Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Capsules for Image Analysis and Synthesis

A capsule is a group of neurons whose activity vector models different properties of the same entity. This paper extends the capsule to a generative version, named variational capsules (VCs). Each VC produces a latent variable for a specific entity, making it possible to integrate image analysis and image synthesis into a unified framework. Variational capsules model an image as a composition of entities in a probabilistic model. Different capsules' divergence with a specific prior distribution represents the presence of different entities, which can be applied in image analysis tasks such as classification. In addition, variational capsules encode multiple entities in a semantically-disentangling way. Diverse instantiations of capsules are related to various properties of the same entity, making it easy to generate diverse samples with fine-grained semantic attributes. Extensive experiments demonstrate that deep networks designed with variational capsules can not only achieve promising performance on image analysis tasks (including image classification and attribute prediction) but can also improve the diversity and controllability of image synthesis.
Societal Sentience

The use of nonhuman animals as models in research and drug testing is a key route through which contemporary scientific knowledge is certified. Given ethical concerns, regulation of animal research promotes the use of less “sentient” animals. This paper draws on a documentary analysis of legal documents and qualitative interviews with Named Veterinary Surgeons and others at a commercial laboratory in the UK. Its key claim is that the concept of animal sentience is entangled with a particular imaginary of how the general public or wider society views animals. We call this imaginary societal sentience. Against a backdrop of increasing ethnographic work on care encounters in the laboratory, this concept helps to stress the wider context within which such encounters take place. We conclude that societal sentience has potential purchase beyond the animal research field, in helping to highlight the affective dimension of public imaginaries and their ethical consequences. Researching and critiquing societal sentience, we argue, may ultimately have more impact on the fate of humans and nonhumans in the laboratory than focusing wholly on ethics as situated practice.
The Cambridge Intensive Weight Management Programme Appears to Promote Weight Loss and Reduce the Need for Bariatric Surgery in Obese Adults

Objectives: To investigate the impact of the Cambridge Intensive Weight Management Programme (IWMP) on weight change, eligibility for bariatric surgery, HbA1c, and blood pressure. Design: Prospective non-randomized intervention. Setting: The IWMP is a multi-disciplinary weight loss intervention for severely obese patients to avoid or optimize their physiological state thus enabling bariatric surgery. It uses dietary interventions, pharmacotherapy, and physical activity along with behavior change counseling. Participants: Severely obese patients (Body Mass Index, BMI≥40 kg/m2). Interventions: IWMP is a prospective intervention conducted in a National Health Service Tier 3 obesity service. It includes 3 phases of 8 weeks each: weight loss, weight stabilization, and weight maintenance. In each phase, patients adhered to a prescribed dietary regime and attended regular clinic visits. Data included in this analysis are from those who enrolled in IWMP between 2009 and 2013. Primary and secondary measures: The primary outcome was weight change between baseline and completion of the programme. Secondary outcomes included changes in blood pressure, HbA1c and eligibility for bariatric surgery pre-assessment. Changes in outcomes were compared by age, sex, smoking status, and employment. Results: Of n = 222 eligible patients, complete data were available for n = 141 patients (63.5%). At baseline, the mean (SD) BMI was 49.7 (9.2) kg/m2 for women, and 47.9 (7.2) kg/m2 for men. Mean (SD) weight change for women was −18.64 (8.36) kg and −22.46 (10.98) kg for men. N = 97 (69%) of patients achieved ≥10% weight loss. Individuals aged ≤ 50 years lost significantly more weight than those aged >50 years [mean (SD) weight loss: 22.18 (10.9) kg vs. 18.32 (7.92) kg, p = 0.020]. Changes in weight were non-significant by smoking status or employment. Median (IQR) change in systolic and diastolic blood pressure was −6 (−14.6) mmHg and 0 (−8.6) mmHg (non-significant), respectively. There was ~50% reduction in the need for bariatric surgery. Conclusions: For the majority of the patients, IWMP is promoting weight loss and allowing for avoidance of, or optimization before, bariatric surgery.
DeepDiff: DEEP‐learning for predicting DIFFerential gene expression from histone modifications

Motivation Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the functional heterogeneity of cells through influencing differential gene regulation. Recent studies either failed to capture combinatorial effects on differential prediction or primarily only focused on cell type‐specific analysis. In this paper we develop a novel attention‐based deep learning architecture, DeepDiff, that provides a unified and end‐to‐end solution to model and to interpret how dependencies among histone modifications control the differential patterns of gene regulation. DeepDiff uses a hierarchy of multiple Long Short‐Term Memory (LSTM) modules to encode the spatial structure of input signals and to model how various histone modifications cooperate automatically. We introduce and train two levels of attention jointly with the target prediction, enabling DeepDiff to attend differentially to relevant modifications and to locate important genome positions for each modification. Additionally, DeepDiff introduces a novel deep‐learning based multi‐task formulation to use the cell‐type‐specific gene expression predictions as auxiliary tasks, encouraging richer feature embeddings in our primary task of differential expression prediction. Results Using data from Roadmap Epigenomics Project (REMC) for ten different pairs of cell types, we show that DeepDiff significantly outperforms the state‐of‐the‐art baselines for differential gene expression prediction. The learned attention weights are validated by observations from previous studies about how epigenetic mechanisms connect to differential gene expression. Availability and implementation Codes and results are available at deepchrome.org. Supplementary information Supplementary data are available at Bioinformatics online.
Towards a more general many-objective evolutionary optimizer using multi-indicator density estimation

Recently, it was shown that Many-Objective Evolutionary Algorithms (MaOEAs) that employ a set of convex weight vectors are overspecialized in solving certain benchmark problems. This over-specialization is due to a high correlation between the Pareto fronts of the test problems and the simplex formed by the weight vectors. In furtherance of avoiding this issue, we propose a novel steady-state MaOEA that does not require weight vectors and adaptively chooses between two density estimators: one based on the IGD+ indicador that strengthens convergence to the Pareto front and another one, based on the s-energy indicator, which improves the diversity of the solutions. This approach, called sIGD+-MOEA, is compared with respect to NSGA-III, MOEA/D, IGD+-EMOA and MOMBI2 (which are MaOEAs that employ convex weight vectors) on the test suites WFG and WFG-1, using the hypervolume indicator. Experimental results show that sIGD+-MOEA is a promising alternative that can solve many-objective optimization problems whose Pareto fronts present different geometries.
Substance P Receptor in the Rat Heart and Regulation of Its Expression in Long-Term Diabetes

Substance P (SP) is a neuropeptide engaged in the signal transmission of neural C fibers afferents in the myocardium. The actions of SP in the heart are extensive and they are mediated by the neurokinin 1 receptor (NK1R), a member of the tachykinin subfamily of G-protein coupled receptors. The receptors have been found in the heart, but to our knowledge, their exact localization in the heart has not been described yet. Here, we investigated the presence of NK1R protein in separate rat heart compartments by means of western blot and its tissue distribution by means of immunofluorescence. Specificity of NK1R immunolabeling was controlled by preabsorption of the antiserum with its corresponding peptide. Additionally, we investigated abundance of gene for NK1R in separated heart chambers by means of quantitative real-time PCR (RT-PCR). Relative abundance of NK1R mRNA was expressed as a ratio of target gene Cq value to Cq value of control gene – beta-actin. Finally, we studied abundance of NK1R mRNA in different cell types of heart isolated by laser capture microdissection. Immunofluorescence showed NK1R immunoreactivity on the surface of some intracardiac neurons and smooth muscle cells of coronary vessels. The results of quantitative RT-PCR indicate abundance of mRNA for NK1R in all heart chambers with highest level in the left atrium. The presence of NK1R mRNA was detected in some samples of dissected intracardiac neurons, but not in cardiomyocytes or smooth muscle cells of coronary vessels. In the course of long-term diabetes, a significant downregulation of the NK1R mRNA was seen in the right atrium and upregulation in the right ventricle 53 weeks after the induction of diabetes. Our results indicate localization of NK1R in some intracardiac neurons and smooth muscle cells. Impaired transcription of the NK1R gene in the diabetic heart may be induced by unidentified genes or factors involved in the development of diabetic cardiomyopathy.
EyeSpyVR

Low cost virtual reality (VR) headsets powered by smartphones are becoming ubiquitous. Their unique position on the user's face opens interesting opportunities for interactive sensing. In this paper, we describe EyeSpyVR, a software-only eye sensing approach for smartphone-based VR, which uses a phone's front facing camera as a sensor and its display as a passive illuminator. Our proof-of-concept system, using a commodity Apple iPhone, enables four sensing modalities: detecting when the VR head set is worn, detecting blinks, recognizing the wearer's identity, and coarse gaze tracking - features typically found in high-end or specialty VR headsets. We demonstrate the utility and accuracy of EyeSpyVR in a series of studies with 70 participants, finding a worn detection of 100%, blink detection rate of 95.3%, family user identification accuracy of 81.4%, and mean gaze tracking error of 10.8° when calibrated to the wearer (12.9° without calibration). These sensing abilities can be used by developers to enable new interactive features and more immersive VR experiences on existing, off-the-shelf hardware.
Web Intelligence Data Clustering by Bare Bone Fireworks Algorithm Combined with K-Means

Data mining and clustering are important elements of various applications in different fields. One of the areas were clustering is rather frequently used is web intelligence, which nowadays represents an important research area. Data collected from the web are usually very complex, dynamic, without structure and rather large. Traditional clustering techniques are not efficient enough and need to be improved. In this paper, we propose combination of recent swarm intelligence algorithm, bare bones fireworks algorithm, and k-means for clustering web intelligence data. The proposed method was compared with other approaches from literature. Based on the experimental results, it can be concluded that the proposed method has very promising characteristics in terms of the quality of clustering, as well as the execution time.
Hierarchical-Pointer Generator Memory Network for Task Oriented Dialog.

End-to-end networks trained for task-oriented dialog, such as for recommending restaurants to a user, suffer from out-of-vocabulary (OOV) problem -- the entities in the Knowledge Base (KB) may not be seen by the network at training time, making it hard to use them in dialog. We propose a novel Hierarchical Pointer Generator Memory Network (HyP-MN), in which the next word may be generated from the decode vocabulary or copied from a hierarchical memory maintaining KB results and previous utterances. This hierarchical memory layout along with a novel KB dropout helps to alleviate the OOV problem. Evaluating over the dialog bAbI tasks, we find that HyP-MN outperforms state-of-the-art results, with considerable improvements (10% on OOV test set). HyP-MN also achieves competitive performances on various real-world datasets such as CamRest676 and In-car assistant dataset.
Deep-speare: A joint neural model of poetic language, meter and rhyme

In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We assess the quality of generated poems using crowd and expert judgements. The stress and rhyme models perform very well, as generated poems are largely indistinguishable from human-written poems. Expert evaluation, however, reveals that a vanilla language model captures meter implicitly, and that machine-generated poems still underperform in terms of readability and emotion. Our research shows the importance expert evaluation for poetry generation, and that future research should look beyond rhyme/meter and focus on poetic language.
Semi-Supervised Clustering with Neural Networks

Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learning or their dependence on large set of labeled data samples. In this paper, we propose ClusterNet that uses pairwise semantic constraints from very few labeled data samples (< 5% of total data) and exploits the abundant unlabeled data to drive the clustering approach. We define a new loss function that uses pairwise semantic similarity between objects combined with constrained k-means clustering to efficiently utilize both labeled and unlabeled data in the same framework. The proposed network uses convolution autoencoder to learn a latent representation that groups data into k specified clusters, while also learning the cluster centers simultaneously. We evaluate and compare the performance of ClusterNet on several datasets and other clustering approaches.
Collaborative Filtering Method for Handling Diverse and Repetitive User-Item Interactions

Most collaborative filtering models assume that the interaction of users with items take a single form, e.g., only ratings or clicks or views. In fact, in most real-life recommendation scenarios, users interact with items in diverse ways. This in turn, generates complex usage data that contains multiple and diverse types of user feedback. In addition, within such a complex data setting, each user-item pair may occur more than once, implying on repetitive preferential user behaviors. In this work we tackle the problem of building a Collaborative Filtering model that takes into account such complex datasets. We propose a novel factor model, CDMF, that is capable of incorporating arbitrary and diverse feedback types without any prior domain knowledge. Moreover, CDMF is inherently capable of considering user-item repetitions. We evaluate CDMF against stateof- the-art methods with highly favorable results.
Negative Momentum for Improved Game Dynamics

Games generalize the single-objective optimization paradigm by introducing different objective functions for different players. Differentiable games often proceed by simultaneous or alternating gradient updates. In machine learning, games are gaining new importance through formulations like generative adversarial networks (GANs) and actor-critic systems. However, compared to single-objective optimization, game dynamics are more complex and less understood. In this paper, we analyze gradient-based methods with momentum on simple games. We prove that alternating updates are more stable than simultaneous updates. Next, we show both theoretically and empirically that alternating gradient updates with a negative momentum term achieves convergence in a difficult toy adversarial problem, but also on the notoriously difficult to train saturating GANs.
A Deep Joint Network for Session-based News Recommendations with Contextual Augmentation

Session-based recommendations have drawn more and more attention in many recommendation settings of modern online services. Unlike many other domains such as books and music, news recommendations suffer from new challenges of fast updating rate and recency issues of news articles and lack of user profiles. In this paper, we proposed a method that combines user click events within session and news contextual features to predict next click behavior of a user. The model consists of two different kinds of hierarchical neutral networks to learn article contextual properties and temporal sequential patterns in streams of clicks. Character-level embedding over input features is adopted to allow integrating different types of data and reduce engineering computation. Besides, we also introduced a time-decay method to compute the freshness of news articles within a time slide. Experimental results on two real-world datasets show significant improvements over several baselines and state-of-the-art methods on session-based neural networks.
Stationary vs. Non-stationary Mobile Learning in MOOCs

Mobile devices enable users to access information ubiquitously, including in the online learning scenario. This though requires users to multitask and divide their attention between several tasks at once whilst "on-the-go" (e.g. watching a video, walking down the street and keeping track of the traffic at the same time). In order to accommodate learners in this situation, most of today's Massive Open Online Course (MOOC) platforms provide mobile access to their content. Prior works have conducted lab studies to investigate the impact the learning condition (in particular stationary vs. on-the-go) has on mobile MOOC learners. User studies beyond the lab setting though are scarce. We here describe a study in a more realistic setup where 36 participants each participated in two mini-MOOCs while in a stationary and real-life mobile learning situation. We find participants' learning gains slightly lowered in the on-the-go condition (-7%). We also find that on average participants spend 10% more time on video-watching and 23% less time on question-answering in the learning on-the-go compared to the stationary condition.
Observations and perspectives on the prebiotic sequence evolution

The post-genomic era has brought opportunities to bridge traditionally separate fields of early history of life and brought new insight into origin and evolution of biodiversity. According to distributions of codons in genome sequences, I found a relationship between the genetic code and the tree of life. This remote and profound relationship involves the origin and evolution of the genetic code and the diversification and expansion of genomes. Here, a prebiotic picture of the triplex nucleic acid evolution is proposed to explain the origin of the genetic code, where the transition from disorder to order in the origin of life might be due to the increasing stabilities of triplex base pairs. The codon degeneracy can be obtained in detail based on the coevolution of the genetic code with amino acids, or equivalently, the coevolution of tRNAs with aaRSs. This theory is based on experimental data such as the stability of triplex base pairs and the statistical features of genomic codon distributions. Several experimentally testable proposals have been developed. This study should be regarded as an exploratory attempt to reveal the early evolution of life based on sequence information in a statistical manner.
FAST Approaches to Scalable Similarity-Based Test Case Prioritization

Many test case prioritization criteria have been proposed for speeding up fault detection. Among them, similarity-based approaches give priority to the test cases that are the most dissimilar from those already selected. However, the proposed criteria do not scale up to handle the many thousands or even some millions test suite sizes of modern industrial systems and simple heuristics are used instead. We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items. FAST techniques provide scalable similarity-based test case prioritization in both white-box and black-box fashion. The results from experimentation on real world C and Java subjects show that the fastest members of the family outperform other black-box approaches in efficiency with no significant impact on effectiveness, and also outperform white-box approaches, including greedy ones, if preparation time is not counted. A simulation study of scalability shows that one FAST technique can prioritize a million test cases in less than 20 minutes.
Dobutamine stress-induced ischemic right ventricular dysfunction in patients with three-vessel coronary artery disease

Background Dobutamine stress echocardiography (DSE) is a non-invasive technique to detect coronary artery diseases (CAD). There are limited studies on evaluation of the right ventricular function by stress echocardiography. The appropriate evaluation of RV function and early diagnosis of its failure can help to improve outcomes for the patients undergoing cardiac surgery. Objective To determine right ventricular dysfunction in patients with three-vessel CAD by using DSE. Methods This cross-sectional study was among 13 patients who were candidates for coronary artery bypass grafting (CABG) referred to Ghaem Hospital, Mashhad, Iran; from September 2015 to May 2016. After a physical examination and initial measures, DSE was performed and echocardiographic parameters were recorded by a cardiologist. Paired-samples t-test was performed using SPSS Software v.16. 0 for data analysis. Results The study included 13 patients (9 males) with a mean age of 65.4±7.6 years. The mean of TAPS was 16.9±4.5 mm and 15.7±2.9 mm before and after stress echocardiography, respectively (p=0.69). Systolic right ventricular (SRV) peak increased from before DSE compared with after DSE (8.0±2.2 vs. 13.7±4.2 mm/s, p<0.001). In addition, after dobutamine injection, right ventricular (RV) cardiac output decreased in 7 patients and one patient was affected by post-ejection shortening. Conclusion It seems that TAPS and RV cardiac output after injection of dobutamine, can be used as markers for the recognition of ischemic RV dysfunction.