Miracle-dz/UniFAR-FTUs huggingface dataset:
Taste and flavor liking : Neurobiological correlates and behavioral diversity

Different people prefer different foods. This can be problematic for the food industry as researchers often study the "average individual". However, when it comes to taste, there is no average individual. Taking inter-individual differences into account is crucial to the enhanced prediction of whether a food product will be sold. Furthermore, food preference may be strongly related to emotions evoked by food consumption. Jelle Dalenberg, working at the Neuroimaging center in Groningen, Netherlands, has implemented a method, which is able to take inter-individual differences into account. By dividing a large group of people into smaller groups who are very similar in their responses, he was able to develop a better understanding about which people prefer which types of foods. Dalenberg has shown that emotions evoked by food consumption are a good predictor of what foods a person will choose for consumption. The relation between taste and emotion is apparent from functional brain imaging. Dalenberg asked people to taste different drinks while brain function was visualized in an MRI scanner. After tasting each drink, participants were instructed to indicate how much they liked it. Dalenberg demonstrated that tasting and taste preference are mainly processed in brain areas involved in emotional processing. Inter-individual differences in taste preferences can also be found within these brain areas.
Title BGCA : bandwidth guarded channel adaptive routing for ad hocnetworks

To support truly peer-to-peer applications in ad hoc wireless networks, a judicious and efficient ad hoc routing protocol is needed. Much research has been done on designing ad hoc routing protocols and some well known protocols are also being implemented in practical situations. However, one major drawback in existing state-of-the-art protocols, such as the AODV (ad hoc on demand distance vector) routing protocol, is that the time-varying nature of the wireless channels among the mobile terminals is ignored, let alone exploited. In this paper, by using a previously proposed adaptive channel coding and modulation scheme which allows a mobile terminal to dynamically adjust the data throughput via changing the amount of error protection incorporated , we devise a new ad hoc routing algorithm that dynamically changes the routes according to the channel conditions. Extensive simulation results indicate that our proposed protocol is more efficient in that shorter delays and higher rates are achieved.
Verification of multi-owner shared data with collusion resistant user revocation in cloud

These days users can easily store and share their data with each other using the cloud technology. Large numbers of users are not assured about integrity of their data by the reason of threats to security in a cloud. Many mechanisms are proposed and being used to verify the integrity or the correctness of single owner shared data. They suggest attaching signatures to the data. Proposed system will provide public auditing on multi-owner shared data. When user is revoked from the group, there must be some method to resign those blocks that are signed by that revoked user. Proposed system will also provide efficient user revocation with collusion resistance i.e. even if cloud colludes with any revoked users; it will not understand the contents of the data which is stored on cloud.
Uenew : A Cross-Browsing Communication System Based on Peer-to-Peer Networks

In this paper, we propose a communication system that enables users to share experiences of web browsing in real-time. This system groups web users of each web page dynamically and enables them to share comments in each group. In general, high performance servers are required to manage a huge number of groups. In contrast, the proposed system does not need any server to realize large-scale communication system because it uses the Peer-to-Peer technologies. To realize the proposed system, we use Scribe, which is one of the algorithms for application layer multicast. We extended Scribe so that it could treat archives of multicast group. We implemented the extended algorithm as APIs in Java, and evaluated them by some emulation experiments.
Compositional Divergence and Convergence in Local Communities and Spatially Structured Landscapes

Community structure depends on both deterministic and stochastic processes. However, patterns of community dissimilarity (e.g. difference in species composition) are difficult to interpret in terms of the relative roles of these processes. Local communities can be more dissimilar (divergence) than, less dissimilar (convergence) than, or as dissimilar as a hypothetical control based on either null or neutral models. However, several mechanisms may result in the same pattern, or act concurrently to generate a pattern, and much research has recently been focusing on unravelling these mechanisms and their relative contributions. Using a simulation approach, we addressed the effect of a complex but realistic spatial structure in the distribution of the niche axis and we analysed patterns of species co-occurrence and beta diversity as measured by dissimilarity indices (e.g. Jaccard index) using either expectations under a null model or neutral dynamics (i.e., based on switching off the niche effect). The strength of niche processes, dispersal, and environmental noise strongly interacted so that niche-driven dynamics may result in local communities that either diverge or converge depending on the combination of these factors. Thus, a fundamental result is that, in real systems, interacting processes of community assembly can be disentangled only by measuring traits such as niche breadth and dispersal. The ability to detect the signal of the niche was also dependent on the spatial resolution of the sampling strategy, which must account for the multiple scale spatial patterns in the niche axis. Notably, some of the patterns we observed correspond to patterns of community dissimilarities previously observed in the field and suggest mechanistic explanations for them or the data required to solve them. Our framework offers a synthesis of the patterns of community dissimilarity produced by the interaction of deterministic and stochastic determinants of community assembly in a spatially explicit and complex context.
The Chief Role of Frontal Operational Module of the Brain Default Mode Network in the Potential Recovery of Consciousness from the Vegetative State: A Preliminary Comparison of Three Case Reports

It has been argued that complex subjective sense of self is linked to the brain default-mode network (DMN). Recent discovery of heterogeneity between distinct subnets (or operational modules - OMs) of the DMN leads to a reconceptualization of its role for the experiential sense of self. Considering the recent proposition that the frontal DMN OM is responsible for the first-person perspective and the sense of agency, while the posterior DMN OMs are linked to the continuity of ‘I’ experience (including autobiographical memories) through embodiment and localization within bodily space, we have tested in this study the hypothesis that heterogeneity in the operational synchrony strength within the frontal DMN OM among patients who are in a vegetative state (VS) could inform about a stable self-consciousness recovery later in the course of disease (up to six years post-injury). Using EEG operational synchrony analysis we have demonstrated that among the three OMs of the DMN only the frontal OM showed important heterogeneity in VS patients as a function of later stable clinical outcome. We also found that the frontal DMN OM was characterized by the process of active uncoupling (stronger in persistent VS) of operations performed by the involved neuronal assemblies.
A Two-Tier Adaptive Data Aggregation Approach for M2M Group-Communication

Network lifetime is the time interval in which the nodes are operational. Considering that machine-to-machine (M2M) devices have limited energy resources, an important challenge in M2M communications is to prolong the network lifetime. The constrained application protocol (CoAP) supports multi-target monitoring applications in M2M communications, allowing the creation and maintenance of groups, as well as their periodic communication. It is essential to aggregate the CoAP group-communication over the paths to increase the network lifetime of low-power M2M devices, since data aggregation reduces the use of energy-consuming hardware (e.g., central processing unit and wireless interface). However, the current data aggregation solutions do not specify how to support data aggregation with multiple CoAP-based groups in multi-target monitoring applications. In this paper, the proposed approach, called two-tier aggregation for multi-target applications (TTAMAs), aggregates the data originated from nodes belonging to either the same or different CoAP groups. Furthermore, TTAMA is an adaptive solution because it performs the data aggregation in accordance with the CoAP configurations, such as communication periodicity and data aggregation functions. We compare TTAMA with current data aggregation approaches that use minimum spanning tree and shortest path tree. The results show that TTAMA outperforms the related works in terms of network lifetime and energy consumption.
Assessing Inflation Persistence: Micro Evidence on an Inflation Targeting Economy

The paper provides an empirical analysis of inflation persistence in one of the inflation targeting countries, the Czech Republic, using 412 detailed product-level consumer price indexes underlying the consumer basket over the period from 1994:M1 to 2005:M12. Subject to various sensitivity tests, the authors' results suggest that raw goods and non-durables, followed by services, display smaller inflation persistence than durables and processed goods. Inflation seems to be somewhat less persistent after the adoption of inflation targeting in 1998. There is also evidence for aggregation bias, that is, aggregate inflation is found to be more persistent than the underlying detailed components. Price dispersion, as a proxy for the degree of competition, is found to be negatively related to inflation persistence, suggesting that competition is not conducive to reducing persistence.
Finger Tracking and Gesture Recognition with Kinect

This paper presents a novel and real-time system for human-computer interaction with hand gestures at a distance. The system follows the principle of multi-touch techniques but is touch-free and only needs an ordinary computer screen, while common implementation of multi-touch demands creating special hardware platforms. We use Microsoft's Kinect sensor as the input device and take advantage of its depth information to help segment human body from the cluttered background. Skin color is also utilized in the process of segmentation. Besides, our system includes hand identification and tracking, as well as gesture recognition. The k-nearest neighbors (KNN) algorithm is chosen to solve the problem of object tracking. Then the tracked blob information is transmitted to applications by Tangible User Interface Object (TUIO) protocol. Thus a natural user interface is realized which is characterized by non-contact and barehanded control.
A Neural Model of MST and MT Explains Perceived Object Motion during Self-Motion

When a moving object cuts in front of a moving observer at a 90° angle, the observer correctly perceives that the object is traveling along a perpendicular path just as if viewing the moving object from a stationary vantage point. Although the observer's own (self-)motion affects the object's pattern of motion on the retina, the visual system is able to factor out the influence of self-motion and recover the world-relative motion of the object (Matsumiya and Ando, 2009). This is achieved by using information in global optic flow (Rushton and Warren, 2005; Warren and Rushton, 2009; Fajen and Matthis, 2013) and other sensory arrays (Dupin and Wexler, 2013; Fajen et al., 2013; Dokka et al., 2015) to estimate and deduct the component of the object's local retinal motion that is due to self-motion. However, this account (known as “flow parsing”) is qualitative and does not shed light on mechanisms in the visual system that recover object motion during self-motion. We present a simple computational account that makes explicit possible mechanisms in visual cortex by which self-motion signals in the medial superior temporal area interact with object motion signals in the middle temporal area to transform object motion into a world-relative reference frame. The model (1) relies on two mechanisms (MST-MT feedback and disinhibition of opponent motion signals in MT) to explain existing data, (2) clarifies how pathways for self-motion and object-motion perception interact, and (3) unifies the existing flow parsing hypothesis with established neurophysiological mechanisms. SIGNIFICANCE STATEMENT To intercept targets, we must perceive the motion of objects that move independently from us as we move through the environment. Although our self-motion substantially alters the motion of objects on the retina, compelling evidence indicates that the visual system at least partially compensates for self-motion such that object motion relative to the stationary environment can be more accurately perceived. We have developed a model that sheds light on plausible mechanisms within the visual system that transform retinal motion into a world-relative reference frame. Our model reveals how local motion signals (generated through interactions within the middle temporal area) and global motion signals (feedback from the dorsal medial superior temporal area) contribute and offers a new hypothesis about the connection between pathways for heading and object motion perception.
Semantic Clustering-Based Community Detection in an Evolving Social Network

Classic community detection methods in social networks are usually based on graph clustering algorithms which employ the structural information for group identification. They cluster nodes into groups topologically. These methods count purely on the linkage structure of the underlying social media. However, in many applications, it is possible to take into account content issued by users of social media to guide the clustering process. Messages issued by users may express relations between users/entities, which can be utilized for community detection. in this paper, we propose to detect hidden structures in a social network, uses the semantic information extracted from posts in the social media.
Keeping Engaged During Deployment: The Interplay Between Self-Efficacy, Family Support, and Threat Exposure

This study investigated the importance of 2 resilience resources for service members’ ability to deal with threat during deployment. Military self-efficacy and family support were measured before deployment and related to work engagement and burnout levels of service members during deployment. We hypothesized that in high threat situations, low self-efficacy would lead to unfavorable outcomes, whereas in low threat situations, high self-efficacy could have negative consequences. In addition, we hypothesized that family support would compensate for both effects. The results showed these expected 3-way interactions. We found that strong self-efficacy helped service members deal with exposure to threatening situations during deployment, leading to more work engagement and less burnout. However, having strong self-efficacy without being exposed to threat during deployment reduced service members’ work engagement and increased burnout. In addition, we found that the presence of family support compensated for these effects. Service members with low self-efficacy benefitted from family support when threat exposure was high, whereas service members with high self-efficacy benefitted from family support when threat exposure was low. As such, family support seemed to act as a compensatory mechanism for the potential negative effects of self-efficacy. This underlines the importance of studying the interplay between resources that help service members deal with deployment experiences. Practical implications relate to supporting service members’ resilience through enhancing multiple resources.
Impact of Signaling System Performance on QoE in Next Generation Networks

The first experience of quality by multimedia applications’ users takes place during the setup phase of a new connection. If the setup phase is not accepted or “slowly accepted”, the confidence of the user decreases. The user becomes more sensitive when he/she pays the connections with assured quality of service (QoS). In this case, the process of call request should be also accomplished with QoS guarantees. This paper presents the signaling sub-system implemented within the EuQoS system. The EuQoS signaling process follows main assumptions of next generation networks (NGN) architecture and performs tasks related with codec agreement between multimedia end users, admission control and resource reservation functions. In this paper, we present analytical, simulation and experimental results showing the impact of signaling system performance on quality of experience (QoE) for the potential users of multi-layer EuQoS system. In particular, the presented approach aims at ensuring user QoE of the connection setup phase by ensuring QoS for transferring signaling messages by the network. Keywords—call setup delay, class of service, heterogeneous networks, next generation networks, quality of experience, quality of service, signaling system.
Entry Deterrence and Learning Prevention on eBay ∗

Internet auctions (such as eBay) differ from the traditional auction format in that participants 1) typically face a choice over several simultaneous auctions and 2) often have limited information about rival bidders. Since existing economic models do not account for these features of the bidding environment, it should not be surprising that even casual empiricism reveals a sharp discrepancy between the predictions of existing theory and the actual behavior of bidders. In particular, despite the second-price structure of eBay auctions (which, along with private values, implies that bidders submit a single bid), eBay bidders frequently bid multiple times over the course of a single auction and cluster their bids at the very end. In this paper, I show that the presence of multiple, contemporaneous auctions for similar items coupled with uncertainty regarding rival entry can explain both features. The multi-auction structure of eBay induces two types of aggressive bidding behavior: entry deterrence and learning prevention. While the incentive to deter entry leads to aggressive early bidding, the incentive to prevent rivals from learning the unobserved features of the auction by observing the price process can lead to late bidding. I analyze these features in a continuoustime stochastic auction model with endogenous entry, in which bidder types are differentiated by their initial information regarding the entry process. I establish non-parametric identification of the structural model and develop a computationally attractive method for estimating its parameters. Empirical estimates using eBay auctions of pop-music CDs confirm my theoretical prediction that the rate of entry depends on price. I then test my model against alternative explanations of observed bidding behavior using a detailed field experiment. The method that I develop for identification and estimation of continuous-time interaction models is applicable to many settings in which agents compete in large dynamic markets. Corresponding author: Denis Nekipelov, Department of Economics, Duke University, e-mail: denis.nekipelov@duke.edu I am deeply indebted to Han Hong as well as Pat Bajari, Chris Timmins, Paul Ellickson, and Curt Taylor for their help and continuous encouragement. I would like to thank Peter Arcidiacono, Pat Bayer, Lanier Benkard, Arie Beresteanu, Hanming Fang, Jesus Fernandez-Villaverde, Joseph Hotz, Rachel Kranton, John Lazarev, Jon Levin, Marjorie McElroy, Robert McMillan, Juan Rubio-Ramirez, Andrew Sweeting, and Duncan Thomas for their suggestions and fruitful discussions, Drew Beauchamp and Ralph Boleslavsky for their comments on the earlier draft of this paper, all participants of the Microeconometrics and the Microeconomic Theory lunches at Duke, Econometrics Lunch and IO workshop at Stanford, and Structural Lunch at Stanford GSB. I am grateful to the Micro-Incentives Research Center at Duke for financial support, and to Jason Blevins and Anna Kormilitsina for tremendous help with the field experiment. All errors are my own.
Biometric sensor image fusion for identity verification: A case study with wavelet-based fusion rules graph matching

Multibiometric systems have many advantages over the uni-biometric systems. However, multibiometric systems lacking in many respects, such as multimodal systems not only acquire relevant and viable information for fusion, but also acquire some irrelevant and redundant information which are associated to the feature sets or with the match score sets, and this may lead to the resultant performance to be degraded. This paper deals with a biometric authentication system that uses image fusion convention for face and palmprint images using wavelet decomposition. The proposed work uses a few selected wavelet fusion rules subject to fusion of biometric face and palmprint images at low-level. While fusion is accomplished with two high-resolution biometric images, SIFT operator is used to extract invariant features from spatially enhanced fused image. Finally, identity is verified by probabilistic relational graph with posteriori attributes matching between a pair of fused images. Matching is employed by searching corresponding feature points in both the database and query fused images using the iterative relaxation algorithm. The experimental results show that the proposed multimodal biometric system through image fusion outperforms feature level fusion methods, while all the fusion schemes are implemented in the same feature space, i.e., in the scale invariant feature space.
Conceptual and methodological issues relevant to cytokine and inflammatory marker measurements in clinical research

Purpose of reviewTo provide clinical investigators with an understanding of factors to consider when wishing to add cytokine and inflammatory marker measurements to their studies. Recent findingsInflammation involves complex and coordinated responses of the immune system to tissue damage. In the absence of tools to routinely assess inflammation within living tissues, measurements of humoral factors such as cytokines and other inflammatory mediators or markers can provide predictive clinical information and insights into disease mechanisms. Historically, enzyme-linked immunosorbent assays (ELISAs) became the gold standard, yet this approach of measuring a single protein in each sample limits the amount of information which can be obtained from limited amounts of human sample. In recent years, commercially available multiplex technologies which detect large numbers of proteins in a limited volume have provided investigators with opportunities to begin addressing the complexity of inflammatory responses. Nevertheless, great attention needs to be paid to many aspects of study design, sample collection, sample measurement and data analysis. These considerations are especially significant when using technologies for which experience remains limited. SummaryWhereas measurements of peripheral levels of inflammatory markers can add important mechanistic elements to human subject research, careful attention to conceptual and methodological considerations is essential, especially when using novel technologies.