Miracle-dz/UniFAR-FTUs huggingface dataset:
Video Annotation by Active Learning and Cluster Tuning

Supervised and semi-supervised learning are frequently applied methods to annotate videos by map.ing low-level features into high-level semantic concepts. Though they work well for certain concepts, the performance is still far from reality due to the large gap between the features and the semantics. The main constraint of these methods is that the information contained in a limited number of labeled training samples can hardly represent the distributions of the semantic concepts. In this paper, we propose a novel semi-automatic video annotation framework, active learning with clustering tuning, to tackle the disadvantages of current video annotation solutions. In this framework, firstly an initial training set is constructed based on clustering the entire video dataset. And then a SVM-based active learning scheme is proposed, which aims at maximizing the margin of the SVM classifier by manually selectively labeling a small set of samples. Moreover, in each round of active learning, we tune/refine the clustering results based on the prediction results of current stage, which is beneficial for selecting the most informative samples in the active learning process, as well as helps further improve the final annotation accuracy in the post-processing step. Experimental results show that the proposed scheme performs superior to typical active learning algorithms in terms of both annotation accuracy and stability.
Natural Products from Three Nudibranchs: Nembrotha kubaryana, Hypselodoris infucata and Chromodoris petechialis

Nudibranchs are shell-less molluscs that are often brightly colored and seemingly defenseless against predation. However, these beautiful animals usually contain large amounts of diet-derived natural products that help defend them against predation. We have isolated a blue tetrapyrrole from Nembrotha kubaryana, the known nakafuran-8 and -9 from Hypselodoris infucata and spongiane-16-one from Chromodoris petechialis. These compounds have previously been found in other marine organisms, thus supporting a link between diet and natural products in the nudibranchs.
A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features

Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.
Dietary patterns of Akwesasne Mohawk adolescents

Abstract Background: Native American youth have greater rates of overweight/obesity than same-aged youth from the general population. Even though dietary shifts are suspected, surprisingly little information exists concerning the dietary patterns of contemporary Native American adolescents. Aim: This study examines the dietary composition of Native American adolescents residing in upstate New York at the Akwesasne Mohawk Nation. The goal of this investigation is to assess the food patterns of Akwesasne adolescents via a total diet approach. Participants/setting and methods: The sample is comprised of 246 Mohawk adolescents between the ages of 10–16.9 years of age residing at the Akwesasne Mohawk Nation. Food frequency data was collected from adolescents via interview during a cross-sectional study investigating their exposure to environmental pollutants. Results and conclusion: Nutrient-dilute but energy-dense foods characterize most of the top 10 dietary sources of energy, carbohydrates, and fat. Although micronutrient intakes are by and large adequate in the sample, micronutrients are most often derived from highly fortified food sources. Adolescent diets contain few naturally-occurring sources of many micronutrients, especially folate and iron. A narrow variety of foods dominate the top dietary sources across both macronutrient and micronutrients, strongly suggesting the need for increased dietary diversity within this age group.
Intermittency, local isotropy, and non‐Gaussian statistics in atmospheric surface layer turbulence

(Received 26 May 1993; accepted 8 March 1994) Orthonormal wavelet expansions are applied to atmospheric surface layer velocity and temperature measurements above a uniform bare soil ‘ surface that exhibit a long inertial subrange energy spectrum. In order to investigate intermittency effects on Kolmogorov’ s theory, a direct relation between the &h-order structure function and the wavelet coefficients is derived. This relation is used to examine deviations from the classical Kolmogorov theory for velocity and temperature in the inertial subrange. The local nature of the orthonormal wavelet transform in physical space aided the identification of events directly contributing to intermittency buildup at inertial subrange scales. These events occur at edges of large eddies and contaminate the Kolmogorov inertial subrange scaling. By suppressing these events, the statistical structure of the inertial subrange for the velocity and temperature, as described by Kolmogorov’ s theory, is recovered. The suppression of intermittency on the nth-order structure function is carried out via a conditional wavelet sampling scheme. The conditioned wavelet statistics reproduced the Kolmogorov scaling (up to n = 6) in the inertial subrange and result in a zero intermittency factor. The conditional wavelet statistics for the mixed velocity temperature structure functions are also presented. It was found that the conditional wavelet statistics for these mixed moments result in a thermal intermittency parameter consistent with other laboratory and field measurements. The relationship between Kolmogorov’ s theory and near-Gaussian statistics for velocity and temperature gradients is also considered. 1. INTRODUCTION From Kolmogorov’ theory (K41), the ensemble average of the nth order velocity difference (Auij between two points separated by spatial distance (Y), in the inertial sub- range, can be computed using (~hz#)=K,((~))~‘ ~r~‘ ~, (1) where E is the turbulent energy dissipation rate
Delegation of Auditing Operation to Auditor by Data Owner with Multiple Authorities for Secure Cloud Storage

In cloud storage service, users upload their data together with authentication information to cloud storage server . Using this services user can enjoy the on-demand highquality applications and services, without burden of local data storage and maintenance . Those advantages are causes of security and privacy problem. So the integrity protection is very challenging task in cloud computing. Hence developer introduce a Third Party Auditor (TPA) to check the integrity of outsource data . In this paper we proposed a method that allow the data owner to delegate the auditing task to untrusted TPA in secure manner, that 1)the data owner can verify whether the TPA has indeed performed the specified audit task, and 2) whether the TPA did the audit task at the right time specified by the data owner. And extended our result to using multiple authorities achieves anonymous cloud data access control and provide a more security for user data. Our security analysis shows the proposed schema are more secure and highly efficient in cloud computing. Keywords— Secure Delegation of Auditing, public auditability, Multiple -Authorities, privileges tree.
Highly sensitive and quantitative evaluation of the EGFR T790M mutation by nanofluidic digital PCR

The mutation of T790M in EGFR is a major mechanism of resistance to treatment with EGFR-TKIs. Only qualitative detection (presence or absence) of T790M has been described to date, however. Digital PCR (dPCR) analysis has recently been applied to the quantitative detection of target molecules in cancer with high sensitivity. In the present study, 25 tumor samples (13 obtained before and 12 after EGFR-TKI treatment) from 18 NSCLC patients with activating EGFR mutations were evaluated for T790M with dPCR. The ratio of the number of T790M alleles to that of activating mutation alleles (T/A) was determined. dPCR detected T790M in all 25 samples. Although T790M was present in all pre-TKI samples from 13 patients, 10 of these patients had a low T/A ratio and manifested substantial tumor shrinkage during treatment with EGFR-TKIs. In six of seven patients for whom both pre- and post-TKI samples were available, the T/A ratio increased markedly during EGFR-TKI treatment. Highly sensitive dPCR thus detected T790M in all NSCLC patients harboring activating EGFR mutations whether or not they had received EGFR-TKI treatment. Not only highly sensitive but also quantitative detection of T790M is important for evaluation of the contribution of T790M to EGFR-TKI resistance.
Porous maltodextrin nanoparticles for the intranasal delivery of vaccines

Nanoparticles technology for mucosal delivery of vaccines received a growing interest in the last decades. Intranasal administration owns great advantages for immune system stimulation, such as local and systemic protection against infectious diseases. However delivery systems and adjuvants are often required to efficiently trigger mucosal and systemic immune responses. In this thesis, nanoparticles (NP) have been evaluated as delivery system for a nasal universal influenza vaccine in a People Program of the European Union Seventh Framework Program FP7 called UniVacFlu. The aim of the UniVacFlu network is to develop a universal influenza vaccine administered through the mucosal route. We used porous maltodextrin nanoparticles with a lipidic core (NPL). We loaded an adjuvanted antigen named CTA1-3M2e-DD in the NPL. CTA1-3M2e-DD is composed of the A1 subunit of the cholera toxin and a conserved epitope of influenza A virus (M2e), while DD, dimer of the synthetic analogue of the Staphyloccous aureus protein A, targets B cells. Interestingly the antigen loading in NPL was quantitative for the antigen: NPL 1:5 mass ratio and the formulation was stable for at least six months at 4°C. We assessed the successful delivery of the antigen by NPL in airway epithelial cells and macrophages. These formulations are currently evaluated by the UniVacFlu consortium in mice.One of the main issues of intranasal vaccines is the toxicity that can be elicited by the nose-brain passage of one of their components. We investigated the loading of antigens in NPL and their delivery in airway mucosa. We observed a high endocytosis of NPL and an increased protein delivery into the cells. On a transwell model of the airway mucosa we assessed the absence of transcytosis and paracellular passage of the NPL. In vivo results confirmed the lack of nose-brain passage of the NPL, as NPL were found not to cross the mucosa. Interestingly, we observed an increased nasal residence time of the protein targeted by NPL. The particles after having delivered their payload are totally eliminated through the gastrointestinal tract, making these nanoparticles good candidates for mucosal delivery system. These results highlight the interest of NPL as vectors for mucosal delivery of drugs.
Extraction of chemical-induced diseases using prior knowledge and textual information

We describe our approach to the chemical–disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtasks: automatic disease-named entity recognition and normalization (DNER), and extraction of chemical-induced diseases (CIDs) from Medline abstracts. For the DNER subtask, we used our concept recognition tool Peregrine, in combination with several optimization steps. For the CID subtask, our system, which we named RELigator, was trained on a rich feature set, comprising features derived from a graph database containing prior knowledge about chemicals and diseases, and linguistic and statistical features derived from the abstracts in the CDR training corpus. We describe the systems that were developed and present evaluation results for both subtasks on the CDR test set. For DNER, our Peregrine system reached an F-score of 0.757. For CID, the system achieved an F-score of 0.526, which ranked second among 18 participating teams. Several post-challenge modifications of the systems resulted in substantially improved F-scores (0.828 for DNER and 0.602 for CID). RELigator is available as a web service at http://biosemantics.org/index.php/software/religator.
Spray Combustion Analysis of Humins

Second generation biomass is an attractive renewable feedstock for transport fuels. Its sulfur content is generally negligible and the carbon cycle is reduced from millions to tens of years. One hitherto non-valorized feedstock are so-called humins, a residual product formed in the conversion of sugars to platform chemicals, such as hydroxymethylfurfural and methoxymethylfurfural, intermediates in the production of FDCA, a building block used to produce the polyethylene furanoate (PEF) bottle by Avantium. The focus of this study is to investigate the spray combustion behavior of humins as a renewable alternative for heavy fuel oil (HFO) under large two-stroke engine-like conditions in an optically accessible constant volume chamber. To reduce the viscosity to HFO levels of the otherwise crystalline humins, methyl levulinate (ML), another side-stream from the same sugar dehydration process, is blended to the former compound at 25 wt. -%; a ratio comparable to that actually produced in many dehydration processes. Various fuel properties of interest, including elemental composition, heating value, density, ignition quality, acid number, flash point, pour point, carbon residue, sediment, water and ash content are measured for the resulting humins-ML blend. The blend is injected into an optically accessible constant volume chamber, the dimensions, injector characteristics and prevailing ambient conditions of which are representative of those found in large two-stroke marine engines. Commercial HFO is used as a benchmark. The combustion process is evaluated by means of shadow imaging and OH∗-chemiluminescence. The former and latter optical techniques are used to determine the phasing and overall magnitude of the heat release event, and ignition delay/location and flame lift off length, respectively. From the results becomes clear that the average ignition delay is comparable to that of HFO, albeit at a higher cycle-to-cycle variation. Notwithstanding a longer lift off length and more downstream ignition kernels, the overall results suggest that the proposed bio-blend is a feasible renewable alternative for HFO in terms of technical feasibility.
SCALABLE OBJECT RECOGNITION USING SUPPORT VECTOR MACHINES

Automatic recognition of objects in images now typically relies on robust local image features. For scalable search through a large database, image features are quantized using a scalable vocabulary tree (SVT) which forms a large visual dictionary. In this project, we design support vector machine (SVM) classifiers for tree histograms calculated from SVT quantization. We explore several practical kernels that naturally capture the statistics of image features. A baseline Naive Bayes classifier for tree histograms is also created for comparison. After Naive Bayes or SVM classification, we further perform a geometric verification step to avoid false positive matches, using either affine or scale consistency check. The Naive Bayes and SVM classifiers and the geometric verification algorithms are tested on two real image databases with challenging image distortions.
A double-strand break can trigger immunoglobulin gene conversion

All three B cell-specific activities of the immunoglobulin (Ig) gene re-modeling system—gene conversion, somatic hypermutation and class switch recombination—require activation-induced deaminase (AID). AID-induced DNA lesions must be further processed and dissected into different DNA recombination pathways. In order to characterize potential intermediates for Ig gene conversion, we inserted an I-SceI recognition site into the complementarity determining region 1 (CDR1) of the Ig light chain locus of the AID knockout DT40 cell line, and conditionally expressed I-SceI endonuclease. Here, we show that a double-strand break (DSB) in CDR1 is sufficient to trigger Ig gene conversion in the absence of AID. The pattern and pseudogene usage of DSB-induced gene conversion were comparable to those of AID-induced gene conversion; surprisingly, sometimes a single DSB induced multiple gene conversion events. These constitute direct evidence that a DSB in the V region can be an intermediate for gene conversion. The fate of the DNA lesion downstream of a DSB had more flexibility than that of AID, suggesting two alternative models: (i) DSBs during the physiological gene conversion are in the minority compared to single-strand breaks (SSBs), which are frequently generated following DNA deamination, or (ii) the physiological gene conversion is mediated by a tightly regulated DSB that is locally protected from non-homologous end joining (NHEJ) or other non-homologous DNA recombination machineries.
Comprehending and Generating Apt Metaphors: A Web-driven, Case-based Approach to Figurative Language

Examples of figurative language can range from the explicit and the obvious to the implicit and downright enigmatic. Some simpler forms, like simile, often wear their meanings on their sleeve, while more challenging forms, like metaphor, can make cryptic allusions more akin to those of riddles or crossword puzzles. In this paper we argue that because the same concepts and properties are described in either case, a computational agent can learn from the easy cases (explicit similes) how to comprehend and generate the hard cases (nonexplicit metaphors). We demonstrate that the markedness of similes allows for a large case-base of illustrative examples to be easily acquired from the web, and present a system, called Sardonicus, that uses this case-base both to understand property-attribution metaphors and to generate apt metaphors for a given target on demand. In each case, we show how the text of the web is used as a source of tacit knowledge about what categorizations are allowable and what properties are most contextually appropriate. Overall, we demonstrate that by using the web as a primary knowledge source, a system can achieve a robust and scalable competence with metaphor while minimizing the need for handcrafted resources like WordNet.
Pre analysis and clustering of uncertain data from manufacturing processes

With increasing complexity of manufacturing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume make any kind of manual data analysis infeasable. At this point, data mining techniques become interesting. The application of current techniques is of complex nature because most of the data is captured by sensor measurement tools. Therefore, every measured value contains a specific error. In this paper, we propose an erroraware extension of the density-based algorithm DBSCAN. Furthermore, we discuss some quality measures that could be utilized for further interpretations of the determined clustering results. Additionally, we introduce the concept of pre-analysis during a necessary data integration step for the proposed algorithm. With this concept, the runtime of the error-aware clustering algorithm can be optimized and the integration of data mining in the overall software landscape can be promoted further .
The Association of Intergroup Encounters, Dominance Status, and Fecal Androgen and Glucocorticoid Profiles in Wild Male White‐Faced Capuchins (Cebus capucinus)

Androgens play a role in male reproductive competition, frequently via aggression, while glucocorticoids are associated with the stress response. However, the relationships of these hormones with different sources of competition (intra‐ vs. intergroup) and dominance status are highly variable. Here, we consider the fecal androgen (fA) and glucocorticoid (fGC) profiles of alpha and subordinate male Cebus capucinus in the context of intergroup competition during a rare period of low intragroup competition (i.e. all females were either pregnant or lactating). Intergroup encounters (IGEs) are a long‐term reproductive strategy in male white‐faced capuchins, enabling them to assess the composition of neighboring groups. IGEs pose a threat to resident males as these can result in injury or death, loss of dominance rank, group eviction, and group takeovers that are frequently associated with infanticide. From February to July 2007, fecal samples were collected from eight males in three groups of white‐faced capuchins in the Santa Rosa Sector of the Área de Conservación Guanacaste, Costa Rica. IGE rate was positively associated with both fA and fGC levels, indicating that IGEs are perceived as reproductive challenges by resident males, and may be associated with elevated metabolic costs. Alpha males sire the majority of group offspring and, accordingly, the threat of IGEs to both future (via rank loss or eviction) and current (via infanticide) reproductive success is greater than for subordinate males. Consistent with this observation, alpha males had higher fA and fGC levels than subordinate males. Given that all females were either pregnant or lactating and pronounced overt intragroup competition was absent, we interpret the difference in hormone profiles of alpha and subordinate males as being primarily associated with variation in the perceived threats of IGEs according to dominance status. Future studies should focus on the interaction of intra‐ and intergroup competition by examining hormone levels in the presence of periovulatory females. Am. J. Primatol. 75:107‐115, 2013. © 2012 Wiley Periodicals, Inc.