Miracle-dz/UniFAR-FTUs huggingface dataset:
Binary Quasars in the Sloan Digital Sky Survey: Evidence for Excess Clustering on Small Scales

We present a sample of 221 new quasar pairs with proper transverse separations Rprop < 1 h-1 Mpc over the redshift range 0.5 < z < 3.0, discovered from an extensive follow-up campaign to find companions around the Sloan Digital Sky Survey and 2dF QSO Redshift Survey quasars. This sample includes 26 new binary quasars with separations Rprop < 50 h-1 kpc (θ < 10″), more than doubling the number of such systems known. We define a statistical sample of binaries selected with homogeneous criteria and compute its selection function, taking into account sources of incompleteness. The first measurement of the quasar correlation function on scales 10 h-1 kpc < Rprop < 400 h-1 kpc is presented. For Rprop ≲ 40 h-1 kpc, we detect an order of magnitude excess clustering over the expectation from the large-scale (Rprop ≳ 3 h-1 Mpc) quasar correlation function, extrapolated down as a power law (γ = 1.53) to the separations probed by our binaries. The excess grows to ∼30 at Rprop ∼ 10 h-1 kpc and provides compelling evidence that the quasar autocorrelation function gets progressively steeper on submegaparsec scales. This small-scale excess can likely be attributed to dissipative interaction events that trigger quasar activity in rich environments. Recent small-scale measurements of galaxy clustering and quasar-galaxy clustering are reviewed and discussed in relation to our measurement of small-scale quasar clustering.
The Costs of Bankruptcy: Chapter 7 Liquidation versus Chapter 11 Reorganization

Our paper explores a comprehensive sample of small and large corporate bankruptcies in Arizona and New York from 1995 to 2001. Bankruptcy costs are very heterogeneous and sensitive to the measurement method used. We find that Chapter 7 liquidations appear to be no faster or cheaper (in terms of direct expense) than Chapter 11 reorganizations. However, Chapter 11 seems to preserve assets better, thereby allowing creditors to recover relatively more. Our paper also provides a large number of further empirical regularities. OUR PAPER ANALYZES THE LARGEST SAMPLE OF CORPORATE bankruptcies to date. We research approximately 300 cases from the Arizona and New York federal bankruptcy courts from 1995 to 2001. These cases consist of (1) both publicly traded and privately held corporations, and (2) both Chapter 7 liquidations and Chapter 11 reorganizations. This sample is practically the entire population of unique corporate bankruptcies in these courts. Our paper’s primary objective is to measure how the two available bankruptcy procedures differ, especially but not only in terms of cost. We explore four variables, namely: the change in the estate’s value during bankruptcy (a measure of indirect costs), the time spent in bankruptcy (another and more common measure of indirect costs), the expenses submitted to and approved by the bankruptcy court (a measure of direct costs), and the recovery rates for creditors and APR violations. Our most important findings are that Chapter 7 liquidations are not cheaper than Chapter 11 reorganizations, particularly after we control for endogenous self-selection of firms into bankruptcy procedure. Bankruptcy professionals (attorneys, accountants, trustees) regularly end up with most of the post-bankruptcy firm’s value in Chapter 7. ∗ Arturo Bris is at IMD Yale School of Management, and ECGI. Ivo Welch is at Brown Uni
The Effect of Religiosity on Adolescent Risky Behaviors

We investigate the relationship between religiosity and risky behaviors in adolescence using data from a large and detailed cohort study of 14 year olds who have been followed for seven years. We focus on the effect of the self-reported importance of religion and on the risk of youths having early sexual intercourse, drinking underage, trying cigarettes, trying cannabis, and being involved in fighting at ages 14–17. We use school and individual fixed effects, and we control for a rich set of adolescent, school, and family characteristics, including achievements in standardized test scores at age 11, parental employment, and marital status. We also control for information on personality traits, such as work ethic, self-esteem, and external locus of control. Our results show that individuals with low religiosity are more likely to engage in risky health behaviors, whatever their combination of personality traits. These effects are robust to separate estimations for boys and girls and to the control variables used. Moreover, the results are essentially unchanged when we use Inverse Probability Weighted Regression Adjustment estimation methods – which provide causal estimates conditional on selection on observables only.
Surprise! An Unexpected Color Singleton Does Not Capture Attention in Visual Search

The existence of a pure form of stimulus-driven attentional control has been aligned exclusively with the existence of attentional capture. Unfortunately, there has been little evidence provided in support of attentional capture. The present study investigated whether the repeated failure to observe attentional capture might be due to the way in which attentional capture has been measured. A new visual search procedure was used to investigate whether attention would be captured by an initial and unexpected encounter with a color singleton. Despite this important change in procedure, the color singleton still did not capture attention. Further evidence showed that visual search for the color singleton could be highly efficient, but only after its relevance became established, and that the failure to observe capture in the present experiment was not due to other potentially detrimental effects of surprise. The present results suggest that new conceptions of stimulus-driven attentional control are required.
Expatriates adjustment through transformation of social identity of Chinese expatriates working in Pakistan

Purpose The purpose of this paper is to investigate the process of Chinese expatriates’ adjustment in Pakistan through integrating the U-curve model and social identity theory. It has also highlighted the importance of engaging in strong social ties and their contribution in expatriates’ adjustment. Design/methodology/approach Data were collected through semi-structured interviews from 30 Chinese expatriates working as directors, deputy directors, senior managers, managers and assistant managers in Chinese organizations in Pakistan. After verbatim transcription of all the interviews, data were analyzed using the NVivo software. Findings The findings of this paper indicate that expatriates’ identities somewhat changed in terms of their behaviors and work habits, while fewer changes were observed in their belief and value system. These changes were related to expatriates’ perceptions of host country groups, attraction to these groups and similarity in beliefs, which resulted in the development of interdependent beliefs and depersonalization. However, a few Chinese expatriates predominantly interacted among themselves at work, and their reduced socialization with Pakistanis lessened their chances of change. Research limitations/implications The results of the study suggest that expatriates’ adjustment is a multi-stage process and social identity change is one of the powerful mechanisms through which they adjust in the host country’s environment; hence host organizations should facilitate expatriates in strengthening their bond with local people. Originality/value This paper has proposed a model that explains the mechanism of Chinese expatriates’ adjustment in Pakistan through improved interactions between expatriates’ and host country nationals, resulting in minor social identity changes, leading to further positive relationship building, and then major social identity changes.
Criminality, thinking patterns and treatment effects – evaluation of the Swedish cognitive intervention programme ‘new challenges’ targeting adult men with a criminal lifestyle

ABSTRACT The cognitive intervention programme ‘New Challenges’ targeting adult men with a criminal lifestyle was evaluated in a pilot study. The participants were divided into a cognitive treatment group (n = 32) and a control group (n = 11). In the control group, six participants had no treatment and five participated in 12-step treatment. The participants were measured pre and post using the Psychological Inventory of Criminal Thinking Styles (PICTS), the abridged version of sense of coherence (SOC), Positive and Negative Affect Scale, and Bergström’s quality of programme delivery (QPD). The results of the treatment group showed that criminal thinking patterns dropped significantly from high values to close to normal level. SOC and positive affect increased significantly in the treatment group. Both SOC and positive affect showed positive correlation with QPD. Regarding the possible influence of the 12-step treatment, there was no difference in the control group between participants receiving 12-step treatment and those not receiving treatment. The main conclusion is that the cognitive treatment programme ‘New Challenges’ can contribute to reduced criminal thinking and increased SOC and positive affect, which may prove to be important precursors of reduced criminality.
Evolutionary Analysis by Whole-Genome Comparisons

ABSTRACT A total of 37 complete genome sequences of bacteria, archaea, and eukaryotes were compared. The percentage of orthologous genes of each species contained within any of the other 36 genomes was established. In addition, the mean identity of the orthologs was calculated. Several conclusions result: (i) a greater absolute number of orthologs of a given species is found in larger species than in smaller ones; (ii) a greater percentage of the orthologous genes of smaller genomes is contained in other species than is the case for larger genomes, which corresponds to a larger proportion of essential genes; (iii) before species can be specifically related to one another in terms of gene content, it is first necessary to correct for the size of the genome; (iv) eukaryotes have a significantly smaller percentage of bacterial orthologs after correction for genome size, which is consistent with their placement in a separate domain; (v) the archaebacteria are specifically related to one another but are not significantly different in gene content from the bacteria as a whole; (vi) determination of the mean identity of all orthologs (involving hundreds of gene comparisons per genome pair) reduces the impact of errors in misidentification of orthologs and to misalignments, and thus it is far more reliable than single gene comparisons; (vii) however, there is a maximum amount of change in protein sequences of 37% mean identity, which limits the use of percentage sequence identity to the lower taxa, a result which should also be true for single gene comparisons of both proteins and rRNA; (viii) most of the species that appear to be specifically related based upon gene content also appear to be specifically related based upon the mean identity of orthologs; (ix) the genes of a majority of species considered in this study have diverged too much to allow the construction of all-encompassing evolutionary trees. However, we have shown that eight species of gram-negative bacteria, six species of gram-positive bacteria, and eight species of archaebacteria are specifically related in terms of gene content, mean identity of orthologs, or both.
Perceptual beginnings to language acquisition

In this article, I present a selective review of research on speech perception development and its relation to reference, word learning, and other aspects of language acquisition, focusing on the empirical and theoretical contributions that have come from my laboratory over the years. Discussed are the biases infants have at birth for processing speech, the mechanisms by which universal speech perception becomes attuned to the properties of the native language, and the extent to which changing speech perception sensitivities contribute to language learning. These issues are reviewed from the perspective of both monolingual and bilingual learning infants. Two foci will distinguish this from my previous reviews: first and foremost is the extent to which contrastive meaning and referential intent are not just shaped by, but also shape, changing speech perception sensitivities, and second is the extent to which infant speech perception is multisensory and its implications for both theory and methodology.
Effects of the initial test interval and feedback timing on L2 vocabulary retention

ABSTRACT Testing-effect literature has shown the benefits of retrieval practice to enhance retention and attenuate forgetting. However, research in memory reconsolidation has demonstrated that a memory trace can be rendered labile by retrieval and requires restabilisation to persist. This study investigated the effects of the initial test interval and feedback timing on vocabulary retention while controlling the retention interval in the naturalistic setting. Participants were 60 undergraduate students from four intact sessions of a lower-level ESL course. The four groups completed three spaced, repeated tests on a vocabulary list taken from their textbook glosses. The initial test interval (short and long) and feedback timing (immediate and delayed) were manipulated. It was revealed that the optimal schedule to promote retention was a short-interval initial test followed by a delayed feedback. The results showed a U-shaped pattern of a downward fluctuation in the intermediate test in the presence of interference but an upward fluctuation in the final test across groups. Groups in the delayed feedback condition produced better final test performance than groups in the immediate feedback condition. In line with reconsolidation theory, the results suggested that memory is susceptible to decay and disruption after retrieval. Testing might not insulate memory strength and content from modification, but repeated retrievals might reduce the susceptibility to interference. The research findings have classroom implications for teachers in regard to scheduling tests and delivering feedback for durable learning.
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition

In computer vision pixelwise dense prediction is the task of predicting a label for each pixel in the image. Convolutional neural networks achieve good performance on this task, while being computationally efficient. In this paper we carry these ideas over to the problem of assigning a sequence of labels to a set of speech frames, a task commonly known as framewise classification. We show that dense prediction view of framewise classification offers several advantages and insights, including computational efficiency and the ability to apply batch normalization. When doing dense prediction we pay specific attention to strided pooling in time and introduce an asymmetric dilated convolution, called time-dilated convolution, that allows for efficient and elegant implementation of pooling in time. We show results using time-dilated convolutions in a very deep VGG-style CNN with batch normalization on the Hub5 Switchboard-2000 benchmark task. With a big n-gram language model, we achieve 7.7% WER which is the best single model single-pass performance reported so far.
Analysis of multilocus DNA reveals hybridization in a contact zone betweenEmpidonaxflycatchers

Contact zones between recently diverged taxa offer unique opportunities to test whether the forms are reproductively isolated and therefore distinct species. The Pacific-slope flycatcher Empidonax difficilis and Cordilleran flycatcher Empidonax occidentalis are closely related taxa that were officially separated into two species in 1989, a treatment that has been controversial due to reports of phenotypically intermediate birds across the southern interior of British Columbia and Alberta. We present the first analysis of molecular variation across this region, in order to determine whether there is genetic introgression between the taxa. Allopatric populations of Pacific-slope and Cordilleran flycatchers belong to distinct mitochondrial clades, and all of the individuals sampled in interior southwestern Canada have the Pacific-slope haplotype. In contrast, variation in nuclear DNA (AFLPs) indicates hybridization between Pacific-slope and Cordilleran flycatchers in this region. We suggest that the discordance between the mitochondrial and nuclear markers most likely results from stochastic loss of Cordilleran mitochondrial haplotype lineages facilitated by asymmetries in mating due to earlier arrival and greater abundance of Pacific-slope flycatchers in the contact zone. The discovery of hybridization between Pacific-slope and Cordilleran flycatchers in southwestern Canada may call into question the decision to split them into two species. On the other hand, allopatric populations are genetically distinct in both mitochondrial and nuclear DNA, and the hybridization might not affect populations outside of the contact zone. This study highlights the importance of employing multiple genetic markers in studies of contact zones between closely related species.
Joint Symbol Detection and Channel Estimation for MIMO-OFDM Systems via the Variational Bayesian EM Algorithm

In this paper, a new joint symbol detection and channel estimation algorithm is proposed for MIMO-OFDM systems over frequency-selective fading channels using the variational Bayesian expectation-maximization (VBEM) algorithm. Since the VBEM algorithm can provide distribution-estimates of the parameters, the statistical information about the channel uncertainty is exploited to improve the evaluation of the extrinsic information in the soft-input soft-output detector which identifies the significant symbol combinations via list sphere decoder. In addition, two channel estimators are derived based on the posterior distributions of the transmitted symbols which are obtained from the space-time detection. The VBEM iterations are embedded in the turbo-processing of the receiver. Simulation results demonstrate that the proposed VBEM algorithm has more robust performance over the conventional EM techniques.
Text-informed speech enhancement with deep neural networks

A speech signal captured by a distant microphone is generally contaminated by background noise, which severely degrades the audible quality and intelligibility of the observed signal. To resolve this issue, speech enhancement has been intensively studied. In this paper, we consider a text-informed speech enhancement, where the enhancement process is guided by the corresponding text information, i.e., a correct transcription of the target utterance. The proposed deep neural network (DNN)based framework is motivated by the recent success in the textto-speech (TTS) research in employing DNN as well as high audible-quality output signal of the corpus-based speech enhancement which borrows knowledge from the TTS research field. Taking advantage of the nature of DNN that allows us to utilize disparate features in an inference stage, the proposed method infers the clean speech features by jointly using the observed signal and widely-used TTS features derived from the corresponding text. In this paper, we first introduce the background and the details of the proposed method. Then, we show how the text information can be naturally integrated into speech enhancement by utilizing DNN and improve the enhancement performance. Index Terms: speech enhancement, text-to-speech, deep neural network
Wavelet‐transform based edge detection approach to derivation of snowmelt onset, end and duration from satellite passive microwave measurements

We developed a new method for deriving the onset date, end date, duration and spatial extent of snowmelt using satellite passive microwave measurements. Our method exploits the fact that apparent edges are present on the brightness temperature (Tb ) time series curve corresponding to sharp and abrupt melt‐induced transitions of brightness temperature. Through a wavelet transform of daily Tb observations, our method identifies and tracks significant upward and downward edges on the Tb curves. Through variance analysis and bi‐modal Gaussian curve fitting, an optimal edge strength threshold is statistically determined to differentiate real snowmelt edges from weak edges caused by noisy perturbations and other non‐melt processes. Based on the principle of spatial autocorrelation, a neighbourhood operator is designed to detect and correct possible errors in the melt computations that are purely based on temporal analysis of individual Tb curves. We have implemented the method using C++ programming language and successfully applied it to Special Sensor Microwave/Imager (SSM/I) data collected in 2001–2002 over the Antarctic ice sheet. The computation results were evaluated through visual interpretation of brightness temperature time series and examination of historical near‐surface air temperature records.
"Climate Change" Frames Detection and Categorization Based on Generalized Concepts

The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for long time in political science and communications research. Media framing offers "interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts. In this paper, we develop a new type of textual features that generalize (subject, verb, object) triplets extracted from text, by clustering them into high-level concepts. We utilize these concepts as features to detect frames in text. Our corpus comprises more than 45,000 climate change related sentences. Expert coders annotated those sentences as frame/non-frame and framed sentences were mapped into one of four general frame categories: solution, problem threat, cause, and motivation. Compared to unigram and bigram based models, classification using our generalized concepts yielded better discriminating features and a higher accuracy classifier with a 12% boost (i.e. from 74% to 83% in f-measure) for frame/no frame detection.