Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Hybrid Bilayer Membranes with Neutron Reflectometry: Probing the Interactions of Melittin

Recent improvements in neutron reflectometry methodology have afforded enhanced sensitivity for the study of biomimetic membranes. The technique has been used to probe the interactions of the peptide toxin, melittin, with supported bilayers of phospholipid and octadecanethiol or thiahexa(ethylene oxide) alkane on gold. Improvements in instrumentation and experimental design permit neutron reflectivity measurements out to a wavevector transfer of 0.7 A-1 and down to reflectivities approaching 10-8, allowing unprecedented resolution of structural details in the bilayer. The data indicate that melittin strongly perturbs the phospholipid headgroup region and also affects the alkane chain region of the bilayer. There is no evidence for hydration of the ethylene oxide spacer region between the gold and alkane regions of the thiahexa(ethylene oxide) alkane/phospholipid bilayer, but a distinct shift of up to 3 A in the apparent location of the interface between the alkane and phospholipid regions is observed. Thi.
Reducing Signaling Overhead in Distributed Mobility Management via Bloom Filter

Distributed mobility management (DMM) is a promising technology to address the mobile data traffic expl osion problem. However, DMM can incur significant signaling overhead to search or distribute the location information. To reduce the signaling overhead, we introduce a timer-based Bloom filter aggregation technique for distributing the location information. Preliminary simulation results show that the timer should be carefully selected to attain the optimal performance.
Bioecology and movements of bull sharks, Carcharhinus leucas , caught in a long-term longline survey off northeastern Brazil

A robust understanding of habitat usage by coastal shark species, and how it overlaps with human presence in denselypopulated regions is needed to inform the development of efficient conservation strategies for these important top predators. An intensive longline survey conducted in nearshore waters off northeastern Brazil from 2004 through 2014 caught a total of 18 bull sharks (Carcharhinus leucas) (male-female ratio = 0.63:1), which can be dangerous to humans. Although most sharks were sexually mature, there was no evidence that this region could be used as a parturition or nursery area. Prey items identified in the guts of the sharks comprised teleosts, mollusks and elasmobranchs. Additionally, one satellite-tagged bull shark covered a great distance (> 3,000 km) in 75 days at liberty, making most use of shallow waters (< 20 m depth) and presumably also entering an estuarine area. Although bull sharks are not an important fishery resource in this region, such a reduced abundance coupled with its affinity for coastal and inshore habitats highlights the potential vulnerability of C. leucas to deleterious anthropic interferences off northeastern Brazil.
Cortical suppressive waves shape the representation of long-range apparent motion

How does the brain link visual stimuli across space and time? Visual illusions provide an experimental paradigm to study these processes. When two stationary dots are flashed in close spatial and temporal succession, human observers experience a percept of motion. Large spatio-temporal separation challenges the visual system to keep track of object identity along the apparent motion path. Here, we utilize voltage-sensitive dye imaging in primary visual cortex (V1) of the awake monkey to investigate whether intra-cortical connections within V1 can shape cortical dynamics to represent the illusory motion. We find that the arrival of the second stimulus in V1 creates a suppressive wave traveling toward the retinotopic representation of the first. Computational approaches show that this suppressive wave can be explained by recurrent gain control fed by the intra-cortical network and contributes to precisely encode the expected motion velocity. We suggest that non-linear intra-cortical dynamics preformat population responses in V1 for optimal read-out by downstream areas.
A Variational Method for Flicker Removal in High Speed Video Sequences

Deflickering of color image sequences constitutes a fundamental task in many video applications. Recently, it has received increased attention with the popularity of consumer-level high speed video acquisition devices, for instance in newer smartphone generations or sports/action cameras. These are capable of acquiring videos at more than 100 frames per second. In this context, flicker corresponds to undesirable intensity and chroma variations due to the interaction between the acquisition frequency on the one hand, and the alternating current powering artificial light sources on the other. Non-uniform periodic flicker is not easy to correct since its estimation requires object tracking, and since most tracking techniques assume consistent illumination. In this paper, we adopt a new optimization approach to solve this problem. We propose causal and non-causal robust correction schemes using a block matching technique paired with color variation estimation. A post processing step is introduced to remove block artifacts in an efficient manner. A multi-resolution approach is also developed to reduce computation time without affecting the quality of our results. We demonstrate the efficacy of our method both on simulated and real data.
Brain Developmental Disorders’ Modelling based on Preschoolers Neuro-Physiological Profiling

Frequently misunderstood by their teachers as being low performers, children with learning disabilities (LDs) such as dyslexia, ADHD, and Asperger’s Syndrome develop low self-confidence and poor self-esteem that may lead to the risk of developing psychological and emotional problems. On contrary, research has shown that a substantial number of these children are capable of learning, and hence, are high-functioning. Therefore, there is a need to provide for the early detection of LDs and instruction that focuses on their needs based on their profiles. Profiling is normally done through observations on the psychological manifestations of LDs by parents and teachers as third-party observers. The first party experience, which is reflected through brain manifestations, is often overlooked. Hence the aim of this paper is to present an alternative solution to profile young children with LDs using electroencephalogram (EEG) that capture brain signals to measure brain functionalities and correlate them with the different LDs. Studies on neurophysiological signals and their relationship to LDs are used to develop Computational Neuro-Physiological (CN-P) model to be an alternative in quantifying the children brain activation function related to learning experience. It is envisaged that such model can profile children with learning disabilities to provide effective intervention in timely manner which can help teachers to provide differentiated instruction for children with LDs. This is in line with the thrust of the Education National Key Result Area (NKRA), the Malaysia Education Blueprint 2013-2025, and the Special Education Regulations 2013.
Marking Vertices to Find Graph Isomorphism Mapping Based on Continuous-Time Quantum Walk

The isomorphism problem involves judging whether two graphs are topologically the same and producing structure-preserving isomorphism mapping. It is widely used in various areas. Diverse algorithms have been proposed to solve this problem in polynomial time, with the help of quantum walks. Some of these algorithms, however, fail to find the isomorphism mapping. Moreover, most algorithms have very limited performance on regular graphs which are generally difficult to deal with due to their symmetry. We propose IsoMarking to discover an isomorphism mapping effectively, based on the quantum walk which is sensitive to topological structures. Firstly, IsoMarking marks vertices so that it can reduce the harmful influence of symmetry. Secondly, IsoMarking can ascertain whether the current candidate bijection is consistent with existing bijections and eventually obtains qualified mapping. Thirdly, our experiments on 1585 pairs of graphs demonstrate that our algorithm performs significantly better on both ordinary graphs and regular graphs.
Contextual Slot Filling for Task-Oriented Conversational Agents

Slot ﬁlling aims to extract the relevant values of certain types of slots from a given utterance in a dialogue. Previous research has either used probabilistic models, requiring little data and having an explicit feature extraction step, or neural networks, using vector representations of words and achieving state-of-the-art results. In this thesis, this problem is solved by using a neural model with two speciﬁc properties: (1) a memory component, giving the model unrestricted access to previous utterances, eﬀectively adding context to the current input utterance. (2) an explicit feature extraction step that uses the dialogue acts as features. The resulting model and two baseline methods are evaluated on a multi-turn dialogue dataset, outperforming both by using the beneﬁts of both probabilistic models and neural networks. The model requires little data and uses an explicit feature extraction step, a memory component and word embeddings. It performs signiﬁcantly better than the baselines on smaller data-set sizes, allowing the model to be used in practice when building task-oriented conversational agents.
S Single-Molecule Fluorescence Resonance Energy Transfer

In this entry, we review smFRET, a powerful technique for measuring distances and monitoring dynamics at the molecular scale. In this technique, the researcher monitors distances between two or more individual fluorescent labels by measuring how efficiently electronic energy is transferred between them, a phenomenon known as Förster resonance energy transfer (FRET). Its power lies in its ability to detect distance changes as small as ~0.3 nm in individual molecules (or pairs of molecules) within a heterogeneous population (Roy et al. 2008). Although it requires a specialized fluorescence microscope, smFRET is employed in many laboratories worldwide and has been used to address questions about topics ranging from intermolecular interactions to macromolecular folding and catalysis. Although smFRET has only been technically possible since the mid-1990s, its theoretical foundations were laid much earlier through the pioneering work of Theodor Förster in the 1940s and through others who elucidated the distance dependence of FRET and its use as a molecular ruler (Stryer 1978). A separate branch of inquiry, the first single-molecule measurements of ion channels using the patch clamp technique, already established some of the core aspects of analyzing time-lapsed recordings from individual molecules in the 1970s and early 1980s (Sakmann and Neher 2009). By the mid-1990s, improvements in fluorescence detection brought about by total internal reflection fluorescence microscopy made it possible to measure the weak emission of single organic fluorophores under ambient conditions and, soon thereafter, smFRETwas realized (Ha et al. 1996).
Doubly penalized estimation in additive regression with high-dimensional data

Additive regression provides an extension of linear regression by modeling the signal of a response as a sum of functions of covariates of relatively low complexity. We study penalized estimation in high-dimensional nonparametric additive regression where functional semi-norms are used to induce smoothness of component functions and the empirical L 2 norm is used to induce sparsity. The functional semi-norms can be of Sobolev or bounded variation types and are allowed to be different amongst individual component functions. We establish oracle inequalities for the predictive performance of such methods under three simple technical conditions: a sub-Gaussian condition on the noise, a compatibility condition on the design and the functional classes under consideration and an entropy condition on the functional classes. For random designs, the sample compatibility condition can be replaced by its population version under an additional condition to ensure suitable convergence of empirical norms. In homogeneous settings where the complexities of the component functions are of the same order, our results provide a spectrum of minimax convergence rates, from the so-called slow rate without requiring the compatibility condition to the fast rate under the hard sparsity or certain L q sparsity to allow many small components in the true regression function. These results signiﬁcantly broaden and sharpen existing ones in the literature.
Identity Vector Extraction by Perceptual Wavelet Packet Entropy and Convolutional Neural Network for Voice Authentication

Recently, the accuracy of voice authentication system has increased significantly due to the successful application of the identity vector (i-vector) model. This paper proposes a new method for i-vector extraction. In the method, a perceptual wavelet packet transform (PWPT) is designed to convert speech utterances into wavelet entropy feature vectors, and a Convolutional Neural Network (CNN) is designed to estimate the frame posteriors of the wavelet entropy feature vectors. In the end, i-vector is extracted based on those frame posteriors. TIMIT and VoxCeleb speech corpus are used for experiments and the experimental results show that the proposed method can extract appropriate i-vector which reduces the equal error rate (EER) and improve the accuracy of voice authentication system in clean and noisy environment.
Fast Interference-Aware Scheduling of Multiple Wireless Chargers

Nowadays, breakthroughs in wireless power transfer make it possible to transfer energy over a long distance. Existing works mainly focused on maximizing network lifetime, optimizing charging efficiency, and optimizing charging quality. All these works use a charging model with the linear superposition, which may not be the most accurate in a real life situation. We use a concurrent charging model, which has a nonlinear superposition, and we consider the Fast Charging Scheduling problem (FCS): given multiple chargers and a group of sensor nodes, how can the chargers be optimally scheduled over the time dimension so that the total charging time is minimized and each sensor node has at least energy E? We prove that FCS is NP-complete and propose algorithms to solve the problem in 1D line and 2D plane respectively. Unlike other algorithms, our algorithm does not need to calculate the combined energy of every possible combination of chargers in advance, which greatly reduces the complexity. We obtain a bound in 2D cases when chargers and sensors are uniformly distributed. Extensive simulations demonstrate that the performance of our algorithm is almost as good as the optimal algorithm when the distribution of chargers is not very dense.
Settling the Staggered Board Debate

We address the heated debate over the staggered board. One theory claims that a staggered board facilitates entrenchment of inefficient management and thus harms corporate value. Consequently, some institutional investors and shareholder rights advocates have argued for the elimination of the staggered board. The opposite theory is that staggered boards are value enhancing since they enable the board to focus on long-term goals. Both theories are supported by prior and conflicting studies and theoretical law review articles. We show that neither theory has empirical support and on average, a staggered board has no significant effect on firm value. Prior studies did not include important explanatory variables in their analysis or account for the changing nature of the firm over time. When we correct for these issues in a sample of up to 2,961 firms from 1990 to 2013 we find that the effect of a staggered board on firm value becomes statistically insignificant after controlling for variables that affect both value and the incidence of a staggered board. Notably, we find that the adoption of a staggered board, its retention, and its removal are not random and exogenous but are rather endogenous, being related to firm characteristics and performance. The effect of a staggered board is idiosyncratic; for some firms it increases value, while for other firms it is value destroying. Our results suggest caution about legal solutions which advocate wholesale adoption or repeal of the staggered board and instead point to an individualized firm approach.
Evaluation of microfluidic channels with optical coherence tomography

Application of time domain, ultra high resolution optical coherence tomography (UHR-OCT) in evaluation of microfluidic channels is demonstrated. Presented study was done using experimental UHR-OCT device based on a Kerr-lens mode locked Ti:sapphire femtosecond laser, a photonic crystal fibre and modified, free-space Michelson interferometer. To show potential of the technique, microfluidic chip fabricated by VTT Center for Printed Intelligence (Oulu, Finland) was measured. Ability for full volumetric reconstruction in non-contact manner enabled complete characterization of closed entity of a microfluidic channel without contamination and harm for the sample. Measurement, occurring problems, and methods of postprocessing for raw data are described. Results present completely resolved physical structure of the channel, its spatial dimensions, draft angles and evaluation of lamination quality.
A BRIGHT MOLECULAR CORE IN A CRAB NEBULA FILAMENT

In a sub-arcsec near-infrared survey of the Crab Nebula using the new Spartan Infrared Camera, we have found several knots with high surface brightness in the H2 2.12 μm line and a very large H2 2.12 μm to Brγ ratio. The brightest of these knots has an intensity ratio I(H2 2.12 μm)/I(Brγ) = 18 ± 9, which we show sets a lower limit on the ratio of masses in the molecular and recombination (i.e., ionized) zones Mmol/Mrec ≥ 0.9, and a total molecular mass within this single knot Mmol ≥ 5 × 10−5 M☉. We argue that the knot discussed here probably is able to emit so strongly in the 2.12 μm line because its physical conditions are better tuned for such emission than is the case in other filaments. It is unclear whether this knot has an unusually large Mmol/Mrec ratio, or if many other Crab filaments also have similar amounts of molecular gas which is not emitting because the physical conditions are not so well tuned.
YouTube Spam Comment Detection Using Support Vector Machine and K–Nearest Neighbor

Information security are becoming an important aspect of organizations. Organisations also are progressively conscious of its important in their business strategy. The awareness make organisations are currently applying for information security management system (ISMS) to effectively manage their information assets. Therefore, this research aims to provide an Information Structure Framework for ISMS planning and certification. Then Likert structured questionnaire was distributed and the findings have been analyzed using Rasch Measurement Model (RMM). The results from this study, managed to develop Information Structure Framework for ISMS. The proposed framework consists of information structure focuses on providing the information outline which is structured in a way, in which the components are put together to form a meaningful structure which can be navigated at any time. The framework contributes to the field of ISMS and certification area. The framework provides an awareness on knowing beforehand what to do and to what extent they are already conquering the information needed for getting clear direction and to develop ISMS.
Learning Scalar Adjective Intensity from Paraphrases

Adjectives like “warm”, “hot”, and “scalding” all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair “really hot” <–> “scalding” suggests that “hot” < “scalding”. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to “yes/no” questions.
A Cost-Aware Auto-Scaling Approach Based on Workload Predictions in Service Clouds

Service platforms have become a very popular way to provide services. But service platforms have disadvantages such as they have long construction periods, low resource utilizations and isolated constructions. Migrating service platforms into clouds can solve these problems. Some researches point out that the scalability become important for taking advantages of service cloud computing technologies. But there are not so many researchers working on the scalability of service clouds. The main contributions of this work are as follows: (1) We propose a linear regression model to predict the workload of service cloud platforms; (2) We present a novel service cloud architecture which offers different services and an auto-scaling approach in service clouds. It combines the real-time scaling and the pre-scaling, and we consider three scaling techniques at different resource levels: self-healing scaling, resource-level scaling and VM-level scaling [1]; (3) Our experiment results show that our auto-scaling approach can meet the user SLA with less costs.
A Collaborative Cache Off-Loading Algorithm for Content Centric Networks

Content centric networking (CCN) is suggested as one of the key troubleshooter technologies for addressing increasing Internet traffics efficiently. However, physical capacity of in-network cache is restricted despite of a major component in CCN due to the limited hardware technology. Therefore the caching performance of each CCN router can be very limited to deal with huge amount of contents. In this paper, we present a new cache management algorithm to cooperate with neighbor routers’ caches. Our preliminary simulation study verifies that the proposed algorithm effectively deals with huge amount of contents with reduced network load.
Mechanochemical Functionalization of Carbon Black at Room Temperature

Carbon nanomaterials such as carbon blacks are intrinsically hydrophobic with limited wettability in aqueous media, thus restricting their potential applications. To improve their hydrophilicity, common methods tend to utilize harmful chemicals and conditions, such as a mixture of KMnO4 and H2SO4 or a complex and expensive synthesis setup. In our work, we report a simple method to improve the wettability of these materials by a mechanochemical treatment completed within 1 h at room-temperature utilizing a NH3 solution. Besides increasing the specific surface area of the carbon black from 67 m2·g−1 up to 307 m2·g−1, our process also incorporates nitrogen- and oxygen-containing functional groups into the carbon. This reduces the contact angle from 80° to 30°, confirming an enhanced wettability. Our work presents an easy, fast, and straightforward pathway towards the functionalization of carbon nanomaterials and can be of use in various applications where aqueous wettability is advantageous.