Miracle-dz/UniFAR-FTUs huggingface dataset:
Information fusion for rural land-use classification with high-resolution satellite imagery

We propose an information fusion method for the extraction of land-use information based on both the panchromatic and multispectral Indian Remote Sensing Satellite 1C (IRS-1C) satellite imagery. It integrates spectral, spatial and structural information existing in the image. A thematic map was first produced with a maximum-likelihood classification (MLC) applied to the multispectral imagery. Probabilistic relaxation (PR) was then performed on the thematic map to refine the classification with neighborhood information. Furthermore, we incorporated edges extracted from the higher resolution panchromatic imagery in the classification. An edge map was generated using operations such as edge detection, edge thresholding and edge thinning. Finally, a modified region-growing approach was used to improve image classification. The procedure proved to be more effective in land-use classification than conventional methods based only on multispectral data. The improved land-use map is characterized with sharp interregional boundaries, reduced number of mixed pixels and more homogeneous regions. The overall kappa statistics increased considerably from 0.52 before the fusion to 0.75 after.
Crosslingual adaptation of semi-continuous HMMS using maximum likelihood and maximum a posteriori convex regression

In this work we present a novel adaptation design for semicontinuous HMMs (SCHMM). The method, which is developed in the scope of a crosslingual model adaptation task, consists in adjusting the states' mixture weights associated to the prototype densities of the codebook. The mixture weights of the target language are modelled as convex combinations of prototype weights. They are defined by an acoustic regression scheme applied to the source models, followed by a refinement using probabilistic latent semantic analysis (PLSA). In order to find suitable combination weights for the convex combinations we present a maximum likelihood (ML) as well as a maximum a posteriori (MAP) estimate. Thus, we name them maximum likelihood convex regression (MLCR) and maximum a posteriori convex regression (MAPCR). Finally, a crosslingual model adaptation task transferring multilingual Spanish-English-German HMMs to Slovenian demonstrates the performance of the method.
Complete Genome Sequence of Campylobacter jejuni Strain S3

ABSTRACT Campylobacter jejuni is one of the leading causes of bacterial gastroenteritis in the world; however, there is only one complete genome sequence of a poultry strain to date. Here we report the complete genome sequence and annotation of the second poultry strain, C. jejuni strain S3. This strain has been shown to be nonmotile, to be a poor invader in vitro, and to be a poor colonizer of poultry after minimal in vitro passage.
The effects of feeding various forages on fatty acid composition of bovine milk fat: A review

The nutritional image of bovine milk fat has suffered for years because of the association of saturated fatty acids and coronary heart disease. Thus the alteration of fatty acid composition has been a long-term strategy. Forages, even though containing a relatively low level of lipids, are the cheapest and often the major source of beneficial unsaturated fatty acids in ruminant diets. Recent progress in the research of factors affecting fatty acid content and composition in fresh and preserved forages and the associations between feeding such forages and milk fat profile are reviewed. Milk from cows grazed or fed fresh forage, especially from species-rich grasslands or forage legumes, has a considerably higher ratio of unsaturated to saturated fatty acids and a higher content of nutritionally beneficial trans-fatty acids (e.g. CLA, vaccenic acid) than milk from cows fed silage or hay. Grass and legume silages seem to affect the fatty acid profile more propitiously than maize silage.
Communicating risk in disaster risk management systems – experimental evidence of the perceived usefulness of risk descriptions

Disaster risk management (DRM) requires the collaboration of a variety of stakeholders working in different sectors. They depend on each other to share risk information, and effective collaboration requires efficient communication. Clearly, the communication of risk descriptions is a key issue for the success of DRM activities. This study investigated the communication of risk-related information between DRM actors, and how to present such information in order to improve its usefulness as a basis for decision-making. Two experiments were conducted to investigate the issue. The first related to the type of hazard, while the second examined the presentation of likelihood and consequences. We addressed two research questions: Does the presentation of a risk description influence its perceived usefulness for decision-making in a DRM system? If so, which format is perceived as most useful? Our findings indicated that the type of hazard did not affect perceived usefulness to any significant extent. However, the presentation of likelihood and consequences did have a significant effect. These results indicate that the presentation of risk information greatly influences stakeholders’ ability to make well-informed decisions. Specifically, quantitative scale and semi-quantitative ranking scale formats are perceived as most useful.
Smooth delivery of layered video stream in P2P networks

Layered streaming in peer-to-peer (P2P) networks has drawn great interests since not only it can accommodate a large number of users but also it handles heterogeneities of peers. However, there's still a lack of comprehensive study on smoothing and layer selection for layered streaming. In this work, we aim to smooth the layered stream by selecting the appropriate layers and reducing the amplitude and frequency of layer changes. We propose three objectives that should be achieved by our proposed mechanism: smoothing, minimizing useless chunk ratio and handling bandwidth fluctuation. The performance evaluation demonstrates that the proposed mechanism provides a significant improvement in the received video quality in terms of lowering the number of layer changes and useless layers chunks.
Recognizing human actions based on silhouette energy image and global motion description

In this paper, we propose a spatio-temporal silhouette representation, called silhouette energy image (SEI), and variability models, to characterize motion and shape properties for automatic recognition of human actions in daily life. To address the variability in the recognition of human actions, several parameters, such as anthropometry of the person, speed of the action, phase (starting and ending state of an action), camera observations (distance from camera, slanting motion, and rotation of human body), and view variations are proposed. We construct the variability models based on SEI and the variability parameters. The global shape-based motions express the spatio-temporal properties of SEI and variability models. Our construction of the optimal model for each action and view is based on the support vectors of motion descriptions of combined action models. We recognize different daily human actions of different styles successfully in the indoor and outdoor environment. Our experimental results show that the proposed method of human action recognition is robust, flexible and efficient.
TXA709, an FtsZ-Targeting Benzamide Prodrug with Improved Pharmacokinetics and Enhanced In Vivo Efficacy against Methicillin-Resistant Staphylococcus aureus

ABSTRACT The clinical development of FtsZ-targeting benzamide compounds like PC190723 has been limited by poor drug-like and pharmacokinetic properties. Development of prodrugs of PC190723 (e.g., TXY541) resulted in enhanced pharmaceutical properties, which, in turn, led to improved intravenous efficacy as well as the first demonstration of oral efficacy in vivo against both methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant S. aureus (MRSA). Despite being efficacious in vivo, TXY541 still suffered from suboptimal pharmacokinetics and the requirement of high efficacious doses. We describe here the design of a new prodrug (TXA709) in which the Cl group on the pyridyl ring has been replaced with a CF3 functionality that is resistant to metabolic attack. As a result of this enhanced metabolic stability, the product of the TXA709 prodrug (TXA707) is associated with improved pharmacokinetic properties (a 6.5-fold-longer half-life and a 3-fold-greater oral bioavailability) and superior in vivo antistaphylococcal efficacy relative to PC190723. We validate FtsZ as the antibacterial target of TXA707 and demonstrate that the compound retains potent bactericidal activity against S. aureus strains resistant to the current standard-of-care drugs vancomycin, daptomycin, and linezolid. These collective properties, coupled with minimal observed toxicity to mammalian cells, establish the prodrug TXA709 as an antistaphylococcal agent worthy of clinical development.
Anomaly detection via statistical learning in industrial communication networks

In this paper, we discuss a novel statistical learning algorithm that predicts normal flows of process data in a distributed control system, i.e., process data evolutions that characterise the normal behaviour of a cyber-physical system such as a power plant. The algorithm's prediction capability allows for determining whether the payload of a network packet that is about to be processed by a computer device in a distributed control system is normal or malicious. This classification is based on whether or not the process data evolution that a network packet under inspection has potential to cause is predicted as normal by the algorithm. In this paper, we also discuss a probabilistic validation of the algorithm. We construct stochastic activity networks with activity-marking oriented reward structures that model pertinent aspects of the normal operation of a cyber-physical system as a whole as perceived by the algorithm. The solution of these models via a tool such as Mbius indicates whether the algorithm's perception of normalcy is correct. We have implemented the algorithm in the MATLAB programming language, and thus in the paper we also discuss practical testing and evaluation of the effectiveness of the algorithm in a testbed that resembles a power plant.
Differential Phagocytosis of White versus Opaque Candida albicans by Drosophila and Mouse Phagocytes

The human fungal pathogen Candida albicans resides asymptomatically in the gut of most healthy people but causes serious invasive diseases in immunocompromised patients. Many C. albicans strains have the ability to stochastically switch between distinct white and opaque cell types, but it is not known with certainty what role this switching plays in the physiology of the organism. Here, we report a previously undescribed difference between white and opaque cells, namely their interaction with host phagocytic cells. We show that both Drosophila hemocyte-derived S2 cells and mouse macrophage-derived RAW264.7 cells preferentially phagocytose white cells over opaque cells. This difference is seen both in the overall percentage of cultured cells that phagocytose white versus opaque C. albicans and in the average number of C. albicans taken up by each phagocytic cell. We conclude that susceptibility to phagocytosis by cells of the innate immune system is an important distinction between white and opaque C. albicans, and propose that one role of switching from the prevalent white form into the rarer opaque form may be to allow C. albicans to escape phagocytosis.
Different relative expression from two murine leukemia virus long terminal repeats in unintegrated transfected DNA and in integrated retroviral vector proviruses

Results of transient-expression studies have suggested a correlation between tissue-specific pathogenicity of murine leukemia viruses and the relative transcriptional activities of their long terminal repeats in various cell types. To test whether transient-expression ratios are representative of those of integrated proviruses, we developed a system for generation of retroviral transmission vectors differing only in U3. Vectors with the long terminal repeats of leukemogenic SL3-3 and nonleukemogenic Akv viruses were used for infection of a lymphoid cell line. We then compared expression in infected cells with transient expression after DNA transfection. In contrast to a high SL3-3/Akv reporter gene expression ratio in the transient assays, the ratio in stably infected populations was low. Sets of random cell clones from the two infected populations showed wide variation, with a mean value ratio identical to the population ratio but a considerably higher ratio between lowest values. We suggest that the lower expression levels, like transient expression, reflect inherent enhancer strength and that the higher levels represent chromosomal influence. The different pathogenicity, despite the moderate difference in average expression, may then relate to a different capacity for insertional oncogene activation owing to the different inherent enhancer strengths revealed by the transient-expression assays and the least active proviruses.
A Novel Deep Embedding Network for Building Shape Recognition

Building shape, as a key structured element, plays a significant role in various urban remote sensing applications. However, because of high complexity and intraclass variations between building structures, the capability of building shape description and recognition becomes limited or even impoverished. In this letter, a novel deep embedding network is proposed for building shape recognition, which combines the strength of the unsupervised feature learning of convolutional neural networks (CNNs) and a novel triplet loss. Specifically, we take advantage of the strong discriminative power of CNNs to learn an efficient building shape representation for shape recognition. With this deep embedding network, the high-dimensional image space can be mapped into a low-dimensional feature space, and the deep features can effectively reduce the intraclass variations while increasing the interclass variation between different building shape images. Afterward, the derived deep features are exploited for the process of building shape recognition. This method consists of two stages. In the first stage, for standard building shape image queries stored in the shape primitives library and the building shape data set, two sets of deep features are extracted with the deep embedding network. In the second stage, we formulate the shape recognition task into a feature matching problem and the final building shape recognition results can be achieved by set-to-set feature matching method. Experiments on the VHR-10 and UCML data sets demonstrate the effectiveness and precision of the proposed method.
Node embeddings in social network analysis

We introduce a distributed representation of nodes, node embeddings, in social network analysis. We compute embeddings for nodes based on their attributes and links. These embeddings can support many social network applications - including analyses of community homogeneity, distance, and detection of community connectors (inter-community outliers, people who connect communities) - thanks to the convenient yet efficient computation provided by node embeddings for structural comparisons. Our experimental results include many interesting insights about the computer science literature network (DBLP). For example, in DBLP prior to 2013 the best way for research in Natural Language & Speech to gain impact toward "best-paper" recognition was to emphasize aspects related to Machine Learning & Pattern Recognition.
A reinforcement-learning approach to understanding loss-chasing behavior in rats

Risky decisions are inherently characterized by the potential to receive gains and losses from these choices, and gains and losses have distinct effects on global risky choice behavior and the likelihoods of making risky choices depending on the outcome of the previous choice. One translationally-relevant phenomenon of risky choice is loss-chasing, in which individuals make risky choices following losses. However, the mechanisms of loss-chasing are poorly understood. The goal of two experiments was to illuminate the mechanisms governing individual differences in loss-chasing and risky choice behaviors. In two experiments, rats chose between a certain outcome that always delivered reward and a risky outcome that probabilistically delivered reward. In Experiment 1, loss processing and loss-chasing behavior were assessed in the context of losses-disguised-as-wins (LDWs), or loss outcomes presented along with gain-related stimuli. The rats presented with LDWs were riskier and less sensitive to differential losses. In Experiment 2, these behaviors were assessed relative to the number of risky losses that could be experienced. Here, the addition of reward omission or a small non-zero loss to the possible risky outcomes elicited substantial individual differences in risky choice, with some rats increasing, decreasing, or maintaining their previous risky choice preferences. Several reinforcement learning (RL) models were fit to individual rats’ data to elucidate the possible psychological mechanisms that best accounted for individual differences in risky choice and loss-chasing behaviors. The RL analyses indicated that the critical predictors of risky choice and loss-chasing behavior were the different rates that individuals updated value estimates with newly experienced gains and losses. Thus, learning deficits may predict individual differences in maladaptive risky decision making. Accordingly, targeted interventions to alleviate learning deficits may ultimately increase the likelihood of making more optimal and informed choices. A REINFORCEMENT-LEARNING APPROACH TO UNDERSTANDING LOSS-CHASING BEHAVIOR IN RATS
Forecasting of photovoltaic power using regularized ensemble Extreme Learning Machine

The increasing penetration of renewable energy sources with intermittent nature generation challenges the grid operator to accurately plan and schedule their generators. In this context accurate forecasting model are vital to ensure smooth day-to-day operation with high renewable energy sources. Artificial Neural Network (ANN) have shown promising ability for accurate forecast. The ANN proposed in this paper are trained using historical dataset and training algorithm, Extreme Learning Machine (ELM). ELM requires randomly initialized parameters which affect the forecasting model. This paper propose a method to reduce the randomness of ELM by adding a regularizing term and combining multiple ELM. The ANN is implemented using MATLAB and trained using real-life data. The result shows that the randomness are greatly reduce and has a higher forecasting accuracy than a single ELM.
Design and Performance Evaluation of A New Proposed Fittest Job First Dynamic Round Robin(FJFDRR) Scheduling Algorithm

In this paper, we have proposed a new variant of Round Robin scheduling algorithm by executing the processes according to the new calculated Fit Factor f and using the concept of dynamic time quantum. We have compared the performance of our proposed Fittest Job First Dynamic Round Robin(FJFDRR) algorithm with the Priority Based Static Round Robin(PBSRR) algorithm. Experimental results show that our proposed algorithm performs better than PBSRR in terms of reducing the number of context switches, average waiting time and average turnaround time.
ODYS: an approach to building a massively-parallel search engine using a DB-IR tightly-integrated parallel DBMS for higher-level functionality

Recently, parallel search engines have been implemented based on scalable distributed file systems such as Google File System. However, we claim that building a massively-parallel search engine using a parallel DBMS can be an attractive alternative since it supports a higher-level (i.e., SQL-level) interface than that of a distributed file system for easy and less error-prone application development while providing scalability. Regarding higher-level functionality, we can draw a parallel with the traditional O/S file system vs. DBMS. In this paper, we propose a new approach of building a massively-parallel search engine using a DB-IR tightly-integrated parallel DBMS. To estimate the performance, we propose a hybrid (i.e., analytic and experimental) performance model for the parallel search engine. We argue that the model can accurately estimate the performance of a massively-parallel (e.g., 300-node) search engine using the experimental results obtained from a small-scale (e.g., 5-node) one. We show that the estimation error between the model and the actual experiment is less than 2.13% by observing that the bulk of the query processing time is spent at the slave (vs. at the master and network) and by estimating the time spent at the slave based on actual measurement. Using our model, we demonstrate a commercial-level scalability and performance of our architecture. Our proposed system ODYS is capable of handling 1 billion queries per day (81 queries/sec) for 30 billion Web pages by using only 43,472 nodes with an average query response time of 194 ms. By using twice as many (86,944) nodes, ODYS can provide an average query response time of 148 ms. These results show that building a massively-parallel search engine using a parallel DBMS is a viable approach with advantages of supporting the high-level (i.e., DBMS-level), SQL-like programming interface.
One-pass logic synthesis for graphene-based Pass-XNOR logic circuits

Electrostatically controlled graphene P-N junctions are devices built on a single layer graphene sheet that can be turned-ON/OFF via external potential difference. Their electrical behavior resembles a CMOS transmission gate with an embedded XNOR Boolean functionality. Recent works presented an efficient design style, the Pass-XNOR logic (PXL), which allows the implementation of adiabatic logic circuits with ultra low-power features. In this work we introduce Gemini, a one-pass logic synthesis methodology for PXL circuits. It consists of a dedicated XNOR-expansion algorithmthat combines logic optimization and technology mapping in a single step carried out through a common data structure, the Pass Diagram. Experimental results demonstrate (i) the superior of PXL circuits in terms of area and performance w.r. t. graphene circuits based on P-N junctions obtained using a CMOS-like synthesis/mapping methodology, and (ii) the power consumption in PXL circuits is governed by the adiabatic-charging principle which guarantees large power/energy savings w.r. t. non-adiabatic counterparts.
Predicting high school GPA using HEXACO personality domains and the secondary school admission test

Predictive studies of non-cognitive personality traits and SAT on college GPA are well established, but far less is known about the association between personality traits and comparable secondary school admission tests on secondary school GPA. This study assessed the relationship between the personality domains in the HEXACO: Personality Inventory-Revised (HEXACO: PI-R) inventory and secondary school GPA, while controlling for performance on the Secondary School Admission Test (SSAT). The results of the hierarchical regression analysis demonstrated no association between the HEXACO domains and GPA and a significant association between SSAT and GPA. The use of the personality domains in the HEXACO: PI-R inventory does not significantly add to prediction of high school GPA, while the SSAT is a strong predictor of academic success in a secondary school population, accounting for approximately 31% of the variance.