Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship of marital adjustment and posttraumatic growth in female breast cancer patients and their husbands

ABSTRACT Posttraumatic growth (PTG) is very important for breast cancer couples to cope with cancer. Individual marital adjustment can affect PTG, however, it is still unknown that the effect of marital adjustment on one’s own and their spouses’ PTG in breast cancer couples. To investigate the status of PTG and marital adjustment and explore the relation between PTG and marital adjustment in breast cancer patient-husband dyads. General data, marital adjustment and PTG scores of breast cancer patients (N = 206) and their husbands (N = 206) were collected through a general information questionnaire, the Post-traumatic Growth Inventory (PTGI) and Marital Adjustment Test (MAT). T-tests and structural equation models were applied to explore the relations between marital adjustment and PTG among breast cancer patients and their husbands. The PTG among breast cancer patients was significantly higher than among their husbands (P < 0.05); the score of patients’ marital adjustment was 96.18 ± 22.08, and that of their husbands was 96.22 ± 22.27. The participants’ marital adjustment had a positive predictive effect on their own PTG (P < 0.05), and patients’ marital adjustment also had a positive predictive effect on their husbands’ PTG (P < 0.05). Breast cancer patients experienced more PTG than their husbands; patients’ PTG was promoted by their own marital adjustment, while husbands’ PTG was promoted by both their own and the patient’s marital adjustment. In order to improve breast cancer patients’ and their husbands’ PTG, it is essential to promote their marital adjustment.
The first 72 hours: Suicide loss survivor experiences with early responders

Abstract Exposure to a suicide carries risks for loss survivors including significant and long-standing emotional distress. Early responders play a critical role in shaping survivors’ experience and trajectory for seeking support services. Yet, few prior studies explore the nuances of early responders’ interactions with loss survivors. The current study explored suicide-bereaved adults’ experiences with early responders in the first 72 h after their loss through four group interviews. Participants (N = 15) indicated that interactions with early responders were positive overall. Yet, the lack of resources and referrals provided, and perceived stigmatization of suicide may contribute to survivors’ delay in obtaining support services.
The safety of exercise for older patients with severe aortic stenosis undergoing conservative management: A narrative review

The incidence of aortic stenosis (AS) increases with age and is a serious problem in an aging society. In recent years, transcatheter aortic valve implantation (TAVI) has been performed widely; however, older patients may be ineligible for TAVI or surgical treatment because of medical ineligibility. Symptom‐based rehabilitation is required for these patients to maintain and improve their physical function and ability to perform activities of daily living. No studies have examined exercise safety for older patients with severe AS who are ineligible for TAVI or surgery. We summarized the safety of exercise for older patients with severe AS, collecting 7 studies on maximal exercise stress tests and 16 studies on preoperative physical examinations. From this review, it may be unlikely that exercise under appropriate management can cause hemodynamic changes, leading to death. However, there were no studies on exercise intervention for older patients with AS who are chosen for conservative management. The optimal exercise intensity for symptomatic older patients with AS undergoing conservative management and the effects of continuous exercise intervention require future study.
Interfering RNA Production in Dendritic Cells Using Small Immune Modulation by Silencing IL-12

RNA interference is a mechanism of posttranscriptional gene silencing that functions in most eukaryotic cells, including human and mouse. Speciﬁc gene silencing is mediated by short strands of duplex RNA of (cid:1) 21 nt in length (termed small interfering RNA or siRNA) that target the cognate mRNA sequence for degradation. We demonstrate here that RNAi can be used for immune modulation by targeting dendritic cell (DC) gene expression. Transfection of DC with siRNA speciﬁc for the IL-12 p35 gene resulted in potent suppression of gene expression and blockade of bioactive IL-12 p70 production without affecting unrelated genes or cellular viability. Inhibition of IL-12 was associated with increased IL-10 production, which endowed the DC with the ability to stimulate production of Th2 cytokines from allogenic T cells in vitro. Furthermore, siRNA-silenced DC lacking IL-12 production were poor allostimulators in MLR. IL-12-silenced and KLH-pulsed DC polarized the immune response toward a Th2 cytokine proﬁle in an Ag-speciﬁc manner. These data are the ﬁrst to demonstrate that RNA interference is a potent and speciﬁc tool for modulating DC-mediated immune responses. The Journal of Immunology, 2003, 171: 691–696. the utility of RNAi for immune modulation has never been reported. We describe here the induction of RNAi in DC using siRNA speciﬁc for IL-12 p35 (siRNA-IL12p35). We show that bioactive IL-12 p70 production in bone marrow-derived DC is inhibited after stimulation with LPS plus TNF- (cid:1) and is accompanied by an increase in IL-10 production. Moreover, when siRNA-IL12p35-treated DC were cultured with allogenic T cells, a Th2 polarization was observed since T cell expression of IFN- (cid:2) was reduced while IL-4 was increased. Inhibiting IL-12 production using siRNA-IL12p35 was associated with suppressed DC allostimulatory function. In vivo, initiation of Ag-speciﬁc Th2 responses was observed when DC treated with siRNA-IL12 p35 were pulsed with keyhole limpet hemocyanin (KLH) and used for immunization experiments. These results provide the ﬁrst evidence that RNAi can be
Foundations for an Agile Governance Manifesto: a bridge for business agility

Context : People are a key element of change in organizations and its driving force. The need to understand people as an essential and creative component of the structures and processes is a critical success factor for governance initiatives. Objective : Inspired by the Manifesto for Agile Software Development, this paper proposes the Agile Governance Manifesto as a behavioral reference for individuals and teams in order to help implement agile governance in organizations. Method : This proposal emerges as an inductive and structuralist look over the findings from a broad research developed to describe and analyze the agile governance phenomena, supported by several complementary studies: systematic literature review, observation and interaction of professional groups based on social networks, semi structured interviews, and explanatory survey with representative agents of the phenomena under study. Results : We characterize the agile governance phenomena with six meta-principles and nine meta-values are discussed in order to guide future researches and especially to drive practices on agile governance. Conclusion : This proposal is to be used as an instrument to build an appreciative and positive organizational culture in order to pave the way for smoother and gradual adoption of agile governance.
Six Sigma critical success factors in Brazilian industry

Purpose – The purpose of this paper is to identify critical success factors (CSFs) that are regarded as the most important in Six Sigma programs in Brazil and to compare these rankings with those in international literature. Design/methodology/approach – A sample of industrial companies was selected to complete a survey. In total, 104 questionnaires were obtained. The results were compared with a literature review consisting of 26 papers from 13 countries. In total, 70 CSFs were found in the papers, but 19 CSFs were analyzed and reduced to ten. A multivariate factor analysis further reduced this number to two underlying constructs. Findings – The authors identified a CSFs common denominator/ranking based on the sample of international articles. The authors found that there are four CSFs that are more prevalent in Brazil and in the international papers studied and that there are no differences between the importance of CSFs in terms of hierarchical levels. Three gaps, five levers and two CSFs constructs were identified. Social implications – This study may initiate cooperation between the studied companies and academia, thus possibly increasing these organization’ knowledge regarding Six Sigma. Originality/value – The originality of this study is that the survey was conducted with companies in Brazil, a country where little information exists on Six Sigma programs. The authors also contributed a literature review on CSFs, a comparison based on most of the consulted papers and the use of a robust methodological strategy that was made possible by the sample size.
Lateral Sclerosis Pathology in a Rat Model of Amyotrophic The Complement Factor C5a Contributes to

Complement activation products are elevated in the cerebrospinal ﬂuid and spinal cord of patients with amyotrophic lateral sclerosis (ALS). In this study, we demonstrate complement system involvement in a rodent model of ALS (human SOD1 G93A transgenic rats). With end-stage disease, SOD1 G93A rats displayed marked deposition of C3/C3b, and a signiﬁcant up-regulation of the C5aR in the lumbar spinal cord. This was associated with increased numbers of C5aR-positive astrocytes. However, expression of C5L2, the alternative receptor for C5a, was highest on motor neurons early in the disease process. To determine the contribution of C5a to the pathology displayed by this model of ALS, rats were administered an orally active, selective C5aR antagonist (PMX205; 1 mg/kg/day, oral). Animals treated with PMX205 displayed a signiﬁcant extension of survival time and a reduction in end-stage motor scores, as compared with vehicle-treated rats. PMX205-treated animals also displayed reduced levels of astroglial proliferation in the lumbar spinal cord. This study provides the ﬁrst demonstration of an involvement of C5a in an ALS model and suggests that inhibitors of complement activation could be beneﬁcial in the treatment of this neurodegenerative disease. The Journal of Immunology, 2008, 181: 8727–8734.
Signal Processing Techniques Based on Adaptive Radial Basis Function Networks for Chemical Sensor Arrays

The use of a chemical sensor array can help discriminate between chemicals when comparing one sample with another. The ability to classify pattern characteristics from relatively small pieces of information has led to growing interest in methods of sensor recognition. A variety of pattern recognition algorithms, including the adaptive radial basis function network (RBFN), may be applicable to gas and/ or odor classification. In this paper, we provide a broad review of approaches for various types of gas and/or odor identification techniques based on RBFN and drift compensation techniques caused by sensor poisoning and aging.
Transmission of Duobinary Signal in Optical 40 GHz Millimeter-Wave Radio-Over-Fiber Systems Utilizing Dual-Arm LiNbO3 Mach–Zehnder Modulator for Downstream

Abstract In this paper, for the first time transmission of 2.5 Gb/s duobinary signal is investigated for the downlink direction in 40 GHz optical millimeter-wave generation or up-conversion, utilizing a dual-arm LiNbO3${\rm{LiNb}}{{\rm{O}}_3}$ Mach–Zehnder modulator based on different modulation schemes, namely double- and single-sideband (DSB and SSB) and optical carrier suppression (OCS). The up-converted optical millimeter-wave employing OCS modulation scheme indicates the highest back-to-back received optical power and the smallest power penalty after long propagation in the single-mode fiber, in comparison to DSB and SSB. Directly modulated laser in association with OCS modulation scheme has been used to generate duobinary optical millimeter-wave signal in order to minimize the cost and complexity of the system.
Analyzing the impact of undersampling on the benchmarking and configuration of evolutionary algorithms

The stochastic nature of iterative optimization heuristics leads to inherently noisy performance measurements. Since these measurements are often gathered once and then used repeatedly, the number of collected samples will have a significant impact on the reliability of algorithm comparisons. We show that care should be taken when making decisions based on limited data. Particularly, we show that the number of runs used in many benchmarking studies, e.g., the default value of 15 suggested by the COCO environment, can be insufficient to reliably rank algorithms on well-known numerical optimization benchmarks. Additionally, methods for automated algorithm configuration are sensitive to insufficient sample sizes. This may result in the configurator choosing a "lucky" but poor-performing configuration despite exploring better ones. We show that relying on mean performance values, as many configurators do, can require a large number of runs to provide accurate comparisons between the considered configurations. Common statistical tests can greatly improve the situation in most cases but not always. We show examples of performance losses of more than 20%, even when using statistical races to dynamically adjust the number of runs, as done by irace. Our results underline the importance of appropriately considering the statistical distribution of performance values.
Adaptive Mho relay for synchronous generator loss-of-excitation protection: a capability curve limit-based approach

Loss-of-excitation (LOE) relays work under pre-defined operating characteristics. If an abnormal condition should arise, such operating characteristics might not be suitable for current system conditions. In this study, an adaptive real-time data-based analytical methodology is proposed for improving LOE protection performance. The core idea is to generate in real time an adaptive operating characteristic for the Mho relay, based on the well know Berdy's method. The adaptive Mho operating characteristic is derived through the proposed methodology using the generator capability curve. Validation is obtained through comparative simulation test cases using DigSilent Power Factory® program. Test case results show that the proposed protection adaptive analytical methodology displays secure and effective high speed LOE detection, indicating potential for real-life applications.
A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation. In recent years, many new models and algorithms have been designed/proposed to bridge the accuracy gap between NAR generation and AR generation. In this paper, we conduct a systematic survey with comparisons and discussions of various non-autoregressive translation (NAT) models from different aspects. Specifically, we categorize the efforts of NAT into several groups, including data manipulation, modeling methods, training criterion, decoding algorithms, and the benefit from pre-trained models. Furthermore, we briefly review other applications of NAR models beyond machine translation, such as grammatical error correction, text summarization, text style transfer, dialogue, semantic parsing, automatic speech recognition, and so on. In addition, we also discuss potential directions for future exploration, including releasing the dependency of KD, reasonable training objectives, pre-training for NAR, and wider applications, etc. We hope this survey can help researchers capture the latest progress in NAR generation, inspire the design of advanced NAR models and algorithms, and enable industry practitioners to choose appropriate solutions for their applications.
On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages

A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves higher performance as opposed to using a corpus of code written in just one programming language. However, no analysis was made with respect to fine-tuning monolingual PLMs. Furthermore, some programming languages are inherently different and code written in one language usually cannot be interchanged with the others, i.e., Ruby and Java code possess very different structure. To better understand how monolingual and multilingual PLMs affect different programming languages, we investigate 1) the performance of PLMs on Ruby for two popular Software Engineering tasks: Code Summarization and Code Search, 2) the strategy (to select programming languages) that works well on fine-tuning multilingual PLMs for Ruby, and 3) the performance of the fine-tuned PLMs on Ruby given different code lengths. In this work, we analyze over a hundred of pre-trained and fine-tuned models. Our results show that 1) multilingual PLMs have a lower Performance-to-Time Ratio (the BLEU, METEOR, or MRR scores over the fine-tuning duration) as compared to monolingual PLMs, 2) our proposed strategy to select target programming languages to fine-tune multilingual PLMs is effective — it reduces the time to fine-tune yet achieves higher performance in Code Summarization and Code Search tasks, and 3) our proposed strategy consistently shows good performance on different code lengths.
Interface effects on titanium growth on graphene

Poor quality interfaces between metal and graphene cause non-linearity and impair the carrier mobility in graphene devices. Here, we use aberration corrected scanning transmission electron microscopy to observe hexagonally close-packed Ti nano-islands grown on atomically clean graphene, and establish a 30∘ epitaxial relationship between the lattices. Due to the strong binding of Ti on graphene, at the limit of a monolayer, the Ti lattice constant is mediated by the graphene epitaxy, and compared to bulk Ti, is strained by ca. 3.7% to a value of 0.306(3) nm. The resulting interfacial strain is slightly greater than what has been predicted by density functional theory calculations. Our early growth stage investigations also reveal that, in contrast to widespread assumptions, Ti does not fully wet graphene but grows initially in islands with a thickness of 1–2 layers. Raman spectroscopy implies charge transfer between the Ti islands and graphene substrate.
Effects of Graph Convolutions in Deep Networks

Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classiﬁcation problems accompanied by graphical information. We present a rigorous theoretical understanding of the eﬀects of graph convolutions in multi-layer networks. We study these eﬀects through the node classiﬁcation problem of a non-linearly separable Gaussian mixture model coupled with a stochastic block model. First, we show that a single graph convolution expands the regime of the distance between the means where multi-layer networks can classify the data by a factor of at least 1 / 4 √ E deg , where E deg denotes the expected degree of a node. Second, we show that with a slightly stronger graph density, two graph convolutions improve this factor to at least 1 / 4 √ n , where n is the number of nodes in the graph. Finally, we provide both theoretical and empirical insights into the performance of graph convolutions placed in diﬀerent combinations among the layers of a network, concluding that the performance is mutually similar for all combinations of the placement. We present extensive experiments on both synthetic and real-world data that illustrate our results.
Nonlocal cross-interaction systems on graphs: Energy landscape and dynamics

We explore the dynamical behavior and energetic properties of a model of two species that interact nonlocally on finite graphs. The authors recently introduced the model in the context of nonquadratic Finslerian gradient flows on generalized graphs featuring nonlinear mobilities. In a continuous and local setting, this class of systems exhibits a wide variety of patterns, including mixing of the two species, partial engulfment, or phase separation. This work showcases how this rich behavior carries over to the graph structure. We present analytical and numerical evidence thereof.
Sim-2-Sim Transfer for Vision-and-Language Navigation in Continuous Environments

Recent work in Vision-and-Language Navigation (VLN) has presented two environmental paradigms with differing realism -- the standard VLN setting built on topological environments where navigation is abstracted away, and the VLN-CE setting where agents must navigate continuous 3D environments using low-level actions. Despite sharing the high-level task and even the underlying instruction-path data, performance on VLN-CE lags behind VLN significantly. In this work, we explore this gap by transferring an agent from the abstract environment of VLN to the continuous environment of VLN-CE. We find that this sim-2-sim transfer is highly effective, improving over the prior state of the art in VLN-CE by +12% success rate. While this demonstrates the potential for this direction, the transfer does not fully retain the original performance of the agent in the abstract setting. We present a sequence of experiments to identify what differences result in performance degradation, providing clear directions for further improvement.
Modeling Review History for Reviewer Recommendation: A Hypergraph Approach

Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many reviewer recommendation approaches (recommenders) have been developed to support this task which apply a similar strategy, i.e. modeling the review history first then followed by predicting/recommending a reviewer based on the model. Apparently, the better the model reflects the reality in review history, the higher recommender's performance we may expect. However, one typical scenario in a pull-request development paradigm, i.e. one Pull-Request (PR) (such as a revision or addition submitted by a contributor) may have multiple reviewers and they may impact each other through publicly posted comments, has not been modeled well in existing recommenders. We adopted the hypergraph technique to model this high-order relationship (i.e. one PR with multiple reviewers herein) and developed a new recommender, namely HGRec, which is evaluated by 12 OSS projects with more than 87K PRs, 680K comments in terms of accuracy and recommen-dation distribution. The results indicate that HGRec outperforms the state-of-the-art recommenders on recommendation accuracy. Besides, among the top three accurate recommenders, HGRec is more likely to recommend a diversity of reviewers, which can help to relieve the core reviewers' workload congestion issue. Moreover, since HGRec is based on hypergraph, which is a natural and interpretable representation to model review history, it is easy to accommodate more types of entities and realistic relationships in modern code review scenarios. As the first attempt, this study reveals the potentials of hypergraph on advancing the pragmatic solutions for code reviewer recommendation.