On short-time behavior of implied volatility in a market model with indexes

This paper investigates short-term behaviors of implied volatility of derivatives written on indexes in equity markets when the index processes are constructed by using a ranking procedure. Even in simple market settings where stock prices follow geometric Brownian motion dynamics, the ranking mechanism can produce the observed term structure of at-the-money (ATM) implied volatility skew for equity indexes. Our proposed models showcase the ability to reconcile two seemingly contradictory features found in empirical data from equity markets: the long memory of volatilities and the power law of ATM skews. Furthermore, the models allow for the capture of a novel phenomenon termed the quasi-blow-up phenomenon

In vivo biotinylated calpastatin improves the affinity purification of human m-calpain

Recently we established a novel affinity purification method for calpain by exploiting the specific and reversible binding properties of its intrinsically disordered protein inhibitor, calpastatin. The immobilization strategy relied on the strength and specificity of the biotin - streptavidin interaction. Here, we report an improved and optimized method that even enables the general applicability of in vivo biotinylated (intrinsically disordered) proteins in any affinity capture strategy. Since in vitro chemical biotinylation is only accomplished with reagents that lack exact site specificity, it can not only cause sample heterogeneity but it can also hamper the functionality of the biotinylated molecules. Therefore, we have developed a recombinant expression protocol to produce in vivo biotinylated human calpastatin domain 1 (hCSD1) in Escherichia coli. We have experimentally verified that the biotinylated polypeptide tag is compatible with the intrinsically disordered state of hCSD1 and that it does not influence the functional properties of this intrinsically disordered protein (IDP). The in vivo biotinylated hCSD1 was then used without the need of any prepurification step prior to the affinity capturing of its substrate, human m-calpain. This leads to a simplified purification strategy that allows capturing the calpain efficiently from a complex biological mixture with only a single chromatogaphic step and in a considerably reduced timeframe. Our approach is generally applicable through the in vivo biotinylation of any IDP of interest, and its practical implementation will showcase the power to exploit the properties of IDPs in affinity capture strategies. © 2018 The Author

Catalytic Performance of La-Ni/Al2O3 Catalyst for CO2 Reforming of Ethanol

Bio-derived ethanol has been considered as an attractive and alternative feedstock for dry or steam reforming reactions to generate renewable hydrogen, which may be used for replacement of conventional fossil fuels. Ethanol dry reforming (EDR) is an environmentally-friendly process since it transforms greenhouse gas, CO2 to value-added products and ethanol can be easily obtained from biomass which is free of catalyst poisons (i.e. sulphur-containing compounds). However, there are currently limited studies regarding syngas production from EDR [1, 2]. Ni-based catalysts are commonly used for reforming reactions due to its capability of C-C bond rupture, relatively low cost and high availability compared to precious metals [2]. Nevertheless, carbonaceous deposition may considerably deteriorate catalytic activity and stability of Ni-based catalysts. La promoter reportedly hindered carbon deposition and improved catalytic activity [3]. Hence, the objective of this research was to investigate the effect of La promotion on 10%Ni/Al2O3 catalyst for EDR

Sublinear time algorithms for earth mover's distance

We study the problem of estimating the Earth Mover’s Distance (EMD) between probability distributions when given access only to samples of the distributions. We give closeness testers and additive-error estimators over domains in [0, 1][superscript d], with sample complexities independent of domain size – permitting the testability even of continuous distributions over infinite domains. Instead, our algorithms depend on other parameters, such as the diameter of the domain space, which may be significantly smaller. We also prove lower bounds showing the dependencies on these parameters to be essentially optimal. Additionally, we consider whether natural classes of distributions exist for which there are algorithms with better dependence on the dimension, and show that for highly clusterable data, this is indeed the case. Lastly, we consider a variant of the EMD, defined over tree metrics instead of the usual l 1 metric, and give tight upper and lower bounds

Aquaculture in the mountains of the northern Lao PDR and northern Vietnam

This article features the current status of aquaculture in the mountains of the northern Lao PDR and northern Vietnam

FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning

Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private data. Most existing works simply propose typical FL systems for single-modal data, thus limiting its potential on exploiting valuable multimodal data for future personalized applications. Furthermore, the majority of FL approaches still rely on the labeled data at the client side, which is limited in real-world applications due to the inability of self-annotation from users. In light of these limitations, we propose a novel multimodal FL framework that employs a semi-supervised learning approach to leverage the representations from different modalities. Bringing this concept into a system, we develop a distillation-based multimodal embedding knowledge transfer mechanism, namely FedMEKT, which allows the server and clients to exchange the joint knowledge of their learning models extracted from a small multimodal proxy dataset. Our FedMEKT iteratively updates the generalized global encoders with the joint embedding knowledge from the participating clients. Thereby, to address the modality discrepancy and labeled data constraint in existing FL systems, our proposed FedMEKT comprises local multimodal autoencoder learning, generalized multimodal autoencoder construction, and generalized classifier learning. Through extensive experiments on three multimodal human activity recognition datasets, we demonstrate that FedMEKT achieves superior global encoder performance on linear evaluation and guarantees user privacy for personal data and model parameters while demanding less communication cost than other baselines

Rate of red blood cell destruction varies in different strains of mice infected with Plasmodium berghei-ANKA after chronic exposure

<p>Abstract</p> <p>Background</p> <p>Severe malaria anaemia in the semi-immune individuals in the holo-endemic area has been observed to occur at low parasite density with individual variation in the responses. Thus the following has been thought to be involved: auto-immune-mediated mechanisms of uninfected red blood cell destruction, and host genetic factors to explain the differences in individual responses under the same malaria transmission. In this study, the extent of red blood cell (RBC) destruction in different strains of semi-immune mice model at relatively low parasitaemia was studied.</p> <p>Methodology</p> <p>To generate semi-immunity, four strains of mice were taken through several cycles of infection and treatment. By means of immunofluorescent assay and ELISA, sera were screened for anti-erythrocyte auto-antibodies, and their relationship with haematological parameters and parasitaemia in the strains of semi-immune mice was investigated.</p> <p>Results</p> <p>Upon challenge with <it>Plasmodium berghei </it>ANKA after generating semi-immune status, different mean percentage haemoglobin (Hb) drop was observed in the mice strains (Balb/c = 47.1%; NZW = 30.05%; C57BL/6 = 28.44%; CBA = 25.1%), which occurred on different days for each strain (for Balb/c, mean period = 13.6 days; for C57BL/6, NZW, and CBA mean period = 10.6, 10.8, 10.9 days respectively). Binding of antibody to white ghost RBCs was observed in sera of the four strains of semi-immune mice by immunofluorescence. Mean percentage Hb drop per parasitaemia was highest in Balb/c (73.6), followed by C57BL/6 (8.6), CBA (6.9) and NZW (4.0), p = 0.0005. Consequently, auto-antibodies level to ghost RBC were correlated with degree of anaemia and were highest in Balb/c, when compared with the other strains, p < 0.001.</p> <p>Conclusion</p> <p>The results presented in this study seem to indicate that anti-RBC auto-antibodies may be involved in the destruction of uninfected RBC in semi-immune mice at relatively low parasite burden. Host genetic factors may also influence the outcome of auto-immune mediated destruction of RBC due to the variation in Hb loss per % parasitaemia and differences in antibody titer for each semi-immune mice strain. However, further studies at the molecular level ought to be carried out to confirm this.</p

Behaviour-aware Malware Classification: Dynamic Feature Selection

Despite the continued advancements in security research, malware persists as being a major threat in this digital age. Malware detection is a primary defence strategy for most networks but the identification of malware strains is becoming increasingly difficult. Reliable identification is based upon characteristic features being detectable within an object. However, the limitations and expense of current malware feature extraction methods is significantly hindering this process. In this paper, we present a new method for identifying malware based on behavioural feature extraction. Our proposed method has been evaluated using seven classification methods whilst analysing 2,068 malware samples from eight different families. The results achieved thus far have demonstrated promising improvements over existing approaches