Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Hall effect in single wide quantum wells

We study the quantum Hall states in the lowest Landau level for a single wide quantum well. Due to a separation of charges to opposite sides of the well, a single wide well can be modelled as an effective two level system. We provide numerical evidence of the existence of a phase transition from an incompressible to a compressible state as the electron density is increased for specific well width. Our numerical results show a critical electron density which depends on well width, beyond which a transition incompressible double layer quantum Hall state to a mono-layer compressible state occurs. We also calculate the related phase boundary corresponding to destruction of the collective mode energy gap. We show that the effective tunneling term and the interlayer separation are both renormalised by the strong magnetic field. We also exploite the local density functional techniques in the presence of strong magnetic field
Changing look: from Compton‐thick to Compton‐thin, or the rebirth of fossil active galactic nuclei

We discuss the properties of a small sample of type 2 Seyfert galaxies whose X-ray spectra changed appearance on time-scales of years, becoming reflection-dominated from Compton-thin, or vice versa. A reflection-dominated spectrum is usually taken as evidence of Compton-thick absorption, but we instead argue that such a spectrum is due to a temporary switching-off of the nuclear radiation. The observations discussed here may help to explain mismatches between optical and X-ray classifications, and provide new strong and direct evidence for the presence of more than one cold circumnuclear region in type 2 Seyfert galaxies.
Accurate Prediction of the Dynamical Changes within the Second PDZ Domain of PTP1e

Experimental NMR relaxation studies have shown that peptide binding induces dynamical changes at the side-chain level throughout the second PDZ domain of PTP1e, identifying as such the collection of residues involved in long-range communication. Even though different computational approaches have identified subsets of residues that were qualitatively comparable, no quantitative analysis of the accuracy of these predictions was thus far determined. Here, we show that our information theoretical method produces quantitatively better results with respect to the experimental data than some of these earlier methods. Moreover, it provides a global network perspective on the effect experienced by the different residues involved in the process. We also show that these predictions are consistent within both the human and mouse variants of this domain. Together, these results improve the understanding of intra-protein communication and allostery in PDZ domains, underlining at the same time the necessity of producing similar data sets for further validation of thses kinds of methods.
An adaptive timing-driven layout for high speed VLSI

An adaptive timing-driven VLSI layout system, called JUNE, has been developed. The constructive algorithm, which combines placement with the global routing, constructs a placement satisfying timing and routability constraints. The placement problem for each macro is solved hierarchically as a sequence of two optimization problems followed by an adaptive correction procedure. Experimental results for industrial sea-of-gates chips confirmed the effectiveness of this approach. <<ETX>>
Energy saving dynamic source routing for ad hoc wireless networks

In this paper, energy saving dynamic source routing (ESDSR) protocol is introduced to maximize the life-span of a mobile ad hoc network (MANET). Many theoretical studies show that energy consumption in MANET can be significantly reduced using energy-aware routing protocols compared to fixed-power minimum-hop routing protocols. Two approaches are broadly suggested for energy-aware routing protocols - transmission power control approach and load sharing approach. ESDSR integrates the advantages of those two approaches. In ESDSR, the routing decision is based on a load balancing approach. Once a routing decision is made, link by link transmit power adjustment per packet is done based on a transmit power control approach. We modified dynamic source routing (DSR) protocol to make it energy aware by a network simulator (network simulator-2 of University of California). The simulation results show that the proposed ESDSR can save energy up to 40% per packet and it can send 20 % more packets to destinations by spending the same battery power in compare to DSR.
Concatenated Decoding with a Reduced-Search BCJR Algorithm

We apply two reduced-computation variants of the BCJR algorithm to the decoding of serial and parallel concatenated convolutional codes. One computes its recursions at only M states per trellis stage; one computes only at states with values above a threshold. The threshold scheme is much more efficient, and it greatly reduces the computation of the BCJR algorithm. By computing only when the channel demands it, the threshold scheme reduces the turbo decoder computation to one-four nodes per trellis stage after the second iteration.
The context-tree weighting method: basic properties

Describes a sequential universal data compression procedure for binary tree sources that performs the "double mixture. " Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desirable coding distribution for tree sources with an unknown model and unknown parameters. Computational and storage complexity of the proposed procedure are both linear in the source sequence length. The authors derive a natural upper bound on the cumulative redundancy of the method for individual sequences. The three terms in this bound can be identified as coding, parameter, and model redundancy, The bound holds for all source sequence lengths, not only for asymptotically large lengths. The analysis that leads to this bound is based on standard techniques and turns out to be extremely simple. The upper bound on the redundancy shows that the proposed context-tree weighting procedure is optimal in the sense that it achieves the Rissanen (1984) lower bound. >
Model Selection: Beyond the Bayesian/Frequentist Divide

The principle of parsimony also known as "Ockham's razor" has inspired many theories of model selection. Yet such theories, all making arguments in favor of parsimony, are based on very different premises and have developed distinct methodologies to derive algorithms. We have organized challenges and edited a special issue of JMLR and several conference proceedings around the theme of model selection. In this editorial, we revisit the problem of avoiding overfitting in light of the latest results. We note the remarkable convergence of theories as different as Bayesian theory, Minimum Description Length, bias/variance tradeoff, Structural Risk Minimization, and regularization, in some approaches. We also present new and interesting examples of the complementarity of theories leading to hybrid algorithms, neither frequentist, nor Bayesian, or perhaps both frequentist and Bayesian!
Principal response curves: Analysis of time‐dependent multivariate responses of biological community to stress

In this paper a novel multivariate method is proposed for the analysis of community response data from designed experiments repeatedly sampled in time. The long‐term effects of the insecticide chlorpyrifos on the invertebrate community and the dissolved oxygen (DO)–pH–alkalinity–conductivity syndrome, in outdoor experimental ditches, are used as example data. The new method, which we have named the principal response curve method (PRC), is based on redundancy analysis (RDA), adjusted for overall changes in community response over time, as observed in control test systems. This allows the method to focus on the time‐dependent treatment effects. The principal component is plotted against time, yielding a principal response curve of the community for each treatment. The PRC method distills the complexity of time‐dependent, community‐level effects of pollutants into a graphic form that can be appreciated more readily than the results of other currently available multivariate techniques. The PRC method also enables a quantitative interpretation of effects towards the species level.
Effects of classwide peer tutoring (Cwpt) on social interactions of children with and without English proficiency

Effects of Classwide Peer Tutoring (CWPT) on Social Interactions of Children with and without English Proficiency by Yaoying Xu Dr. Jeffrey Gelfer, Examination Committee Chair Professor of Special Education University of Nevada, Las Vegas Learning and Education occur in social contexts. The quality of children’s peer relationships is an important indication of children’s current and later social adjustment. Children with peer problems tend to experience higher levels of loneliness and other undesirable affective consequences, including social dissatisfaction and worrying about peer relations. Because of the limited language proficiency and cultural and ethnic differences among children with LEP, some of their social behaviors are considered inappropriate by their non-LEP teachers and peers. On the other hand, children with LEP may feel that they do not fit in a group or class activity. As a result, these children tend to have less social interaction with their peers than non-LEP children. The purpose of this dissertation study was to evaluate the effects of Classwide Peer Tutoring (CWPT) on social interaction behaviors of children with Limited English Proficiency (LEP) and children who are native English speakers (non-LEP). Two secondgrade classrooms from an elementary school were selected as the research setting for this
Prescription Patterns of General Practitioners in Peshawar, Pakistan

Objectives: To find out prescription patterns of general practitioners in Peshawar. Methods: Cross-sectional survey of drug prescriptions was done at six major hospitals and pharmacies of Peshawar between April and May 2011. A total of 1097 prescriptions that included 3640 drugs, were analyzed to assess completeness, average number of drugs, prescription frequency of various drug classes, and number of brands prescribed. Results: No prescription contained all essential components of a prescription. Legibility was poor in 58.5% prescriptions. Physician’s name and registration number were not mentioned in 89% and 98.2% prescriptions respectively. Over 78% prescriptions did not have diagnosis or indication mentioned. Dosage, duration of use, signature of physician and directions for taking drugs were not written in 63.8%, 55.4%, 18.5% and 10.9% of prescriptions respectively. On average each prescription included 3.32 drugs. Most frequently prescribed drug classes included analgesics (61.7%), anti-infective agents (57.2%), multi-vitamins (37.8%) and gastrointestinal drugs (34.4%). We found 206, 130, 105 and 101 different brands of anti-infective agents, gastrointestinal drugs, analgesics and multivitamins being prescribed. Conclusion: We observed a high number of average drugs per prescription mostly using brand names, and over-prescription of analgesics, antimicrobials, multivitamins and anti-ulcer drugs. Quality of written prescriptions was poor in terms of completeness.
Syllable Structure Universals and Native Language Interference in Second Language Perception and Production: Positional Asymmetry and Perceptual Links to Accentedness

The present study investigated how syllable structure differences between the first Language (L1) and the second language (L2) affect L2 consonant perception and production at syllable-initial and syllable-final positions. The participants were Mandarin-speaking college students who studied English as a second language. Monosyllabic English words were used in the perception test. Production was recorded from each Chinese subject and rated for accentedness by two native speakers of English. Consistent with previous studies, significant positional asymmetry effects were found across speech sound categories in terms of voicing, place of articulation, and manner of articulation. Furthermore, significant correlations between perception and accentedness ratings were found at the syllable onset position but not for the coda. Many exceptions were also found, which could not be solely accounted for by differences in L1–L2 syllabic structures. The results show a strong effect of language experience at the syllable level, which joins force with acoustic, phonetic, and phonemic properties of individual consonants in influencing positional asymmetry in both domains of L2 segmental perception and production. The complexities and exceptions call for further systematic studies on the interactions between syllable structure universals and native language interference with refined theoretical models to specify the links between perception and production in second language acquisition.
Text Detection in Natural Scenes with Multilingual Text

Detecting text in natural scenes is an important prerequisite for further text recognition and other image analysis tasks. Most of text detection methods for scene images usually use a priori knowledge of language to detect text. As a rule such algorithms are evaluated on datasets which contain scenes only with text in English. This paper discusses known text detection algorithms and investigates them for invariance to the language.
Computational models of perirhinal cortex function

I review seven models of the contribution of perirhinal cortex (PRC) or neighboring neocortical regions to cognition. Five of the models address recognition memory function (Sohal and Hasselmo ( 2000 ) Network 11:169‐190; Bogacz et al. ( 2001 ) J Comput Neurosci 10:5‐23; Bogacz and Brown ( 2003a ) Neurocomputing 52:1‐6; Norman and O'Reilly ( 2003 ) Psychol Rev 110:611‐646; Cowell et al. ( 2006 ) J Neurosci 26:12186‐12197) and two account for the role of PRC in visual discrimination learning (Bussey and Saksida ( 2002 ) Eur J Neurosci 15:355‐364; Cowell et al. ( 2010b ) J Cogn Neurosci 22:2460‐2479). The models span a range of biological scales and target a variety of datasets, such that like for like comparison between them is not always possible. I lay out a novel framework for facilitating comparison by defining some general abstract principles concerning the organization of cognition in the brain about which all of the models make a statement. The controversies that are revealed by scrutinizing the models within this framework highlight the fundamental questions that remain to be answered by future research. Ultimately, it is by combining these disparate accounts to build a unified model that bridges several levels of biological scale and accounts for multiple psychological phenomena that a full account of PRC function will be achieved. © 2012 Wiley Periodicals, Inc.
Climate Change Risk Perception and Perceptions of Adaptation Measures in Egypt: A Mixed Methods Study of Predictors and Implications

Egypt is among the most susceptible countries in the world to the potential impacts of climate change. The Intergovernmental Panel on Climate Change (IPCC) identified the Nile Delta as one of the most exposed deltas to sea level rise. Despite these alarming predictions, there is a lack of in-depth studies on public risk perceptions of climate change in Egypt. Understanding the public’s risk perception of climate change is vital in informing policy and developing effective risk communication strategies that improve public engagement with climate change and, in turn, encourage actions to address its potentially harmful impacts. This thesis provides a novel contribution to the literature through a mixed methods approach, using an online survey and semi-structured interviews. The research investigates three main topics: (1) Public perceptions of climate change; (2) Predictors of climate change risk perception; and (3) Perceptions of climate change adaptation. Results of the survey and the interviews showed that while participants were concerned about climate change and believed in the human causation of it, they had limited understanding and misconceptions about its causes (for example, erroneously linking climate change to the ozone layer). The Climate Change Risk Perception Model (CCRPM) adopted in this study explained 19.2% of the variance in risk perception. In addition, it revealed that experiential factors (affect and personal experience) were the strongest predictors of climate change risk perception in Egypt, while socio-cultural factors (value orientations) were the weakest predictors. Interviews with participants also highlighted that negative feelings featured prominently when speaking about personal experiences with the impacts of climate change. Moreover, regression analysis showed that personal and societal climate change risk perception had different predictors. These results offer important recommendations for policy, relating to matters such as targeted ways of communicating the science, impacts and risks associated with climate change.
Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications

In this paper we extend our previous work on the stochastic block model, a commonly used generative model for social and biological networks, and the problem of inferring functional groups or communities from the topology of the network. We use the cavity method of statistical physics to obtain an asymptotically exact analysis of the phase diagram. We describe in detail properties of the detectability-undetectability phase transition and the easy-hard phase transition for the community detection problem. Our analysis translates naturally into a belief propagation algorithm for inferring the group memberships of the nodes in an optimal way, i.e., that maximizes the overlap with the underlying group memberships, and learning the underlying parameters of the block model. Finally, we apply the algorithm to two examples of real-world networks and discuss its performance.
Aspect angle for interstellar magnetic field in SN 1006

A number of important processes taking place around strong shocks in supernova remnants (SNRs) depend on the shock obliquity. The measured synchrotron flux is a function of the aspect angle between interstellar magnetic field (ISMF) and the line of sight. Thus, a model of non-thermal emission from SNRs should account for the orientation of the ambient magnetic field. We develop a new method for the estimation of the aspect angle, based on the comparison between observed and synthesized radio maps of SNRs, making different assumptions about the dependence of electron injection efficiency on the shock obliquity. The method uses the azimuthal profile of radio surface brightness as a probe for orientation of ambient magnetic field because it is almost insensitive to the downstream distribution of magnetic field and emitting electrons. We apply our method to a new radio image of SN 1006 produced on the basis of archival Very Large Array and Parkes data. The image recovers emission from all spatial structures with angular scales from a few arcsec to 15 arcmin. We explore different models of injection efficiency and find the following best-fitting values for the aspect angle of SN 1006: Φ o = 70° ± 4.2° if the injection is isotropic, Φ o = 64° ± 2.8° for quasi-perpendicular injection (SNR has an equatorial belt in both cases) and Φ o = 11° ± 0.8° for quasi-parallel injection (polar-cap model of SNR). In the last case, SN 1006 is expected to have a centrally peaked morphology contrary to what is observed. Therefore, our analysis provides some indication against the quasi-parallel injection model.
Steganalysis Based on Bayesion Network and Genetic Algorithm

In this paper, we propose a feature selection and transformation approach for universal steganalysis based on Genetic Algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients of the Bayesion Network Model for blind image steganalysis. The Bayesion Network Model is then used as the classifier. Experimental results show that the GA based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-algorithm. ∗
Prescription Stimulants' Effects on Healthy Inhibitory Control, Working Memory, and Episodic Memory: A Meta-analysis

The use of prescription stimulants to enhance healthy cognition has significant social, ethical, and public health implications. The large number of enhancement users across various ages and occupations emphasizes the importance of examining these drugs' efficacy in a nonclinical sample. The present meta-analysis was conducted to estimate the magnitude of the effects of methylphenidate and amphetamine on cognitive functions central to academic and occupational functioning, including inhibitory control, working memory, short-term episodic memory, and delayed episodic memory. In addition, we examined the evidence for publication bias. Forty-eight studies (total of 1,409 participants) were included in the analyses. We found evidence for small but significant stimulant enhancement effects on inhibitory control and short-term episodic memory. Small effects on working memory reached significance, based on one of our two analytical approaches. Effects on delayed episodic memory were medium in size. However, because the effects on long-term and working memory were qualified by evidence for publication bias, we conclude that the effect of amphetamine and methylphenidate on the examined facets of healthy cognition is probably modest overall. In some situations, a small advantage may be valuable, although it is also possible that healthy users resort to stimulants to enhance their energy and motivation more than their cognition.
Classification of Whereabouts Patterns From Large-scale Mobility Data

Classification of users’ whereabouts patterns is important for many emerging ubiquitous computing applications. Latent Dirichlet Allocation (LDA) is a powerful mechanism to extract recurrent behaviors and high-level patterns (called topics) from mobility data in an unsupervised manner. One drawback of LDA is that it is difficult to give meaningful and usable labels to the extracted topics. We present a methodology to automatically classify the topics with meaningful labels so as to support their use in applications. This mechanism is tested and evaluated using the Reality Mining dataset consisting of about 350000 hours of continuous data on human behavior.