Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature selection and information fusion in hierarchical neural networks for iterative 3D-object recognition

The reliable recognition of three-dimensional objects from two-dimensional camera images is still a major problem in computer vision and artificial intelligence. For this problem exist numerous approaches which in general neither incorporate the inherent hierarchical nature of classification problems nor offer the possibility to learn new objects during the exploration phase. Object recognitions tasks are in general multi-class problems where the number of classes is considerably higher than two. This work offers a novel approach for solving such problems by dividing the complex task into several hierarchically structured sub-problems that are easier to solve. The work at hand covers several aspects of object recognition with hierarchical neural networks: 1. Generation of appropriate classifier hierarchies 2. Evaluation of these hierarchies (information fusion) 3. Selection of suitable features for the classification 4. Extensibility / adaptivity of the hierarchies 5. Generation of similarity preserving sparse binary codes The selection of suitable feature types for the classification of objects from various domains is an important aspect for the recognition of three-dimensional objects. In the context of this work diverse feature types were deployed and evaluated. Different strategies for the retrieval of the combined classification result from the classifier hierarchies were developed and evaluated. The most promising approaches were the retrieval strategy similar to decision trees, the retrieval strategy utilising the Dempster-Shafer evidence theory as well as the retrieval strategy utilising similarity preserving codes and inter-state decision templates.
Improving traffic scheduling in wireless ATM networks

The medium access control (MAC) protocol and the underlying traffic scheduling mechanism for the radio interface are critical components of a wireless ATM network, aiming to provide ATM-like quality of service (QoS) guarantees over the air. A traffic scheduling algorithm for a wireless ATM customer premises network is proposed. The algorithm operates in the mobile terminals, and complements a centralized scheduling mechanism operating in the base stations, for improved performance. The proposed algorithm is delay oriented to meet the requirements of the various traffic classes defined by the ATM architecture. The system performance improvement attained by the proposed algorithm is evaluated via simulations.
Social Movements and Democracy in Africa: The Impact of Women's Struggles for Equal Rights in Botswana

Introduction 1. Situating a Social Movement in Botswana 2. Human Rights and Social Movements in Africa 3. Social Movements and the State 4. Emang Basadi in the Policy Process 5. The Impact of Emang Basadi 6. Conclusions: Social Movements in Africa
SDSS J2125−0813: the evidence for the origination of optical Fe ii emission lines from an accretion disc near a central black hole

We report on the radio-quiet quasar SDSS J2125−0813 which obviously emits optical Fe ii emission lines and double-peaked broad Balmer emission lines. Using the accretion disc model for double-peaked, broad, low-ionization emission lines, we reproduce the composite line spectra at the optical band between 4100 and 5600 A. Broad Fe ii emission lines can be fitted simultaneously with the broad Hβ and Mg i lines, such that all broad lines have an elliptical disc profile with the same disc parameters. This result indicates that the optical Fe ii emission lines originate from the accretion disc near the central black hole which produces the double-peaked broad Balmer emission lines. Furthermore, we find that the object has a dimensionless accretion rate , which is much larger than the accretion rate for the advection-dominated accretion flow (ADAF) mode, and that the energy budget of the accretion disc is enough to power the double-peaked broad Balmer emission lines.
Hashing Pursuit for Online Identification of Heavy-Hitters in High-Speed Network Streams

Distributed Denial of Service (DDoS) attacks have become more prominent recently, both in frequency of occurrence, as well as magnitude. Such attacks render key Internet resources unavailable and disrupt its normal operation. It is therefore of paramount importance to quickly identify malicious Internet activity. The DDoS threat model includes characteristics such as: (i) heavy-hitters that transmit large volumes of traffic towards "victims", (ii) persistent-hitters that send traffic, not necessarily large, to specific destinations to be used as attack facilitators, (iii) host and port scanning for compiling lists of un-secure servers to be used as attack amplifiers, etc. This conglomeration of problems motivates the development of space/time efficient summaries of data traffic streams that can be used to identify heavy-hitters associated with the above attack vectors. This paper presents a hashing-based framework and fast algorithms that take into account the large-dimensionality of the incoming network stream and can be employed to quickly identify the culprits. The algorithms and data structures proposed provide a synopsis of the network stream that is not taxing to fast-memory, and can be efficiently implemented in hardware due to simple bit-wise operations. The methods are evaluated using real-world Internet data from a large academic network.
Extraction of sparse spatial filters using Oscillating Search

Common Spatial Pattern algorithm (CSP) is widely used in Brain Machine Interface (BMI) technology to extract features from dense electrode recordings by using their weighted linear combination. However, the CSP algorithm, is sensitive to variations in channel placement and can easily overfit to the data when the number of training trials is insufficient. Construction of sparse spatial projections where a small subset of channels is used in feature extraction, can increase the stability and generalization capability of the CSP method. The existing ℓ0 norm based sub-optimal greedy channel reduction methods are either too complex such as Backward Elimination (BE) which provided best classification accuracies or have lower accuracy rates such as Recursive Weight Elimination (RWE) and Forward Selection (FS) with reduced complexity. In this paper, we apply the Oscillating Search (OS) method which fuses all these greedy search techniques to sparsify the CSP filters. We applied this new technique on EEG dataset IVa of BCI competition III. Our results indicate that the OS method provides the lowest classification error rates with low cardinality levels where the complexity of the OS is around 20 times lower than the BE.
Matrix Analysis of Coupled Microring Resonator Polygons

The resonant properties of a photonic molecule, composed by N microring resonators forming a regular polygon, are for the first time determined analytically using the transfer matrix method. It is found that the transfer matrix between rings n, n + 2, n + 4 , . is independent of the polygon vertex angle, allowing the application of Floquet theorem for periodic propagation in a cylindrically symmetric structure. Corresponding to even or odd N, the molecule possesses 1 + N/2 or 1 + N discrete resonances, which satisfy the dispersion equation of the straight coupled-resonator optical waveguide (CROW) with infinite rings. The field amplitudes in the rings are determined as eigenvectors of the corresponding eigenvalue problem. By incorporating the molecule into a channel dropping filter system, the presence of these resonances in the transmission spectrum is verified.
Speed-Up of Encoder Optimization Step in Multiple Description Scalar Quantizer Design

The design of optimal multiple description scalar quantizers was pioneered by Vaishampayan with a generalization of Lloyd's algorithm, which alternatively optimizes the decoder, respectively the encoder, while the other component is fixed. We propose an algorithm which speeds up the encoder optimization step from <i>O</i>(<i>N</i> <sup>2</sup>) to <i>O</i>(<i>N</i> log <i>N</i>) time complexity, where <i>N</i> is the number of cells in the central partition.
Galaxy groups in the 2dF Galaxy Redshift Survey: effects of environment on star formation

We estimate the fraction of star-forming galaxies in a catalogue of groups, constructed from the 2dF Galaxy Redshift Survey by Merchan & Zandivarez. We use the η spectral type parameter of galaxies and subdivide the sample of galaxies in groups into four types depending on the values of the η parameter following Madgwick et al. We obtain a strong correlation between the relative fraction of galaxies with high star formation and the parent group virial mass. We find that even in the environment of groups with low virial mass M∼1013 M⊙ the star formation of their member galaxies is significantly suppressed. The relation between the fraction of early-type galaxies and the group virial mass obeys a simple power law spanning over three orders of magnitude in virial mass. Our results show quantitatively the way that the presence of galaxies with high star formation rates is inhibited in massive galaxy systems.
Speech Intelligibility Improvement Using the Constraints on Speech Distortion and Noise Over-estimation

Existing speech enhancement methods can improve speech quality but not speech intelligibility, especially in low SNR conditions. To solve this problem, an algorithm for improving speech intelligibility using the Constraints on Speech Distortion and Noise Over-estimation (CSDNO) is proposed in this paper. Based on the fact that the attenuation distortion and amplification distortion have different impacts on the speech intelligibility, the gain function and noise estimation method used in conventional algorithm are modified in this paper. The performance of the proposed method has been evaluated by Diagnosis Rhyme Test (DRT), fractional Articulation Index (fAI) and frequency-weighted SNR segmental (fwSNRseg) for three types of noises. The experimental results show that, with smaller speech distortion, the proposed method can improve the intelligibility of the enhanced speech in comparison with the reference method.
SAR Interferogram Phase Filtering Using Wavelet Transform

The application of SAR Interferometry (InSAR) especially in Digital Elevation Modeling as well as deformation monitoring encounters problems due to noise in the interferogram phase. The noise is mainly caused by temporal and baseline decorrelation as well as changes in atmospheric conditions that makes the process of phase reconstruction difficult and reduces the accuracy. The quality of digital elevation models and displacement maps can be improved by filtering the interferometric phase. This paper reviews the methods of for noise reduction of interfermetric phase. It discusses a new algorithm based on wavelet transform which reduces the noise without reducing the spatial resolution or using any window over the interferogram. Comparing this algorithm with the other existing methods shows its strength in reducing the noise and preserving the signal. The effectiveness of the proposed filtering algorithm is tested by applying it to the InSAR results of the Bam earthquake. The results show that the filtering algorithm using wavelet transform is very successful in reducing noise in the interferogram.
Reducing CQI Signalling Overhead in HSPA

The efficiency of adaptive modulation and coding (AMC) procedure in high speed Downlink packet access (HSDPA) depends on the frequency of the channel quality information (CQI) reports transmitted by the UE to Node B. The more frequent the reports are the more accurate the link adaptation procedure is. On the other hand, the frequent CQI reports increase uplink interference, reducing thus the signal reception quality at the uplink. In this study, we propose an improved CQI reporting scheme which aims to reduce the required CQI signaling by exploiting a CQI prediction method based on a finite-state Markov chain (FSMC) model of the wireless channel. The simulation results show that under a high downlink traffic load, the proposed scheme has a near-to-optimum performance while produces less interference compared to the respective periodic CQI scheme.
Price transmission and estimations of price elasticity of secondary demand functions: application on commodity market for food grains Cenová transmise a odhady cenové elasticity sekundárních poptávkových funkcí: aplikace na komoditní trh s potravinářským obilím

The paper is focused on the quantitative analysis of the price transmission and on its use for the estimations of the direct price elasticity of the vertical-derived demand functions. The price transmissions were examined between the commodity markets for the food grain and the consumer markets for the bakery products and flour. The data (1995-2002) were taken over from the Czech Statistical Office (CSO), the Price Statistics (PS) and the Statistics of Family Budgets (SFB). The intensity of the inter-market price transmission was assessed by means of the coefficients of the price transmission elasticity (EPT). For enumerating of EPT, the regression linear models were developed. The explicit as well as the implicit time-definition in the models was tested. The explicit dynamic construction was carried out on the basis of the stationary process with the parabolic trend. After the determination of trend functions, the seasonal component in used time-series was thoroughly investigated by means of the harmonic analysis (G-tests of the individual extremes of the developed peri- odograms). The implicit dynamization of the linear models was solved on the basis of the first differences of appropriate commodity prices, respectively price levels on the consumer market. For the quantification of the price transmission elas- ticities, directly dynamized models there were only used only because the model unambiguously achieved better values of characteristics of the statistic verification (correlation index, F-test, T-test). These models also satisfied the economic assumptions in the sense of the vertical price transmissions between the observed market levels and the preservation of the law of diminishing demand. Based on the linear models of the price transmission with parabolic-trend stationarization, it was found out that within the observed period (1995-2002) EPT between commodity market with the food wheat and con- sumer market with the bakery products and flour reached the average level of +0.1602%. Within the same period, the value of EPT between commodity market with the rye and consumer market with the bakery products and flour reached the average level of +0.1067%. These coefficients were subsequently used together with coefficients of the own price elasticity of consumer demand for the bakery products and flour (e) to the estimations of the own price elasticity of the commodity demand for food wheat and rye (e). In accordance with the construction of these estimations: e = e × EPT, it was found out that the average level of the own price elasticity of the demand for food wheat (respectively rye) is about -0.0659% (respe- ctively -0.0441%). Both observed secondary demand functions are therefore strongly inelastic with respect to the reaction on the direct price changes. The commodity demand for the rye seems to be more inelastic.
Non-targeted Metabolite Profiling and Scavenging Activity Unveil the Nutraceutical Potential of Psyllium (Plantago ovata Forsk)

Non-targeted metabolomics implies that psyllium (Plantago ovata) is a rich source of natural antioxidants, PUFAs (ω-3 and ω-6 fatty acids) and essential and sulfur-rich amino acids, as recommended by the FAO for human health. Psyllium contains phenolics and flavonoids that possess reducing capacity and reactive oxygen species (ROS) scavenging activities. In leaves, seeds, and husks, about 76, 78, 58% polyunsaturated, 21, 15, 20% saturated, and 3, 7, 22% monounsaturated fatty acids were found, respectively. A range of FAs (C12 to C24) was detected in psyllium and among different plant parts, a high content of the nutritive indicators ω-3 alpha-linolenic acid (57%) and ω-6 linoleic acid (18%) was detected in leaves. Similarly, total content of phenolics and the essential amino acid valine were also detected utmost in leaves followed by sulfur-rich amino acids and flavonoids. In total, 36 different metabolites were identified in psyllium, out of which 26 (13 each) metabolites were detected in leaves and seeds, whereas the remaining 10 were found in the husk. Most of the metabolites are natural antioxidants, phenolics, flavonoids, or alkaloids and can be used as nutrient supplements. Moreover, these metabolites have been reported to have several pharmaceutical applications, including anti-cancer activity. Natural plant ROS scavengers, saponins, were also detected. Based on metabolomic data, the probable presence of a flavonoid biosynthesis pathway was inferred, which provides useful insight for metabolic engineering in the future. Non-targeted metabolomics, antioxidants and scavenging activities reveal the nutraceutical potential of the plant and also suggest that psyllium leaves can be used as a green salad as a dietary supplement to daily food.
Dynamic Radio Map Construction for WLAN Indoor Location

A novel indoor location algorithm based on dynamic Radio Maps construction in wireless local area network (WLAN) is proposed. The limitation of previous static Radio Map method is that reconstruction work must be taken to adapt the variation of indoor wireless environment. By taking received signal strength (RSS) values varying over time and space into account, a dynamic Radio Map is constructed to avoid this work and thus the cost for location system is reduced significantly. During offline phase, the relationship between RSS of calibration points and reference points is established by artificial neural network (ANN), and then during online phase, the real-time RSS values at reference points are predicted based on the RSS collected at calibration points in real time. Through the simulation of nearest neighbor (KNN) indoor location method for location accuracy, the feasibility and effectiveness of dynamic Radio Map construction based indoor location method is verified.
POSTER: Reuse, don't Recycle: Transforming Algorithms that Throw Away Descriptors

Lock-free algorithms guarantee progress by having threads help one another. Complex lock-free operations facilitate helping by creating descriptor objects that describe how other threads should help them. In many lock-free algorithms, a new descriptor is allocated for each operation. After an operation completes, its descriptor must be reclaimed by a memory reclamation scheme. Allocating and reclaiming descriptors introduces significant space and time overhead. We present a transformation for a class of lock-free algorithms that allows each thread to efficiently reuse a single descriptor. Experiments on a variety of workloads show that our transformation yields significant improvements over implementations that reclaim descriptors.
Experts and Publics in EU Environmental Law

This paper examines two important themes of EU environmental law: the preoccupation with the generation of detailed environmental information and knowledge; and an emphasis on participatory modes of decision-making. Exploring a number of areas of EU environmental law, this paper details the processes for gathering information on ‘facts’ about the world, and for enabling more or less ‘public’ participation in decision-making. It highlights the legitimacy challenges raised by the limits of expertise as a decision-making resource, and by the advantages of industry in shaping that expertise. There are no simple solutions to the challenges raised, but ensuring transparency and inclusion, an opportunity for scrutiny and for hearing different and dissenting voices, are important responses. Decision-makers also need to become more adept at articulating the reasons for a decision that are not to be found in expert advice.
Tools and algorithms to advance interactive intrusion analysis via Tools and algorithms to advance interactive intrusion analysis via Machine Learning and Information Retrieval Machine Learning and Information Retrieval

We consider typical tasks that arise in the intrusion analysis of log data from the perspectives of Machine Learning and Information Retrieval, and we study a number of data organization and interactive learning techniques to improve the analyst’s eﬃciency. In doing so, we attempt to translate intrusion analysis problems into the language of the abovementioned disciplines and to oﬀer metrics to evaluate the eﬀect of proposed techniques. The Kerf toolkit contains prototype implementations of these techniques, as well as data transformation tools that help bridge the gap between the real world log data formats and the ML and IR data models. We also describe the log representation approach that Kerf prototype tools are based on. In particular, we describe the connection between decision trees, automatic classiﬁcation algorithms and log analysis techniques implemented in Kerf.
Reprocessed emission from warped accretion discs with application to X-ray iron line profiles

Fluorescent iron line profiles currently provide the best diagnostic for engine geometries of active galactic nuclei (AGN). Here we construct a method for calculating the relativistic iron line profile from an arbitrarily warped accretion disc, illuminated from above and below by hard X-ray sources. This substantially generalizes previous calculations of reprocessing by accretion discs by including non-axisymmetric effects. We include a relativistic treatment of shadowing by ray-tracing photon paths along Schwarzschild geodesics. We apply this method to two classes of warped discs, and generate a selection of resulting line profiles. New profile features include a time-varying line profile if the warp precesses about the disc, profile differences between ‘twisted’ and ‘twist-free’ warps and the possibility of steeper red and softer blue fall-offs than for flat discs. We discuss some qualitative implications of the line profiles in the context of Type I and II Seyfert AGN and other sources.
Causes of post-fire runoff and erosion: water repellency, cover, or soil sealing?

Few studies have attempted to isolate the various factors that may cause the observed increases in peak flows and erosion after high-severity wildfires. This study evaluated the effects of burning by: (i) comparing soil water repellency, surface cover, and sediment yields from severely burned hillslopes, unburned hillslopes, and hillslopes where the surface cover was removed by raking; and (ii) conducting rainfall simulations to compare runoff, erosion, and surface sealing from two soils with varying ash cover. The fire-enhanced soil water repellency was only stronger on the burned hillslopes than the unburned hillslopes in the first summer after burning. For the first 5 yr after burning, the mean sediment yield from the burned hillslopes was 32 Mg ha ―1 , whereas the unburned hillslopes generated almost no sediment. Sediment yields from the raked and burned hillslopes were indistinguishable when they had comparable surface cover, rainfall erosivity, and soil water repellency values. The rainfall simulations on ash-covered plots generated only 21 to 49% as much runoffand 42 to 67% as much sediment as the plots with no ash cover. Soil thin sections showed that the bare plots rapidly developed a structural soil seal. Successive simulations quickly eroded the ash cover and increased runoff and sediment yields to the levels observed from the bare plots. The results indicate that: (i) post-fire sediment yields were primarily due to the loss of surface cover rather than fire-enhanced soil water repellency; (ii) surface cover is important because it inhibits soil sealing; and (iii) ash temporarily prevents soil sealing and reduces post-fire runoff and sediment yields.