Miracle-dz/UniFAR-FTUs huggingface dataset:
Facing a Clever Predator Demands Clever Responses - Red-Backed Shrikes (Lanius collurio) vs. Eurasian Magpies (Pica pica)

Red-backed shrikes (Lanius collurio) behave quite differently towards two common nest predators. While the European jay (Garrulus glandarius) is commonly attacked, in the presence of the Eurasian magpie (Pica pica), shrikes stay fully passive. We tested the hypotheses that this passive response to the magpie is an alternative defense strategy. Nesting shrikes were exposed to the commonly attacked European kestrel (Falco tinnunculus) in a situation in which i) a harmless domestic pigeon, ii) a commonly attacked European jay, and iii) a non-attacked black-billed magpie are (separately) presented nearby. The kestrel dummy presented together with the magpie dummy was attacked with a significantly lower intensity than when it was presented with the other intruders (pigeon, jay) or alone. This means that the presence of the magpie inhibited the shrike’s defense response towards the other intruder. These results support our previous hypotheses that shrikes use an alternative defense strategy in the magpie’s presence. We hypothesize that the magpie is able to associate the active defense of the shrikes with the close proximity of a nest and that shrikes try not to draw the magpie’s attention to the nest. The reason why this strategy is not used against the jay remains unanswered as jays as well as magpies show very similar cognitive and foraging skills enabling them to individuate the nest presence according to active parental defense.
Periodic Safety Message Broadcasting in VANETs Using Recursive Algorithm

Faster and more reliable connection establishment is the primary purpose of periodic safety broadcasting in VANETs. For this purpose we propose a sublayer in the application layer to increase the reliability of safety applications. Our proposed method uses several rebroadcasting schemes safety messages, which considerably improves the overall reliability. It also handles the synchronized collision problem as well as congestion problem and vehicle-to-vehicle channel loss. We propose a zone based algorithm to increase reliability of safety messages. We also use a Recursive Algorithm to give priority for the safety message being generated. We use wi-fi protocol instead of dsrc protocol in order to improve the reliability. Based on this model, a upper bound for the network coding algorithm is derived. Using the results based on analysis and ns-2 simulations we conclude that our method overcomes the previous problems.
Biological Control of Apple Anthracnose by Paenibacillus polymyxa APEC128, an Antagonistic Rhizobacterium

The present study investigated the suppression of the disease development of anthracnose caused by Colletotrichum gloeosporioides and C. acutatum in harvested apples using an antagonistic rhizobacterium Paenibacillus polymyxa APEC128 (APEC128). Out of 30 bacterial isolates from apple rhizosphere screened for antagonistic activity, the most effective strain was APEC128 as inferred from the size of the inhibition zone. This strain showed a greater growth in brain-heart infusion (BHI) broth compared to other growth media. There was a reduction in anthracnose symptoms caused by the two fungal pathogens in harvested apples after their treatment with APEC128 in comparison with non-treated control. This effect is explained by the increased production of protease and amylase by APEC128, which might have inhibited mycelial growth. In apples treated with different APEC128 suspensions, the disease caused by C. gloeosporioides and C. acutatum was greatly suppressed (by 83.6% and 79%, respectively) in treatments with the concentration of 1 × 108 colony forming units (cfu)/ml compared to other lower dosages, suggesting that the suppression of anthracnose development on harvested apples is dose-dependent. These results indicated that APEC128 is one of the promising agents in the biocontrol of apple anthracnose, which might help to increase the shelf-life of apple fruit during the post-harvest period.
Flow volumes for interactive vector field visualization

Flow volumes are the volumetric equivalent of stream lines. They provide more information about the vector field being visualized than do stream lines or ribbons. Presented is an efficient method for producing flow volumes, composed of transparently rendered tetrahedra, for use in an interactive system. The problems of rendering, subdivision, sorting, composing artifacts, and user interaction are dealt with. Efficiency comes from rendering only the volume of the smoke, and using hardware texturing and compositing. <<ETX>>
Frequency hopping rate and the diversity of a fading channel

A method is presented to determine the diversity gain achieved by increasing the hopping rate in a frequency hopped spread spectrum system using frequency mean square correlation (FMSC). In particular, it is demonstrated that the detailed shape of the delay power profile of a given channel does not play a fundamental role in the determination of the performance saturation point given the FMSC of the channel. Keywords— Frequency Mean Square Correlation, Frequency Hopped Spread Spectrum, Frequency Hopping Rate
Mining Social Emotions from Affective Text

This paper is concerned with the problem of mining social emotions from text. Recently, with the fast development of web 2.0, more and more documents are assigned by social users with emotion labels such as happiness, sadness, and surprise. Such emotions can provide a new aspect for document categorization, and therefore help online users to select related documents based on their emotional preferences. Useful as it is, the ratio with manual emotion labels is still very tiny comparing to the huge amount of web/enterprise documents. In this paper, we aim to discover the connections between social emotions and affective terms and based on which predict the social emotion from text content automatically. More specifically, we propose a joint emotion-topic model by augmenting Latent Dirichlet Allocation with an additional layer for emotion modeling. It first generates a set of latent topics from emotions, followed by generating affective terms from each topic. Experimental results on an online news collection show that the proposed model can effectively identify meaningful latent topics for each emotion. Evaluation on emotion prediction further verifies the effectiveness of the proposed model.
Combined Eigenfunction Expansion and Convergence Accelerator Approach to the Rapid Numerical Evaluation of Hilbert Transforms

The numerical evaluation of a class of Hilbert transforms using an eigenfunction approach is considered. Judicious selection of convergence accelerators allows for the efficient evaluation of the resulting series. The approach is particularly accurate for functions having a Gaussian-like asymptotic behavior. For more slowly decreasing functions, the accuracy of the evaluation decreases.
PET studies of the serotonin system in major depression and its treatment

Cover picture: Summation PET images of an untreated reference subject (left) and of a patient treated with an SSRI (citalopram, right), after intravenous injection of [ 11 C]MADAM. All previously published papers were reproduced with permission from the publisher. ABSTRACT The serotonin system has been implicated in major depression since the 1960s, mainly based on the serotonin enhancing properties of antidepressants. Positron emission tomography, PET, is the in vivo molecular imaging method with the best spatial resolution. There has been a gradual development of suitable radioligands for serotonergic targets since the mid-1990s, widening the scope of PET studies of the serotonin system. [ 11 C]MADAM is an established radioligand selective for the serotonin transporter, and [ 11 C]AZ10419369 is selective for the 5-HT 1B receptor. The aim of this thesis was to study the serotonin system in major depressive disorder and the serotonergic effects of treatment with antidepressive medication or with psychotherapy. In order to understand the results with [ 11 C]AZ10419369 better we examined the sensitivity of this radioligand to baseline serotonin levels. In study I we examined serotonin transporter occupancy with PET and [ 11 C]MADAM in responders to treatment with seven different antidepressants in different doses. Two tricyclic antidepressants (TCAs) and four selective serotonin reuptake inhibitors (SSRIs) were examined. Mirtazapine was included as a serotonin transporter " dummy ". Serotonin transporter occupancy could be confirmed in vivo for all TCAs and SSRIs. There was no significant difference in serotonin transporter occupancy between the old antidepressants, TCAs, and the new, SSRIs. Mirtazapine did not occupy the serotonin transporter. The average serotonin transporter occupancy in SSRIs and TCAs was 67 %, which was significantly lower than the 80 % serotonin transporter occupancy previously postulated important for SSRI effect. In study II we investigated the effect of internet-delivered cognitive behavioural therapy (CBT) for recurrent major depressive disorder on [ 11 C]AZ10419369 binding. Ten patients with an ongoing and untreated major depressive episode finished the study according to protocol and were examined with PET and [ 11 C]AZ10419369 before and after CBT. All patients responded to treatment. The binding potential, BP ND , was reduced by 33 % in the dorsal brain stem, which included the raphe nuclei, from which the serotonergic neurons project. Since the 5-HT 1B receptor acts inhibitory, a reduction of 5-HT 1B receptor density in the raphe nuclei would in theory result in a general stimulation of …
Gradual Changes of Gut Microbiota in Weaned Miniature Piglets

Colonization of gut microbiota in mammals during the early life is vital to host health. The miniature piglet has recently been considered as an optimal infant model. However, less is known about the development of gut microbiota in miniature piglets. Here, this study was conducted to explore how the gut microbiota develops in weaned Congjiang miniature piglets. In contrast to the relatively stabilized gut fungal community, gut bacterial community showed a marked drop in alpha diversity, accompanied by significant alterations in taxonomic compositions. The relative abundances of 24 bacterial genera significantly declined, whereas the relative abundances of 7 bacterial genera (Fibrobacter, Collinsella, Roseburia, Prevotella, Dorea, Howardella, and Blautia) significantly increased with the age of weaned piglets. Fungal taxonomic analysis showed that the relative abundances of two genera (Kazachstania and Aureobasidium) significantly decreased, whereas the relative abundances of four genera (Aspergillus, Cladosporium, Simplicillium, and Candida) significantly increased as the piglets aged. Kazachstania telluris was the signature species predominated in gut fungal communities of weaned miniature piglets. The functional maturation of the gut bacterial community was characterized by the significantly increased digestive system, glycan biosynthesis and metabolism, and vitamin B biosynthesis as the piglets aged. These findings suggest that marked gut microbial changes in Congjiang miniature piglets may contribute to understand the potential gut microbiota development of weaned infants.
Rapid development of application-oriented natural language interfaces

Computerized systems, embedded and mobile devices became everyday artefacts yet with an ever broadening gap between the complexity of the applications and limitations of the interface devices. Controlled natural languages could provide an easy-to-use and easy-to-compute communication interface that fills this gap. In order to facilitate the development of such system components we propose a methodology that relies on the conceptual model of the application interface, from which language rules and vocabulary can be generated. With the addition of a general framework for controlled natural language communication user interface can be created fast-and-simple without any specialized knowledge in language technologies.
ECPC: Preserve Downtime Data Persistence in Disruptive Sensor Networks

In challenging environments, sensor networks may become disruptive even unavailable (downtime), resulting in data losses. In-situ encoding and caching data in a distributed fashion can preserve data persistence during downtime. However, the existing approaches are mainly based on random walks, which incurs significant communication overhead and may itself fail due to network disruptions. In this paper, we present a distributed Erasure Coding with randomized Power Control (ECPC) mechanism to preserve downtime data persistence in disruptive sensor networks. ECPC only requires each node to perform a single broadcast at each of its several randomly selected power levels. Thus it incurs low communication overhead. Moreover the storage space requirement is lower and uniform across the network. It applies randomized power control in localized data broadcast to achieve expected code degree distribution and ensure pseudo-global randomness for erasure coding. Performance comparisons between ECPC and other existing approaches show that ECPC mechanism can reach higher data reliability under varying node failure probabilities. In addition, our ECPC approach is scalable with network sizes.
Differential Dataflow

Existing computational models for processing continuously changing input data are unable to eﬃciently support iterative queries except in limited special cases. This makes it diﬃcult to perform complex tasks, such as social-graph analysis on changing data at interactive timescales, which would greatly beneﬁt those analyzing the behavior of services like Twitter. In this paper we introduce a new model called differential computation , which extends traditional incremental computation to allow arbitrarily nested iteration, and explain—with reference to a publicly available prototype system called Naiad—how diﬀerential computation can be eﬃciently implemented in the context of a declarative data-parallel dataﬂow language. The resulting system makes it easy to program previously intractable algorithms such as incrementally updated strongly connected components, and integrate them with data transformation operations to obtain practically relevant insights from real data streams.
Energy Transfer between Er3+ and Pr3+ for 2.7 μm Fiber laser material

Energy transfer mechanisms between Er3+ and Pr3+ in Er3+/Pr3+ codoped germinate glass are investigated in detail. Under 980 nm LD pumping, 2.7 μm fluorescence intensity enhanced greatly. Meanwhile, 1.5 μm lifetime and fluorescence were suppressed deeply due to the efficient energy transfer from Er3+:4I13/2 to Pr3+:3F3,4, which depopulates the 4I13/2 level and promotes the 2.7 μm transition effectively. The obvious change in J-O parameters indicates that Pr3+ influences the local environment of Er3+ significantly. The increased spontaneous radiative probability in Er3+/Pr3+ glass is further evidence for enhanced 4I11/2 → 4I13/2 transition. The Er3+:4I11/2→Pr3+:1G4 process is harmful to the population accumulation on 4I11/2 level, which inhibits the 2.7 μm emission. The microscopic energy transfer coefficient of Er3+:4I13/2→Pr3+:3F3,4 is 42.25 × 10−40 cm6/s, which is 11.5 times larger than that of Er3+:4I11/2→Pr3+:1G4. Both processes prefer to be non-phonon assisted, which is the main reason why Pr3+ is so efficient in Er3+:2.7 μm emission.
Backtracking search optimization algorithm for fluid-structure interaction problems

Fluidstructure interaction (FSI) problems play an important role in many technical applications, for instance, wind turbines, aircrafts, injection systems, or pumps. Thus, the optimization of such kind of problems is of high practical importance. Optimization algorithms aim to find the best values for a system's parameters under various conditions. In this paper an approach for multi-objective optimization of fluidstructure interaction (FSI) problems is presented. It is based on the Backtracking Search Optimization Algorithm (BSA), a new evolutionary algorithm (EA) for solving real-valued numerical optimization problems. EAs are popular stochastic search algorithms that are widely used to solve non-linear, non-differentiable and complex numerical optimization problems. A shape representation with splines, and a partitioned solution procedure for FSI is used in the numerical simulation and the results of the optimized front obtained using non-dominated BSA algorithm are then presented and compared to Nondominated Neighbor Immune Algorithm (NNIA). The numerical results show not only the effectiveness and the best performance of non-dominated BSA, but also its rapidity and efficiency.
Advanced data analytics education for students and companies

In this paper we introduce a regional education project and a positive experience of quick implementation of a new training package enabled by public funding from the European Social Fund. The project focuses on advanced data analytics (ADA) in business management. In the project, advanced data analytics is taught to both students at University of Eastern Finland and to their potential employers at the local organizations. The university teaching favors effective teaching techniques instead of conventional teaching methods, and the same topics are taught to participants from local organizations as shorter versions. The organizations also have an important role in calibrating the teaching via discussions and maturity reviews. Even-though the project is regional there has been great nationwide interest in the project. This indicates the general need for improving know-how on ADA both at universities and in companies and organizations.
A Neural Network Based Dutch Part of Speech Tagger

In this paper a Neural Network is designed for Part-of-Speech Tagging of Dutch text. Our approach uses the Corpus Gesproken Nederlands (CGN) consisting of almost 9 million transcribed words of spoken Dutch, divided into 15 different categories. The outcome of the design is a Neural Network with an input window of size 8 (4 words back and 3 words ahead) and a hidden layer of 370 neurons. The words ahead are coded based on the relative frequency of the tags in the training set for the word. Special attention is paid to unknown words (words not in the training set) for which such a relative frequency cannot be determined. Based on a 10-fold cross validation an approximation of the relative frequency of tags for unknown words is determined. The performance of the Neural Network is 97.35%, 97.88% on known words and 41.67% on unknown words. This is comparable to state of the art performances found in the literature. The special coding of unknown words resulted of an increase of almost 13% for the tagging of unknown words.
Concurrent Systematic Testing at Scale

Systematic testing, first demonstrated in small, specialized cases 15 years ago, has matured sufficiently for large-scale systems developers to begin to put it into practice. With actual deployment comes new, pragmatic challenges to the usefulness of the techniques. In this report we are concerned with scaling dynamic partial order reduction, a key technique for mitigating the state space explosion problem, to very large clusters. In particular, we present a new approach for distributed dynamic partial order reduction. Unlike previous work, our approach is based on a novel exploration algorithm that 1) enables trading space complexity for parallelism, 2) achieves efficient load-balancing through time-slicing, 3) provides for fault tolerance, 4) has been demonstrated to scale to more than a thousand parallel workers, and 5) is guaranteed to avoid redundant exploration of overlapping portions of the state space. Acknowledgements: This research was sponsored by the U.S. Army Research Office under grant number W911NF0910273. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Further, the authors would like to thank Brian Grant, John Wilkes, Robert Kennedy, Todd Wang, and Walfredo Cirne for their continuous technical feedback and support on the ongoing ETA effort. The authors are also thankful to Google for providing its hardware and software infrastructure for the experimental evaluation presented in this report. Last but not least, we also thank the members and companies of the PDL Consortium (including Actifio, American Power Conversion, EMC Corporation, Emulex, Facebook, Fusion-io, Google, Hewlett-Packard Labs, Hitachi, Huawei Technologies Co., Intel Corporation, Microsoft Research, NEC Laboratories, NetApp, Inc., Oracle Corporation, Panasas, Riverbed, Samsung Information Systems America, Seagate Technology, STEC, Inc., Symantec Corporation, VMware, Inc., and Western Digital) for their interest, insights, feedback, and support.
Taxonomy of Multimedia Component Interactivity A Contribution to the Current Metadata Debate

Preliminary Remark Anyone dealing with the development of modular multimedia learning systems who has had frequent recourse to the metadata drafts of Ariadne (Alliance of Remote Instructional Authoring and Distribution Networks of Europe; http://ariadne.unil.ch), IEEE LOM (Learning Objects Metadata project group of IEEE; http://grouper.ieee.org/groups/ltsc/wg12), or other institutions, can't help being aggravated by the fact that these internationally discussed metadata concepts fail to define important categories like interactivity in multimedia applications and multimedia leaning systems in a form of any practical value for the construction and didactics of such learning systems. The Ariadne project, for example, defines interactivity in the form of a scale ranging from high to low (http://ariadne.unil.ch/Metadata/ariadne_metadata_v3final1.htm). Such a scale can only yield subjective entries from the developers of learning systems, since learning program designers would have to decide for themselves whether their programs require or provide a high, medium, or low form of interactivity. The Learning Objects Metadata (LOM) standard draft from the IEEE organisation, which is based on proposals by Dublin Core and Ariadne, contains the same formal definition of interactivity:
Father's Education and Children's Human Capital: Evidence from the World War II GI Bill

Children who grow up in more highly educated families have better labor market outcomes as adults than those who grow up in less educated families, but we do not know whether this is because education bestows parents with skills that make them better parents or because unobservable endowments that contribute to the parents' education levels are shared by their children. This paper attempts to improve our understanding of the causal processes that contribute to intergenerational mobility by exploiting variation in fathers' education induced by the WWII G.I. Bill. Identificatin rests on the timing of the war: the GI Bill had different effects on different cohorts depending on their likelihood of military service and the probability that schooling had been completed before the war began. I find that a one year increase in a father's education reduces the probability that his child is retained in school by about 2-3 percentage points. This implies that parental schooling levels have an affect on children's outcomes that is independent of their innate ability and suggests that public policies aimed at increasing educational attainment may have important intergenerational effects.