Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Neural Machine Translation with Language Models

Training neural machine translation models is notoriously slow and requires abundant parallel corpora and computational power. In this work we propose an approach of transferring knowledge from separately trained language models to translation systems and investigate several techniques to improve translation quality when there is a lack of parallel data and computational resources. Our method is based on fusion between translation system and language model and initialization of translation system with weights from pretrained language models. We show that this technique gives +2.2 BLEU score on En–Fr pair of WMT europarl-7 dataset and allows us to reach 20.7 BLEU on 20k parallel sentences in less than 8 hours of training on a single NVIDIA GeForce GTX 1080 Ti. We speciﬁcally note, that for this advance we use nothing but monolingual corpora for source and target languages. Signiﬁcant part of presented results was obtained during DeepHack.Babel hackathon on low-resource machine translation organized by iPavlov Lab (Moscow Institute of Physics and Technology).
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task

Chinese grammatical error diagnosis is an important natural language processing (NLP) task, which is also an important application using artificial intelligence technology in language education. This paper introduces a system developed by the Chinese Multilingual & Multimodal Corpus and Big Data Research Center for the NLP-TEA shared task, named Chinese Grammar Error Diagnosis (CGED). This system regards diagnosing errors task as a sequence tagging problem, while takes correction task as a text classification problem. Finally, in the 12 teams, this system gets the highest F1 score in the detection task and the second highest F1 score in mean in the identification task, position task and the correction task.
Named Entity Recognition With Parallel Recurrent Neural Networks

We present a new architecture for named entity recognition. Our model employs multiple independent bidirectional LSTM units across the same input and promotes diversity among them by employing an inter-model regularization term. By distributing computation across multiple smaller LSTMs we find a significant reduction in the total number of parameters. We find our architecture achieves state-of-the-art performance on the CoNLL 2003 NER dataset.
Short-Term Birth Sequelae of the 1918–1920 Influenza Pandemic in the United States: State-Level Analysis

Abstract This paper examines short-term birth sequelae of the influenza pandemic of 1918–1920 in the United States using monthly data on births and all-cause deaths for 19 US states in conjunction with data on maternal deaths, stillbirths, and premature births. The data on births and all-cause deaths are adjusted for seasonal and trend effects, and the residual components of the 2 time series coinciding with the timing of peak influenza mortality are examined for these sequelae. Notable findings include: 1) a drop in births in the 3 months following peak mortality; 2) a reversion in births to normal levels occurring 5–7 months after peak mortality; and 3) a steep drop in births occurring 9–10 months after peak mortality. Interpreted in the context of parallel data showing elevated premature births, stillbirths, and maternal mortality during times of peak influenza mortality, these findings suggest that the main impacts of the 1918–1920 influenza on reproduction occurred through: 1) impaired conceptions, possibly due to effects on fertility and behavioral changes; 2) an increase in the preterm delivery rate during the peak of the pandemic; and 3) elevated maternal and fetal mortality, resulting in late-term losses in pregnancy.
QuantTree: Histograms for Change Detection in Multivariate Data Streams

We address the problem of detecting distribution changes in multivariate data streams by means of histograms. Histograms are very general and flexible models, which have been relatively ignored in the change-detection literature as they often require a number of bins that grows unfeasibly with the data dimension. We present QuantTree, a recursive binary splitting scheme that adaptively defines the histogram bins to ease the detection of any distribution change. Our design scheme implies that i) we can easily control the overall number of bins and ii) the bin probabilities do not depend on the distribution of stationary data. This latter is a very relevant aspect in change detection, since thresholds of tests statistics based on these histograms (e.g., the Pearson statistic or the total variation) can be numerically computed from univariate and synthetically generated data, yet guaranteeing a controlled false positive rate. Our experiments show that the proposed histograms are very effective in detecting changes in high dimensional data streams, and that the resulting thresholds can effectively control the false positive rate, even when the number of training samples is relatively small.
Vis-à-vis with Leonardo. Designing Digital Encounters

The paper describes, frames and analyses the exhibit Leonardo Plays Leonardo. Milan. Life. Nature. at Fondazione Stelline in Milan. This gesture-based interactive installation, located in the cloister of Palazzo delle Stelline, show-cases Leonardo da Vinci and his Milanese years. The project is based on the idea of digital encounter and allows visitors to meet a real size simulated hologram of the Renaissance Master who tells several short stories about his life, his years in Milan and his relationship with nature. The article frames the projects in the context of digital encounters in CH field and embodied interaction systems, describes how the system was realized and presents preliminary data about the usage.
Towards Fast and Scalable Graph Pattern Mining

While there has been a tremendous interest in processing graph-structured data, existing distributed graph processing systems take several minutes or even hours to mine simple patterns on graphs. In this paper, we try to answer the question of whether it is possible to build a graph pattern mining engine that is both fast and scalable. Leveraging the observation that in several pattern mining tasks, providing an approximate answer is good enough, we propose the use of approximation for graph pattern mining. However, we find that existing approximation techniques do not work for this purpose. Based on this, we present a new approach for approximate graph pattern mining that leverages recent advancements in graph approximation theory. Our preliminary evaluations show encouraging results: compared to state-of-the-art, finding 3-motifs in Twitter graph is 165⇥ faster while incurring only 5% error. We conclude by discussing several systems challenges to make our proposal practical.
Performance Study of Uplink and Downlink Splitting in Ultradense Highly Loaded Networks

This paper provides new insights into the performance of Uplink and Downlink Splitting (UDS) in highly loaded wireless communication systems, in terms of both serving nodes and the number of users with high traffic activity. The study puts special focus on the gains that UDS could bring in terms of SINR and throughput when compared with systems with cell range expansion (CRE) in the classic downlink based cell association. CRE not only helps to offload users from macro- to pico-eNBs, but also improves UL service. Instead of an aggregated throughput analysis, a detailed classification of users is performed to figure out the causes of users’ gain or loss after applying each strategy at the system level. Results show marginal gains of a pure path loss based UDS when compared with the intrinsic UL gains of CRE. Given the extra flexibility in radio resource management that splitting both links could bring, using an individual UL adjustable cell offset appears to be an interesting strategy to allow for a finer control of UL interference. The dependency of UDS performance with small cell density has also been a matter of study. Results show that the gains of UDS do decrease after a certain density of pico-cells is surpassed.
Inducible Equilibrium for Security Games

Strong Stackelberg equilibrium (SSE) is the standard solution concept of Stackelberg security games. The SSE assumes that the follower breaks ties in favor of the leader and this is widely acknowledged and justified by the assertion that the defender can often induce the attacker to choose a preferred action by making an infinitesimal adjustment to her strategy. Unfortunately, in security games with resource assignment constraints, the assertion might not be valid. To overcome this issue, inspired by the notion of inducibility and the pessimistic Stackelberg equilibrium, this paper presents the inducible Stackelberg equilibrium (ISE), which is guaranteed to exist and avoids overoptimism as the outcome can always be induced with infinitesimal strategy deviation. Experimental evaluation unveils the significant overoptimism and sub-optimality of SSE and thus, verifies the advantage of the ISE as an alternative solution concept.
On the Dependence of the X-Ray Burst Rate on Accretion and Spin Rate

Nuclear burning and its dependence on the mass accretion rate are fundamental ingredients for describing the complicated observational phenomenology of neutron stars (NSs) in binary systems. Motivated by high-quality burst rate data emerging from large statistical studies, we report general calculations relating the bursting rate to the mass accretion rate and NS rotation frequency. In this first work, we ignore general relativistic effects and accretion topology, although we discuss where their inclusion should play a role. The relations we derive are suitable for different burning regimes and provide a direct link between parameters predicted by theory and what is to be expected in observations. We illustrate this for analytical relations of different unstable burning regimes that operate on the surface of an accreting NS. We also use the observed behavior of the burst rate to suggest new constraints on burning parameters. We are able to provide an explanation for the long-standing problem of the observed decrease of the burst rate with increasing mass accretion that follows naturally from these calculations: when the accretion rate crosses a certain threshold, ignition moves away from its initially preferred site, and this can cause a net reduction of the burst rate due to the effects of local conditions that set local differences in both the burst rate and stabilization criteria. We show under which conditions this can happen even if locally the burst rate keeps increasing with accretion.
Characterizing dark matter interacting with extra charged leptons

In the context of a simplified leptophilic dark matter (DM) scenario where the mediator is a new charged fermion carrying leptonic quantum number and the DM candidate is either scalar or vector, the complementarity of different bounds is analyzed. In this framework, the extra lepton and DM are odd under a Z2 symmetry, and hence the leptonic mediator can only interact with the DM state and Standard Model leptons of various flavors. We show that there is the possibility to characterize the DM spin (scalar or vector), as well as the nature of the mediator, through a combined analysis of cosmological, flavor and collider data. We present an explicit numerical analysis for a set of benchmarks points of the viable parameter space of our scenario.
The Relationship Between Aldosterone and Left Ventricular Hypertrophy in Hypertensive Patients

Background Aldosterone is a pertinent hormone in naturally elevating blood pressure within the body by increasing fluid retention in the body via electrolyte reabsorption in the kidneys. Consequently, aldosterone can have an indirect effect on the incidence of LVH considering the hormone can reinforce high blood pressure. However, recent studies have suggested that aldosterone and the renin-angiotensin-aldosterone-system (RAAS) may have a direct role in leading to an increase in left ventricular mass. Patients with hyperaldosteronism, otherwise elevated circulating aldosterone, have shown high frequencies of LVH regardless of the presence of hypertension. Furthermore, cardiomyocytes have been seen to contain mineralocorticoid receptors that bind to aldosterone and can be affected by different RAAS inactivating medications. Overall, current research suggests there may be a regression between LVH and aldosterone. Methods and Results – A retrospective model comparing plasma aldosterone levels and left ventricular hypertrophy measurements in a hypertensive cohort of African Americans from the AdDReaCH trial. Follow-up over the course of a year allowed for multivariate analysis to determine whether elevated levels of plasma aldosterone induced changes in left ventricular mass and diastolic function independent of blood pressure and other variables. Left ventricular hypertrophy was assessed through various left ventricular measurements from contrast-aided MRI examinations. Though average LVMI was greater in patients with greater aldosterone-renin ratios, multivariate analysis suggested that plasma aldosterone-renin ratio does not have a significant, independent relationship to the incidence and severity of LVH. Results call for further research on the topic, as the current study confounds results from prior studies.
Training Small Networks for Scene Classification of Remote Sensing Images via Knowledge Distillation

Scene classification, aiming to identify the land-cover categories of remotely sensed image patches, is now a fundamental task in the remote sensing image analysis field. Deep-learning-model-based algorithms are widely applied in scene classification and achieve remarkable performance, but these high-level methods are computationally expensive and time-consuming. Consequently in this paper, we introduce a knowledge distillation framework, currently a mainstream model compression method, into remote sensing scene classification to improve the performance of smaller and shallower network models. Our knowledge distillation training method makes the high-temperature softmax output of a small and shallow student model match the large and deep teacher model. In our experiments, we evaluate knowledge distillation training method for remote sensing scene classification on four public datasets: AID dataset, UCMerced dataset, NWPU-RESISC dataset, and EuroSAT dataset. Results show that our proposed training method was effective and increased overall accuracy (3% in AID experiments, 5% in UCMerced experiments, 1% in NWPU-RESISC and EuroSAT experiments) for small and shallow models. We further explored the performance of the student model on small and unbalanced datasets. Our findings indicate that knowledge distillation can improve the performance of small network models on datasets with lower spatial resolution images, numerous categories, as well as fewer training samples.
Shear fronts in shear-thickening suspensions

We study the fronts that appear when a shear-thickening suspension is submitted to a sudden driving force at a boundary. Using a quasi-one-dimensional experimental geometry, we extract the front shape and the propagation speed from the suspension flow field and map out their dependence on applied shear. We find that the relation between stress and velocity is quadratic, as is generally true for inertial effects in liquids, but with a pre-factor that can be much larger than the material density. We show that these experimental findings can be explained by an extension of the Wyart-Cates model, which was originally developed to describe steady-state shear-thickening. This is achieved by introducing a sole additional parameter: the characteristic strain scale that controls the crossover from start-up response to steady-state behavior. The theoretical framework we obtain unifies both transient and steady-state properties of shear-thickening materials.
How to use context to disambiguate overlapping categories: The test case of Japanese vowel length

Infants learn the sound categories of their language and adults successfully process the sounds they hear, even though sound categories often overlap in their acoustics. Most researchers agree that listeners use context to disambiguate overlapping categories. However, they differ in their ideas about how context is used. One idea is that listeners normalize out the systematic effects of context from the acoustics of a sound. Another idea is that contextual information may itself be an informative cue to category membership, due to patterns in the types of contexts that particular sounds occur in. We directly contrast these two ways of using context by applying each one to the test case of Japanese vowel length. We find that normalizing out contextual variability from the acoustics does not improve categorization, but using context in a top-down fashion does so substantially. This reveals a limitation of normalization in phonetic acquisition and processing and suggests that approaches that make use of top-down contextual information are promising to pursue.
How and why systemic inflammation worsens quality of life in patients with advanced cancer

ABSTRACT Introduction: The presence of an innate host systemic inflammatory response has been reported to be a negative prognostic factor in a wide group of solid tumour types in both the operable and advanced setting, both local and distant. In addition, this host systemic inflammatory response is associated with both clinician reported patient performance status and self-reported measures of quality of life in patients with cancer. Areas covered: A variety of mechanisms are thought to underlie this, including the influence of the host immune response on physical symptoms such as pain and fatigue, its effect on organ systems associated with physical ability and well being such as skeletal muscle, and bone marrow. Furthermore, this innate inflammatory response is thought to have a direct negative impact on mood through its action on the central nervous system. Expert commentary: It is clear that the host systemic inflammatory response represents a target for intervention in terms of both improving quality of life and prognosis in patients with advanced cancer. Based on this paradigm, future research should focus both on pathways which might be targeted by novel agents, but also on whether existing anti-inflammatory drugs might be of benefit.
Exploring Students’ Conception of Learning through Narratives

In contrast to studies that solely explore students’ identity development during their time at university and beyond – and so have minimal focus on disciplinary and institutional context – others have sought a richer picture. For example, Stevens et al. developed an engineering-specific threedimensional framework of learning consisting of disciplinary knowledge, identification (as an engineer), and navigation (through engineering education) [1]. And Begel and Simon observed novice software developers in industry and described instructional techniques to better prepare recent college graduates for their careers [2]. We draw on these (and other) works to examine students’ experience of, and the effect of, an engineering education.
Radiological evaluation of the relationship between caudolateral curvilinear osteophyte and joint laxity and degenerative joint disease associated with Canine Hip Dysplasia

The aim of the present study was to investigate the relationship between the caudolateral curvilinear osteophyte (CCO) and canine hip dysplasia (CHD), degenerative joint disease (DJD) and joint laxity as measured using the subluxation index (SI) and Norberg Angle (NA). Three hundred and fifty-three dogs belonging to 8 breeds were evaluated. Following sedation, the hips were evaluated radiographically using the ventro-dorsal hip-extended view and the subluxation view. Radiographic changes examined whether DJD was seen or not. CCO was not considered a sign consistent with DJD but was recorded as an independent finding. The overall prevalence of CHD was detected as 36.11%. DJD and CCO were detected in 16.0% and 34.70% hip joints, respectively. Among hips with CCO, 33.9% had radiographic signs of DJD, however, only 6.5% hips without CCO had DJD. A significantly (P<0.01) positive correlation was determined between CCO and DJD. The mean NA was significantly (P<0.001) higher and the mean SI was significantly lower in dogs without CCO than in dogs with CCO. A significantly (P<0.01) positive correlation was observed between CCO and DJD and CCO and SI, and also a significantly (P<0.01) negative correlation was detected between CCO and NA. This study confirmed the relationship between the CCO and DJD, and reaffirmed the relationship between the SI and DJD. Presently the evidence from this study suggests that by evaluating both CCO and DJD together, the gene pool will be improved to produce better hips if dogs with CCO and greater SI than 0.5 are withheld from the breeding population.
Learning to Align across Languages: Toward Multilingual FrameNet

The FrameNet (FN) project, developed at ICSI since 1997, was the ﬁrst lexical resource based on the theory of Frame Semantics, and documents contemporary English. It has inspired related projects in roughly a dozen other languages, which, while based on frame semantics, have evolved somewhat independently. Multilingual FrameNet (MLFN) is an attempt to ﬁnd alignments between them all. The degree to which these projects have adhered to Berkeley FrameNet frames and the data release on which they are based varies, complicating the alignment problem. To minimize the resources needed to produce the alignments, we will rely on machine learning whenever that’s possible and appropriate. We brieﬂy describe the various FrameNets and their history, and our ongoing work employing tools from the ﬁelds of machine translation and document classiﬁcation to introduce a new relation of similarity between frames, combining structural and distributional similarity, and how this will contribute to the coordination of the FrameNet projects, while allowing them to continue to evolve independently.
Pelvic ultrasonography in children and teenagers

* Trabalho realizado na Escola de Ultra-sonografia de Ribeirão Preto (EURP) e no Departamento de Ginecologia e Obstetrícia do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Ribeirão Preto, SP, Brasil. 1. Doutor, Professor Associado da Escola de Ultra-sonografia de Ribeirão Preto (EURP), Médico Assistente do Departamento de Ginecologia e Obstetrícia do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Ribeirão Preto, SP, Brasil. 2. Especialista, Médica Assistente do Departamento de Ginecologia e Obstetrícia do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Ribeirão Preto, SP, Brasil. 3. Mestre, Aluna de Pós-graduação do Departamento de Ginecologia e Obstetrícia do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Ribeirão Preto, SP, Brasil. Endereço para correspondência: Dr. Wellington de Paula Martins. Avenida Bandeirantes, 3900, 8o andar, Campus Universitário, Monte Alegre. Ribeirão Preto, SP, Brasil, 14048-900. Email: wpmartins@gmail.com Recebido para publicação em 13/6/2009. Aceito, após revisão, em 3/11/2009. sangramento vaginal em crianças, amenorreia primária e dor ou massas pélvicas. Sangramento vaginal na criança pré-púbere pode ser causado por corpo estranho, puberdade precoce ou rabdomiossarcoma vaginal. As causas mais comuns de amenorreia primária em adolescentes incluem: disgenesia gonadal (síndrome de Turner) e anomalias müllerianas (malformação uterovaginal). A dor ou as massas pélvicas em crianças e adolescentes podem ocorrer devido a torsão ovariana, cisto ovariano hemorrágico, doença inflamatória pélvica ou gravidez ectópica.