Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Nonparametric Regression under Huber's ε-contamination Model

We consider the non-parametric regression problem under Huber's $\epsilon$-contamination model, in which an $\epsilon$ fraction of observations are subject to arbitrary adversarial noise. We first show that a simple local binning median step can effectively remove the adversary noise and this median estimator is minimax optimal up to absolute constants over the H\"{o}lder function class with smoothness parameters smaller than or equal to 1. Furthermore, when the underlying function has higher smoothness, we show that using local binning median as pre-preprocessing step to remove the adversarial noise, then we can apply any non-parametric estimator on top of the medians. In particular we show local median binning followed by kernel smoothing and local polynomial regression achieve minimaxity over H\"{o}lder and Sobolev classes with arbitrary smoothness parameters. Our main proof technique is a decoupled analysis of adversary noise and stochastic noise, which can be potentially applied to other robust estimation problems. We also provide numerical results to verify the effectiveness of our proposed methods.
Tagging Named Entities in Croatian Tweets

Named entity extraction tools designed for recognizing named entities in texts written in standard language (e.g., news stories or legal texts) have been shown to be inadequate for user-generated textual content (e.g., tweets, forum posts). In this work, we propose a supervised approach to named entity recognition and classification for Croatian tweets. We compare two sequence labelling models: a hidden Markov model (HMM) and conditional random fields (CRF). Our experiments reveal that CRF is the best model for the task, achieving a very good performance of over 87% micro-averaged F 1 score. We analyse the contributions of different feature groups and influence of the training set size on the performance of the CRF model.
Throbbing Between Two Lives: Resource Pooling in Service Supply Chains

Resource pooling is known to benefit performance through reduced congestion, but primarily in settings with homogenous demand. In settings where demand is heterogeneous, pooling can be counter effective. The effects of pooling of staff when demand is heterogeneous and dependent are not known. We present a simulation model based on a service supply chain that delivers Interactive TV to customers. Customers expect high performance in terms of innovativeness and reliability. Based on the results of simulation analysis, we find that when target innovativeness of the service is increased, pooling outperforms not pooling, but the delays that are involved with pooling will make the system and hence its performance unstable. Stable and high performance can be realized through "unbalanced" hiring. This means that a target performance increase in the upstream stage of the chain (innovation), is accompanied by hiring staff in the downstream stages of the chain (QA and operation).
Analysing Java Identifier Names in the Wild

Identiﬁer names represent the entities manipulated by a computer program and the actions performed on them and are crucial to program comprehension. This research aims to improve understanding of the forms of identiﬁer name created by software developers and advances techniques for analysing names that can also be applied to improve software engineering tools
Flexible page segmentation using the background

This paper introduces a new method for document page segmentation. This method is based on the analysis of the background white space that surrounds the printed regions on the page. It does not make any assumptions about the shape of the regions as opposed to most earlier approaches which assume that printed regions are rectangular. It is capable of identifying and describing regions of complex shapes more accurately than existing methods. It requires no a priori knowledge. The background white space is covered with tiles and the contour of each region is identified by tracing through these white tiles that encircle it. The method can segment page images with severe skew without skew correction. The white tiles on the image can also be used in subsequent document analysis processes such as the classification of the image regions.
Applying ecological site concepts and state‐and‐transition models to a grazed riparian rangeland

Abstract Ecological sites and state‐and‐transition models are useful tools for generating and testing hypotheses about drivers of vegetation composition in rangeland systems. These models have been widely implemented in upland rangelands, but comparatively, little attention has been given to developing ecological site concepts for rangeland riparian areas, and additional environmental criteria may be necessary to classify riparian ecological sites. Between 2013 and 2016, fifteen study reaches on five creeks were studied at Tejon Ranch in southern California. Data were collected to describe the relationship between riparian vegetation composition, environmental variables, and livestock management; and to explore the utility of ecological sites and state‐and‐transition models for describing riparian vegetation communities and for creating hypotheses about drivers of vegetation change. Hierarchical cluster analysis was used to classify the environmental and vegetation data (15 stream reaches × 4 years) into two ecological sites and eight community phases that comprised three vegetation states. Classification and regression tree (CART) analysis was used to determine the influence of abiotic site variables, annual precipitation, and cattle activity on vegetation clusters. Channel slope explained the greatest amount of variation in vegetation clusters; however, soil texture, geology, watershed size, and elevation were also selected as important predictors of vegetation composition. The classification tree built with this limited set of abiotic predictor variables explained 90% of the observed vegetation clusters. Cattle grazing and annual precipitation were not linked to qualitative differences in vegetation. Abiotic variables explained almost all of the observed riparian vegetation dynamics—and the divisions in the CART analysis corresponded roughly to the ecological sites—suggesting that ecological sites are well‐suited for understanding and predicting change in this highly variable system. These findings support continued development of riparian ecological site concepts and state‐and‐transition models to aid decision making for conservation and management of rangeland riparian areas.
Outbreak of mumps virus genotype G infection in tribal individuals during 2016–17 in India

Tribal individuals presented with fever and uni‐ or bi‐lateral parotitis in Galonda and Silli villages (Dadra and Nagar Haveli, India) between 2 October 2016 and 19 March 2017. Consequently, the magnitude and epidemiological characteristics of the outbreak were investigated. Overall, 139 cases of suspected mumps were identified in both the above villages. Most of the suspected cases were 5–15 years old, the exceptions being three adults who had no noticeable complications. Specimens were collected from 42 of the suspected cases and their close contacts (n = 39) for laboratory investigation. Mumps infection was laboratory‐confirmed in 73.8% and 20.5% of the suspected cases and contacts, respectively. Mumps was confirmed in seven adults aged 17–42 years, including three suspected cases and four contacts. To the best of our knowledge, this is the first report of a complete virus genome circulating among tribal individuals. Sequencing and phylogenetic studies revealed circulation of mumps virus genotype G in these tribal villages with 99% identity to a mumps virus detected in the UK (1996) and Canada (2009). Comparison with Indian mumps viruses revealed 99% and 98% identity to previously reported isolates from Pune during 2012 and 1986, respectively. Although the outbreak was large, no major complications were reported in the tribal villages. Detection of asymptomatic mumps in numerous close contacts indicates the importance of laboratory investigations in an outbreak setting.
Title Substance use , anxiety , and self-management efficacy in HIV-positive individuals : A mediation analysis Permalink

Context: In China, the social stigma of both substance use and HIV remains major barrier. HIV-positive individuals have been demonstrated to have higher psychosocial distress in the literature. To ensure quality of life among HIV-positive Chinese individuals, self-efficacy in HIV-related management including substance use and anxiety is the key to suppress viral load and maintain healthy lives. Objectives: We examine the mediation relationship among substance use, anxiety, and self-management efficacy. Method: A cross-sectional study design was used. In total, 137 HIV-positive individuals were recruited from two premier Chinese hospitals: Beijing’s Ditan Hospital and Shanghai’s Public Health Clinic Center (SPHCC). Results: HIV-positive substance users had significantly lower HIV-management efficacy and higher anxiety scores. About a third of the relations between substance use and anxiety were mediated by HIV-management self-efficacy. Those who used substances in the previous week had higher anxiety levels suggesting the presence of a recency effect. Their higher levels of anxiety could be largely explained by their lower HIV-management efficacy. Conclusion: It is useful for healthcare providers to assess substance use behaviors in HIV-positive individuals as well as provide support in managing anxiety in this population. Meanwhile, enhancing self-management efficacy to ensure healthy lifestyles may support achieving optimal lives with HIV. ARTICLE HISTORY Received 24 May 2017 Revised 26 November 2017 Accepted 18 January 2018
Black‐Phosphorus‐Incorporated Hydrogel as a Sprayable and Biodegradable Photothermal Platform for Postsurgical Treatment of Cancer

Photothermal therapy (PTT) is a fledgling therapeutic strategy for cancer treatment with minimal invasiveness but clinical adoption has been stifled by concerns such as insufficient biodegradability of the PTT agents and lack of an efficient delivery system. Here, black phosphorus (BP) nanosheets are incorporated with a thermosensitive hydrogel [poly(d,l‐lactide)‐poly(ethylene glycol)‐poly(d,l‐lactide) (PDLLA‐PEG‐PDLLA: PLEL)] to produce a new PTT system for postoperative treatment of cancer. The BP@PLEL hydrogel exhibits excellent near infrared (NIR) photothermal performance and a rapid NIR‐induced sol–gel transition as well as good biodegradability and biocompatibility in vitro and in vivo. Based on these merits, an in vivo PTT postoperative treatment strategy is established. Under NIR irradiation, the sprayed BP@PLEL hydrogel enables rapid gelation forming a gelled membrane on wounds and offers high PTT efficacy to eliminate residual tumor tissues after tumor removal surgery. Furthermore, the good photothermal antibacterial performance prevents infection and this efficient and biodegradable PTT system is very promising in postoperative treatment of cancer.
Exploring constructs of well-being, happiness and quality of life

Background Existing definitions of happiness, subjective well-being, and quality of life suggest conceptual overlap between these constructs. This study explored the relationship between these well-being constructs by applying widely used measures with satisfactory psychometric properties. Materials and Methods University students (n = 180) completed widely used well-being measures including the Oxford Happiness Questionnaire (OHQ), the World Health Organization Quality of Life Questionnaire, the Satisfaction with Life Scale, and the Positive and Negative Affect Scale. We analyzed the data using correlation, regression, and exploratory factor analysis. Results All included well-being measures demonstrated high loadings on the global well-being construct that explains about 80% of the variance in the OHQ, the psychological domain of Quality of Life and subjective well-being. The results show high positive correlations between happiness, psychological and health domains of quality of life, life satisfaction, and positive affect. Social and environmental domains of quality of life were poor predictors of happiness and subjective well-being after controlling for psychological quality of life. Conclusion Together, these data provide support for a global well-being dimension and interchangeable use of terms happiness, subjective well-being, and psychological quality of life with the current sample and measures. Further investigation with larger heterogeneous samples and other well-being measures is warranted.
Model-free, Model-based, and General Intelligence

During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have indeed close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.
GraKeL: A Graph Kernel Library in Python

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: this https URL .
Mobile Instant Messaging Apps: Usability Evaluation on iOS and Android Platforms and Recommendations for Developers

With the current growth in the use of smartphone devices, and the large amount of Mobile Instant Messaging applications available in the markets, this co-occurrence makes it highly valuable to evaluate the usability of this kind of applications in order to provide more satisfying user experiences. Within this paper, a systematic usability evaluation of Mobile Instant Messaging applications is presented, which will be applied to both iOS and Android platforms. Although applications from both platforms perform similarly, significant differences were found on tasks of adding a new contact and deleting a chat. Based on the results, it is suggested that this type of mobile applications present serious problems in performing tasks, poor user interfaces, how information is presented, and lack of information about privacy and security features. Likewise, along with the results, this paper presents a list of usability recommendations that will help developers to improve their Instant Messaging applications.
Knowledge-based updating of maps by interpretation of aerial images

The aim of this paper is to present our approach for interpretation of aerial images in the context of map updating. We describe concepts for the design of a top-down process that generates goals for detection of changed and new objects. The complexity of aerial photographs requires the use of context. We show that contextual reasoning provides a mechanism to generate goals to search objects by their optional or essential spatial relationship with objects already recognized. Each goal operates on a restricted search area and uses an image processing technique with parameters tailored to the searched object type. Furthermore, a priori knowledge about adaptation of the search area and the parameter setting for image processing is provided. The outcome of each goal is matched with a priori knowledge about properties of the searched object type. The concepts are illustrated by a case study for updating of a road map.
Searching Success in a Successful IS Acquisition

In today’s business world, information systems (IS) are often acquired from an external vendor rather than being developed in-house. Although the number of studies related to the success of IS acquisitions has increased, there is limited understanding of the relationship between acquisition project success, the final IS success, and their role in defining whether the acquisition endeavor was ultimately successful. For instance, in public sector organizations, there is a tendency for the acquisition project to be conducted outside the acquiring unit. This means that success can be evaluated at multiple levels in the organization, and the different levels might not have identical aspirations related to the acquisition. This can cause organizational issues, especially when the evaluation of success is left in the hands of only one of these parties. In this paper, we study the facets of IS acquisition success by presenting differing aspects that are used in defining IS acquisition success, pointing out a contradiction between the formally measured success and the perceived success. As a result, we propose an IS acquisition success model. The study is conducted as a single case study of a public sector organization in Finland.
Contact-implicit trajectory optimization using variational integrators

Contact constraints arise naturally in many robot planning problems. In recent years, a variety of contact-implicit trajectory optimization algorithms have been developed that avoid the pitfalls of mode pre-specification by simultaneously optimizing state, input, and contact force trajectories. However, their reliance on first-order integrators leads to a linear tradeoff between optimization problem size and plan accuracy. To address this limitation, we propose a new family of trajectory optimization algorithms that leverage ideas from discrete variational mechanics to derive higher-order generalizations of the classic time-stepping method of Stewart and Trinkle. By using these dynamics formulations as constraints in direct trajectory optimization algorithms, it is possible to perform contact-implicit trajectory optimization with significantly higher accuracy. For concreteness, we derive a second-order method and evaluate it using several simulated rigid-body systems, including an underactuated biped and a quadruped. In addition, we use this second-order method to plan locomotion trajectories for a complex quadrupedal microrobot. The planned trajectories are evaluated on the physical platform and result in a number of performance improvements.
Spike Sorting by Convolutional Dictionary Learning

Spike sorting refers to the problem of assigning action potentials observed in extra-cellular recordings of neural activity to the neuron(s) from which they originate. We cast this problem as one of learning a convolutional dictionary from raw multi-electrode waveform data, subject to sparsity constraints. In this context, sparsity refers to the number of neurons that are allowed to spike simultaneously. The convolutional dictionary setting, along with its assumptions (e.g. refractoriness) that are motivated by the spike-sorting problem, let us give theoretical bounds on the sample complexity of spike sorting as a function of the number of underlying neurons, the rate of occurrence of simultaneous spiking, and the firing rate of the neurons. We derive memory/computation-efficient convolutional versions of OMP (cOMP) and KSVD (cKSVD), popular algorithms for sparse coding and dictionary learning respectively. We demonstrate via simulations that an algorithm that alternates between cOMP and cKSVD can recover the underlying spike waveforms successfully, assuming few neurons spike simultaneously, and is stable in the presence of noise. We also apply the algorithm to extra-cellular recordings from a tetrode in the rat Hippocampus.
Widespread, long‐term admixture between grey wolves and domestic dogs across Eurasia and its implications for the conservation status of hybrids

Hybridisation between a domesticated species and its wild ancestor is an important conservation problem, especially if it results in the introgression of domestic gene variants into wild species. Nevertheless, the legal status of hybrids remains unregulated, partially because of the limited understanding of the hybridisation process and its consequences. The occurrence of hybridisation between grey wolves and domestic dogs is well documented from different parts of the wolf geographic range, but little is known about the frequency of hybridisation events, their causes and the genetic impact on wolf populations. We analysed 61K SNPs spanning the canid genome in wolves from across Eurasia and North America and compared that data to similar data from dogs to identify signatures of admixture. The haplotype block analysis, which included 38 autosomes and the X chromosome, indicated the presence of individuals of mixed wolf–dog ancestry in most Eurasian wolf populations, but less admixture was present in North American populations. We found evidence for male‐biased introgression of dog alleles into wolf populations, but also identified a first‐generation hybrid resulting from mating between a female dog and a male wolf. We found small blocks of dog ancestry in the genomes of 62% Eurasian wolves studied and melanistic individuals with no signs of recent admixed ancestry, but with a dog‐derived allele at a locus linked to melanism. Consequently, these results suggest that hybridisation has been occurring in different parts of Eurasia on multiple timescales and is not solely a recent phenomenon. Nevertheless, wolf populations have maintained genetic differentiation from dogs, suggesting that hybridisation at a low frequency does not diminish distinctiveness of the wolf gene pool. However, increased hybridisation frequency may be detrimental for wolf populations, stressing the need for genetic monitoring to assess the frequency and distribution of individuals resulting from recent admixture.
Super-resolution estimation of edge images

A hidden Markov model, which describes the evolution of a (binary) edge-image along the resolution axis, is presented. The model integrates two layers: A hidden layer consists of sources having the ability of "breeding" along the resolution axis according to a Markovian rule. A second layer consists of a Gibbs random field which is defined by all the sources. The available image is a realization of this field. After fitting such a model to a given pyramid, it is possible to estimate the super-resolution images by synthesizing additional levels of the process which created the pyramid. The hidden Markov model is found to be a useful tool, allowing us to incorporate selected properties in the process of evolution along the resolution axis, while simultaneously providing an interpretation of this process. The properties incorporated into the model significantly influence the super-resolution image.