Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling

As the first robotic platforms slowly approach our everyday life, we can imagine a near future where service robots will be easily accessible by non-expert users through vocal interfaces. The capability of managing natural language would indeed speed up the process of integrating such platform in the ordinary life. Semantic parsing is a fundamental task of the Natural Language Understanding process, as it allows extracting the meaning of a user utterance to be used by a machine. In this paper, we present a preliminary study to semantically parse user vocal commands for a House Service robot, using a multi-layer Long-Short Term Memory neural network with attention mechanism. The system is trained on the Human Robot Interaction Corpus, and it is preliminarily compared with previous approaches.
PDC-Transitive: An Enhanced Heuristic for Document Clustering Based on Relational Analysis Approach and Iterative MapReduce

Recently, MapReduce-based implementations of clustering algorithms have been developed to cope with the Big Data phenomenon, and they show promising results particularly for the document clustering problem. In this paper, we extend an efficient data partitioning method based on the relational analysis (RA) approach and applied to the document clustering problem, called PDC-Transitive. The introduced heuristic is parallelised using the MapReduce model iteratively and designed with a single reducer which represents a bottleneck when processing large data, we improved the design of the PDC-Transitive method to avoid the data dependencies and reduce the computation cost. Experiment results on benchmark datasets demonstrate that the enhanced heuristic yields better quality results and requires less computing time compared to the original method.
The Brazilian Portuguese Constructicon: Modeling Constructional Inheritance, Frame Evocation and Constraints in FrameNet Brasil

FrameNet Brasil comprises two major resources: a Lexicon, expanded from the Berkeley FrameNet, and a Constructi-con, whose development led to considerable changes in the FrameNet standards for data structure. This paper reports on the development of the Brazilian Portuguese Constructicon, focusing on the changes made to the FrameNet Brasil data-base structure so as to model (a) Inheritance relations be-tween constructions, (b) Evoking relations between constructions and frames, and (c) Constraints applying to the Construct Elements. As a result of such changes, FrameNet Brasil integrates the two resources into one single database, yielding annotations that are able to provide a comprehensive account of both formal and semantic aspects of sentences.
Low health literacy and excess body weight: a systematic review.

OBJECTIVE There is recent evidence that poor health literacy (HL) could be implicated in the aetiology of obesity and could be an important reason behind obese people's inability to encounter difficulties in overcoming obesity issues. The current study reviews the recent scientific evidence investigating the possible link between poor HL levels and excess body weight in adults and children. METHODS The authors performed a thorough systematic computer-assisted literature search from 1 January 2005 up to 31 May 2017. Only English original studies in healthy people, investigating the relationship between HL and excess body weight, were included. RESULTS Twenty-two studies in total were included in this literature review, 17 studies were conducted in adults and 5 in children. In 17 out of 22 studies reviewed, low HL was significantly associated with increased body mass index, overweight and obesity. In case of children and adolescents, the above association seems to be more consistent compared to adults. CONCLUSION There is good evidence that low levels of HL are associated with excess body weight, particularly in children. Initiatives to improve health literacy levels could be a useful tool in the management of the obesity epidemic.
Dynamic Construction Grammar and Steps Towards the Narrative Construction of Meaning

Dynamic construction grammar (DCG) is a neurcomputational framework for learning and generalizing sentence-tomeaning mappings. It is inspired by the cue competition hypothesis of Bates and MacWhinney, and learns regularities in the ordering of open and closed class words and the corresponding mapping to semantic roles for the open class words. The structure of meaning is a crucial aspect of these form to meaning mappings. Here we describe the DCG framework, and the evolution of meaning representations that have been addressed. The first and most basic meaning representation is a predicate-argument form indicating the predicate, agent, object and recipient. We developed an action recognition system, that detected simple actions and used naïve subjects’ narration to train the model to understand. The DCG comprehension model was then extended to address sentence production. The resulting models were then integrated into a cooperative humanoid robotic platform. We then demonstrated how observed actions could be construed from different perspectives, and used the production model to generate corresponding sentences. In order to allow the system to represent and create meaning beyond the single sentence, we introduce the notions of narrative construction and narrative function word. In the same way that grammatical function words operate on relations between open class elements in the sentence, narrative function words operate on events across multiple sentences in a narrative. This motivates the need for an intermediate representation of meaning in the form of a situation model that represents multiple events and relations between their constituents. In this context we can now begin to address how narrative can enrich perceived meaning as suggested by Bruner. Dynamic Construction Grammar The dynamic construction grammar model is a neurocomputational implementation of concepts developed in the Bates and MacWhinney cue competition model (Bates Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. and MacWhinney 1987; Li and Macwhinney 2013) of language. The principal inspiration from cue competition is that, across languages, a limited set of cues are available for encoding grammatical structure necessary for determining who did what to whom. Figure 1. Grammatical construction comprehension. Input sentences are separated into open (semantic) and closed class (grammatical words). Grammatical words stimulate the recurrent reservoir. Learnable connections between reservoir and readouts that indicate for each ordered semantic word its semantic role in the construction. Each Semantic word (SW) can participate in two phrases, so the Predicate, Amongst these cues are word order and grammatical morphology, in the form of free or bound grammatical The AAAI 2017 Spring Symposium on Computational Construction Grammar and Natural Language Understanding Technical Report SS-17-02
Impact of tumor dimensions and lymph node density on the survival of patients with hypopharyngeal squamous cell carcinoma

Purpose To analyze the potential variables affecting the survival of patients undergoing primary surgery for hypopharyngeal squamous cell carcinoma. Patients and methods Between August 2007 and December 2016, 93 patients with primary hypopharyngeal squamous cell carcinomas undergoing radical surgery at Fudan University Shanghai Cancer Center were reviewed. The clinicopathological features were analyzed retrospectively. The optimal cutoff values were determined based on the receiver operating characteristic curve analysis. Pearson correlation coefficients were used to assess the correlations between variables. The Kaplan–Meier and Cox proportional hazard methods were used to evaluate the impact of variables on overall survival (OS), disease-specific survival (DSS), and disease-free survival (DFS). Results Cox multivariate analysis revealed that a depth of invasion (DOI) ≥ 4.3 mm was correlated with inferior OS (P=0.045), DSS (P=0.046), and DFS (P=0.046). A primary tumor volume (PTV) ≥0.36 mL was related to poor OS (P=0.018), DSS (P=0.026), and DFS (P=0.036). A lymph node density (LND) ≥0.07 was also associated with worse OS (P=0.014) and DSS (P=0.045). Moreover, additional prognostic value was observed in the combined use of PTV and LND. Conclusion The DOI, PTV, and LND obtained from the surgical specimens could provide additional valuable information for prognostic stratification and allowed the more appropriate selection of suitable candidates for more aggressive adjuvant therapy.
Recurrent Multi-Graph Neural Networks for Travel Cost Prediction

Origin-destination (OD) matrices are often used in urban planning, where a city is partitioned into regions and an element (i, j) in an OD matrix records the cost (e.g., travel time, fuel consumption, or travel speed) from region i to region j. In this paper, we partition a day into multiple intervals, e.g., 96 15-min intervals and each interval is associated with an OD matrix which represents the costs in the interval; and we consider sparse and stochastic OD matrices, where the elements represent stochastic but not deterministic costs and some elements are missing due to lack of data between two regions. We solve the sparse, stochastic OD matrix forecasting problem. Given a sequence of historical OD matrices that are sparse, we aim at predicting future OD matrices with no empty elements. We propose a generic learning framework to solve the problem by dealing with sparse matrices via matrix factorization and two graph convolutional neural networks and capturing temporal dynamics via recurrent neural network. Empirical studies using two taxi datasets from different countries verify the effectiveness of the proposed framework.
Association of visceral adiposity and insulin resistance with colorectal adenoma and colorectal cancer

Background/Aims To examine whether visceral adiposity serves as a risk factor for colorectal cancer (CRC) and colorectal adenomas. Methods Two hundred healthy subjects, 200 patients with colorectal adenoma, and 151 patients with CRC (46 with early-stage and 105 with advanced-stage cancers) were enrolled at a tertiary referral hospital. All subjects underwent colonoscopy, and had laboratory data, and computed tomography (CT) scan available for abdominal fat measurement. An abdominal CT scan taken 1 to 4 years (mean interval, 20.6 months) before the diagnosis of CRC was also available in the 42 CRC patients. Results The mean areas of visceral adipose tissue (VAT) areas in the control, adenoma, early- and advanced-stage CRC groups were 94.6, 116.8, 110.4, and 99.7 cm2, respectively (P<0.001). The risk of adenoma positively correlated with VAT area and the visceral-to-total fat ratio (P for trend <0.01), but the risk of CRC did not (P>0.05). The risk of both adenoma and CRC positively correlated with fasting plasma glucose levels (P for trend <0.05). In patients with early-stage cancer (n=17), VAT area decreased when the CT scan at diagnosis was compared with that taken before the diagnosis of CRC, but superficial adipose tissue area did not, so visceral-to-total fat ratio significantly decreased (46.6% vs. 50.7%, respectively, P=0.018) Conclusions VAT area is related to the risk of colorectal adenoma. However, VAT decreases from the early stages of CRC. Impaired fasting glucose has a role in colorectal carcinogenesis.
Image Captioning Based on a Hierarchical Attention Mechanism and Policy Gradient Optimization

Automatically generating the descriptions of an image, i.e., image captioning, is an important and fundamental topic in artificial intelligence, which bridges the gap between computer vision and natural language processing. Based on the successful deep learning models, especially the CNN model and Long Short-Term Memories (LSTMs) with attention mechanism, we propose a hierarchical attention model by utilizing both of the global CNN features and the local object features for more effective feature representation and reasoning in image captioning. The generative adversarial network (GAN), together with a reinforcement learning (RL) algorithm, is applied to solve the exposure bias problem in RNN-based supervised training for language problems. In addition, through the automatic measurement of the consistency between the generated caption and the image content by the discriminator in the GAN framework and RL optimization, we make the finally generated sentences more accurate and natural. Comprehensive experiments show the improved performance of the hierarchical attention mechanism and the effectiveness of our RL-based optimization method. Our model achieves state-of-the-art results on several important metrics in the MSCOCO dataset, using only greedy inference.
Risk factors for the development of brain metastases in patients with HER2-positive breast cancer

Background Patients with metastatic human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) frequently experience brain metastases (BM). We aimed to define risk factors for the development of BM in patients with HER2+ BC and to report on their outcome. Methods This is a retrospective analysis of patients diagnosed with HER2+ BC between January 2000 and December 2014 at Institut Jules Bordet, Belgium. Statistical analyses were conducted with SAS V.9. 4 using Kaplan-Meier method and Cox regression analyses. Results A total of 483 patients were included of whom 108 (22.4%) developed metastases and 52 (10.8%) BM. Among 96 metastatic patients without BM at diagnosis, 40 (41.7%) developed BM in the course of their disease. In multivariate analysis, risk factors for the development of BM were age ≤40 years (HR 2.10, 95 % CI 1.02 to 4.36), tumour size >2 cm (HR 4.94, 95% CI 1.69 to 14.47), nodal involvement (HR 3.48, 95% CI 1.47 to 8.25), absence or late start (≥6 months after initial diagnosis) of adjuvant anti-HER2 treatment (HR 3.79, 95% CI 1.52 to 9.43 or HR 2.65, 95% CI 1.03 to 6.82) and the development of lung metastases as first site of relapse (HR 6.97, 95% CI 3.41 to 14.24). Twenty-two patients with HER2+ BC and BM sent to our institute for further treatment were included in the outcome analysis. Asymptomatic patients at the time of BM diagnosis showed a better overall survival than symptomatic patients (HR 0.49, 95% CI 0.25 to 0.94). Conclusion A considerable number of patients with metastatic HER2+ BC will develop BM. Screening of patients with risk factors for BM might lead to early detection and better outcome. However, randomised controlled trials examining the use of MRI as a screening method for BM in patients with metastatic BC are warranted before such an approach can be recommended.
Responsiveness of Physical Rehabilitation Centers in Capital of Iran: Disparities and Related Determinants in Public and Private Sectors

Background: Responsiveness as a non-medical, non-financial goal of the health system is of special importance to people with physical disability. The current study assessed the experiences of people with physical disabilities when they encounter rehabilitation centers in Tehran. Methods: This cross-sectional study was conducted in Tehran, the capital of Iran. The sample consisted of 610 people with physical disabilities referred to 10 comprehensive rehabilitation centers (CRCs) selected by Quota sampling. Data were collected by a standard responsiveness questionnaire proposed by the World Health Organization (WHO) and were analyzed by a standard protocol. Blinder-Oaxaca analysis was done to explain the inequality in performance of public and private sectors. Results: Study participants included 298 (48.7%) women and 312 (51.3%) men. The mean age of the respondents was 46.3 (SD = 14.3) for women and 45.6 (SD = 15.4) for men. Prompt attention (33.3%) and confidentiality (1.3%) were the most and least important reported domains, respectively. Overall poor responsiveness was reported by 20.9% of respondents. Private rehabilitation centers showed significantly better performance in communication, basic amenities and autonomy compared to public centers (P ≤ 0.05). Perceived social class explained 76% of the inequality in autonomy in the private and public sector (P ≤ 0.05). Conclusion: Improving overall responsiveness in domains that are of high importance from the respondents' viewpoint but are performing poorly—areas such as prompt attention and basic amenities—is essential. Additionally, interventions are needed to improve the performance of the public centers and providers in the areas of participation of service users in all social classes in their rehabilitation decisions and procedures, clear communication, and basic amenities.
Technology-Assisted Ankle Rehabilitation Improves Balance and Gait Performance in Stroke Survivors: A Randomized Controlled Study With 1-Month Follow-Up

Many stroke survivors have limited ankle range of motion (ROM) caused by weak dorsiflexors and stiff plantarflexors. Passive ankle stretching exercises with physical therapists or a stretching board are usually recommended, but these treatments have some limitations (e.g., cost and availability of physical therapists). In this paper, we assessed the results of ankle stretching exercises delivered by a robotic ankle stretching system called motorized ankle stretcher (MAS) that we developed or by a stretching board on ankle ROM, balance control, and gait performance. The 16 stroke survivors were randomly assigned to an intervention group (IG) or a control group (CG) and participated in seven sessions of dorsiflexion stretching exercises for three-and-a-half consecutive weeks. Laboratory assessments included pre-assessment (baseline at the beginning of the first exercise session), post-assessment (at the end of the seventh exercise session), and retention assessment (one month after the seventh exercise session). All assessments included ankle ROM for the affected side, static/dynamic balance control with a sensory organization test (SOT), walking speed, walking cadence, and step length for the affected and unaffected sides. During seven sessions of ankle stretching exercises, the IG performed them using the MAS, and the CG used a stretching board. The IG significantly improved ankle ROM, SOT scores (i.e., static/dynamic balance control), walking speeds, walking cadences, and step lengths for the unaffected side after completing the seven exercise sessions of ankle stretching exercises and maintained the enhancements at the retention assessment. The CG did not significantly improve across the majority of outcome measures except for the SOT scores between the pre-assessment and retention assessment. Future work will investigate the ideal intensity, frequency, and duration of exercising with the MAS. Our research on technology-assisted ankle rehabilitation, which can ascertain the level of persistent improvement, long-term performance retention, and carry-over effects in stroke survivors, can be used to inform future designs.
The effect of attachment and environmental manipulations on cooperative behavior in the prisoner’s dilemma game

Cooperation and competition are vital for human survival and for social progress. In this study we examine the impact of external (environmental) and internal (individual differences) factors on the tendency to cooperate or compete in social conflicts. To this end, 53 young adults played blocks of the repeated Prisoner’s Dilemma Game with each other or with a computer. The environmental context was manipulated across blocks, by introducing uncertainty, randomly losing or gaining money. Individual differences were assessed by participants’ attachment style. We found that participants cooperated more when randomly losing money compared to when randomly winning or in the neutral condition. Moreover, in a negative uncertain environment, individuals with higher anxious and avoidant attachment styles cooperated less. The above effects were only observed when playing against a human and not a computer. Overall, the findings highlight the dependency of cooperative behavior on the context as driven by external and internal factors.
Toward effective prescription of inhaled corticosteroids in chronic airway disease

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Neural Likelihoods via Cumulative Distribution Functions

We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs). By the application of automatic differentiation with respect to response variables and then to parameters of this CDF representation, we are able to build black box CDF and density estimators. A suite of families is introduced as alternative constructions for the multivariate case. At one extreme, the simplest construction is a competitive density estimator against state-of-the-art deep learning methods, although it does not provide an easily computable representation of multivariate CDFs. At the other extreme, we have a flexible construction from which multivariate CDF evaluations and marginalizations can be obtained by a simple forward pass in a deep neural net, but where the computation of the likelihood scales exponentially with dimensionality. Alternatives in between the extremes are discussed. We evaluate the different representations empirically on a variety of tasks involving tail area probabilities, tail dependence and (partial) density estimation.
Ranking Based Linear Constraint Handling Method with Adaptive Penalty

A novel linear constraint handling technique for the covariance matrix adaptation evolution strategy (CMA-ES) is proposed. The proposed constraint handling exhibits two invariance properties. One is the invariance to arbitrary element-wise increasing transformation of the objective and constraint functions. The other is the invariance to arbitrary affine transformation of the search space. The proposed technique virtually transforms a constrained optimization problem into an unconstrained optimization problem by considering an adaptive weighted sum of the ranking of the objective function values and the ranking of the constraint violations that are measured by the Mahalanobis distance between each candidate solution to its projection onto the boundary of the constraints. Simulation results are presented and show that the CMA-ES with the proposed constraint handling exhibits the affine invariance and performs similarly to the CMA-ES on unconstrained counterparts.
Complete Genome Sequence of the Model Halovirus PhiH1 (ΦH1)

The halophilic myohalovirus Halobacterium virus phiH (ΦH) was first described in 1982 and was isolated from a spontaneously lysed culture of Halobacterium salinarum strain R1. Until 1994, it was used extensively as a model to study the molecular genetics of haloarchaea, but only parts of the viral genome were sequenced during this period. Using Sanger sequencing combined with high-coverage Illumina sequencing, the full genome sequence of the major variant (phiH1) of this halovirus has been determined. The dsDNA genome is 58,072 bp in length and carries 97 protein-coding genes. We have integrated this information with the previously described transcription mapping data. PhiH could be classified into Myoviridae Type1, Cluster 4 based on capsid assembly and structural proteins (VIRFAM). The closest relative was Natrialba virus phiCh1 (φCh1), which shared 63% nucleotide identity and displayed a high level of gene synteny. This close relationship was supported by phylogenetic tree reconstructions. The complete sequence of this historically important virus will allow its inclusion in studies of comparative genomics and virus diversity.