Noninteractive Verifiable Outsourcing Algorithm for Bilinear Pairing with Improved Checkability

It is well known that the computation of bilinear pairing is the most expensive operation in pairing-based cryptography. In this paper, we propose a noninteractive verifiable outsourcing algorithm of bilinear pairing based on two servers in the one-malicious model. The outsourcer need not execute any expensive operation, such as scalar multiplication and modular exponentiation. Moreover, the outsourcer could detect any failure with a probability close to 1 if one of the servers misbehaves. Therefore, the proposed algorithm improves checkability and decreases communication cost compared with the previous ones. Finally, we utilize the proposed algorithm as a subroutine to achieve an anonymous identity-based encryption (AIBE) scheme with outsourced decryption and an identity-based signature (IBS) scheme with outsourced verification

FederBoost: Private Federated Learning for GBDT

An emerging trend in machine learning and artificial intelligence is federated learning (FL), which allows multiple participants to contribute various training data to train a better model. It promises to keep the training data local for each participant, leading to low communication complexity and high privacy. However, there are still two problems in FL remain unsolved: (1) unable to handle vertically partitioned data, and (2) unable to support decision trees. Existing FL solutions for vertically partitioned data or decision trees require heavy cryptographic operations. In this paper, we propose a framework named FederBoost for private federated learning of gradient boosting decision trees (GBDT). It supports running GBDT over both horizontally and vertically partitioned data. The key observation for designing FederBoost is that the whole training process of GBDT relies on the order of the data instead of the values. Consequently, vertical FederBoost does not require any cryptographic operation and horizontal FederBoost only requires lightweight secure aggregation. We fully implement FederBoost and evaluate its utility and efficiency through extensive experiments performed on three public datasets. Our experimental results show that both vertical and horizontal FederBoost achieve the same level of AUC with centralized training where all data are collected in a central server; and both of them can finish training within half an hour even in WAN.Comment: 15 pages, 8 figure

A simplified climate change model and extreme weather model based on a machine learning method

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change

Photoinduced oxygen release and persistent photoconductivity in ZnO nanowires

Photoconductivity is studied in individual ZnO nanowires. Under ultraviolet (UV) illumination, the induced photocurrents are observed to persist both in air and in vacuum. Their dependence on UV intensity in air is explained by means of photoinduced surface depletion depth decrease caused by oxygen desorption induced by photogenerated holes. The observed photoresponse is much greater in vacuum and proceeds beyond the air photoresponse at a much slower rate of increase. After reaching a maximum, it typically persists indefinitely, as long as good vacuum is maintained. Once vacuum is broken and air is let in, the photocurrent quickly decays down to the typical air-photoresponse values. The extra photoconductivity in vacuum is explained by desorption of adsorbed surface oxygen which is readily pumped out, followed by a further slower desorption of lattice oxygen, resulting in a Zn-rich surface of increased conductivity. The adsorption-desorption balance is fully recovered after the ZnO surface is exposed to air, which suggests that under UV illumination, the ZnO surface is actively "breathing" oxygen, a process that is further enhanced in nanowires by their high surface to volume ratio

Safety and efficacy of ex vivo expanded CD34 stem cells in murine and primate models

Background: Hematopoietic stem cell (HSC) transplantation has been widely applied to the treatment of malignant blood diseases. However, limited number of functional HSCs hinders successful transplantation. The purpose of our current study is to develop a new and cost-efficient medium formulation that could greatly enhance the expansion of HSCs while retaining their long-term repopulation and hematopoietic properties for effective clinical transplantation. Methods: Enriched human CD34(+) cells and mobilized nonhuman primate peripheral blood CD34(+) cells were expanded with a new, cost-efficient expansion medium formulation, named hematopoietic expansion medium (HEM), consisting of various cytokines and nutritional supplements. The long-term repopulation potential and hematologic-lineage differentiation ability of expanded human cells were studied in the non-obese diabetic/severe combined immunodeficiency mouse model. Furthermore, the efficacy and safety studies were performed by autologous transplantation of expanded primate cells in the nonhuman primate model. Results: HEM could effectively expand human CD34(+) cells by up to 129 fold within 9 days. Expanded HSCs retained long-term repopulation potential and hematologic-lineage differentiation ability, as indicated by (1) maintenance (over unexpanded HSCs) of immunophenotypes of CD38(-)CD90(+)CD45RA(-)CD49f(+) in CD34(+) cells after expansion; (2) significant presence of multiple human hematopoietic lineages in mouse peripheral blood and bone marrow following primary transplantation; (3) enrichment (over unexpanded HSCs) in SCID-repopulating cell frequency measured by limiting dilution analysis; and (4) preservation of both myeloid and lymphoid potential among human leukocytes from mouse bone marrow in week 24 after primary transplantation or secondary transplantation. Moreover, the results of autologous transplantation in nonhuman primates demonstrated that HEM-expanded CD34(+) cells could enhance hematological recovery after myelo-suppression. All primates transplanted with the expanded autologous CD34(+) cells survived for over 18 months without any noticeable abnormalities. Conclusions: Together, these findings demonstrate promising potential for the utility of HEM to improve expansion of HSCs for clinical application.State Scientific Key Projects for New Drug Research and Development [2011ZX09102-010-04, 2011ZX09401-027]; International Cooperation and Exchange Program, China [2013DFA30830]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The Effect of Superparamagnetic Iron Oxide Nanoparticle Surface Charge on Antigen Cross-Presentation.

Magnetic nanoparticles (NPs) of superparamagnetic iron oxide (SPIO) have been explored for different kinds of applications in biomedicine, mechanics, and information. Here, we explored the synthetic SPIO NPs as an adjuvant on antigen cross-presentation ability by enhancing the intracellular delivery of antigens into antigen presenting cells (APCs). Particles with different chemical modifications and surface charges were used to study the mechanism of action of antigen delivery. Specifically, two types of magnetic NPs, γF

Dynamic Budget Throttling in Repeated Second-Price Auctions

Throttling is one of the most popular budget control methods in today's online advertising markets. When a budget-constrained advertiser employs throttling, she can choose whether or not to participate in an auction after the advertising platform recommends a bid. This paper focuses on the dynamic budget throttling process in repeated second-price auctions from a theoretical view. An essential feature of the underlying problem is that the advertiser does not know the distribution of the highest competing bid upon entering the market. To model the difficulty of eliminating such uncertainty, we consider two different information structures. The advertiser could obtain the highest competing bid in each round with full-information feedback. Meanwhile, with partial information feedback, the advertiser could only have access to the highest competing bid in the auctions she participates in. We propose the OGD-CB algorithm, which involves simultaneous distribution learning and revenue optimization. In both settings, we demonstrate that this algorithm guarantees an $O(\sqrt{T\log T})$ regret with probability $1 - O(1/T)$ relative to the fluid adaptive throttling benchmark. By proving a lower bound of $\Omega(\sqrt{T})$ on the minimal regret for even the hindsight optimum, we establish the near optimality of our algorithm. Finally, we compare the fluid optimum of throttling to that of pacing, another widely adopted budget control method. The numerical relationship of these benchmarks sheds new light on the understanding of different online algorithms for revenue maximization under budget constraints.Comment: 29 pages, 1 tabl

Arresting-Cable System for Robust Terminal Landing of Reusable Rockets

Recent successful recovery techniques for rockets require that rockets maintain a vertical configuration with zero vertical and lateral velocities; otherwise, landings may fail. To relax this requirement, a new active-arresting system (inspired by the arresting gears used on aircraft carriers) is proposed herein to achieve a robust landing, even if the rocket deviates from the target position or has notable residual velocities and inclinations. The system consists of four deployable onboard hooks above the rocket’s center of mass, an on-ground apparatus containing four arresting cables forming a square capture frame, and four buffer devices to actively catch and passively decelerate the landing rocket. To catch the rocket, the capture frame was controlled by servo motors via a simple proportional–derivative controller. After catching, the buffer devices generate decelerating forces to stop its motion. A flexible multibody model of the proposed system was built to evaluate its robust performance under various combinations of multiple uncertainties, such as noise measurement, time delay in the motor, initial conditions, and wind excitation. Using a quasi-Monte Carlo method, hundreds of deviated landing cases were generated and simulated. The results confirmed the robustness of the proposed system for achieving successful terminal landings