitosis when such explants are cultured in eCSF. Our results suggest that SCO-spondin is a crucial eCSF diffusible factor regulating the balance between proliferation and differentiation of the brain neuroepithelial cells.
Load-aware energy-efficient medium access control for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensors are usually battery-powered. Therefore, it is essential to reduce energy consumption to prolong network lifetime. In this paper, we propose a Load-aware Energy-efficient MAC protocol (LE-MAC) which utilises fuzzy control and the quorum concept. The LE-MAC protocol utilises fuzzy control to dynamically adjust the active/sleep ratio of each sensor node. The quorum concept is adopted to guarantee that two nodes with different active/sleep ratios can meet and communicate with each other. Simulation results verify that our LE-MAC can adapt to various network traffic loads. Furthermore, LE-MAC achieves significant performance improvements in energy consumption, transmission successful ratio, and transmission delay.
A new cascaded spatio-temporal noise reduction scheme for interlaced video

The quality of a television system is significantly determined by the amount of distortions within the displayed image. One of these distortions is white Gaussian noise. So a spatio-temporal algorithm for reducing noise in interlaced video is presented in this paper. It consists of a detail preserving spatial algorithm which uses a set of contour oriented lowpass-filters that are controlled via corresponding highpass masks. The influence of the number of different masks as well as of the coefficients are analyzed. The temporal scheme works in two subbands and makes use of some perception properties of the human eye. The high efficiency of this cascaded system is proved by simulations.
A hydrodynamic scheme for two-component winds from hot stars

Aims. We have developed a time-dependent two-component hydrodynamics code to simulate radiatively-driven stellar winds from hot stars. Methods. We use a time-explicit van Leer scheme to solve the hydrodynamic equations of a two-component stellar wind. Dynamical friction due to Coulomb collisions between the passive bulk plasma and the line-scattering ions is treated by a time-implicit, semianalytic method using a polynomial fit to the Chandrasekhar function. This gives stable results despite the stiffness of the problem. Results. This method was applied to model stars with winds that are both poorly and well-coupled. While for the former case we reproduce the mCAK solution, for the latter case our solution leads to wind decoupling.
A sligthly improved robust adaptive observer for actuator fault detection

In this paper, we address the problem of actuator fault detection and estimation for a plant in the presence of external disturbances. We design a robust fault estimation technique based on an adaptive observer with enhanced speed of adaptation. The construction of the observer is carried out through a transformation of the plant model into its special coordinate basis (SCB) form. This transformation allows a decoupling of the fault estimates from the disturbances. Thanks to this decoupling, the standard adaptive observer is slightly modified to achieve faster fault estimation in presence of disturbances and plant-model uncertainties. Simulation experiments are presented to illustrate the effectiveness of the proposed adaptive scheme.
Generating Memoryless Policies Faster Using Automatic Temporal Abstractions for Reinforcement Learning with Hidden State

Reinforcement learning with eligibility traces has been an effective way to solve problems with hidden state. Under certain conditions, it succeeds to build up a memoryless optimal policy over observations. Automatic generation of temporal abstractions, on the other hand, provides ways to extract and make use of useful sub-policies during reinforcement learning for a fully observable problem setting, so that the agent shall not need to repeatedly learn the same skill. One of the recent automatic abstraction techniques is the extended sequence tree method. We propose a novel way to bring together the extended sequence tree method and reinforcement learning for problems with hidden state. We expand the extended sequence tree method with a mechanism that helps the abstraction procedure to get rid of adverse effects of perceptual aliasing, letting the agent to make use of the remaining useful abstractions. Effectiveness of the method is shown empirically via experimentation on some benchmark problems.
Vehicle Classification with Confidence by Classified Vector Quantization

Automated vehicle classification based on static images is highly practical and directly applicable for various operations such as traffic related investigations. An integrated vehicle detection and classification system is proposed in this paper. A multi-resolution vehicle detection scheme is introduced as an improvement over the cascade boosted classifiers proposed recently by Negri et al. 2008 in the literature. Building on solutions from previous works from Negri et al, the implementation of a new decision strategy renders current detection method to be robust to environmental changes. The vehicle classification is based on the Classified Vector Quantization (CVQ) proposed earlier by Zhang et al. 2009. The justification of choosing CVQ is its advantages in providing classification confidence by incorporating rejection option. The significance of rejection in enhancing the system? s reliability is emphasized and evaluated. A database composed of more than 2800 images of four types of vehicles (cars, vans, light trucks and buses) was created using police surveillance cameras. The proposed scheme offers a performance accuracy of over 95% with a rejection rate of 8%, and reliability over 98% with a rejection rate of 20%. This exhibits promising potentials for implementations into real-world applications.
A Low Mortality, High Morbidity Reduced Intensity Status Epilepticus (RISE) Model of Epilepsy and Epileptogenesis in the Rat

Animal models of acquired epilepsies aim to provide researchers with tools for use in understanding the processes underlying the acquisition, development and establishment of the disorder. Typically, following a systemic or local insult, vulnerable brain regions undergo a process leading to the development, over time, of spontaneous recurrent seizures. Many such models make use of a period of intense seizure activity or status epilepticus, and this may be associated with high mortality and/or global damage to large areas of the brain. These undesirable elements have driven improvements in the design of chronic epilepsy models, for example the lithium-pilocarpine epileptogenesis model. Here, we present an optimised model of chronic epilepsy that reduces mortality to 1% whilst retaining features of high epileptogenicity and development of spontaneous seizures. Using local field potential recordings from hippocampus in vitro as a probe, we show that the model does not result in significant loss of neuronal network function in area CA3 and, instead, subtle alterations in network dynamics appear during a process of epileptogenesis, which eventually leads to a chronic seizure state. The model’s features of very low mortality and high morbidity in the absence of global neuronal damage offer the chance to explore the processes underlying epileptogenesis in detail, in a population of animals not defined by their resistance to seizures, whilst acknowledging and being driven by the 3Rs (Replacement, Refinement and Reduction of animal use in scientific procedures) principles.
The Acquisition and Sentence Alignment for Academic Bilingual Resources Based on Web Paper Libraries

This paper presents an approach for acquiring academic bilingual resources from the web paper libraries. By analyzing the structured information of web pages, we first implement a customized crawler to download these pages including paper details, and then use a parser to transfer them into XML format. Based on the classic statistical method for sentence alignment, we propose an improved approach to align the initial bilingual resources, in which two factors, bilingual keyword pairs and matching patterns are introduced. Experimental results show that our sentence aligner supported by the new approach achieves performance enhancement by 7% in both precision and recall.
Rewriting game theory applied to protein signalling in MAPK cascades

We propose the recent notion of rewriting game theory as a tool for studying biochemical systems. Rewriting game theory is based on a discrete and dynamic notion of Nash-style equilibria for games without structural constraints and with arbitrary payoff values. Our aim here is to show how the formalism can be used to characterise biological information as logical properties of a purely chemical model. Specifically, we address MAPK cascades through a compendium of the involved chemical reactions, with particular focus on the known signalling pathways. We also present preliminary computerised support for our methodology.
Clonal Structure of Two Wild Lotus (Nelumbo nucifera Gaertn.) Populations Inferred from Amplified Fragment Length Polymorphism Fingerprints

The lotus Nelumbo nucifera often forms dense populations at the margins of lakes and ponds. Many aquatic plant species such as N. nucifera are characterized by the ability to reproduce both sexually and asexually, which can affect the genetic diversity of populations growing in different types of environments. We compared the clonal diversities of two natural lotus populations in Japan. Diversity was quantified by DNA fingerprinting of amplified fragment length polymorphisms (AFLPs) in leaves sampled across each population. The DNA fingerprints revealed different amounts of diversity in each population. In 162 samples from Lake Kasumigaura, where lotus has grown continuously for many years, 35 AFLP patterns were detected. One particular clone was distributed widely, suggesting that growth of seed-derived plants was inhibited because of the superior competitive ability of rhizomes, resulting in low genetic diversity within the population. In 214 samples from Uchi-numa Pond in Miyagi Prefecture, where the lotus population was totally destroyed by heavy rains in 1998 and is now recovering, 213 different AFLP profiles were detected. The spatial distance between samples from Uchi-numa Pond was correlated with the genetic distance. These results suggest that, in lotus, sexual reproduction is favored when rapidly varying water level damages the population, because small seedlings are better able to survive in a low-density population. In contrast, vegetative reproduction is favored when environmental conditions are stable.
Globalisation and Employment in Bangladesh and Kenya DISCUSSION PAPER 7

I would like to thank the Centre for Policy Dialogue (CPD), Dhaka, Bangladesh and the Kenya Institute for Public Policy Research and Analysis (KIPPRA), Nairobi, Kenya, for providing excellent research facilities and logistic support during my visits to Bangladesh and Kenya. In particular, I would like to thank Prof. Mwangi Kimenyi, Executive Director, KIPPRA, and Prof. Mustafizur Rahman, Research Director, CPD, for their support and advice. This paper has benefited considerably from discussions with Md. Akhtaruzzaman, Debapriya Bhattacharya, Enamul Haque, Kapil Kapoor, Rafiqul Islam Molla, Narhari Rao, Zaidi Sattar, Binayak Sen and Rehman Sobhan in the case of Bangladesh, and Graham Glenday , Bernard Kagira, Peter Kimuyu, Kulundu Manda, Eng. Masila , Catherine Masinde, Hezron Nyangitu and Terry Ryan in the case of Kenya. The usual disclaimer applies.
A Utility Maximization Framework for Privacy Preservation of User Generated Content

The prodigious amount of user-generated content continues to grow at an enormous rate. While it greatly facilitates the flow of information and ideas among people and communities, it may pose great threat to our individual privacy. In this paper, we demonstrate that the private traits of individuals can be inferred from user-generated content by using text classification techniques. Specifically, we study three private attributes on Twitter users: religion, political leaning, and marital status. The ground truth labels of the private traits can be readily collected from the Twitter bio field. Based on the tweets posted by the users and their corresponding bios, we show that text classification yields a high accuracy of identification of these personal attributes, which poses a great privacy risk on user-generated content. We further propose a constrained utility maximization framework for preserving user privacy. The goal is to maximize the utility of data when modifying the user-generated content, while degrading the prediction performance of the adversary. The KL divergence is minimized between the prior knowledge about the private attribute and the posterior probability after seeing the user-generated data. Based on this proposed framework, we investigate several specific data sanitization operations for privacy preservation: add, delete, or replace words in the tweets. We derive the exact transformation of the data under each operation. The experiments demonstrate the effectiveness of the proposed framework.
Arabic Character Recognition using ANN Networks and Statistical Analysis

In this paper, an Arabic character recognition system based on Artificial Neural Networks (ANN) and statistical analysis of the Arabic characters is presented. In this system, each typed Arabic character is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to an ANN that consists of two layers. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions, especially when the contaminating noise level is low
Statistical Preprocessing for Decision Tree Induction

Some appa.rently simple numeric data sets cause significant problems for existing decision tree induction algorithms, in that no method is able to find a small, accurate tree, even though one exists. One source of this difficulty is the goodness measures used to decide whether a particular node represents a good way to split the data. This paper points out that the commonly-used goodness measures a,re not equipped to take into account some patterns in numeric attribute spaces, and presents a framework for capturing some such patterns into decision tree induction. As a case study, it is demonstrated empirically that supervised clustering, when used as a preprocessing step, can improve the quality of both univa^riate and multivariate decision trees.