Miracle-dz/UniFAR-FTUs huggingface dataset:
Ram pressure stripping of tilted galaxies

Context. Ram pressure stripping of galaxies in clusters can yield gas deficient disks. Previous numerical simulations based on various approaches suggested that, except for near edge-on disk orientations, the amount of stripping depends very little on the inclination angle. Aims. Following our previous numerical and analytical study of face-on stripping, we extend the set of parameters with the disk tilt angle and explore in detail the effects of the ram pressure on the interstellar content (ISM) of tilted galaxies that orbit in various environments of clusters, with compact or extended distributions of the intra-cluster medium (ICM). We further study how results of numerical simulations could be estimated analytically. To isolate the effect of inclination, galaxies on strictly radial orbits are considered. Methods. A grid of numerical simulations with varying parameters is produced using the tree/SPH code GADGET with a modified method for calculating the ISM-ICM interaction. These SPH calculations extend the set of existing results obtained from different codes using various numerical techniques. Results. The simulations confirm the general trend of less stripping at orientations close to edge-on. The dependence on the disk tilt angle is more pronounced for compact ICM distributions, however it almost vanishes for strong ram pressure pulses. Although various hydrodynamical effects are present in the ISM-ICM interaction, the main quantitative stripping results appear to be roughly consistent with a simple scenario of momentum transfer from the encountered ICM. This behavior can also be found in previous simulations. To reproduce the numerical results we propose a fitting formula depending on the disk tilt angle and on the column density of the encountered ICM. Such a dependence is superior to that on the peak ram pressure used in previous simple estimates.
Towards high performance phonotactic feature for spoken language recognition

With the demands of globalization, multilingual speech is increasingly common in conversational telephone speech, broadcast news and internet podcasts. Therefore, automatic spoken language recognition has become an important technology in multilingual speech related applications. For example, automatic spoken language recognition has been used as a preprocessing component for spoken language translation, multilingual speech recognition and spoken document retrieval. Both humans and machines rely on certain informative cues to differentiate one language from another. Inspired by the findings in the discriminative cues for human language recognition, most of the automatic language recognition systems rely on the following three features: acoustic, prosodic and phonotactic. Acoustic features capture spectral characteristics and can be obtained from short-term speech signals. Prosodic features such as tone, intonation, prominence and rhythm can be derived from energy measurements, pitch contour, rate of change. Phonotactic features capture the statistics of lexical constraints and phonotactic patterns. Phonotactic features can be generated from a tokenization front end which converts speech signals into sequences of sound patterns. This thesis focuses on the study of effective phonotactic feature extraction methods for high performance automatic language recognition. Specifically, the main contributions of this thesis are: A novel target-oriented method is proposed to construct parallel phone recognizers for robust phonotactic feature extraction. A subset of the most discriminative phones from an existing phone recognizer is selected to form a target-oriented phone tokenizer (TOPT). The TOPT phone tokenizers, one for each of the target languages, are constructed from an existing phone recognizer without requiring additional transcribed training data.
Multi-camera tracking by joint calibration, association and fusion

To perform surveillance using multiple cameras, camera calibration, measurement-to-object association, fusion of measurements from multiple cameras are three essential components. While these three issues are usually addressed separately, they actually have mutual effects on each other. For example, calibration requires correctly associated objects and measurements with calibration errors will result in wrong associations. In this paper, we present a novel joint calibration, association and fusion approach for multi-camera tracking. More specifically, the expectation-maximization (EM) algorithm is incorporated with the extended Kalman filter (EKF) to give a simultaneous estimate of object states, calibration and association parameters. The real video data collected from two cameras are used to evaluate the tracking performance of the proposed method. Compared to the conventional methods, which perform calibration, association and fusion separately, it is shown that the proposed method can significantly improve the robustness and the accuracy of multi-object tracking.
A review of research in multi-robot systems

Formally, a collection of two or more autonomous mobile robots working together are termed as teams or societies of mobile robots. In multi robot systems simple robots are allowed to coordinate with each other to achieve some well defined goals. In these kinds of systems robots are far less capable as an entity, but the real power lies in cooperation of multiple robots. The simplicity of multi-robots have produced a potentially wide set of applications such as military missions (battlefield surveillance), searching for survivors in disaster hit areas, parallel and simultaneous transportation of vehicles, and delivery of payloads. Although the research on multi-robot systems has attracted considerable attention worldwide in the past decade, the research in this area is still in its infancy. This paper surveys various interaction techniques in multi robot systems which are important with respect to goal attainment and task completion.
The Smolyak Algorithm and Tensor Products of Function Spaces

The Smolyak algorithm represents one possible approach to the approximation of functions of many variables. The natural domains of de nition are given by tensor products of function spaces de ned on R or on some interval I ⊂ R. Here Besov as well as Sobolev spaces of dominating mixed smoothness come into play. They are tensor products of Besov and Sobolev spaces de ned on R.
Autism and EEG Phase Reset: Deficient GABA Mediated Inhibition in Thalamo-Cortical Circuits

The purpose of this study was to explore the relationship between electroencephalogram (EEG) phase reset in autism spectrum disorder (ASD) subjects as compared to age matched control subjects. The EEG was recorded from 19 scalp locations from 54 autistic subjects and 241 control subjects ranging in age from 2.6 years to 11 years. Complex demodulation was used to compute instantaneous phase differences between all pairs of electrodes and the 1st and 2nd derivatives were used to measure phase reset by phase shift duration and phase lock duration. In both short (6 cm) and long (21–24 cm) inter-electrode distances phase shift duration in ASD subjects was significantly shorter in all frequency bands but especially in the alpha-1 frequency band (8–10 Hz) (p < .0001). Phase lock duration was significantly longer in the alpha-2 frequency band (10–12 Hz) in ASD subjects (p < .0001). An anatomical gradient was present with the occipital-parietal regions the most significant. The findings in this study support the hypothesis that neural resource recruitment occurs in the lower frequency bands and especially the alpha-1 frequency band while neural resource allocation occurs in the alpha-2 frequency band. The results are consistent with a general GABA inhibitory neurotransmitter deficiency resulting in reduced number and/or strength of thalamo-cortical connections in autistic subjects.
Business Cycle and Bank Capital Regulation: Basel II Procyclicality

This paper studies the impact of bank capital regulation on business cycle fluctuations. In particular, we study the procyclical nature of Basel II claimed in the literature. To do so, we adopt the Bernanke et al. (1999) ``financial accelerator" model (BGG), to which we augment a banking sector. We first study the impact of a negative shock to entrepreneurs' net worth and a positive monetary policy shock on business cycle fluctuations. We then look at the impact of a negative net worth shock on business cycle fluctuations when the minimum capital requirement increases from 8 percent to 12 percent. Our comparison studies between the augmented BGG model with Basel I bank regulation and the one with Basel II bank regulation suggest that, in the presence of credit market frictions and bank capital regulation, the liquidity premium effect further amplifies the financial accelerator effect through the external finance premium channel, which, in turn, contributes to the amplification of Basel II procyclicality. Moreover, under Basel II bank regulation, in response to a negative net worth shock, the liquidity premium and the external finance premium rise much more if the minimum bank capital requirement increases, which, in turn, amplify the response of real variables. Finally, small adjustments in monetary policy can result in stronger response in the real economy, in the presence of Basel II bank regulation in particular, which is undesirable.
A miR-200c/141-BMI1 autoregulatory loop regulates oncogenic activity of BMI1 in cancer cells

MicroRNAs (miRNAs) are known to function as oncomiRs or tumor suppressors and are important noncoding RNA regulators of oncogenesis. The miR-200c/141 locus on chromosome 12 encodes miR-200c and miR-141, two members of the miR-200 family, which have been shown to function as tumor suppressive miRNAs by targeting multiple oncogenic factors such as polycomb group protein BMI1. Here, we show that BMI1 reciprocally functions as a transcriptional repressor of the miR-200c/141 cluster and that BMI1 inhibitors upregulate expression of miR-200c and miR-141. Our data suggest that BMI1 binds to the miR-200c/141 promoter and regulates it through transcription factor binding motifs E-box 2 and Z-box 1 to repress expression of miR-200c/141 cluster. We also show that PTC-209, a small molecule inhibitor of BMI1 gene expression induces cellular senescence and transcriptionally upregulates expression of miR-200c/141 cluster in breast cancer cells. Furthermore, inhibition of expression of miR-200c or miR-141 overcomes tumor suppressive effects of PTC-209 including induction of cellular senescence and downregulation of breast cancer stem cell phenotype. Therefore, our studies suggest a reciprocal regulation between BMI1 and miR-200c/141 cluster, and that BMI1 inhibitory drugs can further amplify their inhibitory effects on BMI1 via multiple mechanisms including posttranscriptional regulation by upregulating BMI1 targeting miRNAs.
Energy-aware resource allocation algorithm for mobile ad hoc networks

Energy efficiency is a key factor in the design of ad hoc networks. In this paper, we adopt the network utility maximization framework for joint power and rate optimization at both transport and physical layer. Then, we propose a novel energy-aware resource allocation algorithm by integrating the power cost into the objective function. The proposed algorithm has been implemented in a distributed manner. Simulation results show that the proposed algorithm not only has good convergence, but also improve the energy efficiency of the network significantly.
Control allocation for fault tolerant control of a VTOL octorotor

For the fault tolerant control of an eight-rotor VTOL Unmanned Air Vehicle (UAV), a control allocation scheme is proposed. The eight-rotor configuration provides actuator redundancy to ensure safe operation under rotor/motor failures. A PD controller is used to generate total thrust and moment demands. A cascade inverse method of control allocation is proposed to allocate the controller commands to the actuators whilst ensuring that actuator saturation does not occur. If the vehicle is subjected to rotor failures, the scheme re-allocates the commands to maintain the vehicle stability and performance. Until actuator saturation occurs the response of the vehicle is the same when operating with all motors or fewer. The response of the vehicle to several combinations of complete actuator failures is investigated by simulation and it is shown that the proposed method is able to maintain control after failure of up to four actuators. The controller is invarient and the vehicle response to commands is identical until motor saturation occurs.
Comprehensive Biostatistical Analysis of CpG Island Methylator Phenotype in Colorectal Cancer Using a Large Population-Based Sample

Background The CpG island methylator phenotype (CIMP) is a distinct phenotype associated with microsatellite instability (MSI) and BRAF mutation in colon cancer. Recent investigations have selected 5 promoters (CACNA1G, IGF2, NEUROG1, RUNX3 and SOCS1) as surrogate markers for CIMP-high. However, no study has comprehensively evaluated an expanded set of methylation markers (including these 5 markers) using a large number of tumors, or deciphered the complex clinical and molecular associations with CIMP-high determined by the validated marker panel. Metholodology/Principal Findings DNA methylation at 16 CpG islands [the above 5 plus CDKN2A (p16), CHFR, CRABP1, HIC1, IGFBP3, MGMT, MINT1, MINT31, MLH1, p14 (CDKN2A/ARF) and WRN] was quantified in 904 colorectal cancers by real-time PCR (MethyLight). In unsupervised hierarchical clustering analysis, the 5 markers (CACNA1G, IGF2, NEUROG1, RUNX3 and SOCS1), CDKN2A, CRABP1, MINT31, MLH1, p14 and WRN were generally clustered with each other and with MSI and BRAF mutation. KRAS mutation was not clustered with any methylation marker, suggesting its association with a random methylation pattern in CIMP-low tumors. Utilizing the validated CIMP marker panel (including the 5 markers), multivariate logistic regression demonstrated that CIMP-high was independently associated with older age, proximal location, poor differentiation, MSI-high, BRAF mutation, and inversely with LINE-1 hypomethylation and β-catenin (CTNNB1) activation. Mucinous feature, signet ring cells, and p53-negativity were associated with CIMP-high in only univariate analysis. In stratified analyses, the relations of CIMP-high with poor differentiation, KRAS mutation and LINE-1 hypomethylation significantly differed according to MSI status. Conclusions Our study provides valuable data for standardization of the use of CIMP-high-specific methylation markers. CIMP-high is independently associated with clinical and key molecular features in colorectal cancer. Our data also suggest that KRAS mutation is related with a random CpG island methylation pattern which may lead to CIMP-low tumors.
Aliveness Detection for IRIS Biometrics

Various experiments show an alarming lack of anti-spoofing mechanisms in devices already protecting many sensitive areas all over the world, proving that aliveness detection methods must be quickly included in commercial equipment. To introduce and systemize the topic, the paper begins with a survey of possible types of eye forgery, together with possible countermeasures. The authors introduce three solutions of eye aliveness detection, based on analyses of image frequency spectrum, controlled light reflection from the cornea, and pupil dynamics. A body of various fake (printed) eye images was used to test the developed methodologies, including different printers and printout carriers. The proposed methodology was embedded into the NASK iris recognition system and showed its large potential. For a local database of pairs of alive and printed eyes, all methods proposed in the paper revealed zero false acceptance rate of fakes FAR-F. The false rejection rate of genuines FRR-G reached 2.8% for the first proposed solution, and showed null value for the next two proposed methods. This very favorable compares to the commercial equipment tested: two popular iris cameras accepted 73% and 15% of the prepared fake irises
ACOUSTIC DOMAIN ADAPTATION WITH ENROLLMENT DATA FROM CLINICAL TASKS

The growing interest on applying NLP and speech processing techniques to facilitate the administration and making more robust the evaluation of clinical tasks bring the need of assessing the usefulness of the regular techniques on this domain. For example, most research on Acoustic Model (AM) adaptation reported in the literature has been framed around publicly available corpus such as Switchboard and Broadcast News. In this study, we examine the effectiveness of different AM adaptation strategies with data from an enrollment phase of a clinical task – supervised vs. unsupervised adaptation and MLLR vs. MAP adaptation. Then, we examine semi-supervised methods to bridge the gap between supervised and unsupervised adaptation. Our findings provide a few guidelines for best practices in future clinical data col-
Common determinants of bond and stock market liquidity: the impact of financial crises, monetary policy, and mutual fund flows

We study common determinants of daily bid-ask spreads and trading volume for the bond and stock markets over the 1991-98 period. We find that spread changes in one market are affected by lagged spread and volume changes in both markets. Further, spread and volume changes are predictable to a considerable degree using lagged market returns, lagged interest rates, lagged spreads, and lagged volume. During periods of financial crisis, stock and bond spreads and volume are more volatile and become more highly correlated; moreover, at these times, money supply positively affects financial market liquidity, albeit with a lag of two weeks. During normal times, increases in mutual fund flows enhance stock market liquidity and trading volume, but during financial crises, U.S. government bond funds see higher inflows, resulting in increased bond market liquidity. Overall, this study deepens our understanding of the dynamics of liquidity in financial markets and suggests how asset allocation strategies might be designed to reduce trading costs.
Panopticon: An Omniscient Lock Broker for Efficient Distributed Transactions in the Datacenter

For datacenter applications that require tight synchronization, transactions are commonly employed for achieving concurrency while preserving correctness. Unfortunately, distributed transactions are hard to scale due to the decentralized lock acquisition and coordination protocols they employ. In this paper, we show that it is possible to achieve scalability for distributed transactions by using a lock broker architecture, and present the design and development of such a framework, called PANOPTICON. Panopticon achieves scalability by divorcing locks from the data items and striving to improve lock access locality. A lock can be hosted at the lock broker or at a different server than the server that hosts the corresponding data item. The lock broker mediates the access to data shared across servers by migrating the associated locks like tokens, and in the process gets to learn about the access patterns of transactions. We show that the broker can drastically improve the lock access locality and, hence, the performance of distributed transactions by employing simple rules. Our experiments show that Panopticon performed significantly better than distributed transactions as the the number of data items and number of servers involved in transactions increase. Moreover, as the history locality (the probability of using the same objects in consecutive transactions) increase, Panopticon’s lock migration strategies improved lock-access locality and resulted in significantly better performance.
Understanding E-Government: Information Systems in Public Administration

1. Introduction 2. On Technology, Intelligence and the Social 3. The Information Society 4. Bureaucracy and Virtual Organizations 5. The Political Economy of Information Networks 6. E-Government: A Wired Government Takes Shape 7. ICT Evaluation 8. Conclusions and Reflection
Forms in odd degree extensions and self-dual normal bases

Introduction. Let K be a field. Springer has proved that an anisotropic quadratic form over K is also anisotropic over any odd degree extension of K (see [31], [14]). If the characteristic of K is not 2, this implies that two nonsingular quadratic forms that become isomorphic over an extension of odd degree of K are already isomorphic over K (see [31]). In [27], Serre reformulated the latter Statement äs follows: if O is an orthogonal group over K, then the canonical map of Galois cohomology sets
Inference of the molecular mechanism of action from genetic interaction and gene expression data.

Inference of new and useful hypotheses from heterogeneous sources of genome-scale experimental data requires new computational methods that can integrate different types of data. Gene expression and genetic interaction data are two most informative data types, each allowing the identification of genes at different levels of cellular regulatory network hierarchy. We present an integrative data analysis approach, which, rather than correlating the findings from the two data sets, uses each type of data independently to identify the components of molecular pathways and combines them into a single directed network. Our computational genomics approach is based on a set of inference rules traditionally used for reasoning on genetic experiments, which we have formalized and implemented in a software tool. The approach uses chemogenetic interaction and expression data to infer the type of relation between the chemical substance (perturber) and a transcription factor by using previous knowledge on the set of genes whose expression the transcription factor in question regulates. We have used the proposed approach to successfully infer the models for the action of the drug rapamycin and of a DNA damaging agent on their molecular targets and pathways in yeast cells. The developed method is available as a web-based tool at http://www.ailab. si/perturbagen.
Application of Association Rule Mining: A case study on team India

This paper applies Association Rule Mining algorithm to sports management, especially mining relationship from data on performance of Indian cricket team in one day international (ODI) matches. This analysis will help in determining factors associated with the match outcome so as to enable the team to formulate match winning strategies. Data has been obtained from secondary sources to obtain deeper insights on playing conditions and the match outcome. The association among factors such as outcome of toss, playing in a home ground or playing abroad, batting first or batting second, and the match outcome, i.e., win or loss is examined. The outcome of the analysis reveals that Team India has performed well in the last ten years (since 2001 to 2010) as compared to entire period since the team started playing its first match (since 1974 to 2010).
Pre-Coding Aided Differential Spatial Modulation

In this paper, we propose a novel Multiple Input Multiple Output (MIMO) scheme based on Spatial Modulation (SM) and termed as Pre-coding aided Differential Spatial Modulation (PDSM). By mapping information to the receiver side space-time transmit blocks and conducting differential modulation, the PDSM scheme achieves further reduced receiver side complexity in comparison with Pre-coding aided Spatial Modulation (PSM). A general upper bound on the Average Bit Error Probability (ABEP) achieved by the PDSM architecture with an arbitrary number of transmit antennas and two receive antennas is derived, and an exact closed-form ABEP expression is provided for BPSK signaling in Rayleigh fading environment. The PSM architecture with the same system configuration is chosen as a benchmark for performance comparisons. Simulation results validate the analysis and reveal a less-than-3dB Signalto- Noise power Ratio (SNR) penalty of the considered system in comparison with the benchmark.