Miracle-dz/UniFAR-FTUs huggingface dataset:
Rebalancing Spatial Attention: Endogenous Orienting May Partially Overcome the Left Visual Field Bias in Rapid Serial Visual Presentation

In dynamically changing environments, spatial attention is not equally distributed across the visual field. For instance, when two streams of stimuli are presented left and right, the second target (T2) is better identified in the left visual field (LVF) than in the right visual field (RVF). Recently, it has been shown that this bias is related to weaker stimulus-driven orienting of attention toward the RVF: The RVF disadvantage was reduced with salient task-irrelevant valid cues and increased with invalid cues. Here we studied if also endogenous orienting of attention may compensate for this unequal distribution of stimulus-driven attention. Explicit information was provided about the location of T1 and T2. Effectiveness of the cue manipulation was confirmed by EEG measures: decreasing alpha power before stream onset with informative cues, earlier latencies of potentials evoked by T1-preceding distractors at the right than at the left hemisphere when T1 was cued left, and decreasing T1- and T2-evoked N2pc amplitudes with informative cues. Importantly, informative cues reduced (though did not completely abolish) the LVF advantage, indicated by improved identification of right T2, and reflected by earlier N2pc latency evoked by right T2 and larger decrease in alpha power after cues indicating right T2. Overall, these results suggest that endogenously driven attention facilitates stimulus-driven orienting of attention toward the RVF, thereby partially overcoming the basic LVF bias in spatial attention.
Targeted Manipulation of Serotonergic Neurotransmission Affects the Escalation of Aggression in Adult Male Drosophila melanogaster

Dopamine (DA) and serotonin (5HT) are reported to serve important roles in aggression in a wide variety of animals. Previous investigations of 5HT function in adult Drosophila behavior have relied on pharmacological manipulations, or on combinations of genetic tools that simultaneously target both DA and 5HT neurons. Here, we generated a transgenic line that allows selective, direct manipulation of serotonergic neurons and asked whether DA and 5HT have separable effects on aggression. Quantitative morphological examination demonstrated that our newly generated tryptophan hydroxylase (TRH)-Gal4 driver line was highly selective for 5HT-containing neurons. This line was used in conjunction with already available Gal4 driver lines that target DA or both DA and 5HT neurons to acutely alter the function of aminergic systems. First, we showed that acute impairment of DA and 5HT neurotransmission using expression of a temperature sensitive form of dynamin completely abolished mid- and high-level aggression. These flies did not escalate fights beyond brief low-intensity interactions and therefore did not yield dominance relationships. We showed next that manipulation of either 5HT or DA neurotransmission failed to duplicate this phenotype. Selective disruption of 5HT neurotransmission yielded flies that fought, but with reduced ability to escalate fights, leading to fewer dominance relationships. Acute activation of 5HT neurons using temperature sensitive dTrpA1 channel expression, in contrast, resulted in flies that escalated fights faster and that fought at higher intensities. Finally, acute disruption of DA neurotransmission produced hyperactive flies that moved faster than controls, and rarely engaged in any social interactions. By separately manipulating 5HT- and DA- neuron systems, we collected evidence demonstrating a direct role for 5HT in the escalation of aggression in Drosophila.
Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors

In this paper we describe the participation of the Joint Research Centre, EC, in task 14 - Semantic Taxonomy Enrichment at SemEval 2016. The algorithm which we propose transforms each candidate deﬁnition into a term vector, where each dimension represents a term and its value is calculated by TF.IDF. We attach the candidate term as a hyponym to the WordNet synset with the most similar deﬁnition. The results we obtained are encouraging, considering the simplicity of our approach. The obtained F measure is below the average, but above one of the baselines.
Robust Web Image Annotation via Exploring Multi-Facet and Structural Knowledge

Driven by the rapid development of Internet and digital technologies, we have witnessed the explosive growth of Web images in recent years. Seeing that labels can reflect the semantic contents of the images, automatic image annotation, which can further facilitate the procedure of image semantic indexing, retrieval, and other image management tasks, has become one of the most crucial research directions in multimedia. Most of the existing annotation methods, heavily rely on well-labeled training data (expensive to collect) and/or single view of visual features (insufficient representative power). In this paper, inspired by the promising advance of feature engineering (e.g., CNN feature and scale-invariant feature transform feature) and inexhaustible image data (associated with noisy and incomplete labels) on the Web, we propose an effective and robust scheme, termed robust multi-view semi-supervised learning (RMSL), for facilitating image annotation task. Specifically, we exploit both labeled images and unlabeled images to uncover the intrinsic data structural information. Meanwhile, to comprehensively describe an individual datum, we take advantage of the correlated and complemental information derived from multiple facets of image data (i.e., multiple views or features). We devise a robust pairwise constraint on outcomes of different views to achieve annotation consistency. Furthermore, we integrate a robust classifier learning component via $\ell _{2,p}$ loss, which can provide effective noise identification power during the learning process. Finally, we devise an efficient iterative algorithm to solve the optimization problem in RMSL. We conduct comprehensive experiments on three different data sets, and the results illustrate that our proposed approach is promising for automatic image annotation.
Latent Semantic Analysis Boosted Convolutional Neural Networks for Document Classification

Convolutional neural networks (CNNs) have been shown to be effective in document classification tasks. CNNs can be setup using various architectures with many different parameter settings, which may make them difficult to implement. For many document classification tasks, data transformed with ngrams (typically using uni, bi, and trigrams) and term-frequency inverse-document-frequency (TFIDF) weighting are still considered effective baseline models when used with linear classifiers such as logistic regression, especially in smaller datasets with less than 500K observations. A parsimonious CNN baseline model for sentiment classification should replicate the easy use of linear methods. In this study, we introduce a Latent Semantic Analysis (LSA) based CNN model, in which natively trained LSA word vectors are used as input into parallel 1-dimensional convolutional layers (1D-CNNs). The LSA word vector model is obtained by applying singular value decomposition (SVD) on the data transformed by a unigram and TFIDF weighting. Thus, the convolutional layers are designed with window sizes that are best suited for LSA word vectors. This parsimonious LSA -based CNN model exceeds the accuracy of all linear classifiers utilizing ngrams with TFIDF on all analyzed datasets, with average improvement of 0.73% by the top performing LSA-based CNN models. This may be due to the fact that CNNs are better adept at capturing word relationships in phrases and sentences that are not necessarily in the training corpus. Furthermore, the LSA-based CNN model exceeds the performance of word2vec-based CNN models as well. Thus, the success of LSA-based CNNs may potentiate their use as a baseline in classification tasks alongside linear models. Also, we provide guiding principles to simplify the application of LSA-based CNNs in document classification tasks.
Physics-based s-Adaptive Haptic Simulation for Deformable Object

Accurate and fast haptic simulations of deformable objects are desired in many applications such as medical virtual reality. In the haptic interactions with a coarse model, the number of nodes near the haptic interaction region is too small to generate detailed deformation. Thus local refinement techniques need to be developed. Many approaches have employed purely geometric subdivision schemes but they are not proper in describing deformation behavior of deformable object. This paper presents a continuum mechanics-based finite element adaptive method to perform a haptic interaction with a deformable object. It is accomplished by superimposing a local fine mesh upon a global coarse model which consists of the entire deformable object. The coupling between the local mesh and the global mesh is achieved by s-version finite element method (s-FEM) which is generally used to enhance more accurate solutions near the target points. The employment of s-FEM demonstrates the reliable deformation to users in real-time manner.
A Step by Step Design Procedure of PR controller and Capacitor Current Feedback Active Damping for a LCL-Type Grid-tied T-Type Inverter

Distributed power generation systems use power electronics converters as interface for connection to grid. The connection through an LCL filter offers certain advantages, but it brings also the disadvantage of having a resonance frequency. Power electronic converter control is challenging due to filter resonance peak. In this paper a three level ultra-efficient converter called T-Type converter is connected to grid through an LCL filter and its injected current is controlled by PR controller. The resonance peak of the filter is also damped using filter capacitor current feedback. A step by step mathematical design procedure for controller and filter capacitor current feedback coefficient is investigated in details. Simulations are carried out in MATLAB/Simulink environment and results depict suitable performance of the system with designed parameters.
Primary User Enters the Game: Performance of Dynamic Spectrum Leasing in Cognitive Radio Networks

Dynamic spectrum leasing (DSL) is one of the schemes proposed for dynamic spectrum sharing (DSS) in cognitive radio networks. In DSL, spectrum owners, denoted as primary users, dynamically adjust the amount of secondary interference they are willing to tolerate in response to the demand from secondary transmitters. In this correspondence we investigate how much can be gained by primary users if this limited interaction with secondary system is allowed, compared to a scheme in which the interference cap allowed by primary users is fixed {a priori} by a regulatory authority. To that end, we define performance metrics for both primary and secondary systems based on the theoretically achievable multiuser sum-rate of the secondary system and analyze both schemes' behavior with respect to different system parameters. This analysis shows that (i) in dynamic environments DSL based schemes may present an important advantage over other schemes with fixed interference constraints, and (ii) DSL schemes are robust against inaccurate a priori information that may degrade system performance.
Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy

Cell segmentation and tracking in time-lapse fluorescence microscopy images is a task of fundamental importance in many biological studies on cell migration and proliferation. In recent years, level sets have been shown to provide a very appropriate framework for this purpose, as they are well suited to capture topological changes occurring during mitosis, and they easily extend to higher dimensional image data. This model evolution approach has also been extended to deal with many cells concurrently. Notwithstanding its high potential, the multiple-level-set method suffers from a number of shortcomings, which limit its applicability to a larger variety of cell biological imaging studies. In this paper, we propose several modifications and extensions to the coupled-active-surfaces algorithm, which considerably improve its robustness and applicability. Our algorithm was validated by comparing it to the original algorithm and two other cell segmentation algorithms. For the evaluation, four real fluorescence microscopy image datasets were used, involving different cell types and labelings that are representative of a large range of biological experiments. Improved tracking performance in terms of precision (up to 11%), recall (up to 8%), ability to correctly capture all cell division events, and computation time (up to nine times reduction) is achieved.
An explanation for the efficiency of scale invariant dynamics of information fusion in large teams

Large heterogeneous teams will often be in situations where sensor data that is uncertain and conflicting is shared across a peer-to-peer network. Not every team member will have direct access to sensors and team members will be influenced mostly by teammates with whom they communicate directly. Simple models of a large team sharing beliefs to reach conclusions about the world show that the dynamics of such belief sharing systems are characterized by information cascades. These are ripples of belief changes through the system caused by a single additional sensor reading. Glinton et al. showed that such a system will exhibit qualitatively different dynamics sensitive to ranges over system parameters. In addition they showed that In one particular range, the system exhibits behavior known as scale-invariant dynamics which was found empirically to correspond to dramatically more accurate conclusions being reached by team members. In this paper we provide an analytical explanation for the performance of scale invariant dynamics by leveraging signal processing concepts. We show that scale invariant dynamics behave as an adaptive information filter with a response that automatically adjusts to the accuracy of sensor inputs. This adaptation causes the performance gain.
Facial Features and its Importance in Chinese Physiognomy

In this project, we have used different algorithms for detection and extraction of five facial features, i.e. hairline, smile line, skin, wrinkle and melanocytes. These actions were performed using image processing software services and were stored in a cloud platform so that users can periodically browse the changes in these facial features. Chinese medical doctors could also check the features and give some suggestion/comment for patients via internet.
A CNN-Based In-Loop Filter with CU Classification for HEVC

Lossy compression of image and video yields visually annoying artifacts including blocking, blurring, ringing, etc., especially at low bit rates. In-loop filtering techniques can reduce these artifacts, improve quality, and achieve coding gain accordingly. In this paper, we present a convolutional neural network (CNN) based in-loop filter for High Efficiency Video Coding (HEVC). First, we design a new CNN structure that is composed of multiple Variable-filter-size Residue-learning blocks, namely VRCNN-ext, for artifact reduction. VRCNN-ext is trained by natural images as well as their compressed versions at different quality levels. Second, we investigate a new in-loop filter based on the trained VRCNN-ext models. Specifically, we observed that using VRCNN-ext directly on the inter pictures is not effective. To solve this problem, we further train a classifier to decide whether to use VRCNN-ext for each coding unit (CU). The classifier makes decision based on the compressed information, thus avoiding the overhead bits to control the on/off of the CNN-based filter at the CU level. Experimental results show that our scheme achieves significant bits saving than the HEVC anchor, leading to on average 9.2%, 9.6% and 7.4% BD-rate reduction on the HEVC test sequences, under all-intra, low-delay B and random-access configurations, respectively.
An Evaluation of Sciaenid Growth in the Gulf of Mexico

viii CHAPTER 1. GENERAL INTRODUCTION . 1 1.1. Growth Modeling . 1 1.2. Study Species . 2 1.3. Study Outline . 3 1.4. References . 3 CHAPTER 2. TRENDS IN GROWTH MODELING IN FISHERIES . 6 2.
Enriching Wikipedia’s Intra-language Links by their Cross-language Transfer

Although hyperlinks enhance the utility of Wikipedia, embedding them in articles imposes a burden on contributors. To alleviate this burden as well as enrich hyperlinks in Wikipedia articles, we propose a method for transferring intra-language links between different-language articles linked via an interlanguage link. The method avoids anchor selection and disambiguation problems by which usual wikification methods are affected, by exploiting the analogy between different language editions of Wikipedia. The effectiveness of the method was demonstrated through an experiment of transferring intra-language links from English to Japanese. It increased the number of intra-language links in Japanese articles by 40.9%, and the accuracy of anchors selected was estimated to be 96.3%.
Fair Routing in Delay Tolerant Networks

The typical state-of-the-art routing algorithms for delay tolerant networks are based on best next hop hill-climbing heuristics in order to achieve throughput and efficiency. The combination of these heuristics and the social network structure leads the routing to direct most of the traffic through a small subset of good users. For instance, in the SimBet algorithm, the top 10% of users carry out 54% of all the forwards and 85% of all the handovers. This unfair load distribution is not sustainable as it can quickly deplete constraint resources in heavily utilized mobile devices (e.g. storage, battery, budget, etc.). Moreover, because a small number of users carry a significant amount of the traffic, the system is not robust to random failures and attacks. To overcome these inefficiencies, this paper introduces Fair-Route, a routing algorithm for delay tolerant networks inspired by the social processes of perceived interaction strength, where messages are preferably forwarded to users that have a stronger social relation with the target of the message; and assortativity, that limits the exchange of messages to those users with similar "social status". We compare the performance of FairRoute to the state-of-the-art algorithms by extensive simulations on the MIT reality mining dataset. The results show that our algorithm outperforms existing algorithms in the de facto benchmark of throughput vs. forwards. Furthermore, it distributes better the load; the top 10% carry out 26% of the forwards and 28% of the handovers without any loss in performance.
Towards the Extraction of Hierarchical Building Descriptions from 3D Indoor Scans

We present a new method for the hierarchical decomposition of 3D indoor scans and the subsequent generation of an according hierarchical graph-based building descriptor. The hierarchy consists of four basic levels with according entities, building - storey - room - object. All entities are represented as attributed nodes in a graph and are linked to the upper level entity they are located in. Additionally, nodes of the same level are linked depending on their spatial and topological relationship. The hierarchical description enables easy navigation in the formerly unstructured data, measurement takings, as well as carrying out retrieval tasks that incorporate geometric, topological, and also functional building properties describing e.g. the designated use of single rooms according to the objects it contains. In contrast to previous methods which either focus on the segmentation into rooms or on the recognition of indoor objects, our holistic approach incorporates a rather large spectrum of entities on different semantic levels that are inherent to 3D building representations. In our evaluation we show the feasibility of our method for extraction of hierarchical building descriptions for various tasks using synthetic as well as real world data.
Looking back at the Bell-La Padula model

The Bell-La Padula security model produced conceptual tools for the analysis and design of secure computer systems. Together with its sibling engineering initiatives, it identified and elucidated security principles that endure today. This paper reviews those security principles, first in their own time, and then in the context of today's computer and network environment
Use of pyrolysis char as an amendment in soils of the Southeastern United States

Soils in the southeastern United States are characterized by low natural fertility, low soil organic carbon (SOC), and low activity clays. Climate and a history of poor soil management in the region have also resulted in increased rates of organic decomposition and significant loss of soil organic matter. Pyrolysis, the thermal decomposition of biomass in the absence of oxygen, results in a char byproduct. Application of pyrolysis char to soil has the potential to improve soil quality by improving the physical, chemical, and biological properties of soil, as well as increasing soil moisture retention. Char may also be a recalcitrant source of soil carbon that may increase carbon sequestration. The objectives of this study were to determine the effects of char, produced at 400°C with steam, from peanut hull and pine chip feedstocks on soil moisture, soil respiration, and yield of corn (Zea mays L.) in a Tifton loamy sand over two growing seasons in the southeastern Coastal Plain. Overall, char amendments did not significantly affect soil moisture content during either growing season. Soil respiration varied seasonally over the two years (P<0.0001) but there was no significant differences in respiration due to char amendment. This research demonstrated no gain in corn yields or biomass over two growing seasons due to char amendments. Also, yield and biomass decreased during the 2007 season due to drought conditions and nutrient depletion evident in controls and fertilizer checks. It is suggested from this research that char may be applied to agricultural soils as a stable C source without significantly decreasing crop yields. INDEX WORDS: Zea mays L. , Volumetric Water Content, Carbon Sequestration, Soil Respiration, Pyrolysis, Char, Yield, Biomass, Coastal Plain
Leveraging Reusability: Cost-Effective Lexical Acquisition for Large-Scale Ontology Translation

Thesauri and ontologies provide important value in facilitating access to digital archives by representing underlying principles of organization. Translation of such resources into multiple languages is an important component for providing multilingual access. However, the specificity of vocabulary terms in most ontologies precludes fully-automated machine translation using general-domain lexical resources. In this paper, we present an efficient process for leveraging human translations when constructing domain-specific lexical resources. We evaluate the effectiveness of this process by producing a probabilistic phrase dictionary and translating a thesaurus of 56,000 concepts used to catalogue a large archive of oral histories. Our experiments demonstrate a cost-effective technique for accurate machine translation of large ontologies.