Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Chinese lexical inference using probabilistic soft logic

Lexical inference problem is a significant component of some recent core AI and NLP research problems like machine reading and textual entailment. In this paper, we propose method utilizing the Probabilistic Soft Logic (PSL) model for Chinese lexical inference. The proposed PSL model not only can integrate two complementary traditional methods, i.e., the lexical-knowledge-based method and the distributional probabilistic method, but also can optimize the lexical inference network in a global view by the transitivity property of entailment relations. We build a large domain specific verb inference corpus containing 18,029 verb pairs with gold inference labels from math world problems. A five-folded experiment is performed. Results show that the proposed PSL model greatly outperforms our baseline.
A Susceptibility Locus at Chromosome 3p21 Linked to Familial Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) poses one of the serious health problems in southern Chinese, with an incidence rate ranging from 15 to 50/100,000. Chromosome translocation t(1;3) and frequent loss of heterogeneity on short arms of chromosome 3 and 9 have been reported to be associated with NPC, and a genome-wide scan identified an NPC susceptibility locus on chromosome 4p15.1-q12 recently. In our study, we collected samples from 18 families at high risk of NPC from the Hunan province in southern China, genotyped with a panel of polymorphic markers on short arms of chromosomes 3, 9, and 4p15.1-q12. A locus on 3p21 was identified to link to NPC with a maximum logarithm of odds for linkage score of 4.18. Fine mapping located the locus to a 13.6-cM region on 3p21.31-21.2, where a tumor suppressor gene cluster resided. Our findings identified a novel locus for NPC and provided a map location for susceptibility genes candidates. In contrast to a recent study, no significant evidence for NPC linkage to chromosomes 4 and 9 was observed.
Nrac, a Novel Nutritionally-Regulated Adipose and Cardiac-Enriched Gene

Obesity increases the risk of multiple diseases, such as type 2 diabetes and coronary heart diseases, and therefore the current obesity epidemic poses a major public health issue. Therapeutic approaches are urgently needed to treat obesity as well as its complications. Plasma-membrane proteins with restricted tissue distributions are attractive drug targets, because of their accessibility to various drug delivery mechanisms and potentially alleviated side effects. To identify genes involved in metabolism, we performed RNA-Seq on fat in mice treated with a high-fat diet or fasting. Here we show that the gene A530016L24Rik (human ortholog C14orf180), named Nrac, is a novel nutritionally-regulated adipose and cardiac-enriched gene. Nrac is expressed specifically and abundantly in fat and the heart. Both fasting and obesity reduced Nrac expression in white adipose tissue, and fasting reduced its expression in brown fat. Nrac is localized to the plasma membrane, and highly induced during adipocyte differentiation. Nrac is therefore a novel adipocyte marker and has potential functions in metabolism.
Performance study of mapping irregular computations on GPUs

Recently, Graphical Processing Units (GPUs) have become increasingly more capable and well-suited to general purpose applications. As a result of the GPUs high degree of parallelism and computational power, there has been a great deal of interest directed toward the platform for parallel application development. Much of the focus, however, has been on very regular applications that exhibit a high degree of data parallelism, as these applications map well to the GPU. Irregular applications, such as the Breadth First Search discussed in this paper, have not been as extensively studied and are more difficult to implement in an efficient fashion on the GPU. We will present both an implementation of the Breadth First Search algorithm as well as that of a Matrix Parenthesization algorithm. These pair of algorithms showcase similar synchronization behavior when implemented on a GPU using CUDA, enabling a more direct comparison between them. The results obtained can be used to showcase some of the synchronization issues present with irregular algorithms on the GPU.
Evolutionary Parameter Setting of Multi-clustering

Multi-clustering is a technique for amalgamating the results of many runs of a standard clustering algorithm to obtain a clustering of data which avoid artifacts introduced by the underlying metric. Multi-clustering also yields an advisory, called a cut plot, as to the number of "natural" clusters present in the data. In order to perform multi-clustering a number of parameters must be chosen. This paper tests evolutionary algorithms that perform parameter setting for multi-clustering on synthetic data set with designed numbers of clusters. A evolutionary algorithm and an evolution strategy are compared. The superior algorithm, the ES, is then used to set parameters for four microarray-like data sets. Evolutionary parameter setting is found to more than double the range in which the cut plot detects the correct number of clusters when compared to hand-chosen parameters arrived at by serial parameter optimization. This paper also presents a new technique for accelerating multi-clustering, iteration limiting, and demonstrates that the technique may be implemented to speed up multi-clustering without impairing performance. The evolutionary results support the use of iteration limiting in multi-clustering
Operational semantics of multiagent interactions

The social stance advocated by institutional frameworks and most multiagent system methodologies has resulted in a wide spectrum of organizational and communicative abstractions which have found currency in several programming frameworks and software platforms. Still, these tools and frameworks are designed to support a limited range of interaction capabilities that constrain developers to a fixed set of particular, pre-defined abstractions. The main hypothesis motivating this paper is that the variety of multiagent interaction mechanisms -- both, organizational and communicative, share a common semantic core. In the realm of software architectures, the paper proposes a connector-based model of multiagent interactions which attempts to identify the essential structure underlying multiagent interactions. Furthermore, the paper also provides this model with a formal execution semantics which describes the dynamics of social interactions. The proposed model is intended as the abstract machine of an organizational programming language which allows programmers to accommodate an open set of interaction mechanisms.
Response Styles in Cross-cultural Surveys

Measurement equivalence or invariance is the prerequisite for cross‐cultural survey research. One of the most commonly studied measurement errors in cross‐cultural survey research is response style. This chapter focuses on two types of response styles, namely, acquiescent response style (ARS) and extreme response style (ERS), and demonstrates several empirical models for measuring the impact of these two response styles in particular. It presents and compares four empirical methods for measuring and adjusting response styles: OLS regression, confirmatory factor analysis (CFA), latent class analysis (LCA), and multidimensional unfolding models (MUM). In addition to the key predictor, the chapter explains several variables that have been correlated with response style, including gender, age, education, household income, and data collection mode. It reviews analytical models that are convenient tools for measuring the impact of response styles, when researchers attempt to make comparison of the substantive latent constructs.
Secondary genomic alterations in non-Hodgkin’s lymphomas: tumor-specific profiles with impact on clinical behavior

Lymphoid neoplasms are associated with primary genetic alterations – mainly non-random reciprocal chromosomal translocations – and secondary alterations. In this perspective article, Drs. Bea and Campo discuss the importance of secondary chromosomal alterations in non-Hodgkin’s lymphomas. See related articles on pages 670, 680, and 688.
JPMT: A Java Performance Monitoring Tool

This paper describes our Java Performance Monitoring Toolkit (JPMT), which is developed for detailed analysis of the behavior and performance of Java applications. JPMT represents internal execution behavior of Java applications by event traces, where each event represents the occurrence of some activity, such as thread creation, method invocation, and locking contention. JPMT supports event filtering during and after application execution. Each event is annotated by high-resolution performance attributes, e.g., duration of locking contention and CPU time usage by method invocations. JPMT is an open toolkit, its event trace API can be used to develop custom performance analysis applications. JPMT comes with an event trace visualizer and a command-line event trace query tool for scripting purposes. The instrumentation required for monitoring the application is added transparently to the user during run-time. Overhead is minimized by only instrumenting for events the user is interested in and by careful implementation of the instrumentation itself.
Learning Clinical Integration: A Case Study Approach

The present article identifies three waves in the integration of psychology and theology literature including apologetic, model building, and empirical validation, bringing the discussion to a fourth wave, clinical integration. Rather than a model or destination, clinical integration is an emergent phenomenon of the dynamic complexity of the particular therapeutic configuration and therefore is best learned through supervisory experience, which includes observing, hearing, and reading cases. Subsequently, the articles that make up this special volume are case studies. In addition, the article notes the lack of cultural and theological diversity in the integration literature so invited authors were selected not only for their scholarship, but also for being newer voices in the field and for their personal diversity. The rewards, challenges, and ethics of writing about clinical cases are also explored.
Structuring Racist Ideologies in Stephen Crane’s “A Dark Brown Dog”: A Critical Discourse Analysis

This paper deals with the study of how racist ideologies are constructed in Crane’s “A Dark Brown Dog” using the CDA framework. Benefitting from the approaching between literature and linguistics, it focuses on the linguistic examination of the (re)construction of whiteness and blackness based on the assumption that racism is: a social, a discursive, and an ideological construct. This tri-dimensional construct is treated within a theoretical triangulation ranging from cognition, society, and discourse. Based on the view of discourse as a process of coding and decoding of meaning, the text of this story is explored at various levels of language use: a) contextual analysis focuses on the study of how the depiction of the setting and the characters serves to draw the scene of racial inequality; b) textual analysis deals with the way narration contributes to the (re)production of racism; and c) identity analysis is devoted to the study of how racial identity evolves across generations. The results showed that: first, the representation of the characters highlights the binary structure of the southern American society marked by the inferiority of the blacks and the superiority of the whites. Second, the way actions and events are narrated reflects the unequal distribution of power between blacks and whites. The existing asymmetry of power is traced in the duality of punishment and obedience. Third, the racist ideologies and race relations evolve across generations depending on: the amalgamations happening at the family level, the evolution of society, and the evolution of its legislations.
Within-Subject Comparison of Changes in a Pretest-Posttest Design

The authors propose a method to compare the influence of a treatment on different properties within subjects. The properties are measured by several Likert-type—scaled items. The results show that many existing approaches, such as repeated measurement analysis of variance on sum and mean scores, a linear partial credit model, and a graded response model, conceptualize a comparison of changes in a way that depends on the distribution of the pretest values, but in the present article, change is measured in terms of the conditional distributions of posttest values, given the pretest values. A multivariate regression and analysis of covariance approach is unbiased but shows power deficiencies in a simulation study. The authors suggest a new approach based on poststratification (i.e., aggregating change information conditional on each pretest value), which is unbiased and has a superior power. The approach is applied in a study that compares the influence of a certain piece of music on five different basic emotions.
Optimal Multiple Description Image Coding Transform in the Wavelet Domain

Multiple description coding (MDC) addresses the problem of encoding a source into two (or more) bitstreams such that a high quality reconstruction is decodable from the two bitstreams together, while a lower, but still acceptable, quality reconstructions is decodable if either of the two bitstreams is lost. MDC is considered as a joint-source channel coding scheme which has proven to provide effective error resilience. In this paper we propose a multiple description image coding scheme to generate two compressed and balanced rates descriptions in the wavelet domain (Daubechies biorthogonal (9, 7) wavelet) using pairwise correlating transform optimal. Our performance test shown that the proposed method gives more improvement and good quality of the reconstructed image when the wavelet coefficients are normalized by Gaussian scale mixture (GSM) model then the Gaussian one.
Secure Attachment: An Antidote to Sex Addiction? A Thematic Analysis of Therapists' Experiences of Utilizing Attachment-Informed Treatment Strategies to Address Sexual Compulsivity

ABSTRACT Research demonstrates a high correlation between insecure attachment and sex addiction. Insecure attachment prevents the formation of secure relationships and disrupts affect regulation. These can be key factors in the maintenance of sexual addiction. Sex addiction treatment strategies may therefore benefit from incorporating a focus upon developing the capacity for secure attachment, alongside treating sexual compulsivity. This study examines the lived experiences of 6 sex addiction therapists who incorporate an attachment-informed approach in their work. The study aims to elaborate key components of an attachment-based approach that could potentially be incorporated into sex addiction treatment strategies for therapists working across different modalities.
Dynamic control of internal force for visco-elastic contact grasps

This paper deals with robotic grasps exhibiting viscoelastic contact interactions with the manipulated object. Such interactions are frequent in advanced robotic applications since in many cases, i.e. when a great precision is needed, elasticity cannot be neglected. This is particularly true in medical applications, as in telesurgery or in laparoscopy, where precision is obviously basic. The control of internal forces for viscoelastic grasps is explored in this work. This is an important issue in teleoperated robotics (telesurgery and so forth). The presence of nonnegligible compliance at the contacts, implies that the object dynamics cannot be neglected when attempting to control internal forces without affecting the object position. A geometric approach to derive a control law dynamically decoupling the internal force control action from the object dynamics is proposed.
Impacts of Mindfulness Training on Mechanisms Underlying Stress Reduction in Teachers: Results from a Randomized Controlled Trial

This Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. For more information, please contact pdxscholar@pdx.edu. Recommended Citation Choles, Jaiya Rae, "Impacts of Mindfulness Training on Mechanisms Underlying Stress Reduction in Teachers: Results from a Randomized Controlled Trial" (2018). Dissertations and Theses. Paper 4468.
Clustered Genes Encoding 2-Keto-l-Gulonate Reductase and l-Idonate 5-Dehydrogenase in the Novel Fungal d-Glucuronic Acid Pathway

D-Glucuronic acid is a biomass component that occurs in plant cell wall polysaccharides and is catabolized by saprotrophic microorganisms including fungi. A pathway for D-glucuronic acid catabolism in fungal microorganisms is only partly known. In the filamentous fungus Aspergillus niger, the enzymes that are known to be part of the pathway are the NADPH requiring D-glucuronic acid reductase forming L-gulonate and the NADH requiring 2-keto-L-gulonate reductase that forms L-idonate. With the aid of RNA sequencing we identified two more enzymes of the pathway. The first is a NADPH requiring 2-keto-L-gulonate reductase that forms L-idonate, GluD. The second is a NAD+ requiring L-idonate 5-dehydrogenase forming 5-keto-gluconate, GluE. The genes coding for these two enzymes are clustered and share the same bidirectional promoter. The GluD is an enzyme with a strict requirement for NADP+/NADPH as cofactors. The kcat for 2-keto-L-gulonate and L-idonate is 21.4 and 1.1 s-1, and the Km 25.3 and 12.6 mM, respectively, when using the purified protein. In contrast, the GluE has a strict requirement for NAD+/NADH. The kcat for L-idonate and 5-keto-D-gluconate is 5.5 and 7.2 s-1, and the Km 30.9 and 8.4 mM, respectively. These values also refer to the purified protein. The gluD deletion resulted in accumulation of 2-keto-L-gulonate in the liquid cultivation while the gluE deletion resulted in reduced growth and cessation of the D-glucuronic acid catabolism.
Rapid Detection of Carbapenemase-producing Enterobacteriaceae

To rapidly identify carbapenemase producers in Enterobacteriaceae, we developed the Carba NP test. The test uses isolated bacterial colonies and is based on in vitro hydrolysis of a carbapenem, imipenem. It was 100% sensitive and specific compared with molecular-based techniques. This rapid (<2 hours), inexpensive technique may be implemented in any laboratory.
Noninverting Buck–Boost DC–DC Converter Using a Duobinary-Encoded Single-Bit Delta-Sigma Modulator

This paper presents a delta-sigma modulation (DSM) control scheme for noninverting buck–boost (NIBB) converter that features a duobinary encoding for four power switch controls. The proposed scheme converts the single-bit output of the modulator into a 1.5-b signal to enable a three-phase operation comprising the charging, bypassing, and discharging phases. This control method reduces both switching and conduction losses by changing only two switches in each period, thus, achieving high conversion efficiency. A smooth mode transition is provided by the DSM controller, which automatically and continuously determines the operating mode of the converter. Thus, the dead zone can be effectively released with improved transient responses. Furthermore, the spurious tones in the output are effectively eliminated by the robust noise shaping capability of the modulator. The proposed DSM-based NIBB converter was implemented on a 180-nm CMOS. It regulated the output in the range of 2.0–4.6 V with input voltage of 2.5–5.0 V, and the maximal load current was 500 mA. The converter showed a peak efficiency of 94.8% at 90-mA load and the output voltage ripples were maintained under 18 mV. A low noise floor with the first spurious peak located –92 dBc below the signal was achieved across all mode operations. In addition, the converter occupied a small chip area of 1.51 mm2.
Bioinformatics Applications Note Proteowizard: Open Source Software for Rapid Proteomics Tools Development

The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, which has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and commercial projects. In addition to the library, we also introduce a rapidly growing set of companion tools whose implementation helps to illustrate the simplicity of developing applications on top of the ProteoWizard library. Availability: Cross-platform software that compiles using native compilers (i.e. GCC on Linux, MSVC on Windows and XCode on OSX) is available for download free of charge, at http://proteowizard.sourceforge. net. This website also provides code examples, and documentation. It is our hope the ProteoWizard project will become a standard platform for proteomics development; consequently, code use, contribution and further development are strongly encouraged. Supplementary information: Supplementary data are available at Bioinformatics online.
Action learning: understanding interpersonal relationships within learning sets

Purpose – The purpose of this paper is to respond to calls for research into the use of action learning in management education (Hay, 2011). It reports on student experiences of action learning in a final year module for part time Master of Business Administration students. It focuses specifically on the development of an understanding of the interpersonal relationships that existed within those action learning sets; both positive and negative. The paper then discusses the subsequent impact those dynamics may have on the effectiveness of the action learning process. Design/methodology/approach – An interpretivist philosophy underpins the research framework adopted in this paper. Data were captured by means of semi-structured questionnaires distributed at both the beginning and end of the module. The data were thematically analysed using open coding. Findings – The paper used two contrasting views of the interpersonal relationships in action learning sets: Revans’ (1982) “comrades in adversity” and Vince's.
Fire Time Series Forecasting Based on Markov-SVR Model

Based on support vector regression and Markovstate transition, a new prediction model termed as Markovsupportvector regression (MSVR) model is proposed toforecast the fire time series. In the proposed model, a SVR is tobuild an optimal prediction model from a series of fire data,and then uses the Markov state transition to reduce theresiduals errors produced by the mentioned model. Theproposed model is examined using actual fire time series data.The results show that the MSVR model gets the better resultperformance than that of the pure SVR model.