Miracle-dz/UniFAR-FTUs huggingface dataset:
The bilingual researcher’s dilemmas: Reflexive approaches to translation issues

Research conducted by English-speaking researchers about other language speaking subjects is essentially cross-cultural and often multilingual, particularly with qualitative research that involves participants communicating in languages other than English. In research using non-English data, scholars have noted that the translation process can be problematic, and is often glossed over. Thus, they call for greater transparency in accounting for translation decisions. This article is a reflexive account of the author’s doctoral research about international EAL (English as an Additional Language) students’ informal academic learning practices. Nine of the ten research participants were of Chinese ethnicity and, consequently, a substantial portion of the data collected was in Chinese. I am a Chinese Singaporean who is bilingual in English and Chinese. I discuss the dilemmas faced when interpreting and translating data from the Chinese participants, each of whom had his or her own particular social and cultural backgrounds. The dilemmas I faced were related to i) immediate understanding of interview responses, ii) interview transcription for member checking, and iii) translation of data. I argue that these dilemmas can be resolved by member checking during data collection and analysis, and the use of professional translators. However, I also caution against an idealised notion of objective meaning in translation. I conclude that cross-cultural research should take into account the researcher’s cultural and language identity in understanding how data from research participants is represented.
fines, leniency, and rewards in antitrust

This article reports results from an experiment studying how FINES, LENIENCY, and REWARDS for whistleblowers affect cartel formation and prices. Antitrust without LENIENCY reduces cartel formation but increases cartel prices: subjects use costly FINES as punishments. LENIENCY improves antitrust by strengthening deterrence but stabilizes surviving cartels: subjects appear to anticipate the lower postconviction prices after reports/LENIENCY. With REWARDS, prices fall at the competitive level. Overall, our results suggest a strong cartel deterrence potential for well-run LENIENCY and REWARD schemes. These findings may also be relevant for similar white-collar organized crimes, such as corruption and fraud.
Gender Role Stress Mediates Depression Among Veteran Men With Military Sexual Trauma

Compared with survivors of other types of trauma, survivors of sexual trauma, particularly military sexual trauma (MST), are at greater risk for posttraumatic distress. Although traditionally masculine aspects of gender identity may mitigate the deleterious effects of trauma (Valdez & Lilly, 2014), it may, in some cases, exacerbate negative outcomes. Sexual victimization may undermine traditional gender expectations of power among male survivors, potentially explaining the added risk associated with this type of trauma. These dynamics may be even more pronounced for veteran men, given their involvement in a military culture that highly values masculinity. Accordingly, we hypothesized that masculine gender role stress (i.e., stress from not upholding masculine gender norms; Eisler & Skidmore, 1987) would be greater among veteran men with MST than those with other interpersonal trauma and would mediate the relationship between MST and posttraumatic distress (i.e., posttraumatic stress disorder [PTSD] and depressive symptoms). We found that while veteran men with MST (n = 52) reported greater PTSD and depressive symptom severity compared to men without MST (n = 52), there was no significant difference in overall gender role stress. Ad hoc analyses exploring specific aspects of gender role stress found that men with MST reported greater gender role stress related to emotionality and intellectual inferiority. Additionally, gender role stress related to emotionality fully mediated the relationship between MST and depression but not PTSD. The vulnerability and intense emotions that accompany MST are discussed in the context of gender role expectations and in terms of clinical implications.
A Review of Three Techniques for Formally Representing Variable Binding

This paper compares three models for formal reasoning about programming languages with binding. Higher order abstract syntax (hoas) uses meta-level binding to represent object-level binding [PE88]. Nominal Logic couples a concrete representation of bound variables with a formal apparatus for safely manipulating bound variables [Pit03]. The locally named binding representation places bound and free variables in different syntactic sorts [MP99]. This paper surveys each binding model, and compares it to the others and to Gordon and Melham’s axiomatization of the untyped lambda calculus [GM97]. Comparisons are made based on expressive power, transparency to human readers, and suitability for mechanized reasoning of each binding model. Each system excels in one area; hoas is most expressive, Nominal Logic most transparent, and locally named most mechanizable.
Active sampling for constrained clustering

Constrained Clustering is a framework of improving clustering performance by using supervised information, which is generally a set of constraints about data pairs. Since performance of constrained clustering depends on a set of constraints to use, we need a method to select good constraints that are expected to promote clustering performance. In this paper, we propose such a method, which actively select data pairs to be constrained by using variance of clustering iteration. This method consists of a bagging based cluster ensemble algorithm that integrates a set of clusters produced by a constrained k-means with random ordered data assignment. Experimental results show that our method outperforms clustering with random sampling method.
Attacking and Defending Networked Embedded Devices

Currently, work on malware attack and defense focuses primarily on PCs. However, as lightweight computing devices with embedded operating systems become more ubiquitous, they present a new and very disturbing target for botnet developers; and as embedded devices become more integrated and networked with general-purpose computing, they can easily become the launching point for many attacks on the enterprise network in which the embedded devices are deployed. In this paper, we showcase a variety of practical attacks we were able to launch on an enterprise network using already widely deployed set-top-boxes. We identify challenges associated with securely deploying and managing these embedded multimedia boxes. We also present a solution to these attacks by hardening these embedded devices, using the Enforcer LSM and SELinux.
Generating targeted paraphrases for improved translation

Today's Statistical Machine Translation (SMT) systems require high-quality human translations for parameter tuning, in addition to large bitexts for learning the translation units. This parameter tuning usually involves generating translations at different points in the parameter space and obtaining feedback against human-authored reference translations as to how good the translations. This feedback then dictates what point in the parameter space should be explored next. To measure this feedback, it is generally considered wise to have multiple (usually 4) reference translations to avoid unfair penalization of translation hypotheses which could easily happen given the large number of ways in which a sentence can be translated from one language to another. However, this reliance on multiple reference translations creates a problem since they are labor intensive and expensive to obtain. Therefore, most current MT datasets only contain a single reference. This leads to the problem of reference sparsity. In our previously published research, we had proposed the first paraphrase-based solution to this problem and evaluated its effect on Chinese-English translation. In this article, we first present extended results for that solution on additional source languages. More importantly, we present a novel way to generate “targeted” paraphrases that yields substantially larger gains (up to 2.7 BLEU points) in translation quality when compared to our previous solution (up to 1.6 BLEU points). In addition, we further validate these improvements by supplementing with human preference judgments obtained via Amazon Mechanical Turk.
Better Together : Combining Language and Social Interactions in Predictive Models of Individual Behavior

Overview Low-level observational data in social networks often consists of transactions—actions and interactions observed over time, for individuals, pairs, and groups of users. While this observed data provides some information about people and their relationships over time, there is also an important unobserved aspect of the system that influences behavior (e.g., personal beliefs, relational influence, roles, communities). To improve understanding of this behavior, it is critical to use the observed data to make inferences about the unobserved components of the system. For example, to identify the formation of groups and predict their influence on personal beliefs/actions, we need to be able to reason about latent properties such as user roles, relationship types, community affiliations, etc. The technical challenge to developing data-driven methods to learn models of individual and collective behavior lies in the dimensionality of the data and the complexity of the modeling task(s). The data is relational, temporal, and often spatial, and can include a large amount of high-dimensional information due to text (e.g., messages), images (e.g., photos in posts), or actions (e.g., reacting to posts). The modeling task also involves more than just learning to make independent predictions from static views of the data. Modeling relational behavior requires joint representations for learning and inference, and the discovery of hidden temporal patterns requires more complex dynamic, latent variable models that can optimize intermittent signals (i.e., reward) over time in structured data.
Determinacy Under Inflation Targeting Interest Rate Policy in a Sticky Price Model with Investment (and Labor Bargaining)

In a sticky price model with investment spending, recent research shows that inflation-forecast targeting interest rate policy makes determinacy of equilibrium essentially impossible. We examine a necessary and sufficient condition for determinacy under interest rate policy that responds to a weighted average of an inflation forecast and current inflation. This condition demonstrates that the average-inflation targeting policy ensures determinacy as long as both the response to average inflation and the relative weight of current inflation are large enough. We also find that interest rate policy which responds solely to past inflation guarantees determinacy when its response satisfies the Taylor principle and is not large. These results still hold even when wages and hours worked are determined by Nash bargaining.
Synectin-dependent Gene Expression in Endothelial Cells Moreover, Synectin Plays a Major Role in Arterial Morphogenesis and in Growth Factor Signaling in Arterial Endo- Thelial Cells by Regulating Rac1 Activity (

dependent gene expression in endothelial cells. (GIPC1), a receptor scaffold protein, has been isolated by our laboratory as a syndecan-4 cytoplasmic domain binding partner that regulates important aspects of cell motility The present study was carried out to characterize changes in synectin-dependent gene expression induced by homozygous disruption of the gene in endothelial cells. Using a combination of suppression subtraction hybridization and high through-put microarray technology, we have identified aberrant biological processes of transcriptional regulation in synectin Ϫ/Ϫ primary endothelial cells including abnormal basal regulation of genes associated with development , cell organization and biogenesis, intracellular tracking, and cell adhesion. Analysis of gene expression following FGF2 treatment demonstrated significant abnormalities in transcription, cytoskeletal organization and biogenesis, and protein modification and transport in synectin Ϫ/Ϫ compared with synectin ϩ/ϩ endothelial cells. These results confirm synectin involvement in FGF2-dependent signal transduction and provide insights into synectin-dependent gene expression in the endothelium. angiogenesis; functional genomics FIBROBLAST GROWTH FACTORS have long been thought to stimulate intracellular signaling by binding to high affinity receptors (FGF receptors 1– 4) that belong to the classic transmembrane tyrosine kinase family of receptors (19). More recent studies showed that syndecan-4, a nontyrosine kinase transmembrane protein also functions as an FGF receptor (15, 25). While studies of the syndecan-4 signaling cascade focused on its activation of PKC␣, other possibilities are now emerging as well (10, 21). Particularly intriguing is the role played by synectin that has been isolated as a syndecan-4 binding partner (13). Synectin is a single PDZ domain containing protein that interacts with a wide variety of plasma membrane and cyto-plasmic molecules including, in addition to syndecan-4, G protein regulator of signaling interacting protein, cytoplasmic G␣ interacting protein (regulator of G protein signaling) (11), semaphorin 4c (27), Glut-1 glucose transporter (5), neuropi-lin-1 (6), ␣5 and ␣6 integrins (23) among others. We previously demonstrated a PDZ-dependent synectin-syndecan-4 interaction (13), suggesting that synectin may be involved in FGF2 signal transduction since FGF2 signaling in endothelial cells (ECs) requires an intact syndecan-4 PDZ binding domain (15). Moreover, we recently showed that disruption of synectin gene expression in mice results in significant abnormalities in arterial EC migration, proliferation , and in vivo arterial branching morphogenesis resulting from abnormal cellular activation of Rac1 as well as impaired response to FGF2 signaling (8). To further study the molecular role of synectin in FGF2 signaling, we used suppression subtraction hybridization (SSH) to analyze immediate early …
The History of the Psychopharmacology of Schizophrenia

Objective: To review the historical development of the psychopharmacological treatment of schizophrenia. Method: A chronological literature review of the clinical practices and theoretical models that have controlled drug treatment of schizophrenia at different times. Results: Effective treatment of schizophrenia was achieved only after the introduction of antipsychotic drugs, in the 1950s, and is still progressing. Conclusion: Close collaboration between basic neuroscience and careful and informed clinical practice are likely to lead to continued progress.
On the foundations of artificial workload design

The principles on which artificial workload model design is currently based are reviewed. Design methods are found wanting for three main reasons: their resource orientation, with the selection of resources often unrelated to the performance impact of resource demands; their avoiding to define an accuracy criterion for the resulting workload model; and their ignoring the dynamics of the workload to be modeled. An attempt at establishing conceptual foundations for the design of interactive artificial workloads is described. The problems found in current design methods are taken into account, and sufficient conditions for the applicability of these methods are determined. The study also provides guidance for some of the decisions to be made in workload model design using one of the current methods.
Measuring the sea breeze from QuikSCAT Scatterometry

Differences between morning and evening winds from QuikSCAT scatterometer measurements are analyzed to diagnose the diurnal variability of the wind over the ocean. A statistically significant signal, associated with the sea breeze, is present along most of the world's coastlines. Significant diurnal variability is also present mid‐ocean in the easterly trade wind belts.
Notes on practical reasoning

Formal logic, probability and decision theory are not theories of the reasoning process. Internal reasoning in thought must be distinguished from external reasoning with others. It is difficult to distinguish internal reasoning from certain other internal computational processes. Differences between theoretical and practical reasoning tell against trying to reduce one to the other. Internal reasoning tends to be unconscious, although its products may be conscious. There is evidence for a parallel constraint satisfaction model of reasoning toward a reflective equilibrium. Relevant principles in moral reasoning may not be the sort of familiar principles people appeal to in discussion. Emotion may play a significant role in reasoning.
Plain Ewald and PME

where ∑ ~n is the sum over all lattice vectors ~n = (Lxnx, Lyny, Lznz), nx,y,z ∈ N and Lx,y,z are the dimensions of the unit MD cell and ~xij = ~xj − ~xi. The “daggered” ( †) summation indicates the exclusion of all pairsi = j inside the original unit MD cell ( ~n = ~0). Most molecular force fields provide exclusion schemes to exclude additional pairs, e.g., the so-called 1-3 exclusionexcludes directly connected pairs and pairs with a direct common neighbor. Furthermore, some exclusion schemes may also modify the potential by a factor for certain pairs. If the potentialφ satisfies |φ(~x)| ≤ A|~x|−3− (2)
Partial functions in fitness-shared genetic programming

Investigates the use of partial functions and fitness sharing in genetic programming. Fitness sharing is applied to populations of either partial or total functions and the results are compared. Applications to two classes of problem are investigated: learning multiplexer definitions, and learning (recursive) list membership functions. In both cases, fitness sharing approaches outperform the use of raw fitness, by generating more accurate solutions with the same population parameters. On the list membership problem, variants using fitness sharing on populations of partial functions outperform variants using total functions, whereas populations of total functions give better performance on some variants of multiplexer problems.
TurboVG: A HW/SW co-designed multi-core OpenVG accelerator for vector graphics applications with embedded power profiler

TurboVG is a hardware accelerator for the OpenVG 1.1 library that operates sixteen times faster than an optimized software implementation. This improved efficiency stems from a well-designed hardware-software interaction capable of handling massive data transfers across hierarchical layers without performance loss. By combining multiple TurboVG cores, the library can support screen resolutions of up to Full-HD 1080p.
Fungi Identify the Geographic Origin of Dust Samples

There is a long history of archaeologists and forensic scientists using pollen found in a dust sample to identify its geographic origin or history. Such palynological approaches have important limitations as they require time-consuming identification of pollen grains, a priori knowledge of plant species distributions, and a sufficient diversity of pollen types to permit spatial or temporal identification. We demonstrate an alternative approach based on DNA sequencing analyses of the fungal diversity found in dust samples. Using nearly 1,000 dust samples collected from across the continental U.S., our analyses identify up to 40,000 fungal taxa from these samples, many of which exhibit a high degree of geographic endemism. We develop a statistical learning algorithm via discriminant analysis that exploits this geographic endemicity in the fungal diversity to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. In addition, our statistical approach provides a measure of certainty for each prediction, in contrast with current palynology methods that are almost always based on expert opinion and devoid of statistical inference. Fungal taxa found in dust samples can therefore be used to identify the origin of that dust and, more importantly, we can quantify our degree of certainty that a sample originated in a particular place. This work opens up a new approach to forensic biology that could be used by scientists to identify the origin of dust or soil samples found on objects, clothing, or archaeological artifacts.
Where to go goose hunting? Using pattern-oriented modeling to better understand human decision processes

ABSTRACT To predict hunting pressure at a regional level for the adaptive harvest management of a European goose population, we created a predictive model within an existing agent-based model framework. In this paper, we outline the inputs, outputs, and learning from developing this model, using pattern-oriented modeling (POM), to predict the regional distribution of goose hunting locations. Our results showed that social aspects (e.g., crowding, how far hunters are prepared to travel) may influence hunter decisions when choosing hunting locations. However, access to multiple hunting locations and knowledge of goose behavior and likely foraging areas were more important decision drivers. A crucial model outcome was the secondary prediction of the size of the potential pool of goose hunters. We believe that POM is a beneficial framework for those wishing to define, test, and ultimately develop better predictive models of human decision-making and subsequent behaviors and feedbacks.
Extremal optimization for the 3D protein folding based on the lattice model

Much of the previous work in the protein field focused on the 2D protein folding due to its simplicity but ignored the 3D protein folding problem. In this paper, based on the work of 2D protein folding problem by primary extremal optimization (EO) algorithm, we study the more complicated 3D protein folding problem with its variation (τ-EO) for the first time. The parameter τ is set as a random value near a constant in each iteration. For the protein with not too long sequence, the experimental results show that the proposed EO algorithm is very efficient.
Neuropsychological characteristics of mild cognitive impairment subgroups

Objective: To describe the neuropsychological characteristics of mild cognitive impairment (MCI) subgroups identified in the Cardiovascular Health Study (CHS) cognition study. Methods: MCI was classified as MCI-amnestic type (MCI-AT): patients with documented memory deficits but otherwise normal cognitive function; and MCI-multiple cognitive deficits type (MCI-MCDT): impairment of at least one cognitive domain (not including memory), or one abnormal test in at least two other domains, but who had not crossed the dementia threshold. The MCI subjects did not have systemic, neurological, or psychiatric disorders likely to affect cognition. Results: MCI-AT (n = 10) had worse verbal and non-verbal memory performance than MCI-MCDT (n = 28) or normal controls (n = 374). By contrast, MCI-MCDT had worse language, psychomotor speed, fine motor control, and visuoconstructional function than MCI-AT or normal controls. MCI-MCDT subjects had memory deficits, though they were less pronounced than in MCI-AT. Of the MCI-MCDT cases, 22 (78.5%) had memory deficits, and 6 (21.5%) did not. MCI-MCDT with memory disorders had more language deficits than MCI-MCDT without memory disorders. By contrast, MCI-MCDT without memory deficits had more fine motor control deficits than MCI-MCDT with memory deficits. Conclusions: The most frequent form of MCI was the MCI-MCDT with memory deficits. However, the identification of memory impaired MCI groups did not reflect the true prevalence of MCI in a population, as 16% of all MCI cases and 21.5% of the MCI-MCDT cases did not have memory impairment. Study of idiopathic amnestic and non-amnestic forms of MCI is essential for an understanding of the aetiology of MCI.