Miracle-dz/UniFAR-FTUs huggingface dataset:
Informational bounds on equilibria (a survey)

Query complexity and communication complexity of equilibria have been actively studied in the past decade. Recent progress in these fields of informational complexity has led to a quite good understanding of equilibria. This survey summarizes the established results for the three most common solution concepts: Nash equilibria, correlated equilibria, and coarse correlated equilibria. The survey provides a high-level idea of the techniques that are utilized to deduce recently developed lower bounds on Nash equilibria.
“I need to compartmentalize myself”: Appropriation of Instagram for chronic illness management

Recent studies have suggested and explored the rapid increase of social media appropriation globally by users in across different usage contexts. In line with this phenomenon, there has been a surge in the number of chronically ill adults who leverage social media tools as part of their illness management practice. This paper applies an interpretive case study with a mixed method approach to investigate the processes and influences on appropriation of Instagram social media tool by chronically ill adults living with fibromyalgia. Our results highlight the processes of Instagram appropriation which include: Creating a separate account to compartmentalize fibromyalgia from “normal life”; borderless appropriation of multiple social media tools and; adaptive usage patterns to convey support on Instagram. Among a range of influences, information support exchanges to better manage living with fibromyalgia; emotional support exchanges and validation; monetary/financial benefits: reaching a wider, targeted audience; accessibility and ease of use of Instagram were found to be positively influence appropriation of Instagram by chronically ill adults.
Buffer sizing and Video QoE Measurements at Netflix

In this paper we report on measurement of Netflix video traffic, to understand how the size of router buffers affects the quality of video seen by the end-user (i.e. video QoE). In certain locations, Netflix streams video over TCP New Reno from racks of servers that are directly connected to large routers, which in turn directly peer with commercial ISPs. We vary the size of the router buffers during periods of persistent congestion, and log metrics such as the number of rebuffering events, video quality, and video play delay. We observe buffers that are too small and too large, both of which worsen video QoE. Our results are for a specific, but we believe interesting, case: a router carrying large numbers of long-lived TCP flows, shedding light on how buffers should be sized by CDNs and video content providers. We find that the generality of our results is limited by the specific internal workings of the VOQ-based chassis routers used in our experiments, which have a different buffer architecture than prior work. We conclude that the routers complicate---and potentially mask---our clear understanding of buffer sizing.
Prediction of Pedestrian Trajectory in a Crowded Environment Using RNN Encoder-Decoder

Deep learning is the current state-of-the-art technique for most machine learning and data analytics tasks. It has been applied to all aspects of human life. Traditional methods are not competitive for solving pedestrian trajectory prediction problems due to the diversity of its environment and the uncertainty of the original trajectory. Due to the great success of the RNN (Recurrent Neural Networks) architecture in sequence prediction, it has become the first choice to us for solving pedestrian trajectory prediction problems in a crowded environment. In this work, in order to solve the problem that RNN architecture does not have great accuracy when the input is long sequence, we use LSTM (Long Short Term Memory) which produces higher accuracy when the input is long sequence. In the process of building our LSTM based prediction model, we find the best loss function through experiments and data analysis. Then we try various model hyperparameters combinations and find best hyperparameter values and parameter ranges that would make the prediction results more accurate. At the same time, we creatively use velocity values of pedestrian trajectory rather than coordinate values as input of the model. Experiments on several datasets shows that the proposed approach achieves high accuracy when it predicts pedestrian trajectory in a crowded environment.
Enhancing Response Predictions with a Joint Gaussian Process Model for Stochastic Simulation Models

The stochastic Gaussian process model has been widely used in stochastic simulation metamodeling. In practice, the performance of this model can be largely affected by the noise in the observations. In this article, we propose an approach to mitigate the impact of the noisy observations by jointly modeling the response of interest with a correlated but less-noisy auxiliary response. The main idea is to leverage on and learn from the correlated and more accurate response to improve the prediction. To achieve this, we extend the existing deterministic multi-response model for stochastic simulation to jointly model the two responses, use some simplified examples to show the benefit of the proposed model, and investigate the input estimation of this model. Quantile prediction is used to illustrate the efficiency of the proposed approach by jointly modeling it with the expectation, which typically has a less noisy estimator compared with that of the quantile. Several numerical examples are then conducted, and the results show that the joint model can provide better performance. These promising results illustrate the potential of this joint model especially in situations where the response of interest is much noisier or when observations are scarce. We further propose a two-stage design approach based on the multi-response model to more efficiently utilize limited computing budget to improve predictions. We also see from these designs the benefits of the joint model, where the more accurate auxiliary response observations can be used to improve the response of interest.
Sample complexity of single-parameter revenue maximization

The sample complexity of learning Myerson's optimal auction from i.i. d. samples of bidders' values has received much attention since its introduction by Cole and Roughgarden (STOC 2014). This letter gives a brief introduction of a recent work that settles the sample complexity by showing matching upper and lower bounds, up to a poly-logarithmic factor, for all families of value distributions that have been considered in the literature. The upper bounds are unified under a novel framework, which builds on the strong revenue monotonicity by Devanur, Huang, and Psomas (STOC 2016), and an information theoretic argument. This is fundamentally different from the previous approaches that rely on either constructing an ∈-net of the mechanism space, either explicitly, or implicitly via statistical learning theory, or learning an approximately accurate version of the virtual values. To our knowledge, it is the first time information theoretical arguments are used to show sample complexity upper bounds, instead of lower bounds. The lower bounds are also unified under a meta construction of hard instances.
3D Geological Modeling to Support the Assessment of Conventional and Unconventional Geothermal Resources in the Latium Region (Central Italy)

In 1904 the first electricity production from geothermal resources took place in Larderello (Italy) inaugurating the geothermal exploration activity worldwide. After over one century, the peri-Tyrrhenian regions of Tuscany and Latium (Central Italy) has still a relevant unexploited geothermal potential. According to the Italian Ministry of Economic Development, 23 exploration applications in Italy out of 38 are within Latium (data referred at the end of 2013). The Italian National Research Council (CNR), recognizing the value of geothermal energy, has promoted the "Geothermal Atlas Project" in central and southern Italy. The purpose of the project is to update and to organize all the relevant data (i.e., geological, geophysical, geochemical, hydrogeological) in order to evaluate the favourability of the central-southern regions of Italy for the exploration of conventional and unconventional geothermal resources for electricity generation. Through this project, CNR aims to encourage the development of the geothermal resources and to facilitate investment in this energy sector. This work, part of the Geothermal Atlas project, focuses on the northern Latium, which is an area characterized, together with Tuscany, by the highest values of heat-flow in Italy and hence notably interesting for geothermal exploration. The work shows the first results of a review of geological, geophysical and hydrogeological data collected to define the geological structure of the northern Latium and to build up an updated 3D subsurface geological model. The revised structural setting and the 3D geological model of the area will be used in the further stages of the Atlas project to develop the favorability geothermal maps of the study area.
Efficient Inter-Prediction Transcoding Algorithm from H.264/AVC to HEVC

With the development of the High Efficiency Video Coding (HEVC) video compression standard, there is an urgent need for more research on video format transcoding from H.264/Advanced Video Coding (AVC) to HEVC. In this paper, we highlight the difference between the inter-prediction mechanisms of the two standards, and propose detailed steps to complete bit stream transcoding from H.264/AVC to HEVC. In the transcoding process, we use information extracted from the H.264/AVC decoder in conjunction with our proposed algorithm, called "merging smaller blocks into a larger one" (MSBLO), to accurately obtain information regarding the coding unit (CU) size and the motion vector (MV), along with the prediction mode of the prediction unit (PU). This ultimately reduces the computational complexity of the determination of the optimum inter-prediction mode in the transcoding process. Experimental results show that our proposed transcoding algorithm reduces the computational complexity of the current HEVC model by approximately 90% with a lower rate distortion loss of coding efficiency.
Satisfaction and willingness to consume immersive journalism: experiment of differences between VR, 360 video, and article

Immersive journalism has been touted to revolutionize journalism due to its ability to afford a multi-modal engrossing experience. However, hardly any experiments have been conducted whether consumers' satisfaction and consequent intentions to use immersive journalistic media may differ from traditional forms of journalistic content. Therefore, in this study, we investigate the differences in satisfaction and continued use intentions between article, 360 video and VR-based interaction with content. The data was collected via a randomized controlled laboratory experiment with between-subjects design (N = 87). Participants were randomly assigned to reading a written article based on the video (article) and watching the video on a computer screen (2D 360) or in mobile VR (VR 360). The collected data consisted of demographics (age and gender) and reported satisfaction and intention to continue use. Results suggest that those who were assigned to VR 360 had higher intentions to continue use, but not greater satisfaction than those in the other two conditions. However, the intention was predicted to an extent by satisfaction as suggested by previous literature. Finally, age and gender did not predict continued use. These findings imply that users prefer the new media technology for consuming journalism content and support previous findings of the relationship between satisfaction and intention to continue use. Finally, avenues for further research are presented.
Chronic Mild Stress Modified Epigenetic Mechanisms Leading to Accelerated Senescence and Impaired Cognitive Performance in Mice

Cognitive and behavioural disturbances are a growing public healthcare issue for the modern society, as stressful lifestyle is becoming more and more common. Besides, several pieces of evidence state that environment is crucial in the development of several diseases as well as compromising healthy aging. Therefore, it is important to study the effects of stress on cognition and its relationship with aging. To address these queries, Chronic Mild Stress (CMS) paradigm was used in the senescence-accelerated mouse prone 8 (SAMP8) and resistant 1 (SAMR1). On one hand, we determined the changes produced in the three main epigenetic marks after 4 weeks of CMS treatment, such as a reduction in histone posttranslational modifications and DNA methylation, and up-regulation or down-regulation of several miRNA involved in different cellular processes in mice. In addition, CMS treatment induced reactive oxygen species (ROS) damage accumulation and loss of antioxidant defence mechanisms, as well as inflammatory signalling activation through NF-κB pathway and astrogliosis markers, like Gfap. Remarkably, CMS altered mTORC1 signalling in both strains, decreasing autophagy only in SAMR1 mice. We found a decrease in glycogen synthase kinase 3 β (GSK-3β) inactivation, hyperphosphorylation of Tau and an increase in sAPPβ protein levels in mice under CMS. Moreover, reduction in the non-amyloidogenic secretase ADAM10 protein levels was found in SAMR1 CMS group. Consequently, detrimental effects on behaviour and cognitive performance were detected in CMS treated mice, affecting mainly SAMR1 mice, promoting a turning to SAMP8 phenotype. In conclusion, CMS is a feasible intervention to understand the influence of stress on epigenetic mechanisms underlying cognition and accelerating senescence.
Improving cognitive abilities through gestural interaction

The psychology of learning and the neurosciences sustain an ongoing effort in the study of memory in order to facilitate teaching-learning processes. Special attention goes to Working Memory (WM), the conscious, short-term system where learning originates. Persons with cognitive deficiency, for example with Down Syndrome, present limiting conditions in phonological and visuospatial WM, with the latter being less affected. Gamified, digital learning resources, in a gestural interaction environment between human and computer, are proposed for stimulating new learning. We use a quasi-experimental methodology to measure the memory factor in image and text resources, both in a single class lesson and between lessons (Δt=1 wk.), in a population of three students (8 < cognitive age < 12) selected by their regular classroom teachers with the aim of a balanced sample. A lesson plan was personalized for each student, with 3 or 4 work sessions in a row, each lasting 6-12 minutes, and adapted to the computerized gestural interaction platform Tango:H. This lesson plan was repeated for 3 weeks with no changes in the learning resources or strategies. Results show a significant variation in recall between lessons, with a 99.95% confidence level; 100% recall was attained in 33% of the cases. There was no distinction between image- or text-based learning resources in terms of the recall factor; no significant difference in learning outcomes was found between different resource types.
Efficient Neural Matrix Factorization without Sampling for Recommendation

Recommendation systems play a vital role to keep users engaged with personalized contents in modern online platforms. Recently, deep learning has revolutionized many research fields and there is a surge of interest in applying it for recommendation. However, existing studies have largely focused on exploring complex deep-learning architectures for recommendation task, while typically applying the negative sampling strategy for model learning. Despite effectiveness, we argue that these methods suffer from two important limitations: (1) the methods with complex network structures have a substantial number of parameters, and require expensive computations even with a sampling-based learning strategy; (2) the negative sampling strategy is not robust, making sampling-based methods difficult to achieve the optimal performance in practical applications. In this work, we propose to learn neural recommendation models from the whole training data without sampling. However, such a non-sampling strategy poses strong challenges to learning efficiency. To address this, we derive three new optimization methods through rigorous mathematical reasoning, which can efficiently learn model parameters from the whole data (including all missing data) with a rather low time complexity. Moreover, based on a simple Neural Matrix Factorization architecture, we present a general framework named ENMF, short for Efficient Neural Matrix Factorization. Extensive experiments on three real-world public datasets indicate that the proposed ENMF framework consistently and significantly outperforms the state-of-the-art methods on the Top-K recommendation task. Remarkably, ENMF also shows significant advantages in training efficiency, which makes it more applicable to real-world large-scale systems.
PII: S0010-0277(01)00157-3

For nearly two decades it has been known that infants’ perception of speech sounds is affected by native language input during the first year of life. However, definitive evidence of a mechanism to explain these developmental changes in speech perception has remained elusive. The present study provides the first evidence for such a mechanism, showing that the statistical distribution of phonetic variation in the speech signal influences whether 6and 8-month-old infants discriminate a pair of speech sounds. We familiarized infants with speech sounds from a phonetic continuum, exhibiting either a bimodal or unimodal frequency distribution. During the test phase, only infants in the bimodal condition discriminated tokens from the endpoints of the continuum. These results demonstrate that infants are sensitive to the statistical distribution of speech sounds in the input language, and that this sensitivity influences speech perception. q 2002 Elsevier Science B.V. All rights reserved.
HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention

This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model. Based on this chatbot framework, we build HHH, an online question-and-answer (QA) Healthcare Helper system for answering complex medical questions. HHH maintains a knowledge graph constructed from medical data collected from the Internet. HHH also implements a novel text representation and similarity deep learning model, Hierarchical BiLSTM Attention Model (HBAM), to find the most similar question from a large QA dataset. We compare HBAM with other state-of-the-art language models such as bidirectional encoder representation from transformers (BERT) and Manhattan LSTM Model (MaLSTM). We train and test the models with a subset of the Quora duplicate questions dataset in the medical area. The experimental results show that our model is able to achieve a superior performance than these existing methods.
Using Count Data Models to Predict Epiphytic Bryophyte Recruitment in Schima superba Gardn. et Champ. Plantations in Urban Forests

Epiphytic bryophytes are known to perform essential ecosystem functions, but their sensitivity to environmental quality and change makes their survival and development vulnerable to global changes, especially habitat loss in urban environments. Fortunately, extensive urban tree planting programs worldwide have had a positive effect on the colonization and development of epiphytic bryophytes. However, how epiphytic bryophytes occur and grow on planted trees remain poorly known, especially in urban environments. In the present study, we surveyed the distribution of epiphytic bryophytes on tree trunks in a Schima superba Gardn. et Champ. urban plantation and then developed count data models, including tree characteristics, stand characteristics, human disturbance, terrain factors, and microclimate to predict the drivers on epiphytic bryophyte recruitment. Different counting models (Poisson, Negative binomial, Zero-inflated Poisson, Zero-inflated negative binomial, Hurdle-Poisson, Hurdle-negative binomial) were compared for a data analysis to account for the zero-inflated data structure. Our results show that (i) the shaded side and base of tree trunks were the preferred locations for bryophytes to colonize in urban plantations, (ii) both hurdle models performed well in modeling epiphytic bryophyte recruitment, and (iii) both hurdle models showed that the tree height, diameter at breast height (DBH), leaf area index (LAI), and altitude (ALT) promoted the occurrence of epiphytic bryophytes, but the height under branch and interference intensity of human activities opposed the occurrence of epiphytic bryophytes. Specifically, DBH and LAI had positive effects on the species richness recruitment count; similarly, DBH and ALT had positive effects on the abundance recruitment count, but slope had a negative effect. To promote the occurrence and growth of epiphytic bryophytes in urban tree planting programs, we suggest that managers regulate suitable habitats by cultivating and protecting large trees, promoting canopy closure, and controlling human disturbance.
Complex interplayof faulting, glacioeustatic variations and halokinesis duringdepositionof upperVis eanunits over thick salt in thewestern Cumberland Basinof Atlantic Canada

The late Palaeozoic Cumberland Basin of Nova Scotia and New Brunswick (eastern Canada) developed as a strike-slip basin in the aftermath of the Middle Devonian Acadian Orogeny. Following deposition of thick salt during the middle Vis ean (middle Mississippian), this basin mainly accommodated fault-controlled continental deposits during the late Vis ean, which generated halokinesis from clastic loading. The Mississippian halokinetic history of this basin is cryptic, as it was severely distorted by subsequent tectonic and halokinetic overprints. After minor structural restoration, the study of upper Vis ean minibasin units in wide coastal sections and deep wells allowed a fairly detailed reconstruction of the Mississippian halokinetic setting to be made. Paleoenvironments and depositional settings in the western part of the basin include sectors that were proximal to three fault-bounded source areas and characterized by alluvial fan systems transitioning laterally into gravelly to sandy braidplain environments. More central areas of the basin were characterized by tidal flats transitioning laterally into shallow marine environments. Because of halokinesis, the marine body was eventually forced to subdivide into three separate salt expulsion minibasins. Although late Vis ean marine incursions were short-lived in the rest of eastern Canada due to ongoing glacioeustatic variations, there are sedimentologic and stratigraphic lines of evidence for the long-lasting entrapment of restricted marine bodies in salt expulsion minibasins of the western Cumberland Basin. In one minibasin that was characterized by especially high accommodation rates, NE of Hopewell Cape (New Brunswick), the proximal conglomerates and marine carbonates of a fan-delta setting transition laterally into thick sulphate over a short distance, away from freshwater inputs from the source area. The vertical continuity of the latter sulphate succession suggests that this entrapped evaporitic basin was cut-off from significant marine influxes, even at times of glacioeustatic highstands. This is in contrast with salt expulsion minibasins in open marine shelf settings, which always remain open to global marine transgressions and regressions.