Miracle-dz/UniFAR-FTUs huggingface dataset:
Disposition Index in Active Acromegaly

Background: The relative contribution of reduced insulin sensitivity (Si) or insulin secretion to impaired fasting glucose (IFG) or diabetes mellitus (DM) has not been clarified in active acromegaly. An intravenous glucose tolerance test (IVGTT) was never used for the calculation of Si, acute insulin response (AIRg), and disposition index (DI) in this population. Our aim was to assess Si, AIRg and DI using an IVGTT in acromegaly with normal (NGT) and abnormal glucose tolerance. Methods: We performed an IVGTT in 13 patients (8 NGT, 2 IFG, and 3 DM; 5 males, age 47.9 ± 11 years, body mass index 29.7 ± 4.1 kg/m2) with active acromegaly (insulin-like growth factor-1 4.1 ± 1.3 × upper limit of normal, basal GH 29.1 ± 25 ng/mL) and 3 healthy controls (2 males, age 39 ± 3 years, body mass index 23 ± 5 kg/m2). No patient had any growth hormone- or glucose-lowering medication. Results: NGT patients had significantly lower Si than healthy controls but higher AIRg. Hyperglycemic and normoglycemic patients had similar Si. DM patients had severely diminished AIRg (5–260 pmol × min/L) while IFG patients maintained their insulin secretion (3,862 and 912 pmol × min/L). Patients with abnormal glucose tolerance (IFG + DM) had a significantly lower DI (353 ± 350) than both NGT patients (3,685 ± 2,544) and healthy controls (5,837 ± 1,894; p < 0.01 for ANOVA). Conclusions: Disposition index suggests that although reduced insulin sensitivity is characteristic of active acromegaly it is the impaired insulin secretion that mainly drives glucose intolerance. The clinical utility of DI in predicting DM in acromegaly must be further investigated.
Cold Induced Depot-Specific Browning in Ferret Aortic Perivascular Adipose Tissue

Brown adipose tissue is responsible for facultative thermogenesis to produce heat and increase energy expenditure in response to proper stimuli, e.g., cold. Acquisition of brown-like features (browning) in perivascular white adipose tissue (PVAT) may protect against obesity/cardiovascular disease. Most browning studies are performed in rodents, but translation to humans would benefit from a closer animal model. Therefore, we studied the browning response of ferret thoracic aortic PVAT (tPVAT) to cold. We performed global transcriptome analysis of tPVAT of 3-month-old ferrets acclimatized 1 week to 22 or 4°C, and compared the results with those of inguinal subcutaneous adipose tissue. Immunohistochemistry was used to visualize browning. Transcriptome data revealed a stronger cold exposure response of tPVAT, including increased expression of key brown/brite markers, compared to subcutaneous fat. This translated into a clear white-to-brown remodeling of tPVAT, with the appearance of multilocular highly UCP1-stained adipocytes. The pathway most affected by cold exposure in tPVAT was immune response, characterized by down-regulation of immune-related genes, with cardio protective implications. On the other hand, subcutaneous fat responded to cold by increasing energy metabolism based on increased expression of fatty acid oxidation and tricarboxylic acid cycle genes, concordant with lower inguinal adipose tissue weight in cold-exposed animals. Thus, ferret tPVAT responds to cold acclimation with a strong induction of browning and immunosuppression compared to subcutaneous fat. Our results present ferrets as an accessible translational animal model displaying functional responses relevant for obesity and cardiovascular disease prevention.
VR Re-Embodiment in the Neurorobotics Platform

We present the re-embodiment of human users inside the Neurorobotics Platform [8] as a work-in-progress. The Neu-rorobotics Platform (NRP) is built to design and perform robot embodiment experiments in simulations. Virtual experiments consist of a simulated environment as well as artificial actors in the form of robots with simulated brains. The goal is to allow users to spawn their personal humanoid robotic body into an experiment, take control over it through VR hardware and with this become active participants in experiments alongside and in cooperation with the simulated robots/brains.
Towards a Logging Framework for Evaluation and Management of Information Radiators

In the CommunityMirror project we are researching networked information radiators that are based on large interactive screens, showing information from di ﬀ erent sources. For addressing our constructive research questions – e.g. to ﬁ nd design parameters, to ﬁ nd solutions for implementing features and processes - we are facing di ﬀ erent problems acquiring the information about usage and non-usage in long-term deployments. While working on the issues we found an interesting parallel: Most of the information needed for scienti ﬁ c evaluation is also needed for (platform) management issues. In this paper we brie ﬂ y present the problem scenario – and the current concept for addressing this issue in the CommunityMirror implementation – in the form of a concept for a logging framework.
Analysis of an Integrated Photovoltaic Thermal System by Top Surface Natural Circulation of Water

The performance of photovoltaic (PV) panel is extremely sensitive to its operating temperature. Most of the energy absorbed by the panel is wasted in the form of heat and provides no value. Integrated photovoltaic thermal system can be a great solution to this problem. In this study, a numerical and experimental work is conducted on hybrid photovoltaic – thermal water heating system with front surface water cooling. First, a numerical analysis is conducted which is capable of describing various thermal parameters affecting the performance of photovoltaic panel and collector. The effect of supplying different mass flow rates of water over the individual photovoltaic and collector system and the overall system are examined thoroughly by experiment. The results of the numerical analysis are found in good agreement with the experimental analysis. Due to active cooling, it was also observed that the overall efficiency of the whole system is approximately five times higher than the efficiency of PV panel alone. Top surface cooling method significantly dropped the panel temperature and increased the panel efficiency by almost 1.5% with an overall system efficiency of around 80% where, PV and collector efficiencies are approximately 16% and 64% respectively.
Intuitive Interfaces? Interface Design and its Impact on Human-Robot Interaction

One goal of developing humanoid robots and virtual agents is to allow for intuitive and natural interaction with technical systems. However, up to now existing robotic systems do not live up to this promise. Based on an empirical approach that combines the analysis of the interface design of three different robot/agent systems with the micro-analysis of empirical encounters between humans and respective systems, we show that and how the systems provide contradictory ’affordances’, which make it systematically difficult to start and continue satisfying interactions with them.
On the right track! Analysing and Predicting Navigation Success in Wikipedia

Understanding and modeling user navigation behaviour in the web is of interest for different applications. For example, e-commerce portals can be adjusted to strengthen customer engagement or information sites can be optimized to improve the availability of relevant content to the user. In web navigation, the users goal and whether she reached it, is typically unknown. This makes navigation games particularly interesting to researchers, since they capture human navigation towards a known goal and allowbuilding labelled datasets suitable for supervised machine learning models. In this work, we show that a recurrent neural network model can predict game success from a partial click trail without knowledge of the users navigation goal. We evaluate our approach on data from WikiSpeedia and WikiGame, two well known navigation games and achieve an AUC of 86% and 90%, respectively. Furthermore, we show that our model outperforms a baseline that leverages the navigation goal on the WikiSpeedia dataset. A detailed analysis of both datasets with regards to structural and content related properties reveals significant differences in navigation behaviour, which confirms the applicability of our approach to different settings.
StoryTime: eliciting preferences from children for book recommendations

We present StoryTime, a book recommender for children. Our web-based recommender is co-designed with children and uses images to elicit their preferences. By building on existing solutions related to both visual interfaces and book recommendation strategies for children, StoryTime can generate suggestions without historical data or adult guidance. We discuss the benefits of StoryTime as a starting point for further research exploring the cold start problem, incorporating historical data, and needs related to children as a complex audience to enhance the recommendation process.
Concept to code: deep learning for multitask recommendation

Deep Learning has shown significant results in Computer Vision, Natural Language Processing, Speech and recommender systems. Promising techniques include Embedding, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and its variant Long Short-Term Memory (LSTM and Bi-directional LSTMs), Attention, Autoencoders, Generative Adversarial Networks (GAN) and Bidirectional Encoder Representations from Transformer (BERT). Multi-task learning (MTL) has led to successes in many applications of machine learning. We are proposing a tutorial for applying MTL for recommendation, improving recommendation and providing explanation. We cover few recent and diverse techniques which will be used for hands-on session. We believe that a self-contained tutorial giving good conceptual understanding of MTL technique with sufficient mathematical background along with actual code will be of immense help to RecSys participants.
Anticancer Effect and Mechanism of Hydroxygenkwanin in Oral Squamous Cell Carcinoma

The incidence and mortality of oral squamous cell carcinoma (OSCC) are high, and the number of oral cancers had risen in the world. However, chemotherapy drugs have numerous side effects. There is an urgent requirement to develop a novel drug that can be used to treat oral cancer. Hydroxygenkwanin (HGK) is a nature flavonoid extracted from Daphne genkwa Sieb. et Zucc. (Thymelaeaceae). Previous studies had demonstrated that HGK exhibits anticancer effect, but the effect is still unclear in oral cancer. HGK inhibited cell growth dose-dependently in SAS and OCEM1 cells. The functional enrichment analysis showed the significant pathway in cellular movement, cell cycle and cellular growth and proliferation. We further demonstrated the HGK induced the cell cycle arrest by flow cytometry and inhibited colony formation ability and cell movement. The western blot showed that HGK induced cell cycle arrest through p21 activation and caused intrinsic cell apoptosis pathway. HGK inhibited the cell invasion and migration through down-regulation vimentin. HGK might be an effective natural product for oral cancer therapy.
Domain adaptation in display advertising: an application for partner cold-start

Digital advertisements connects partners (sellers) to potentially interested online users. Within the digital advertisement domain, there are multiple platforms, e.g., user re-targeting and prospecting. Partners usually start with re-targeting campaigns and later employ prospecting campaigns to reach out to untapped customer base. There are two major challenges involved with prospecting. The first challenge is successful on-boarding of a new partner on the prospecting platform, referred to as partner cold-start problem. The second challenge revolves around the ability to leverage large amounts of re-targeting data for partner cold-start problem. In this work, we study domain adaptation for the partner cold-start problem. To this end, we propose two domain adaptation techniques, SDA-DANN and SDA-Ranking. SDA-DANN and SDA-Ranking extend domain adaptation techniques for partner cold-start by incorporating sub-domain similarities (product category level information). Through rigorous experiments, we demonstrate that our method SDA-DANN outperforms baseline domain adaptation techniques on real-world dataset, obtained from a major online advertiser. Furthermore, we show that our proposed technique SDA-Ranking outperforms baseline methods for low CTR partners.
Evaluating Acceptance of Video Games using Convolutional Neural Networks for Sentiment Analysis of User Reviews

Video game and interactive media are currently among the most profitable industries in the world. In this competitive marketing, game producers are interested in designing products with aspects that increase user acceptance, such as a well written story, stable servers for multiplayer games, and fluid combat mechanics. Although user-expressed feelings about game aspects seem to correlate with user acceptance, sentiment analysis is under-exploited for video games user acceptance evaluation. In this poster, we propose an approach to evaluate the user acceptance of video games by using convolutional neural networks for aspect-based sentiment analysis of user text reviews.
Predictability limits in session-based next item recommendation

Session-based recommendations are based on the user's recent actions, for example, the items they have viewed during the current browsing session or the sightseeing places they have just visited. Closely related is sequence-aware recommendation, where the choice of the next item should follow from the sequence of previous actions. We study seven benchmarks for session-based recommendation, covering retail, music and news domains to investigate how accurately user behavior can be predicted from the session histories. We measure the entropy rate of the data and estimate the limit of predictability to be between 44% and 73% in the included datasets. We establish some algorithm-specific limits on prediction accuracy for Markov chains, association rules and k-nearest neighbors methods. With most of the analyzed methods, the algorithm design limits their performance with sparse training data. The session based k-nearest neighbors are least restricted in comparison and have room for improvement across all of the analyzed datasets.
The Maximum Label Propagation Algorithm on Sparse Random Graphs

In the Maximum Label Propagation Algorithm (Max-LPA), each vertex draws a distinct random label. In each subsequent round, each vertex updates its label to the label that is most frequent among its neighbours (including its own label), breaking ties towards the larger label. It is known that this algorithm can detect communities in random graphs with planted communities if the graphs are very dense, by converging to a different consensus for each community. In [17] it was also conjectured that the same result still holds for sparse graphs if the degrees are at least C log n. We disprove this conjecture by showing that even for degrees n, for some ε > 0, the algorithm converges without reaching consensus. In fact, we show that the algorithm does not even reach almost consensus, but converges prematurely resulting in orders of magnitude more communities. 2012 ACM Subject Classification Mathematics of computing → Random graphs; Theory of computation → Distributed algorithms
DualDiv: diversifying items and explanation styles in explainable hybrid recommendation

In recommender systems, item diversification and explainable recommendations improve users' satisfaction. Unlike traditional explainable recommendations that display a single explanation for each item, explainable hybrid recommendations display multiple explanations for each item and are, therefore, more beneficial for users. When multiple explanations are displayed, one problem is that similar sets of explanation styles (ESs) such as user-based, item-based, and popularity-based may be displayed for similar items. Although item diversification has been studied well, the question of how to diversify the ESs remains underexplored. In this paper, we propose a method for diversifying ESs and a framework, called DualDiv, that recommends items by diversifying both the items and the ESs. Our experimental results show that DualDiv can increase the diversity of the items and the ESs without largely reducing the recommendation accuracy.
A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation

Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no single objective can be further improved without hurting the others. However existing approaches to Pareto efficient multi-objective recommendation still lack good theoretical guarantees. In this paper, we propose a general framework for generating Pareto efficient recommendations. Assuming that there are formal differentiable formulations for the objectives, we coordinate these objectives with a weighted aggregation. Then we propose a condition ensuring Pareto efficiency theoretically and a two-step Pareto efficient optimization algorithm. Meanwhile the algorithm can be easily adapted for Pareto Frontier generation and fair recommendation selection. We specifically apply the proposed framework on E-Commerce recommendation to optimize GMV and CTR simultaneously. Extensive online and offline experiments are conducted on the real-world E-Commerce recommender system and the results validate the Pareto efficiency of the framework. To the best of our knowledge, this work is among the first to provide a Pareto efficient framework for multi-objective recommendation with theoretical guarantees. Moreover, the framework can be applied to any other objectives with differentiable formulations and any model with gradients, which shows its strong scalability.
Augmenting Everyday Objects - A Reflection on Reuse as an Interface Design Method

This paper presents three projects which are based on the reuse of existing objects as new Tangible User Interfaces for the purpose of (a) education, (b) exploration and (c) creative expression. The already existing familiarity of the users with the basic interaction concepts of such systems can help to (a) breakdown barriers and increase accessibility of technologies as well as (b) can create playful and enjoyable experiences. The authors give a short overview over their conducted work and the resulting insights as well as a reflection about the process in retrospective.
Constraints on sequence processing speed in biological neuronal networks

Sequence processing has been proposed to be the universal computation performed by the neocortex. The Hierarchical Temporal Memory (HTM) model provides a mechanistic implementation of this form of processing. While the model accounts for a number of neocortical features, it is based on networks of highly abstract neuron and synapse models updated in discrete time. Here, we reformulate the model in terms of a network of spiking neurons with continuous-time dynamics to investigate how neuronal parameters such as cell-intrinsic time constants and synaptic weights constrain the sequence-processing speed.
Exploring Trust in Human-Agent Collaboration

Collaborative ensembles will increasingly involve agents like robots in the near future. A key part of collaboration is trust. While trust has been mainly studied between humans in CSCW, trust in human-agent research has been mostly studied in dyadic formations divorced from the broader context. This exploratory paper critically discusses previous work on trust across CSCW and HCI-related areas, taking into account recent practice approaches in CSCW and what they can contribute for understanding trust in human-agent collaboration. To make better sense of how trust emerges in collaborative ensembles with agents, we suggest that concepts that have been proposed in the ﬁeld of human-agent interaction need to be further explored in real-life settings, while concepts embraced in CSCW can lead to a more thorough understanding of the situatedness and dynamics of trust going beyond the attributes of the agent itself
Correlates of concurrent partnerships and patterns of condom use among men who have sex with men and transgender women in Peru

Background In Peru, there is an ongoing high-incidence HIV epidemic among men who have sex with men (MSM) and transgender women (TW). Sexual concurrency, or having sex with a partner in between two acts of sex with another partner, may be a key factor in onward HIV transmission. In this study, we quantify concurrency, evaluate factors associated with concurrency, and assess condom use with concurrent partners among MSM and TW in Peru. Methods We conducted a secondary analysis of data from the 2011 Peruvian Biobehavioral Survey. Pearson’s Chi-squared test was used to identify individual-level characteristics associated with concurrency. We estimated the association between participant characteristics, concurrent partnerships, partnership type (stable vs. non-stable), and CLAI within the context of concurrent partnerships using multivariate and repeated-measure Poisson regression. Results 3-month cumulative prevalence of concurrency was higher among TW compared to MSM (30.7% vs 25.2%, p = 0.014). Among those with concurrent stable and non-stable partners, 45% used condoms with both partners (95% CI: 40%-50%) and 30% preferentially had CLAI with the stable partner only (95%CI: 26%-35%). Factors associated with CLAI within the context of concurrent partnerships varied between MSM and TW. Conclusions Although concurrency is common among TW and MSM in Peru, patterns of concurrency and differential condom use may vary between TW and MSM. Future research may explore differential condom use with stable and non-stable partners to better understand behavioral factors that may alter vulnerability to HIV in TW compared to MSM.