Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Image Dehazing via Conditional Generative Adversarial Network

In this paper, we present an algorithm to directly restore a clear image from a hazy image. This problem is highly ill-posed and most existing algorithms often use hand-crafted features, e.g., dark channel, color disparity, maximum contrast, to estimate transmission maps and then atmospheric lights. In contrast, we solve this problem based on a conditional generative adversarial network (cGAN), where the clear image is estimated by an end-to-end trainable neural network. Different from the generative network in basic cGAN, we propose an encoder and decoder architecture so that it can generate better results. To generate realistic clear images, we further modify the basic cGAN formulation by introducing the VGG features and an L1-regularized gradient prior. We also synthesize a hazy dataset including indoor and outdoor scenes to train and evaluate the proposed algorithm. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on both synthetic dataset and real world hazy images.
Records of Cyathea spinulosa Wallich ex Hooker (Cyatheaceae) and Cycas pectinata Griff.(Cycadaceae) from the Churiya Hills of Eastern Nepal

Nepal hosts 16 plant species of CITES appendix. The CITES appendix II includes Cyathea spp. (tree fern, Family Cyatheaceae) and the appendix III includes Cycas pectinata (Family Cycadaceae). During an ecological survey of Churiya hills of eastern Nepal, these two rare and endangered plants have been recorded in their natural habitats. The Churiya in eastern Nepal receives relatively high precipitation, and for its steep slopes with erodible poor soil, Churiya has very little to offer for agriculture and human settlement keeping the natural forest intact. However, the human population growth in the area has put pressure on the habitat and threatened the rare plants. Key words : Ecology; Forest; Fossil plants; Threatened species DOI: 10.3126/njst.v10i0. 2827 Nepal Journal of Science and Technology Volume 10, 2009 December Page: 69-72
A Demonstration of the OtterTune Automatic Database Management System Tuning Service

Database management systems (DBMSs) have a plethora of tunable knobs that control almost everything in the system. The performance of a DBMS is highly dependent on these configuration knobs, however, getting this tuning right is hard. Many organizations resort to hiring experts to configure these knobs, but this is prohibitively expensive. As databases grow in both size and complexity, optimizing a DBMS has surpassed the abilities of even the best human experts. We recently introduced OtterTune, a tuning service that is able to automatically find good settings for a DBMS's configuration knobs. OtterTune leverages data collected from previous tuning efforts to train machine learning models, and recommends new configurations that are as good as or better than ones generated by existing tools or a human expert. In this demonstration, we showcase OtterTune's ability to automatically select a configuration that improves a DBMS's performance.
Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?

Do unsupervised methods for learning rich, contextualized token representations obviate the need for explicit modeling of linguistic structure in neural network models for semantic role labeling (SRL)? We address this question by incorporating the massively successful ELMo embeddings (Peters et al., 2018) into LISA (Strubell and McCallum, 2018), a strong, linguistically-informed neural network architecture for SRL. In experiments on the CoNLL-2005 shared task we find that though ELMo out-performs typical word embeddings, beginning to close the gap in F1 between LISA with predicted and gold syntactic parses, syntactically-informed models still out-perform syntax-free models when both use ELMo, especially on out-of-domain data. Our results suggest that linguistic structures are indeed still relevant in this golden age of deep learning for NLP.
EC NEUROLOGY Mini Review Neural Connections and Mental States: The Need for a Neurocognitive Framework

The extensive research into the functional connectivity of the human brain – the human connectome – holds significant promise for the understanding of both the normal brain and a broad range of neurological conditions. There is, however, a growing realization that the human connectome research faces a number of challenges that need to be addressed in order to harvest the full potential of such research. Some of the major issues to be addressed have been emphasized as being: (a) Scales, (b) Individual variability, (c) Structural plasticity, and (d) Structure-function relationship. In the present communication it is argued that an important contribution to the solution of such problems is to conduct human connectome research within the framework of integrative models bridging neural connections and mental states via a computational level. As an example of such an approach the REF (Reorganization of Elementary Functions) framework is presented. The REF framework (the original REF model, the REFCON and REFGEN models) emphasizes dynamic and integrative processes as well as the importance of situationally and experience-dependent strategies. With such an emphasis, this framework appears to be a promising basis for both the design and interpretation of human connectome research.
Aznalcóllar dam failure. Part 1: Field observations and material properties

The paper describes the failure of Aznalcollar dam, in southern Spain, in April 1998. The rockfill dam slid forward and released a flow of acid-saturated tailings. The geology and geomorphology of the site are described. The results of a detailed laboratory testing programme on the tailings material and on the overconsolidated high-plasticity foundation clay are given. Special attention is paid to the identification of cementation of the tailings and to the strength and consolidation properties of the foundation clay. Failure features are interpreted on the basis of the field evidence, taking into account the measured geotechnical properties of the materials involved.
Relation between Democratic Leadership and Organizational Cynicism

This study intends to analyze the relation between school principals' leadership styles and teachers' perception of organizational cynicism. The study group consists of 268 participants teaching at high schools in the Balikesir district of Turkey during 2014-2015 academic year. In the study, which used relational survey model, data was collected through Democratic Leadership Scale and Organizational Cynicism Scale and analyzed by mean scores, independent t-test, one-way analysis of variance (ANOVA), and simple linear regression. The results revealed that democratic leadership is a significant predictor of organizational cynicism, and it is negatively connected with organizational cynicism. In addition, it was found that there were significant differences between seniority and gender groups as regards democratic leadership, between seniority groups as regards all dimensions of organizational cynicism, and between gender as regards affective cynicism. All the results of the study showed that the democratic leadership style displayed by school principals influences the organizational cynicism perceived by teachers.
Regional variations of antioxidant capacity and phenolic properties in the Iranian jujube collection

Background & Aim: Fruits from the jujube plant (Ziziphus jujuba) possess health-promoting effects and medicinal properties. Jujube has a long history of usage as a remedy in Iranian traditional medicine and is recommended for the treatment of some diseases. This study investigated the variations in antioxidant capacities (AOX), total phenol (TPH), total flavonoid (TF) and total anthocyanin (TA) contents of fruit extracts among 29 jujube accessions originating from 7 provinces of Iran. Experimental: The fruits from 29 accessions were extracted with methanol (85%). DPPH assay were performed for determination of antioxidant capacity of each extract. The total phenols were measured by Folin- Ciocalteu method and total flavonoids evaluated using the aluminum chloride colorimetric procedure while total anthocyanins were determined by pH-differential method. Results & Discussion: The results from the analysis of variance (ANOVA) revealed that the examined accessions of Z. jujuba were highly variable in all evaluated phytochemical traits (P <0.01). Principal component analysis (PCA) based on four phytochemical characters confirmed this broad variation and also identified three distinct group among the accessions. The accessions Hamidabad (Mazandaran), Bayaziyeh (Isfahan) and kuhpayeh2 (Isfahan) can considered by their high values for the TPH, TF, and TA values, respectively. On the other hand, accessions Ardestan (Isfahan), Ghaziolya (Ghom) and Maroon (Ghom) showed high AOX. No correlation was found between total phenol content and antioxidant activity. Industrial and practical recommendations: These findings suggest the importance of genotype and origin for determining antioxidant potential and phenolic contents of jujube plants. The knowledge of the diversities, found in this study, will allow a better plant exploitation related to pharmaceutical uses and also a better conduction of breeding programs for Ziziphus jujuba ecotypes.
Avian blood parasites in the National Natural Park Chingaza: High Andes of Colombia

Birds from the high Andes in the National Natural Park Chingaza, Colombia, were surveyed for blood parasites in April 2002 and March 2003. Of 136 birds examined, representing 40 species of 14 families, 38 were infected with blood parasites. Parasites of the genera Leucocytozoon (21.3% prevalence), Plasmodium (8.1%), Hepatozoon (2.9%), Microfilariae (2.9%) and Haemoproteus (1.5%) were recorded. Twenty five avian species in this survey were examined for blood parasites for the first time and 49 new parasite-host associations were identified. The most striking observation of this study was the high prevalence of Leucocytozoon spp. In the Neotropics, the prevalence of Leucocytozoon spp. has not been reported to be greater than 0.2% and is closely associated with the presence of migrant birds. Its presence in native avian host suggests a local transmission cycle in the zone.
Protective Effect of the Nitric Oxide Pathway in L-Citrulline Renal Ichaemia-Reperfusion Injury in Rats (L-citrulline/ischaemia-reperfusion/nitricoxidesynthase/L-NAME)

To observe the protective effects of L-cit- rulline on the renal I/R injury and elucidate the mechanisms involved, 48 rats were randomized into eight groups: Group 1: sham operated; Group 2: I/R (45 min renal ischaemia and 24 h reperfusion); Group 3: I/R + L-citrulline (300 mg/kg, i.g.); Group 4: I/R + L-citrulline (600 mg/kg, i.g.); Group 5: I/R + L-citrulline (900 mg/kg, i.g.); Group 6: I/R + normal saline (NS, i.g.); Group 7: I/R + N sup ω nitro-L-ar- ginine ester (L-NAME, 20 mg/kg, i.p.); Group 8: I/R + L-citrulline (900 mg/kg, i.g.) + L-NAME (20 mg/ kg, i.p.). At the end of the reperfusion period, serum was collected and the kidneys underwent histological and biochemical examinations. Our results showed
SAM: Pushing the Limits of Saliency Prediction Models

The prediction of human eye fixations has been recently gaining a lot of attention thanks to the improvements shown by deep architectures. In our work, we go beyond classical feed-forward networks to predict saliency maps and propose a Saliency Attentive Model which incorporates neural attention mechanisms to iteratively refine predictions. Experiments demonstrate that the proposed strategy overcomes by a considerable margin the state of the art on the largest dataset available for saliency prediction. Here, we provide experimental results on other popular saliency datasets to confirm the effectiveness and the generalization capabilities of our model, which enable us to reach the state of the art on all considered datasets.
Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings

Neural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data. One way to improve NMT for lower-resource settings is to initialize a word-based NMT model with pretrained word embeddings. However, rare words still suffer from lower quality word embeddings when trained with standard word-level objectives. We introduce word embeddings that utilize morphological resources, and compare to purely unsupervised alternatives. We work with Arabic, a morphologically rich language with available linguistic resources, and perform Ar-to-En MT experiments on a small corpus of TED subtitles. We find that word embeddings utilizing subword information consistently outperform standard word embeddings on a word similarity task and as initialization of the source word embeddings in a low-resource NMT system.
Epistemic Attack Semantics

We present a probabilistic interpretation of the plausibility of attacks in abstract argumentation frameworks by extending the epistemic approach to probabilistic argumentation with probabilities on attacks. By doing so we also generalise the previously proposed attack semantics by Villata et al. to the probabilistic setting and provide a fine-grained assessment of the plausibility of attacks. We also consider the setting where partial probabilistic information on arguments and/or attacks is given and missing probabilities have to be derived.
An interpretation of the interaction between lodgepole pine , the mountain pine beetle and its associated blue stain fungi in western Canada

The interaction between lodgepole pine (Pinus contorta Dougl.) and the mountain pine beetle (Dendroctonus ponderosae Hopk.), with its associated blue stain fungi (Ceratocystis montia Rumb. and Europhium clavigerum Robinson and Davidson), is described, as are consequences of this interaction for lodgepole pine trees and stands. A map of hazard ratings for western Canada based upon climatic variables affecting this interaction is proposed. Finally, guidelines for management of lodgepole pine to minimize losses from the mountain pine beetle are included.
Neural Metaphor Detecting with CNN-LSTM Model

Metaphors are figurative languages widely used in daily life and literatures. It’s an important task to detect the metaphors evoked by texts. Thus, the metaphor shared task is aimed to extract metaphors from plain texts at word level. We propose to use a CNN-LSTM model for this task. Our model combines CNN and LSTM layers to utilize both local and long-range contextual information for identifying metaphorical information. In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task. We also incorporated some additional features such as part of speech (POS) tags and word cluster to improve the performance of model. Our best model achieved 65.06% F-score in the all POS testing subtask and 67.15% in the verbs testing subtask.
Popcorn : a replicated-kernel OS based on Linux

In recent years, the number of CPUs per platform has continuously increased, affecting almost all segments of the computer market. Because of this trend, many researchers have investigated the problem of how to scale operating systems better on high core-count machines. While many projects have used Linux as a vehicle for this investigation, others have proposed new OS designs. Among them, the replicated-kernel OS model, specifically the multikernel, has gained traction. In this paper, we present Popcorn: a replicated-kernel OS based on Linux. Popcorn boots multiple Linux kernel instances on multicore hardware, one per core or group of cores. Kernels communicate to give to applications the illusion that they are running on top of a single OS. Applications can freely migrate between kernels, exploiting all the hardware resources available on the platform, as in SMP Linux.
How Can Personalized Tourniquet Systems Accelerate Rehabilitation of Wounded Warriors, Professional Athletes and Orthopaedic Patients?

Rehabilitation of wounded warriors, professional athletes and orthopaedic patients has profound health, economic, psychological, and social implications. This has motivated development and investigation of a new technique, Personalized Blood Flow Restriction Training (PBFRT), which may substantially reduce recovery time and improve rehabilitation. PBFRT involves exercising well below maximum intensity using an optimal personalized restrictive pressure (PRP) in a tourniquet cuff encircling a limb, for brief and repeated exercise periods according to a rehabilitation protocol. Although the exact mechanism is not fully understood, many studies have shown beneficial effects of blood flow restricted training on skeletal muscle strength and hypertrophy, and preliminary evidence suggests it may also promote bone formation. Advances in the development of modern tourniquet systems made within our group in Canada allow PBFRT to be performed with optimal safety and repeatability, establishing a PRP that automatically accounts for important variables including individual limb shape and size, muscle tone, blood pressure, gender, race, tourniquet cuff characteristics and application technique.
Comparison of Representations of Time Series for Clustering Smart Meter Data

Deployment of smart grids gives a space to emergence of new methods of machine learning and data analysis. Smart grids can contain of millions of smart meters, which produce large amount of data of electricity consumption. These data can be used to support intelligent grid control, make accurate forecast or to detect anomalies. The purpose of this paper is to prove that clustering of electricity consumers helps to improve accuracy of the load consumption forecast. To achieve this goal various representations of time series are compared, some of them newly proposed by us. The best results achieve data adaptive and robust model-based representation methods. We show that results of clustering can be effectively used in classifying new consumers without loss of the accuracy forecast, so there is no need of exhausting reclustering. These conclusions are evaluated on experiments made on smart meter data comming from enterprises and consumers from residences.
Frost flowers on sea ice as a source of sea salt and their influence on tropospheric halogen chemistry

Frost flowers grow on newly‐formed sea ice from a saturated water vapour layer. They provide a large effective surface area and a reservoir of sea salt ions in the liquid phase with triple the ion concentration of sea water. Recently, frost flowers have been recognised as the dominant source of sea salt aerosol in the Antarctic, and it has been speculated that they could be involved in processes causing severe tropospheric ozone depletion events during the polar sunrise. These events can be explained by heterogeneous autocatalytic reactions taking place on salt‐laden ice surfaces which exponentially increase the reactive gas phase bromine (“bromine explosion”). We analyzed tropospheric bromine monoxide (BrO) and the sea ice coverage both measured from satellite sensors. Our model based interpretation shows that young ice regions potentially covered with frost flowers seem to be the source of bromine found in bromine explosion events.
A mechanistic model for bromodeoxyuridine dilution naturally explains labelling data of self-renewing T cell populations

Bromodeoxyuridine (BrdU) is widely used in immunology to detect cell division, and several mathematical models have been proposed to estimate proliferation and death rates of lymphocytes from BrdU labelling and de-labelling curves. One problem in interpreting BrdU data is explaining the de-labelling curves. Because shortly after label withdrawal, BrdU+ cells are expected to divide into BrdU+ daughter cells, one would expect a flat down-slope. As for many cell types, the fraction of BrdU+ cells decreases during de-labelling, previous mathematical models had to make debatable assumptions to be able to account for the data. We develop a mechanistic model tracking the number of divisions that each cell has undergone in the presence and absence of BrdU, and allow cells to accumulate and dilute their BrdU content. From the same mechanistic model, one can naturally derive expressions for the mean BrdU content (MBC) of all cells, or the MBC of the BrdU+ subset, which is related to the mean fluorescence intensity of BrdU that can be measured in experiments. The model is extended to include subpopulations with different rates of division and death (i.e. kinetic heterogeneity). We fit the extended model to previously published BrdU data from memory T lymphocytes in simian immunodeficiency virus-infected and uninfected macaques, and find that the model describes the data with at least the same quality as previous models. Because the same model predicts a modest decline in the MBC of BrdU+ cells, which is consistent with experimental observations, BrdU dilution seems a natural explanation for the observed down-slopes in self-renewing populations.
Depressive Symptoms in Asian Americans 3 A Symptom Profile Analysis of Depression in a Nationally

Past research has suggested the existence of differences in depressive symptoms, diagnosis, and treatment of depression among ethnic and racial groups. In particular, Asian Americans have been found to experience depression differently than European Americans. Using a symptom profile approach, the presentation of depressive symptoms was examined in a nationally representative sample of Asian Americans and compared to that of European Americans. This study used data from the Collaborative Psychiatric Epidemiology Surveys, which include the National Latino and Asian American Study (NLAAS) and the National Comorbidity SurveyReplication (NCS-R). Depressive symptom profiles of Asian Americans and European Americans who reported depressive experiences were compared in order to analyze the phenomenology of depression in these groups. Findings suggested that Asian Americans reported somatic and affective depressive symptoms equally. When compared to European Americans though, they endorsed a variety of symptoms less frequently. Clinical implications and directions for future research are discussed. Depressive Symptoms in Asian Americans 3 A Symptom Profile Analysis of Depression in a Nationally Representative Sample of Asian
Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification

The lack of high-quality labeled training data has been one of the critical challenges facing many industrial machine learning tasks. To tackle this challenge, in this paper, we propose a semi-supervised learning method to utilize unlabeled data and user feedback signals to improve the performance of ML models. The method employs a primary model Main and an auxiliary evaluation model Eval, where Main and Eval models are trained iteratively by automatically generating labeled data from unlabeled data and/or users’ feedback signals. The proposed approach is applied to different text classification tasks. We report results on both the publicly available Yahoo! Answers dataset and our e-commerce product classification dataset. The experimental results show that the proposed method reduces the classification error rate by 4% and up to 15% across various experimental setups and datasets. A detailed comparison with other semi-supervised learning approaches is also presented later in the paper. The results from various text classification tasks demonstrate that our method outperforms those developed in previous related studies.