Query: Aggregating Content and Network Information to Curate Twitter User Lists

Relevant passages:
Political Polarization on Twitter <=SEP=> In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between leftand right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.

Measuring User Influence in Twitter: The Million Follower Fallacy <=SEP=> Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user’s influence on others—a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.

TwitterRank: finding topic-sensitive influential twitterers <=SEP=> This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.

Author cocitation: A literature measure of intellectual structure <=SEP=> 

Detecting and Tracking Political Abuse in Social Media <=SEP=> We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation. We present promising preliminary results with better than 96% accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections.

From content delivery today to information centric networking <=SEP=> Today, content delivery is a heterogeneous ecosystem composed by various independent infrastructures. The ever increasing growth of Internet traffic has encouraged the proliferation of different architectures to serve content provider needs and user demand. Despite the differences among the technology, their low level implementation can be characterized in a few fundamental building blocks: network storage, request routing, and data transfer. Existing solutions are inefficient because they try to build an information centric service model over a network infrastructure which was designed to support host-to-host communications. Information-Centric Networking (ICN) paradigm has been proposed as a possible solution to this mismatch. ICN integrates content delivery as a native network feature. The rationale is to architect a network that automatically interprets, processes, and delivers content (information) independently of its location. This paper makes the following contributions: 1) it identifies a set of building blocks for content delivery, 2) it surveys the most popular approaches to realize the above building blocks, 3) it compares content delivery solutions relying on the current Internet infrastructure with novel ICN approaches.

"I spy with my little eye!": breadth of attention, inattentional blindness, and tactical decision making in team sports. <=SEP=> Failures of awareness are common when attention is otherwise engaged. Such failures are prevalent in attention-demanding team sports, but surprisingly no studies have explored the inattentional blindness paradigm in complex sport game-related situations. The purpose of this paper is to explore the link between breadth of attention, inattentional blindness, and tactical decision-making in team ball sports. A series of studies revealed that inattentional blindness exists in the area of team ball sports (Experiment 1). More tactical instructions can lead to a narrower breadth of attention, which increases inattentional blindness, whereas fewer tactical instructions widen the breadth of attention in the area of team ball sports (Experiment 2). Further meaningful exogenous stimuli reduce inattentional blindness (Experiment 3). The results of all experiments are discussed in connection with consciousness and attention theories as well as creativity and training in team sports.

State of the art of Trust and Reputation Systems in E-Commerce Context <=SEP=> This article proposes in depth comparative study of the most popular, used and analyzed Trust and Reputation System (TRS) according to the trust and reputation literature and in terms of specific trustworthiness criteria. This survey is realized relying on a selection of trustworthiness criteria that analyze and evaluate the maturity and effectiveness of TRS. These criteria describe the utility, the usability, the performance and the effectiveness of the TRS. We also provide a summary table of the compared TRS within a detailed and granular selection of trust and reputation aspects.

Building a corpus of "real" texts for deception detection <=SEP=> Text-based deception detection is currently emerging as a vital multidisciplinary field due to its indisputable theoretical and practical value (police, security, and customs, including predatory communications, such as Internet scams). A very important issue associated with deception detection is designing valid text corpora. Most research has been carried out using texts produced in laboratory settings. There is a lack of "real" deceptive texts written when the stakes for deception are high as they are obviously difficult to collect and label. In addition, studies in text-based deception detection have mostly been performed for Romance and Germanic languages. There are few studies dealing with deception detection in Slavic languages. In this paper one can find an overview of available text corpora used for studying text-based deception detection as well as the description of how the first corpus of "real" deceptive texts for Slavic languages was collected and labeled. We expect this corpus once finished to be a handy tool for developing and testing new deception detection techniques and for carrying out related cross-cultural studies.

3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks <=SEP=> Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.

Formal analysis of privacy requirements specifications for multi-tier applications <=SEP=> Companies require data from multiple sources to develop new information systems, such as social networking, e-commerce and location-based services. Systems rely on complex, multi-stakeholder data supply-chains to deliver value. These data supply-chains have complex privacy requirements: privacy policies affecting multiple stakeholders (e.g. user, developer, company, government) regulate the collection, use and sharing of data over multiple jurisdictions (e.g. California, United States, Europe). Increasingly, regulators expect companies to ensure consistency between company privacy policies and company data practices. To address this problem, we propose a methodology to map policy requirements in natural language to a formal representation in Description Logic. Using the formal representation, we reason about conflicting requirements within a single policy and among multiple policies in a data supply chain. Further, we enable tracing data flows within the supply-chain. We derive our methodology from an exploratory case study of Facebook platform policy. We demonstrate the feasibility of our approach in an evaluation involving Facebook, Zynga and AOL-Advertising policies. Our results identify three conflicts that exist between Facebook and Zynga policies, and one conflict within the AOL Advertising policy.

e-health first impressions and visual evaluations: key design principles for attention and appeal <=SEP=> Design plays a critical role in the development of e-health, greatly impacting the outreach potential for pertinent health communication. Design influences viewers' initial evaluations of electronic displays of health information, as well as directly impacting the likelihood one will attend to and favorably evaluate the information, essential actions for processing the health concepts presented. Individuals with low health literacy, representing a hard-to-reach audience susceptible to worsened health outcomes, will benefit greatly from the application of theory-based design principles. Design principles that have been shown to appeal and engage audiences are the necessary first step for effective message delivery. Design principles, which directly impact increased attention, favorable evaluations, and greater information processing abilities, include: web aesthetics, visual complexity, affordances, prototypicality, and persuasive imagery. These areas of theory-driven design research should guide scholars in e-health investigation with research goals of broader outreach, reduction of disparities, and potential avenues for reduced health care costs. Improving design by working with this hard-to-reach audience will simultaneously improve practice, as the applications of key design principles through theory-driven design research will allow practitioners to create effective e-health that will benefit people more broadly.

Towards SVC-based Adaptive Streaming in information centric networks <=SEP=> HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services. In HAS, each video is segmented and stored in different qualities. The client can dynamically select the most appropriate quality level to download, allowing it to adapt to varying network conditions. As the Internet was not designed to deliver such applications, optimal support for multimedia delivery is still missing. Information Centric Networking (ICN) is a recently proposed disruptive architecture that could solve this issue, where the focus is given to the content rather than to end-to-end connectivity. Due to the bandwidth unpredictability typical of ICN, standard AVC-based HAS performs quality selection sub-optimally, thus leading to a poor Quality of Experience (QoE). In this article, we propose to overcome this inefficiency by using Scalable Video Coding (SVC) instead. We individuate the main advantages of SVC-based HAS over ICN and outline, both theoretically and via simulation, the research challenges to be addressed to optimize the delivered QoE.

A novel compact printed ACS fed dual-band antenna for Bluetooth/WLAN/WiMAX applications <=SEP=> In this research article, a compact dual band asymmetric coplanar strip-fed printed antenna is designed and presented for Bluetooth, WLAN/WiMAX and public safety applications. The dual frequency operating bands (2.45 GHz and 5.25 GHz) have been achieved by attaching two simple meander shaped radiating strips to the ACS feed line. The proposed antenna geometry is printed on a low cost FR4 substrate having thickness of 1.6mm with overall dimensions of 13 × 21.3m including uniplanar ground plane. The -10 dB impedance bandwidth of the meandered ACS-fed dual band monopole antenna is about 140MHz from 2.36-2.5 GHz, and 2500MHz from 4.5-7.0 GHz respectively, which can cover 2.4 GHz Bluetooth/WLAN, 5.2/5.8 GHz WLAN, 5.5 GHz WiMAX and 4.9 GHz US public safety bands. In addition to the simple geometry and wide impedance bandwidth features, proposed structure perform bidirectional and omnidirectional radiation pattern in both E and H-plane respectively.

Table-top spatially-augmented realty: bringing physical models to life with projected imagery <=SEP=> Despite the availability of high-quality graphics systems, architects and designers still build scaled physical models of buildings and products. These physical models have many advantages, however they are typically static in structure and surface characteristics. They are inherently lifeless. In contrast, high-quality graphics systems are tremendously flexible, allowing viewers to see alternative structures, facades, textures, cut-away views, and even dynamic effects such as changing lighting, moving automobiles, people, etc. We introduce a combination of these approaches that builds on our previously-published projector-based Spatially-Augmented Reality techniques. The basic idea is to aim multiple ceiling-mounted light projectors inward to graphically augment table-top scaled physical models of buildings or products. This approach promises to provide very compelling hybrid visualizations that afford the benefits of both traditional physical models, and modern computer graphics, effectively "bringing to life" table-top

Visual Event Summarization on Social Media using Topic Modelling and Graph-based Ranking Algorithms <=SEP=> Due to the increasing popularity of microblogging platforms, the amount of messages (posts) related to public events, especially posts encompassing multimedia content, is steadily increasing. The inclusion of images can convey much more information about the event, compared to their text, which is typically very short (e.g., tweets). Although such messages can be quite informative regarding different aspects of the event, there is a lot of spam and redundancy making it challenging to extract pertinent insights. In this work, we describe a summarization framework that, given a set of social media messages about an event, aims to select a subset of images derived from them, that, at the same time, maximizes the relevance of the selected images and minimizes their redundancy. To this end, we propose a topic modeling technique to capture the relevance of messages to event topics and a graph-based algorithm to produce a diverse ranking of the selected high-relevance images. A user-centered evaluation on a large Twitter dataset around several real-world events demonstrates that the proposed method considerably outperforms a number of state-of-the-art summarization algorithms in terms of result relevance, while at the same time it is also highly competitive in terms of diversity. Namely, we get an improvement of 25% in terms of precision compared to the second best result, and 7% in terms of diversity.

Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning <=SEP=> In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks. To achieve this, we figure out a way to perturb affine transformations of neurons, and loss functions used in deep neural networks. In addition, our mechanism intentionally adds "more noise" into features which are "less relevant" to the model output, and vice-versa. Our theoretical analysis further derives the sensitivities and error bounds of our mechanism. Rigorous experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is highly effective and outperforms existing solutions.

Calibration of RGB camera with velodyne LiDAR <=SEP=> Calibration of the LiDAR sensor with RGB camera finds its usage in many application fields from enhancing image classification to the environment perception and mapping. This paper presents a pipeline for mutual pose and orientation estimation of the mentioned sensors using a coarse to fine approach. Previously published methods use multiple views of a known chessboard marker for computing the calibration parameters, or they are limited to the calibration of the sensors with a small mutual displacement only. Our approach presents a novel 3D marker for coarse calibration which can be robustly detected in both the camera image and the LiDAR scan. It also requires only a single pair of camera-LiDAR frames for estimating large sensors displacement. Consequent refinement step searches for more accurate calibration in small subspace of calibration parameters. The paper also presents a novel way for evaluation of the calibration precision using projection error.

Sketch2normal: deep networks for normal map generation <=SEP=> Normal maps are of great importance for many 2D graphics applications such as surface editing, re-lighting, texture mapping and 2D shading etc. Automatically inferring normal map is highly desirable for graphics designers. Many researchers have investigated the inference of normal map from intuitive and flexiable line drawing based on traditional geometric methods while our proposed deep networks-based method shows more robustness and provides more plausible results.

Delayed enhancement of multitasking performance: Effects of anodal transcranial direct current stimulation on the prefrontal cortex <=SEP=> BACKGROUND
The dorsolateral prefrontal cortex (DLPFC) has been proposed to play an important role in neural processes that underlie multitasking performance. However, this claim is underexplored in terms of direct causal evidence.


OBJECTIVE
The current study aimed to delineate the causal involvement of the DLPFC during multitasking by modulating neural activity with transcranial direct current stimulation (tDCS) prior to engagement in a demanding multitasking paradigm.


METHODS
The study is a single-blind, crossover, sham-controlled experiment. Anodal tDCS or sham tDCS was applied over left DLPFC in forty-one healthy young adults (aged 18-35 years) immediately before they engaged in a 3-D video game designed to assess multitasking performance. Participants were separated into three subgroups: real-sham (i.e., real tDCS in the first session, followed by sham tDCS in the second session 1 h later), sham-real (sham tDCS first session, real tDCS second session), and sham-sham (sham tDCS in both sessions).


RESULTS
The real-sham group showed enhanced multitasking performance and decreased multitasking cost during the second session, compared to first session, suggesting delayed cognitive benefits of tDCS. Interestingly, performance benefits were observed only for multitasking and not on a single-task version of the game. No significant changes were found between the first and second sessions for either the sham-real or the sham-sham groups.


CONCLUSIONS
These results suggest a causal role of left prefrontal cortex in facilitating the simultaneous performance of more than one task, or multitasking. Moreover, these findings reveal that anodal tDCS may have delayed benefits that reflect an enhanced rate of learning.

Binarization-free OCR for historical documents using LSTM networks <=SEP=> A primary preprocessing block of almost any typical OCR system is binarization, through which it is intended to remove unwanted part of the input image, and only keep a binarized and cleaned-up version for further processing. The binarization step does not, however, always perform perfectly, and it can happen that binarization artifacts result in important information loss, by for instance breaking or deforming character shapes. In historical documents, due to a more dominant presence of noise and other sources of degradations, the performance of binarization methods usually deteriorates; as a result the performance of the recognition pipeline is hindered by such preprocessing phases. In this paper, we propose to skip the binarization step by directly training a 1D Long Short Term Memory (LSTM) network on gray-level text lines. We collect a large set of historical Fraktur documents, from publicly available online sources, and form train and test sets for performing experiments on both gray-level and binarized text lines. In order to observe the impact of resolution, the experiments are carried out on two identical sets of low and high resolutions. Overall, using gray-level text lines, the 1D LSTM network can reach 24% and 19% lower error rates on the low- and high-resolution sets, respectively, compared to the case of using binarization in the recognition pipeline.

Joint effects of emotion and color on memory. <=SEP=> Numerous studies have shown that memory is enhanced for emotionally negative and positive information relative to neutral information. We examined whether emotion-induced memory enhancement is influenced by low-level perceptual attributes such as color. Because in everyday life red is often used as a warning signal, whereas green signals security, we hypothesized that red might enhance memory for negative information and green memory for positive information. To capture the signaling function of colors, we measured memory for words standing out from the context by color, and manipulated the color and emotional significance of the outstanding words. Making words outstanding by color strongly enhanced memory, replicating the well-known von Restorff effect. Furthermore, memory for colored words was further increased by emotional significance, replicating the memory-enhancing effect of emotion. Most intriguingly, the effects of emotion on memory additionally depended on color type. Red strongly increased memory for negative words, whereas green strongly increased memory for positive words. These findings provide the first evidence that emotion-induced memory enhancement is influenced by color and demonstrate that different colors can have different functions in human memory.

Design of a Real-Time Micro-Winch Controller for Kite-Power Applications Master ’ s Thesis in Embedded <=SEP=> Airborne wind energy is a technology to extract energy from high altitude winds. This technology is under heavy development by several companies and universities. An actual problem with the commercialization of the technology is the reliability and safety of the system. In this thesis a real time environment suitable to perform research and further development of the prototype steering and depower control is proposed. Additionally, the overload prevention of the kite lines is researched. This thesis presents a method to estimate the tension on the kite control tapes using only one tension sensor. Thus, reducing the amount of hardware needed to protect the kite from overloads. The method relies on the characterization of the powertrain efficiency and can be used to estimate the tensions at high loads. An algorithm to limit the forces on the steering lines by depowering the kite is shown; it controls the depower state of the kite based on the desired depower state, the actual tension, and previous tensions on the KCU’s tapes. The tensions history is used to calculate a higher depower state to prevent future overloads, this reduces the amount of action needed by the motors and enable the system to use a brake to save energy. The limiter output is used as an input to a position controller, which allows the project to use off the shelf solutions to build the KCU prototype. The controller was implemented in a real time system and is able to run as fast as 20 Hz being the communication protocol the execution time bottleneck. The control algorithms were tested using a mathematical model of the kite, the environment, and trajectory control inputs from FreeKiteSim. Three scenarios were considered for the model test, normal operation, overload operation without tension limitation, and overload operation with tension limitation. The apparent wind speed during the reel out phase of the normal scenario is approximately 30 m/s and 35 m/s for the overload scenarios. During the overload scenario the limiter spent roughly 22% more energy than the normal operation scenario to counteract an increase of 5 m/s in the apparent wind during 3.5 hours of operation, but it spent 15% less energy than the overload scenario without tension limitation.

Analyzing News Media Coverage to Acquire and Structure Tourism Knowledge <=SEP=> Destination image significantly influences a tourist’s decision-making process. The impact of news media coverage on destination image has attracted research attention and became particularly evident after catastrophic events such as the 2004 Indian Ocean earthquake that triggered a series of lethal tsunamis. Building upon previous research, this article analyzes the prevalence of tourism destinations among 162 international media sites. Term frequency captures the attention a destination receives—from a general and, after contextual filtering, from a tourism perspective. Calculating sentiment estimates positive and negative media influences on destination image at a given point in time. Identifying semantic associations with the names of countries and major cities, the results of co-occurrence analysis reveal the public profiles of destinations, and the impact of current events on media coverage. These results allow national tourism organizations to assess how their destination is covered by news media in general, and in a specific tourism context. To guide analysts and marketers in this assessment, an iterative analysis of semantic associations extracts tourism knowledge automatically, and represents this knowledge as ontological structures.

Composite Connectors for Composing Software Components <=SEP=> In a component-based system, connectors are used to compose components. Connectors should have a semantics that makes them simple to construct and use. At the same time, their semantics should be rich enough to endow them with desirable properties such as genericity, compositionality and reusability. For connector construction, compositionality would be particularly useful, since it would facilitate systematic construction. In this paper we describe a hierarchical approach to connector definition and construction that allows connectors to be defined and constructed from sub-connectors. These composite connectors are indeed generic, compositional and reusable. They behave like design patterns, and provide powerful composition connectors.

Measured Gene-Environment Interactions in Psychopathology: Concepts, Research Strategies, and Implications for Research, Intervention, and Public Understanding of Genetics. <=SEP=> There is much curiosity about interactions between genes and environmental risk factors for psychopathology, but this interest is accompanied by uncertainty. This article aims to address this uncertainty. First, we explain what is and is not meant by gene-environment interaction. Second, we discuss reasons why such interactions were thought to be rare in psychopathology, and argue instead that they ought to be common. Third, we summarize emerging evidence about gene-environment interactions in mental disorders. Fourth, we argue that research on gene-environment interactions should be hypothesis driven, and we put forward strategies to guide future studies. Fifth, we describe potential benefits of studying measured gene-environment interactions for basic neuroscience, gene hunting, intervention, and public understanding of genetics. We suggest that information about nurture might be harnessed to make new discoveries about the nature of psychopathology.

Oral stimulation for promoting oral feeding in preterm infants. <=SEP=> BACKGROUND
Preterm infants (< 37 weeks' postmenstrual age) are often delayed in attaining oral feeding. Normal oral feeding is suggested as an important outcome for the timing of discharge from the hospital and can be an early indicator of neuromotor integrity and developmental outcomes. A range of oral stimulation interventions may help infants to develop sucking and oromotor co-ordination, promoting earlier oral feeding and earlier hospital discharge.


OBJECTIVES
To determine the effectiveness of oral stimulation interventions for attainment of oral feeding in preterm infants born before 37 weeks' postmenstrual age (PMA).To conduct subgroup analyses for the following prespecified subgroups.• Extremely preterm infants born at < 28 weeks' PMA.• Very preterm infants born from 28 to < 32 weeks' PMA.• Infants breast-fed exclusively.• Infants bottle-fed exclusively.• Infants who were both breast-fed and bottle-fed.


SEARCH METHODS
We used the standard search strategy of the Cochrane Neonatal Review Group to search the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE via PubMed (1966 to 25 February 2016), Embase (1980 to 25 February 2016) and the Cumulative Index to Nursing and Allied Health Literature (CINAHL; 1982 to 25 February 2016). We searched clinical trials databases, conference proceedings and the reference lists of retrieved articles.


SELECTION CRITERIA
Randomised and quasi-randomised controlled trials comparing a defined oral stimulation intervention with no intervention, standard care, sham treatment or non-oral intervention in preterm infants and reporting at least one of the specified outcomes.


DATA COLLECTION AND ANALYSIS
One review author searched the databases and identified studies for screening. Two review authors screened the abstracts of these studies and full-text copies when needed to identify trials for inclusion in the review. All review authors independently extracted the data and analysed each study for risk of bias across the five domains of bias. All review authors discussed and analysed the data and used the GRADE system to rate the quality of the evidence. Review authors divided studies into two groups for comparison: intervention versus standard care and intervention versus other non-oral or sham intervention. We performed meta-analysis using a fixed-effect model.


MAIN RESULTS
This review included 19 randomised trials with a total of 823 participants. Almost all included trials had several methodological weaknesses. Meta-analysis showed that oral stimulation reduced the time to transition to oral feeding compared with standard care (mean difference (MD) -4.81, 95% confidence interval (CI) -5.56 to -4.06 days) and compared with another non-oral intervention (MD -9.01, 95% CI -10.30 to -7.71 days), as well as the duration of initial hospitalisation compared with standard care (MD -5.26, 95% CI -7.34 to -3.19 days) and compared with another non-oral intervention (MD -9.01, 95% CI -10.30 to -7.71 days).Investigators reported shorter duration of parenteral nutrition for infants compared with standard care (MD -5.30, 95% CI -9.73 to -0.87 days) and compared with another non-oral intervention (MD -8.70, 95% CI -15.46 to -1.94 days). They could identify no effect on breast-feeding outcomes nor on weight gain.


AUTHORS' CONCLUSIONS
Although the included studies suggest that oral stimulation shortens hospital stay, days to exclusive oral feeding and duration of parenteral nutrition, one must interpret results of these studies with caution, as risk of bias and poor methodological quality are high overall. Well-designed trials of oral stimulation interventions for preterm infants are warranted. Such trials should use reliable methods of randomisation while concealing treatment allocation, blinding caregivers to treatment when possible and paying particular attention to blinding of outcome assessors.

The Economics of State Fragmentation-Assessing the Economic Impact of Secession <=SEP=> This paper provides empirical evidence that declaring independence significantly lowers per capita GDP based on a large panel of countries covering the period 1950-2016. To do so, we rely on a semi-parametric identification strategy that controls for the confounding effects of past GDP dynamics, anticipation effects, unobserved heterogeneity, model uncertainty and effect heterogeneity. Our baseline results indicate that declaring independence reduces per capita GDP by around 20% in the long run. We subsequently propose a quadruple-difference procedure to demonstrate that the results are not driven by simulation and matching inaccuracies or spillover effects. A second methodological novelty consists of the development of a two-step estimator that relies on the control function approach to control for the potential endogeneity of the estimated independence payoffs and their potential determinants, to shed some light on the primary channels driving our results. We find tentative evidence that the adverse effects of independence decrease in territorial size, pointing to the presence of economies of scale, but that they are mitigated when newly independent states liberalize their trade regime or use their new-found political autonomy to democratize.

AlterEgo: A Personalized Wearable Silent Speech Interface <=SEP=> We present a wearable interface that allows a user to silently converse with a computing device without any voice or any discernible movements - thereby enabling the user to communicate with devices, AI assistants, applications or other people in a silent, concealed and seamless manner. A user's intention to speak and internal speech is characterized by neuromuscular signals in internal speech articulators that are captured by the AlterEgo system to reconstruct this speech. We use this to facilitate a natural language user interface, where users can silently communicate in natural language and receive aural output (e.g - bone conduction headphones), thereby enabling a discreet, bi-directional interface with a computing device, and providing a seamless form of intelligence augmentation. The paper describes the architecture, design, implementation and operation of the entire system. We demonstrate robustness of the system through user studies and report 92% median word accuracy levels.

Be Quiet? Evaluating Proactive and Reactive User Interface Assistants <=SEP=> This research examined the ability of an anthropomorphic interface assistant to help people learn and use an unfamiliar text-editing tool, with a specific focus on assessing proactive assistant behavior. Participants in the study were introduced to a text editing system that used keypress combinations for invoking the different editing operations. Participants then were directed to make a set of prescribed changes to a document with the aid either of a paper manual, an interface assistant that would hear and respond to questions orally, or an assistant that responded to questions and additionally made proactive suggestions. Anecdotal evidence suggested that proactive assistant behavior would not enhance performance and would be viewed as intrusive. Our results showed that all three conditions performed similarly on objective editing performance (completion time, commands issued, and command recall), while the participants in the latter two conditions strongly felt that the assistant’s help was valuable.