Enhancing Access Privacy of Range Retrievals over B+Trees

National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ

DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

Deep learning can achieve higher accuracy than traditional machine learning algorithms in a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn tremendous attention from information security community, in which neither training data nor the training model is expected to be exposed. Federated learning is a popular learning mechanism, where multiple parties upload local gradients to a server and the server updates model parameters with the collected gradients. However, there are many security problems neglected in federated learning, for example, the participants may behave incorrectly in gradient collecting or parameter updating, and the server may be malicious as well. In this paper, we present a distributed, secure, and fair deep learning framework named DeepChain to solve these problems. DeepChain provides a value-driven incentive mechanism based on Blockchain to force the participants to behave correctly. Meanwhile, DeepChain guarantees data privacy for each participant and provides auditability for the whole training process. We implement a DeepChain prototype and conduct experiments on a real dataset for different settings, and the results show that our DeepChain is promising

Biophysical Interactions Control the Progression of Harmful Algal Blooms in Chesapeake Bay: A Novel Lagrangian Particle Tracking Model with Mixotrophic Growth and Vertical Migration

Climate change and nutrient pollution contribute to the expanding global footprint of harmful algal blooms. To better predict their spatial distributions and disentangle biophysical controls, a novel Lagrangian particle tracking and biological (LPT-Bio) model was developed with a high-resolution numerical model and remote sensing. The LPT-Bio model integrates the advantages of Lagrangian and Eulerian approaches by explicitly simulating algal bloom dynamics, algal biomass change, and diel vertical migrations along predicted trajectories. The model successfully captured the intensity and extent of the 2020 Margalefidinium polykrikoides bloom in the lower Chesapeake Bay and resolved fine-scale structures of bloom patchiness, demonstrating a reliable prediction skill for 7-10 d. The fully coupled LPT-Bio model initialized/calibrated by remote sensing and controlled by ambient environmental conditions appeared to be a powerful approach to predicting transport pathways, identifying bloom hotspots, resolving concentration variations at subgrid scales, and investigating responses of HABs to changing environmental conditions and human interference

Geochemistry of the Late Cretaceous Pandan Formation in Cebu Island, Central Philippines: Sediment Contributions From the Australian Plate Margin During the Mesozoic

The Late Cretaceous Pandan Formation in Cebu Island is one of the oldest sedimentary units in the Central Philippines. The inconsistencies in geological descriptions and interpretation of the depositional environment of the Pandan Formation complicated efforts to determine the origin and tectonic history of the basement of Cebu Island. This study therefore looks into the petrological and geochemical characteristics of the Pandan Formation and their implications for the tectonic development of the Philippine Arc during the late Mesozoic. Petrographic analyses indicate significant contribution from mafic sources with additional inputs from felsic rocks, siliciclastics and metamorphic sources. Enrichment of detrital quartz from felsic volcanic and plutonic rocks, as well as from siliciclastic and metamorphic sources, has shifted the SiO2 composition of the Pandan clastics from a mafic to a more intermediate source. Whole-rock geochemical analyses revealed low SiO2/Al2O3 = 4.21, low K2O/Na2O = 1.16, low Th/Sc = 0.13, low Th/U = 2.78, high La/Th = 4.51, significantly low REEs = ca 76.45 ppm and low LaN/YbN = 4.28. A slight negative chondrite-normalized Eu/Eu* (0.91) anomaly and significantly high PAAS-normalized positive Eu/Eu* (1.39) values are consistent with derivation from a young undissected magmatic arc terrane. Tectonic discrimination diagrams suggest formation in an oceanic island arc to active margin/collision zone modelled to be located at the oceanic leading edge of Australia. Rapid uplift and erosion of the magmatic arc and older allochthonous blocks gave way to the rapid deposition of the Pandan Formation in the Late Cretaceous at the subequatorial region

Direct Neighbor Search

In this paper we study a novel query type, called direct neighbor query. Two objects in a dataset are direct neighbors (DNs) if a window selection may exclusively retrieve these two objects. Given a source object, a DN search computes all of its direct neighbors in the dataset. The DNs define a new type of affinity that differs from existing formulations (e.g., nearest neighbors, nearest surrounders, reverse nearest neighbors, etc) and finds application in domains where user interests are expressed in the form of windows, i.e., multi-attribute range selections. Drawing on key properties of the DN relationship, we develop an I/O optimal processing algorithm for data indexed with a spatial access method. In addition to plain DN search, we also study its K-DN and all-DN variants. The former relaxes the DN condition – two objects are K-DNs if a window query may retrieve them and only up to K − 1 other objects – whereas the all-DN variant computes the DNs of every object in the dataset. Using real, large-scale data

Dynamics of Persistent and Acute Deformed Wing Virus Infections in Honey Bees, Apis mellifera

The dynamics of viruses are critical to our understanding of disease pathogenesis. Using honey bee Deformed wing virus (DWV) as a model, we conducted field and laboratory studies to investigate the roles of abiotic and biotic stress factors as well as host health conditions in dynamics of virus replication in honey bees. The results showed that temperature decline could lead to not only significant decrease in the rate for pupae to emerge as adult bees, but also an increased severity of the virus infection in emerged bees, partly explaining the high levels of winter losses of managed honey bees, Apis mellifera, around the world. By experimentally exposing adult bees with variable levels of parasitic mite Varroa destructor, we showed that the severity of DWV infection was positively correlated with the density and time period of Varroa mite infestation, confirming the role of Varroa mites in virus transmission and activation in honey bees. Further, we showed that host conditions have a significant impact on the outcome of DWV infection as bees that originate from strong colonies resist DWV infection and replication significantly better than bee originating from weak colonies. The information obtained from this study has important implications for enhancing our understanding of host‑pathogen interactions and can be used to develop effective disease control strategies for honey bees

Cost-Time Sensitive Decision Tree with Missing Values 1

Abstract. Cost-sensitive decision tree learning is very important and popular in machine learning and data mining community. There are many literatures focusing on misclassification cost and test cost at present. In real world application, however, the issue of time-sensitive should be considered in costsensitive learning. In this paper, we regard the cost of time-sensitive in costsensitive learning as waiting cost (referred to WC), a novelty splitting criterion is proposed for constructing cost-time sensitive (denoted as CTS) decision tree for maximal decrease the intangible cost. And then, a hybrid test strategy that combines the sequential test with the batch test strategies is adopted in CTS learning. Finally, extensive experiments show that our algorithm outperforms the other ones with respect to decrease in misclassification cost.