Miracle-dz/UniFAR-FTUs huggingface dataset:
Light-induced intracellular hydrogen peroxide generation through genetically encoded photosensitizer KillerRed-SOD1

Abstract Hydrogen peroxide (H2O2) plays an important role in various biological processes in numerous organisms. Depending on the concentration and the distribution within the cell, it can act as stressor or redox signalling molecule. To analyse the effects of H2O2 and its diffusion within the cell we developed the new genetically encoded photosensitizer KillerRed-SOD1 which enables a light-induced spatially and temporally controlled generation of H2O2 in living cells. The KillerRed-SOD1 is a fusion protein of the photosensitizer KillerRed (KR) and the cytosolic superoxide dismutase isoform 1 (SOD1) connected by a helix-forming peptide linker. Light irradiation at a wavelength of 560 nm induced superoxide radical formation at the KR domain which was transformed to H2O2 at the SOD1 domain. H2O2 was specifically detected under live cell conditions using the fluorescent sensor protein HyPer. Genetically encoded photosensitizers have the advantage that appropriate tag sequences can determine the localisation of the protein within the cell. Herein, it was exemplarily shown that the peroxisomal targeting sequence 1 directed the photosensitizer KR-SOD1 to the peroxisomes and enabled H2O2 formation specifically in these organelles. In summary, with the photosensitizer KR-SOD1 a new valuable tool was established which allows a controlled intracellular H2O2 generation for the analysis of H2O2 effects on a subcellular level.
Feature Re-Learning with Data Augmentation for Content-based Video Recommendation

This paper describes our solution for the Hulu Content-based Video Relevance Prediction Challenge. Noting the deficiency of the original features, we propose feature re-learning to improve video relevance prediction. To generate more training instances for supervised learning, we develop two data augmentation strategies, one for frame-level features and the other for video-level features. In addition, late fusion of multiple models is employed to further boost the performance. Evaluation conducted by the organizers shows that our best run outperforms the Hulu baseline, obtaining relative improvements of 26.2% and 30.2% on the TV-shows track and the Movies track, respectively, in terms of recall@100. The results clearly justify the effectiveness of the proposed solution.
Patients' reasons for consulting a GP when experiencing a dental problem: a qualitative study.

BACKGROUND There are approximately 380 000 dental consultations in UK general practice every year. AIM To explore the reasons why patients may consult a GP rather than a dentist when experiencing problems with their teeth or gums. DESIGN AND SETTING A qualitative semi-structured interview study with adults who had consulted a UK GP with a dental problem in the previous 12 months. METHOD Participants were recruited via print and social media; internet adverts; HealthWise Wales, the Welsh national population research cohort; and word of mouth. In total, 39 telephone interviews were conducted, and transcripts thematically analysed. RESULTS Participants' consultation behaviour was influenced by their interpretation of their symptoms; their perceptions of the scope of practice of primary care practitioners; the comparative ease of navigating medical and dental care systems; previous experiences of dental care, including dental anxiety and dissatisfaction with prior treatment; and willingness and ability to pay for dental care. CONCLUSION There are several reasons why patients may consult a GP with a dental problem. Effective interventions will need to break down the barriers preventing access to dental care. Accessible public-facing information on where to seek care for dental problems is required, and general practice teams should be able to signpost patients who present with dental problems, if appropriate. Dental providers should also be encouraged to maintain timely access to urgent care for their patients.
Convolutional Neural Networks with Multi-task Loss for Polyphonic Sound Event Detection

The1 sound event detection methods which use convolution neural networks as classifiers have recently been demonstrated to improve the performance. Although the cross-entropy loss functions for classification used in general settings, it is controversial that loss functions shoul be tailored to specific tasks. Multi-task systems improve the generalization capability for some tasks. We use a multi-task loss function to couple with different neural networks and apply it to a polyphonic sound event detection task. The proposed method is compared with DNN, CNN and CBRNN methods. And experimental results show that the proposed method has made a considerable improvement for three different datasets.
Cascaded Feature Augmentation with Diffusion for Image Retrieval

Recently, as an effective re-ranking technique, diffusion has attracted considerable attention in research on image retrieval. It inherits from random surfer model and is effective to deeply explore data manifold structure. However, as a common practice, diffusion is performed at query time which relies heavily on initial retrieval shortlists and suffers the bottleneck of online time-efficiency. To this end, in this paper, we present a more generalized method named CFA (cascaded feature augmentation) based on diffusion. First of all, we transfer diffusion process from online stage to offline stage and innovatively utilize output of diffusion to augment database features in a cascaded mode, which can eliminate iteration process at query time radically. Second, to scale the diffusion method to large image database, we propose a cascaded cluster diffusion technique for feature augmentation which largely reduces computational cost. Third, we extend our cascaded feature augmentation scheme to cases with multiple features without involving extra memory and time cost. Our CFA is compatible with other re-ranking methods. Extensive experiments on four public datasets demonstrate the effectiveness of our proposed algorithm.
Explicit Preference Elicitation for Task Completion Time

Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completing tasks. They can also use forums such as Turker Nation1 to exchange preferences on tasks and requesters. In fact, crowdsourcing platforms rely heavily on observing workers and inferring their preferences implicitly. On the contrary, we believe that asking workers to indicate their preferences explicitly will allow us to improve different processes in crowdsourcing platforms. We initiate a study that leverages explicit elicitation from workers to capture the evolving nature of worker preferences and we propose an optimization framework to better understand and estimate task completion time. We design a Worker model to estimate task completion time whose accuracy is improved iteratively by requesting worker preferences for task factors, such as, required skills, task payment, and task relevance. We develop efficient solutions with guarantees, run extensive experiments with large-scale real-world data that show the benefit of explicit preference elicitation over implicit ones with statistical significance.
End2End Semantic Segmentation for 3D Indoor Scenes

This research is concerned with semantic segmentation of 3D point clouds arising from videos of 3D indoor scenes. It is an important building block of 3D scene understanding and has promising applications such as augmented reality and robotics. Although various deep learning based approaches have been proposed to replicate the success of 2D semantic segmentation in 3D domain, they either result in severe information loss or fail to model the geometric structures well. In this paper, we aim to model the local and global geometric structures of 3D scenes by designing an end-to-end 3D semantic segmentation framework. It captures the local geometries from point-level feature learning and voxel-level aggregation, models the global structures via 3D CNN, and enforces label consistency with high-order CRF. Through preliminary experiments conducted on two indoor datasets, we describe our insights on the proposed approach, and present some directions to be pursued in the future.
Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information

Tone mapping operators and multi-exposure fusion methods allow us to enjoy the informative contents of high dynamic range (HDR) images with standard dynamic range devices, but also introduce distortions into HDR contents. Therefore methods are needed to evaluate tone-mapped image quality. Due to the complexity of possible distortions in a tonemapped image, information from different scales and different levels should be considered when predicting tone-mapped image quality. So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model. After being aggregated, the extracted features are mapped to quality predictions by regression. The proposed method is tested on the largest public database for TMIQA and compared to existing no-reference methods. The experimental results show that the proposed method achieves better performance.
Relationships between vegetarian dietary habits and daily well-being

ABSTRACT The goal of the present study was to examine differences in the daily experiences of vegetarians and non-vegetarians. At the end of each day for two weeks, a convenience sample of American undergraduates described how they felt and how they thought about themselves that day, and they described the events that occurred to them that day. Multilevel modeling analyses (days nested within persons) found that vegetarians (individuals who avoided all meat and fish, n = 24) reported lower self-esteem, lower psychological adjustment, less meaning in life, and more negative moods than semi-vegetarians (individuals who ate some meat and/or fish, n = 56) and omnivores (individuals who did not restrict their intake of meat or fish, n = 323). Vegetarians also reported more negative social experiences than omnivores and semi-vegetarians. Although women were more likely than men to identify as vegetarians and semi-vegetarians, controlling for participant gender did not change the results of the analyses. The differences we found are consistent with other research that suggests that vegetarians are less psychologically well-adjusted than non-vegetarians. The implications of the present results for understanding relationships between dietary habits and well-being are discussed.
X-Rank: Explainable Ranking in Complex Multi-Layered Networks

In this paper we present a web-based prototype for an explainable ranking algorithm in multi-layered networks, incorporating both network topology and knowledge information. While traditional ranking algorithms such as PageRank and HITS are important tools for exploring the underlying structure of networks, they have two fundamental limitations in their efforts to generate high accuracy rankings. First, they are primarily focused on network topology, leaving out additional sources of information (e.g. attributes, knowledge). Secondly, most algorithms do not provide explanations to the end-users on why the algorithm gives the specific ranking results, hindering the usability of the ranking information. We developed Xrank, an explainable ranking tool, to address these drawbacks. Empirical results indicate that our explainable ranking method not only improves ranking accuracy, but facilitates user understanding of the ranking by exploring the top influential elements in multi-layered networks. The web-based prototype (Xrank: http://www.x-rank.net) is currently online - we believe it will assist both researchers and practitioners looking to explore and exploit multi-layered network data.
UbiLibrary: Situated Large Public Display as Interactive Interface to Library Services

We present the UbiLibrary service developed for a large public display deployed at the lobby of a municipal library. The design of the system was driven by library surveys, interviews of library users and library staff, and analyzing the use of library's current digital services. The system provides library customers with rich information on library's services and events through a metadata engine that semantically complements the library's database with supplementary metadata crawled from online sources and further personalizes information according to the age and gender of the user estimated through face recognition. The service was assessed with a task-based evaluation by library customers, expert evaluation by professional librarians and in the wild field study. The service was found easy to use and regarded as a valuable addition to the library's digital services. Consequently, it was also deployed on two smaller displays at book collections.
Impact of Document Representation on Neural Ad hoc Retrieval

Neural embeddings have been effectively integrated into information retrieval tasks including ad hoc retrieval. One of the benefits of neural embeddings is they allow for the calculation of the similarity between queries and documents through vector similarity calculation methods. While such methods have been effective for document matching, they have an inherent bias towards documents that are sized relatively similarly. Therefore, the difference between the query and document lengths, referred to as the query-document size imbalance problem, becomes an issue when incorporating neural embeddings and their associated similarity calculation models into the ad hoc document retrieval process. In this paper, we propose that document representation methods need to be used to address the size imbalance problem and empirically show their impact on the performance of neural embedding-based ad hoc retrieval. In addition, we explore several types of document representation methods and investigate their impact on the retrieval process. We conduct our experiments on three widely used standard corpora, namely Clueweb09B, Clueweb12B and Robust04 and their associated topics. Summarily, we find that document representation methods are able to effectively address the query-document size imbalance problem and significantly improve the performance of neural ad hoc retrieval. In addition, we find that a document representation method based on a simple term-frequency shows significantly better performance compared to more sophisticated representation methods such as neural composition and aspect-based methods.
Context-Aware Unsupervised Text Stylization

In this work, we present a novel algorithm to stylize the text without supervision, which provides a flexible and convenient way to invoke fantastic text expressions. Rather than employing the fixed pair of target text and source style images, our unsupervised framework establishes an implicit mapping for them by using an abstract imagery of the style image as bridges. Based on the mapping, we progressively narrow the visual discrepancy between text and style images by the proposed legibility-preserving structure transfer and texture transfer algorithms, which effectively balance the text legibility and style consistency. Furthermore, we explore a seamless composition of the stylized text and a background image, in which the optimal text layout is determined by a context-aware layout design algorithm utilizing cues for both seamlessness and aesthetics. Given the layout, the text can be seamlessly embedded into the background by texture synthesis under a context-aware boundary constraint. Experimental results demonstrate the effectiveness of the proposed method in automatic artistic typography creation and visual-textual presentation synthesis.
Towards Effective Extraction and Linking of Software Mentions from User-Generated Support Tickets

Software support tickets contain short and noisy text from the customers. Software products are often represented by various surface forms and informal abbreviations. Automatically identifying software mentions from support tickets and determining the official names and versions are helpful for many downstream applications, \eg routing the support tickets to the right expert groups for support. In this work, we study the problem ofsoftware product name extraction andlinking from support tickets. We first annotate and analyze sampled tickets to understand the language patterns. Next, we design features using local, contextual, and external information sources, for extraction and linking models. In experiments, we show that linear models with the proposed features are able to deliver better and more consistent results, compared with the state-of-the-art baseline models, even on dataset with sparse labels.
Validity and reliability of the Eating Problem Checklist

ABSTRACT The aim of this study was to validate the latest version of the Eating Problem Checklist (EPCL), a tool designed to assess eating-disorder behaviours and psychopathology in patients with eating disorders, session-by-session. The EPCL was completed at baseline by participants with eating disorders (n = 161) and a healthy control group (n = 379) and then administered session-by-session in a subgroup of 75 participants with eating disorders. The EPCL demonstrated good internal consistency, test–retest reliability, and concurrent and criterion validity, and principal axis analysis of the session-by-session data identified two factors (‘eating concerns’ and ‘body image concerns’) that accounted for 51.3% of the variance. Furthermore, session-by-session analysis indicated that the EPCL is able to identify specific weekly improvements and/or deterioration in eating-disorder psychopathology. These findings suggest that the EPCL is a valid and reliable self-report questionnaire that provides relevant clinical information regarding weekly changes in eating-disorder behaviours and psychopathology in patients with eating disorders.
Activity Recognition in New Smart Home Environments

Activity recognition is important to health care in smart homes. It provides information about the activities of the residents. Many health care services are based on it. To collect data about the activities, sensor networks consist of binary sensors are widely used. Activity recognition is performed based on their readings. Most of existing activity recognition methods are based on supervised classification algorithms. One drawback of these methods is that the classification model learned in one smart home environment usually cannot be used in another. For a new smart home environment, sufficient sensor readings have to be collected and labeled to learn the needed classification model. This process is time consuming and expensive. In this paper, we propose a method for smart home activity recognition with binary sensors. Our method utilizes the characteristics of binary sensors, the semantic information of the sensors and the activities, and the time information. The classification model learned with the data in one smart home environment can be used in the activity recognition in another, which has different sensor networks and label spaces. Experiments on real world datasets show the effectiveness of our method.
REDfly: the transcriptional regulatory element database for Drosophila

Abstract The REDfly database provides a comprehensive curation of experimentally-validated Drosophila transcriptional cis-regulatory elements and includes information on DNA sequence, experimental evidence, patterns of regulated gene expression, and more. Now in its thirteenth year, REDfly has grown to over 23 000 records of tested reporter gene constructs and 2200 tested transcription factor binding sites. Recent developments include the start of curation of predicted cis-regulatory modules in addition to experimentally-verified ones, improved search and filtering, and increased interaction with the authors of curated papers. An expanded data model that will capture information on temporal aspects of gene regulation, regulation in response to environmental and other non-developmental cues, sexually dimorphic gene regulation, and non-endogenous (ectopic) aspects of reporter gene expression is under development and expected to be in place within the coming year. REDfly is freely accessible at http://redfly.ccr. buffalo.edu, and news about database updates and new features can be followed on Twitter at @REDfly_database.
Deep Graph Convolutional Encoders for Structured Data to Text Generation

Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.
Jet-paddling jellies: swimming performance in the Rhizostomeae jellyfish Catostylus mosaicus

ABSTRACT Jellyfish are a successful and diverse class of animals that swim via jet propulsion, with swimming performance and propulsive efficiency being related to the animal's feeding ecology and body morphology. The Rhizostomeae jellyfish lack tentacles but possess four oral lobes and eight trailing arms at the centre of their bell, giving them a body morphology quite unlike that of other free-swimming medusae. The implications of this body morphology on the mechanisms by which thrust is produced are unknown. Here, we determined the wake structure and propulsive efficiency in the blue-blubber jellyfish Catostylus mosaicus (order: Rhizostomeae). The animal is propelled during both bell contraction and bell relaxation by different thrust-generating mechanisms. During bell contraction, a jet of fluid is expelled from the subumbrellar cavity, which results from the interaction between the counter-rotating stopping (from the preceding contraction cycle) and starting vortices, creating a vortex superstructure and propulsion. This species is also able to utilise passive energy recapture, which increases the animal's swimming velocity towards the end of the bell expansion phase when the bell diameter is constant. The thrust produced during this phase is the result of the flexible bell margin manoeuvring the stopping vortex into the subumbrellar cavity during bell relaxation, enhancing its circulation, and creating a region of high pressure on the inner surface of the bell and, consequently, thrust. These mechanisms of thrust generation result in C. mosaicus having a relatively high propulsive efficiency compared with other swimmers, indicating that economical locomotion could be a contributing factor in the ecological success of these medusan swimmers. Summary: Blue-blubber jellyfish have a relatively high propulsive efficiency compared with other swimmers, which they achieve by manipulating the water around them with their flexible bodies.