Miracle-dz/UniFAR-FTUs huggingface dataset:
Physician Orders for Life-Sustaining Treatment (POLST): How Do Nursing Facilities Implement the POLST Program?

The Physician Orders for Life-Sustaining Treatment (POLST) is a novel end of life advance planning tool. In form, the POLST is a medical order. However the document is completed before the onset of a medical crisis, and can be used by patients to direct medical treatment much like a living will. End-of-life care is an important public health concern, and it is hoped that the POLST will give individuals greater control over their medical care. I surveyed 19 nursing homes using the POLST form in one Pennsylvania county to determine whether the POLST is being implemented in the way intended by its designers. The survey asked whether the POLST is presented to residents as required, recommended, or optional; whether the POLST is offered to all nursing home residents or only certain types of residents; whether the facility requires a patient or surrogate signature on the form; whether the facility updates the form at resident care conferences or upon a change in medical condition; whether the facility screens the POLST for errors and makes sure it is signed by an appropriate medical professional before it is put into the chart; and how much time it typically takes to complete a POLST for residents with and without a living will. The survey also asked whether facilities were complying with state law procedural requirements for end-of-life decision making when using the POLST. The survey found that most nursing facilities reported that they present the form as being required, and they offer it to all residents regardless of medical condition. Approximately half of the facilities follow the recommendation of the National POLST Paradigm Task Force to obtain a resident or surrogate signature on the POLST. Almost all respondents said they typically complete a POLST for a resident who does not have a living will in less than an hour. Most facilities update the form upon a change in medical condition and check the form for errors. Many of the surveyed nursing homes are not complying with procedural requirements of state law when using the POLST to implement decisions regarding end-of-life medical treatment.
Dietary intake and dental caries in children

Dental caries is a common childhood disease and important health problem in the United States and throughout the world. Most studies that have assessed risk factors for dental caries focused on non-modifiable risk factors such as previous caries experience and socioeconomic status. It is also important to investigate modifiable risk factors that can be used in developing guidelines for risk assessment and prevention. The present dissertation assessed mainly the associations between dental caries and modifiable factors, including dietary factors, water fluoride levels and toothbrushing frequency in children, while adjusting for non-modifiable factors. Data were obtained from subjects who were participants in the Iowa Fluoride Study. Dietary data were collected using 3day dietary diaries from 1.5 months to 8.5 years and detailed questionnaires from 9 years to 13 years. Dental caries examinations were conducted at about 5, 9 and 13 years of age. There are three main analyses. The first analysis assessed risk factors for a 4 group primary dentition caries experience variable: the caries-free (reference group), the d1, the d2+f, and the d1d2+f groups. The dietary consumption frequencies (from ages 3 to 5 years) for the children in the 4 caries groups were compared using multivariable multinomial regression analyses. Lower consumption frequency of milk at meals and greater consumption frequency of pre-sweetened cereal at meals significantly increased the likelihood of being in the d1 group. Greater consumption frequency of regular soda pop at snacks significantly increased the likelihood of being in the d1d2+f group. Greater consumption frequency of added sugars at snacks significantly increased chance to be in the d2+f group and the d1d2+f group. The second manuscript assessed risk factors for new mixed dentition cavitated caries determined based on surface-specific transitions from the primary to mixed dentition exams on 16 teeth using logistic regression analysis. Greater consumption
Developing Scheme and 802.11 Mesh Networks

Unified pervasive modalities have led to many private advances, including kernels and local-area networks. In fact, few systems engineers would disagree with the simulation of SCSI disks, demonstrates the significant importance of distributed systems. Our focus in this position paper is not on whether the seminal virtual algorithm for the simulation of compilers by V. A. Jackson et al. is NP-complete, but rather on motivating a novel heuristic for the study of IPv4 (Boomdas).
Development of Wavelet-Based Tools for Event Related Potentials’ N400 Detection: Application to Visual and Auditory Vowelling and Semantic Priming in Arabic Language

Neurological signals are generally very weak in amplitude and strongly noisy. As a result, one of the major challenges in neuroscience is to be able to eliminate noise and thus exploit the maximum amount of information contained in neurological signals (EEG, ERP, Evoked Potentials, .). In our project, we aim to highlight the ERP's N400 wave which the behavior, the amplitude and the latency may reflect the effects of vowelling and semantic priming in Arabic language. For that reason, we consider a nonlinear filtering method based on discrete 10th order Daubechies discrete wavelet transform combined to principal component analysis, to improve the quality of the recorded ERP signals. Thus, among all tested wavelets, the Daubechies one allows a significant improvement of the signal to noise ratio while using only 10 ERP trials. In addition, we compare and illustrate the effectiveness of this method to that obtained using the averaging technique implemented on EEGLab toolbox. In a second step, the Mexican Hat function have been used to achieve continuous wavelet analysis of the filtered signals. This method permits us to get an alternative representation of the ERPs and to detect avec more accuracy the N400 wave.
Neural Cross-Lingual Event Detection with Minimal Parallel Resources

The scarcity in annotated data poses a great challenge for event detection (ED). Cross-lingual ED aims to tackle this challenge by transferring knowledge between different languages to boost performance. However, previous cross-lingual methods for ED demonstrated a heavy dependency on parallel resources, which might limit their applicability. In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources. Specifically, to construct a lexical mapping between different languages, we devise a context-dependent translation method; to treat the word order difference problem, we propose a shared syntactic order event detector for multilingual co-training. The efficiency of our method is studied through extensive experiments on two standard datasets. Empirical results indicate that our method is effective in 1) performing cross-lingual transfer concerning different directions and 2) tackling the extremely annotation-poor scenario.
Neural Conversation Recommendation with Online Interaction Modeling

The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis. It presents a concrete challenge for individuals to better discover and engage in social media discussions. In this paper, we present a novel framework to automatically recommend conversations to users based on their prior conversation behaviors. Built on neural collaborative filtering, our model explores deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. Furthermore, to identify salient characteristics from interleaving user interactions, our model incorporates graph-structured networks, where both replying relations and temporal features are encoded as conversation context. Experimental results on two large-scale datasets collected from Twitter and Reddit show that our model yields better performance than previous state-of-the-art models, which only utilize lexical features and ignore past user interactions in the conversations.
Energy-Efficient Resource Allocation in Multicarrier NOMA Systems With Fairness

Non-orthogonal multiple access (NOMA) has attracted both academic and industrial interest since it has been considered as one of the promising 5G technologies in order to increase connectivity and spectral efficiency. In this paper, we focus on a downlink multicarrier (MC) NOMA network, where a single base station serves a set of users through multiple subchannels. The goal is to jointly optimize energy efficiency (EE) and fairness among users with respect to the subcarrier and power allocation parameters. To achieve this with acceptable complexity, a novel greedy subcarrier assignment scheme based on the worst-user first principle is proposed. Due to the fractional form of the EE expression and the existence of interference, the power allocation problem is non-convex and NP-hard. To this end, we first transform this into an equivalent subtractive form, which is then solved by using fractional programming with sequential optimization of the inter/intra-subchannel power allocation vectors. Simulation results reveal the effectiveness of the proposed scheme in terms of EE and fairness among users compared to baseline schemes. Finally, the proposed algorithms are of fast convergence, low complexity, and insensitive to the initial values.
ConvergenceClubs: A Package for Performing the Phillips and Sul's Club Convergence Clustering Procedure

This paper introduces package ConvergenceClubs, which implements functions to perform the Phillips and Sul (2007, 2009) club convergence clustering procedure in a simple and reproducible manner. The approach proposed by Phillips and Sul to analyse the convergence patterns of groups of economies is formulated as a nonlinear time varying factor model that allows for different time paths as well as individual heterogeneity. Unlike other approaches in which economies are grouped a priori, it also allows the endogenous determination of convergence clubs. The algorithm, usage, and implementation details are discussed.
Successful Pressing Sequences in Simple Pseudo-Graphs

Motivated by the study of genomes evolving by reversals, the primary topic of this thesis is “successful pressing sequences” in simple pseudo-graphs. Pressing sequences where first introduced by Hannenhali and Pevzner in 1999 where they showed that sorting signed permutation problem can be solved in polynomial time, therefore demonstrating that the length of a most parsimonious solution to the genome inversion only rearrangement problem can be determined efficiently. A signed permutation is an integer permutation where each entry is given a sign: plus or minus. A reversal in a signed permutation is the operation of reversing a subword and flipping the signs of the subword’s entries. The primary computational problem of sorting signed permutations by reversals is to find the minimum number of reversals needed to transform a signed permutation into the positive identity permutation. Hannenhalli and Pevzner showed that the signed sorting problem can be solved in polynomial-time in contrast to the problem of sorting unsigned permutations, which is known to be NP-hard in general. At the core of the argument given by Hannenhali and Pevzner is the study of successful pressing sequences on vertex 2-colored graphs. The connection between permutation sorting and phylogenetics dates back to at least the 1930’s, when two biologists, Dobzhansky and Sturtevant, wrote a series of papers in which they argued that the relationships between possible gene arrangements within a given chromosome encode critical information about the evolutionary history of species containing those genomes. In particular, they introduced the idea that the degree of disorder between the genes in two genomes is an indicator of the
On Distributed Averaging for Stochastic k-PCA

In the stochastic k-PCA problem, we are given i.i. d. samples from an unknown distribution over vectors, and the goal is to compute the top k eigenvalues and eigenvectors of the moment matrix. In the simplest distributed variant, we have 'm' machines each of which receives 'n' samples. Each machine performs some computation and sends an O(k) size summary of the local dataset to a central server. The server performs an aggregation and computes the desired eigenvalues and vectors. The goal is to achieve the same effect as the server computing using m*n samples by itself. The main choices in this framework are the choice of the summary, and the method of aggregation. We consider a slight variant of the well-studied "distributed averaging" approach, and prove that this leads to significantly better bounds on the dependence between 'n' and the eigenvalue gaps. Our method can also be applied directly to a setting where the "right" value of the parameter k (i.e., one for which there is a non-trivial eigenvalue gap) is not known exactly. This is a common issue in practice which prior methods were unable to address.
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning

Knowledge Graph (KG) reasoning aims at finding reasoning paths for relations, in order to solve the problem of incompleteness in KG. Many previous path-based methods like PRA and DeepPath suffer from lacking memory components, or stuck in training. Therefore, their performances always rely on well-pretraining. In this paper, we present a deep reinforcement learning based model named by AttnPath, which incorporates LSTM and Graph Attention Mechanism as the memory components. We define two metrics, Mean Selection Rate (MSR) and Mean Replacement Rate (MRR), to quantitatively measure how difficult it is to learn the query relations, and take advantages of them to fine-tune the model under the framework of reinforcement learning. Meanwhile, a novel mechanism of reinforcement learning is proposed by forcing an agent to walk forward every step to avoid the agent stalling at the same entity node constantly. Based on this operation, the proposed model not only can get rid of the pretraining process, but also achieves state-of-the-art performance comparing with the other models. We test our model on FB15K-237 and NELL-995 datasets with different tasks. Extensive experiments show that our model is effective and competitive with many current state-of-the-art methods, and also performs well in practice.
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations

Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-by-word matching. Such requirements and assumptions are infeasible for most languages, especially for languages with large linguistic distances, e.g., English and Chinese. In this work, we propose a Multilingual Language Model with deep semantic Alignment (MLMA) to generate language-independent representations for cross-lingual sequence labeling. Our methods require only monolingual corpora with no bilingual resources at all and take advantage of deep contextualized representations. Experimental results show that our approach achieves new state-of-the-art NER and POS performance across European languages, and is also effective on distant language pairs such as English and Chinese.
Induction Networks for Few-Shot Text Classification

Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the sample-wise level. However, this sample-wise comparison may be severely disturbed by the various expressions in the same class. Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries. In this paper, we propose a novel Induction Network to learn such a generalized class-wise representation, by innovatively leveraging the dynamic routing algorithm in meta-learning. In this way, we find the model is able to induce and generalize better. We evaluate the proposed model on a well-studied sentiment classification dataset (English) and a real-world dialogue intent classification dataset (Chinese). Experiment results show that on both datasets, the proposed model significantly outperforms the existing state-of-the-art approaches, proving the effectiveness of class-wise generalization in few-shot text classification.
Learning to Build Natural Audio Production Interfaces

Improving audio production tools provides a great opportunity for meaningful enhancement of creative activities due to the disconnect between existing tools and the conceptual frameworks within which many people work. In our work, we focus on bridging the gap between the intentions of both amateur and professional musicians and the audio manipulation tools available through software. Rather than force nonintuitive interactions, or remove control altogether, we reframe the controls to work within the interaction paradigms identified by research done on how audio engineers and musicians communicate auditory concepts to each other: evaluative feedback, natural language, vocal imitation, and exploration. In this article, we provide an overview of our research on building audio production tools, such as mixers and equalizers, to support these kinds of interactions. We describe the learning algorithms, design approaches, and software that support these interaction paradigms in the context of music and audio production. We also discuss the strengths and weaknesses of the interaction approach we describe in comparison with existing control paradigms.
Trauma in Patients with Serious Mental Illness: The Acceptability and Impact of a Brief Psychoeducational Intervention for Trauma in the General Acute Inpatient Psychiatric Setting

Prevalence rates of posttraumatic stress disorder (PTSD) for individuals with serious mental illnesses (SMIs) are high, with an estimated 49% to 100% being exposed to potentially traumatic events. The consequences of these disorders are serious and lasting, with PTSD being the costliest of mental health disorders. At the same time, established evidence-based treatments for PTSD are often not feasible in treatment settings utilized by individuals with SMIs, namely the general acute inpatient psychiatric hospital. Psychoeducational approaches have been incorporated as a component of evidence-based interventions for trauma and have been feasibly implemented in the general acute inpatient psychiatric hospital. The objective of this study was to examine the acceptability and impact of a two-session psychoeducational intervention for trauma for individuals in a general acute inpatient psychiatric facility. Participants (N = 70) on a general acute inpatient psychiatric unit were assigned to one of two conditions: a) twosession psychoeducational intervention or b) general group treatment as usual. Measurements of participant PTSD symptoms and knowledge of PTSD were collected before and after treatment. Participants were also assessed subsequent to the intervention to examine treatment acceptability, as well as readiness to engage in future traumafocused treatment. Results indicated that participants in the two-session psychoeducational group rated the group as significantly more acceptable than participants rated the acceptability of the general treatment group. Involvement in the two session psychoeducational group did not result in an increase in PTSD symptomatology. No significant differences in knowledge of PTSD or readiness for future trauma-focused treatment were found. Nonetheless, these findings have implications for the treatment and TRAUMA IN PATIENTS WITH SERIOUS MENTAL ILLNESS v dissemination of evidence-based treatment of PTSD for patients with SMI in the general acute inpatient psychiatric hospital. TRAUMA IN PATIENTS WITH SERIOUS MENTAL ILLNESS vi Table of
Improved Regret Bounds for Bandit Combinatorial Optimization

\textit{Bandit combinatorial optimization} is a bandit framework in which a player chooses an action within a given finite set $\mathcal{A} \subseteq \{ 0, 1 \}^d$ and incurs a loss that is the inner product of the chosen action and an unobservable loss vector in $\mathbb{R} ^ d$ in each round. In this paper, we aim to reveal the property, which makes the bandit combinatorial optimization hard. Recently, Cohen et al.~\citep{cohen2017tight} obtained a lower bound $\Omega(\sqrt{d k^3 T / \log T})$ of the regret, where $k$ is the maximum $\ell_1$-norm of action vectors, and $T$ is the number of rounds. This lower bound was achieved by considering a continuous strongly-correlated distribution of losses. Our main contribution is that we managed to improve this bound by $\Omega( \sqrt{d k ^3 T} )$ through applying a factor of $\sqrt{\log T}$, which can be done by means of strongly-correlated losses with \textit{binary} values. The bound derives better regret bounds for three specific examples of the bandit combinatorial optimization: the multitask bandit, the bandit ranking and the multiple-play bandit. In particular, the bound obtained for the bandit ranking in the present study addresses an open problem raised in \citep{cohen2017tight}. In addition, we demonstrate that the problem becomes easier without considering correlations among entries of loss vectors. In fact, if each entry of loss vectors is an independent random variable, then, one can achieve a regret of $\tilde{O}(\sqrt{d k^2 T})$, which is $\sqrt{k}$ times smaller than the lower bound shown above. The observed results indicated that correlation among losses is the reason for observing a large regret.
Using ScratchJr to Foster Young Children’s Computational Thinking Competence: A Case Study in a Third-Grade Computer Class

This study investigated young children’s computational thinking (CT) development by integrating ScratchJr into a programming curriculum. Twelve third graders (six males and six females) voluntarily participated in an experiment-based computer class conducted at a public elementary school in Taiwan. This study adopted a case study methodology to investigate research questions in one specific case (8-week CT educational training). A one-group quasi-experimental pretest and posttest design with the support of qualitative observation was used to examine four research topics: CT competence progress, programming behaviors in a CT framework, factors influencing CT competence, and learning responses to CT training. The quantitative results indicated that students immersing in weekly programming projects significantly improved in terms of their CT competence, which was mostly retained 1 month after completion of the class. The programming behaviors indicated that students’ CT concepts (sequence, event, and parallelism) and practice (testing and debugging as well as reusing and remixing) significantly improved. Moreover, parents’ active involvement in take-home assignments influenced students’ long-term CT competence retention. The qualitative results indicated that students enjoyed using tablet computers to learn ScratchJr programming and demonstrated various leaning behaviors in a three-stage instructional design model.