Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerated Methods for Non-Convex Optimization

We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second derivatives. In a time $O(\epsilon^{-7/4} \log(1/ \epsilon) )$, the method finds an $\epsilon$-stationary point, meaning a point $x$ such that $\|\nabla f(x)\| \le \epsilon$. The method improves upon the $O(\epsilon^{-2} )$ complexity of gradient descent and provides the additional second-order guarantee that $\lambda_{\min}(\nabla^2 f(x)) \gtrsim -\epsilon^{1/2}$ for the computed $x$. Furthermore, our method is Hessian free, i.e., it only requires gradient computations, and is therefore suitable for large-scale applications.
Cooperative Dynamic Voltage Scaling and Radio Resource Allocation for Energy-Efficient Multiuser Mobile Edge Computing

Mobile-Edge Computing (MEC) could relieve computing pressure of resource-constrained Smart Mobile Devices (SMDs) by offloading computation-intensive tasks to nearby/MEC server. However, how to achieve energy efficient computation offloading for SMDs under application-dependent latency constraints remains challenging in multiuser MEC systems. Specifically, the optimal system operations are not only inner- coupled for each SMD due to parallel local and cloud execution, but also inter-coupled among SMDs due to competition for limited radio resource. Additionally, the inner- and inter-coupling influence each other, which further complicates multiuser offloading strategy design. In this paper, we address such a challenge by jointly optimizing computational speed of SMDs via Dynamic Voltage Scaling (DVS) technology, subcarrier allocation, transmit power per subcarrier, data size sent per subcarrier, and offloading ratio, to minimize weighted sum of mobile energy consumption, resulting in a mixed-integer optimization problem. To tackle this NP-hard problem, we propose a fast-convergent suboptimal algorithm with the Lagrangian dual decomposition. Additionally, simulation results verify that the algorithm converges fast and significantly outperforms existing schemes in energy consumption reduction. Meanwhile, we discover that given latency mean, total mobile energy consumption remains stable or increases with the variance of latency requirements, which could direct admission control in practice.
Functional Connectivity within the Primate Extended Amygdala Is Heritable and Associated with Early-Life Anxious Temperament

Children with an extremely inhibited, anxious temperament (AT) are at increased risk for anxiety disorders and depression. Using a rhesus monkey model of early-life AT, we previously demonstrated that metabolism in the central extended amygdala (EAc), including the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST), is associated with trait-like variation in AT. Here, we use fMRI to examine relationships between Ce–BST functional connectivity and AT in a large multigenerational family pedigree of rhesus monkeys (n = 170 females and 208 males). Results demonstrate that Ce–BST functional connectivity is heritable, accounts for a significant but modest portion of the variance in AT, and is coheritable with AT. Interestingly, Ce–BST functional connectivity and AT-related BST metabolism were not correlated and accounted for non-overlapping variance in AT. Exploratory analyses suggest that Ce–BST functional connectivity is associated with metabolism in the hypothalamus and periaqueductal gray. Together, these results suggest the importance of coordinated function within the EAc for determining individual differences in AT and metabolism in brain regions associated with its behavioral and neuroendocrine components. SIGNIFICANCE STATEMENT Anxiety disorders directly impact the lives of nearly one in five people, accounting for substantial worldwide suffering and disability. Here, we use a nonhuman primate model of anxious temperament (AT) to understand the neurobiology underlying the early-life risk to develop anxiety disorders. Leveraging the same kinds of neuroimaging measures routinely used in human studies, we demonstrate that coordinated activation between the central nucleus of the amygdala and the bed nucleus of the stria terminalis is correlated with, and coinherited with, early-life AT. Understanding how these central extended amygdala regions work together to produce extreme anxiety provides a neural target for early-life interventions with the promise of preventing lifelong disability in at-risk children.
Integrating reciprocal teaching in an online environment with an annotation feature to enhance low-achieving students’ English reading comprehension

ABSTRACT Reciprocal teaching (RT) has been used to improve English as Foreign Language (EFL) students’ reading comprehension in face-to-face instruction. However, little was known about how they use the RT to comprehend English texts in an online environment. This study explored how the implementation of RT strategies with the use of an annotation tool to improve low-achieving students’ English reading comprehension in an online environment. A total of 22 low-achieving EFL students participated in this study. The pre- and post- reading comprehension tests showed that the students’ English reading comprehension improved after practicing RT strategies with annotation tools. Questioning and predicating strategies were ranked as the two most useful strategies, as both promoted successful collaborative reading among students. Summarizing and clarifying were less useful than questioning and predicting strategies because the low-achieving EFL students faced language difficulties when summarizing and clarifying. The annotations supported RT strategies by (1) establishing a collaborative environment for students to discuss RT strategies any time, (2) organizing and indexing reading content in multimodal forms, and (3) helping students review and revise their comprehension.
Closing the Performance Gap Between Volatile and Persistent Key-Value Stores Using Cross-Referencing Logs

Key-Value (K-V) stores are an integral building block of modern datacenter applications. With byte-addressable persistent memory (PM) technologies, such as Intel/Micron's 3D XPoint, on the horizon, there has been an influx of new high performance K-V stores that leverage PM for performance. However, there remains a significant performance gap between PM optimized K-V stores and DRAM resident ones, largely reflecting the gap between projected PM latency relative to that of DRAM. We address that performance gap with Bullet, a K-V store that leverages both the byte-addressability of PM and the lower latency of DRAM, using a technique called cross-referencing logs (CRLs) to keep most PM updates off the critical path. Bullet delivers performance approaching that of DRAM resident K-V stores by maintaining two hash tables, one in the slower (backend) PM and the other in the faster (frontend) DRAM. CRLs are a scalable persistent logging mechanism that keeps the two copies mutually consistent. Bullet also incorporates several critical optimizations, such as dynamic load balancing between frontend and backend threads, support for nonblocking Gets, and opportunistic omission of stale updates in the backend. This combination of implementation techniques delivers performance within 5% of that of DRAM-only key-value stores for realistic (read-heavy) workloads. Our general approach, based on CRLs, is "universal" in that it can be used to turn any volatile K-V store into a persistent one (or vice-versa, provide a fast cache for a persistent K-V store).
Similarities and differences in the associations between patient safety culture dimensions and self-reported outcomes in two different cultural settings: a national cross-sectional study in Palestinian and Belgian hospitals

Objectives To investigate the relationships between patient safety culture (PSC) dimensions and PSC self-reported outcomes across different cultures and to gain insights in cultural differences regarding PSC. Design Observational, cross-sectional study. Setting Ninety Belgian hospitals and 13 Palestinian hospitals. Participants A total of 2836 healthcare professionals matched for profession, tenure and working hours. Primary and secondary outcome measures The validated versions of the Belgian and Palestinian Hospital Survey on Patient Safety Culture were used. An exploratory factor analysis was conducted. Reliability was tested using Cronbach’s alpha (α). In this study, we examined the specific predictive value of the PSC dimensions and its self-reported outcome measures across different cultures and countries. Hierarchical regression and bivariate analyses were performed. Results Eight PSC dimensions and four PSC self-reported outcomes were distinguished in both countries. Cronbach’s α was α≥0.60. Significant correlations were found between PSC dimensions and its self-reported outcome (p value range <0.05 to <0.001). Hierarchical regression analyses showed overall perception of safety was highly predicted by hospital management support in Palestine (β=0.16, p<0.001) and staffing in Belgium (β=0.24, p<0.001). The frequency of events was largely predicted by feedback and communication in both countries (Palestine: β=0.24, p<0.001; Belgium: β=0.35, p<0.001). Overall grade for patient safety was predicted by organisational learning in Palestine (β=0.19, p<0.001) and staffing in Belgium (β=0.19, p<0.001). Number of events reported was predicted by staffing in Palestine (β=−0.20, p<0.001) and feedback and communication in Belgium (β=0.11, p<0.01). Conclusion To promote patient safety in Palestine and Belgium, staffing and communication regarding errors should be improved in both countries. Initiatives to improve hospital management support and establish constructive learning systems would be especially beneficial for patient safety in Palestine. Future research should address the association between safety culture and hard patient safety measures such as patient outcomes.
Subword-level Word Vector Representations for Korean

Research on distributed word representations is focused on widely-used languages such as English. Although the same methods can be used for other languages, language-specific knowledge can enhance the accuracy and richness of word vector representations. In this paper, we look at improving distributed word representations for Korean using knowledge about the unique linguistic structure of Korean. Specifically, we decompose Korean words into the jamo-level, beyond the character-level, allowing a systematic use of subword information. To evaluate the vectors, we develop Korean test sets for word similarity and analogy and make them publicly available. The results show that our simple method outperforms word2vec and character-level Skip-Grams on semantic and syntactic similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis.
TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring

Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To close this gap, a two-stage deep neural network (TDNN) is proposed. In particular, in the first stage, using the rated essays for non-target prompts as the training data, a shallow model is learned to select essays with an extreme quality for the target prompt, serving as pseudo training data; in the second stage, an end-to-end hybrid deep model is proposed to learn a prompt-dependent rating model consuming the pseudo training data from the first step. Evaluation of the proposed TDNN on the standard ASAP dataset demonstrates a promising improvement for the prompt-independent AES task.
Tackling the Story Ending Biases in The Story Cloze Test

The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. There have been a variety of models tackling the SCT so far. Although the original goal behind the SCT was to require systems to perform deep language understanding and commonsense reasoning for successful narrative understanding, some recent models could perform significantly better than the initial baselines by leveraging human-authorship biases discovered in the SCT dataset. In order to shed some light on this issue, we have performed various data analysis and analyzed a variety of top performing models presented for this task. Given the statistics we have aggregated, we have designed a new crowdsourcing scheme that creates a new SCT dataset, which overcomes some of the biases. We benchmark a few models on the new dataset and show that the top-performing model on the original SCT dataset fails to keep up its performance. Our findings further signify the importance of benchmarking NLP systems on various evolving test sets.
Epigenetic reprogramming by histone acetyltransferase HAG1/AtGCN5 is required for pluripotency acquisition in Arabidopsis

Shoot regeneration can be achieved in vitro through a two‐step process involving the acquisition of pluripotency on callus‐induction media (CIM) and the formation of shoots on shoot‐induction media. Although the induction of root‐meristem genes in callus has been noted recently, the mechanisms underlying their induction and their roles in de novo shoot regeneration remain unanswered. Here, we show that the histone acetyltransferase HAG1/AtGCN5 is essential for de novo shoot regeneration. In developing callus, it catalyzes histone acetylation at several root‐meristem gene loci including WOX5, WOX14, SCR, PLT1, and PLT2, providing an epigenetic platform for their transcriptional activation. In turn, we demonstrate that the transcription factors encoded by these loci act as key potency factors conferring regeneration potential to callus and establishing competence for de novo shoot regeneration. Thus, our study uncovers key epigenetic and potency factors regulating plant‐cell pluripotency. These factors might be useful in reprogramming lineage‐specified plant cells to pluripotency.
Multi-dimensional long short-term memory networks for artificial Arabic text recognition in news video

This study presents a novel approach for Arabic video text recognition based on recurrent neural networks. In fact, embedded texts in videos represent a rich source of information for indexing and automatically annotating multimedia documents. However, video text recognition is a non-trivial task due to many challenges like the variability of text patterns and the complexity of backgrounds. In the case of Arabic, the presence of diacritic marks, the cursive nature of the script and the non-uniform intra/inter word distances, may introduce many additional challenges. The proposed system presents a segmentation-free method that relies specifically on a multi-dimensional long short-term memory coupled with a connectionist temporal classification layer. It is shown that using an efficient pre-processing step and a compact representation of Arabic character models brings robust performance and yields a low-error rate than other recently published methods. The authors' system is trained and evaluated using the public AcTiV-R dataset under different evaluation protocols. The obtained results are very interesting. They also outperform current state-of-the-art approaches on the public dataset ALIF in terms of recognition rates at both character and line levels.
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design

Bayesian optimization methods are promising for the optimization of black-box functions that are expensive to evaluate. In this paper, a novel batch Bayesian optimization approach is proposed. The parallelization is realized via a multi-objective ensemble of multiple acquisition functions. In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions. The batch of inputs are then selected from the Pareto front. The Pareto front represents the best trade-off between the multiple acquisition functions. Such a policy for batch Bayesian optimization can significantly improve the efficiency of optimization. The proposed method is compared with several state-of-the-art batch Bayesian optimization algorithms using analytical benchmark functions and real-world analog integrated circuits. The experimental results show that the proposed method is competitive compared with the state-of-the-art algorithms.
Quantitative Analysis of the Bacteria in Blepharitis With Demodex Infestation

Bacterial infection or Demodex infestation has been reported to contribute to chronic blepharitis. The association between Demodex mites and bacterial flora in this disease remains to be elucidated. Fifty-six consecutive patients diagnosed with chronic blepharitis and 46 healthy volunteers were recruited for this study. Using specimens of three epilated eyelashes and lid margin swabs, Demodex were identified microscopically and bacteria were determined by cultures, followed by colony counting and mass spectrometry. We found 191 Demodex mites, 161 D. folliculorum and 30 D. brevis, in 45 patients. Correspondingly, 101 Demodex, 63 D. folliculorum and 38 D. brevis, were found in 21 controls (p < 0.05, both). Bacterial culture-positivity was obtained in samples (eyelashes, lid margins, or both) from 54 patients and in eyelashes from 37 controls. The total colony counts and the incidences and colony counts of Propinibacterium acnes and Staphylococcus aureus from patients’ eyelashes were significantly higher than that of the controls. Furthermore, bacterial colony counts in blepharitis patients’ eyelashes with D. folliculorum were higher than that of controls with D. folliculorum (p < 0.01). Similarly, P. acnes colonies increased significantly in patients’ eyelashes with D. folliculorum (p < 0.05). These results suggest that D. folliculorum and P. acnes have a role in the occurrence of the chronic blepharitis. Further studies are required to reveal the relationship between these two organisms in blepharitis.
Generating and applying textual entailment graphs for relation extraction and email categorization

Recognizing that the meaning of one text expression is semantically related to the meaning of another can be of help in many natural language processing applications. One semantic relationship between two text expressions is captured by the textual entailment paradigm, which is defined as a relation between exactly two text expressions. Entailment relations holding among a set of more than two text expressions can be captured in the form of a hierarchical knowledge structure referred to as entailment graphs. Despite the fact that several people have worked on building entailment graphs for different types of textual expressions, little research has been carried out regarding the applicability of such entailment graphs in NLP applications. This thesis fills this research gap by investigating how entailment graphs can be generated and used for addressing two specific NLP tasks: First, the task of validating automatically derived relation extraction patterns and, second, the task of automatically categorizing German customer emails. After laying a theoretical foundation, the research problem is approached in an empirical way, i.e., by drawing conclusions from analyzing, processing, and experimenting with specific task-related datasets. The experimental results show that both tasks can benefit from the integration of semantic knowledge, as expressed by entailment graphs.
Detection limits in the high-dimensional spiked rectangular model

We study the problem of detecting the presence of a single unknown spike in a rectangular data matrix, in a high-dimensional regime where the spike has fixed strength and the aspect ratio of the matrix converges to a finite limit. This setup includes Johnstone's spiked covariance model. We analyze the likelihood ratio of the spiked model against an "all noise" null model of reference, and show it has asymptotically Gaussian fluctuations in a region below---but in general not up to---the so-called BBP threshold from random matrix theory. Our result parallels earlier findings of Onatski et al.\ (2013) and Johnstone-Onatski (2015) for spherical spikes. We present a probabilistic approach capable of treating generic product priors. In particular, sparsity in the spike is allowed. Our approach is based on Talagrand's interpretation of the cavity method from spin-glass theory. The question of the maximal parameter region where asymptotic normality is expected to hold is left open. This region is shaped by the prior in a non-trivial way. We conjecture that this is the entire paramagnetic phase of an associated spin-glass model, and is defined by the vanishing of the replica-symmetric solution of Lesieur et al.\ (2015).
Nurse education and understanding related to domestic violence and abuse against women: An integrative review of the literature

The aim of this study was to explore previous literature related to nurses understanding of Intimate partner violence (IPV) or domestic violence and abuse (DVA) against women and to identify the gaps in nursing education so as to use the findings as a baseline to inform potential intervention strategies, curriculum development and outline implications for future nursing practice.
From KLM-style conditionals to defeasible modalities, and back

Abstract We investigate an aspect of defeasibility that has somewhat been overlooked by the non-monotonic reasoning community, namely that of defeasible modes of reasoning. These aim to formalise defeasibility of the traditional notion of necessity in modal logic, in particular of its different readings as action, knowledge and others in specific contexts, rather than defeasibility of conditional forms. Building on an extension of the preferential approach to modal logics, we introduce new modal osperators with which to formalise the notion of defeasible necessity and distinct possibility, and that can be used to represent expected effects, refutable knowledge, and so on. We show how KLM-style conditionals can smoothly be integrated with our richer language. We also propose a tableau calculus which is sound and complete with respect to our modal preferential semantics, and of which the computational complexity remains in the same class as that of the underlying classical modal logic.
Prediction Models for Risk of Type-2 Diabetes Using Health Claims

This study focuses on highly accurate prediction of the onset of type-2 diabetes. We investigated whether prediction accuracy can be improved by utilizing lab test data obtained from health checkups and incorporating health claim text data such as medically diagnosed diseases with ICD10 codes and pharmacy information. In a previous study, prediction accuracy was increased slightly by adding diagnosis disease name and independent variables such as prescription medicine. Therefore, in the current study we explored more suitable models for prediction by using state-of-the-art techniques such as XGBoost and long short-term memory (LSTM) based on recurrent neural networks. In the current study, text data was vectorized using word2vec, and the prediction model was compared with logistic regression. The results obtained confirmed that onset of type-2 diabetes can be predicted with a high degree of accuracy when the XGBoost model is used.