Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX

With the advent of big data era and the development of artificial intelligence and other technologies, data security and privacy protection have become more important. Recommendation systems have many applications in our society, but the model construction of recommendation systems is often inseparable from users' data. Especially for deep learning-based recommendation systems, due to the complexity of the model and the characteristics of deep learning itself, its training process not only requires long training time and abundant computational resources but also needs to use a large amount of user data, which poses a considerable challenge in terms of data security and privacy protection. How to train a distributed recommendation system while ensuring data security has become an urgent problem to be solved. In this paper, we implement two schemes, Horizontal Federated Learning and Secure Distributed Training, based on Intel SGX(Software Guard Extensions), an implementation of a trusted execution environment, and TensorFlow framework, to achieve secure, distributed recommendation system-based learning schemes in different scenarios. We experiment on the classical Deep Learning Recommendation Model (DLRM), which is a neural network-based machine learning model designed for personalization and recommendation, and the results show that our implementation introduces approximately no loss in model performance. The training speed is within acceptable limits.Comment: 5 pages, 8 figure

Monobutyl phthalate induces the expression change of G-Protein-Coupled Receptor 30 in rat testicular Sertoli cells

The aim of the study was to explore whether G-Protein-Coupled Receptor 30 (GPR30) was expressed in rat testicular Sertoli cells and to assess the impact of monobutyl phthalate (MBP) on the expression of GPR30 in Sertoli cells. By using RT-PCR, Western-Blot and immunofluorescent microscopy, the expression of GPR30 in rat Sertoli cells was found at both gene and protein level. Cultures of Sertoli cells were exposed to MBP (10– –1000 mM) or a vehicle. The results indicated that the expression of GPR30 increased at gene and protein levels in Sertoli cells following administration of MBP even at a relatively low concentration. We suggest that changes of GPR30 expression may play an important role in the effects of the xenoestrogen MBP on Sertoli cell function. (Folia Histochemica et Cytobiologica 2013, Vol. 51, No. 1, 18–24

Phytoplankton community structure in the Western Subarctic Gyre of the Pacific Ocean during summer determined by a combined approach of HPLC-pigment CHEMTAX and metabarcoding sequencing

The Western Subarctic Gyre (WSG) is a cyclonic upwelling gyre in the northwest subarctic Pacific, which is a region with a high concentration of nutrients but low chlorophyll. We investigated the community structure and spatial distribution of phytoplankton in this area by using HPLC-pigment CHEMTAX (a chemotaxonomy program) and metabarcoding sequencing during the summer of 2021. The phytoplankton community showed significant differences between the two methods. The CHEMTAX analyses identified eight major marine phytoplankton assemblages. Cryptophytes were the major contributors (24.96%) to the total Chl a, followed by pelagophytes, prymnesiophytes, diatoms, and chlorophytes. The eukaryotic phytoplankton OTUs obtained by metabarcoding were categorized into 149 species in 96 genera of 6 major groups (diatoms, prymnesiophytes, pelagophytes, chlorophytes, cryptophytes, and dinoflagellates). Dinoflagellates were the most abundant group, accounting for 44.74% of the total OTUs obtained, followed by cryptophytes and pelagophytes. Sixteen out of the 97 identified species were annotated as harmful algal species, and Heterocapsa rotundata, Karlodinium veneficum, and Aureococcus anophagefferens were assigned to the abundant group (i.e., at least 0.1% of the total reads). Nutrients were more important in shaping the phytoplankton community than temperature and salinity. The 24 stations were divided into southern and northern regions along 44°N according to the k-means method, with the former being dominated by high Chl a and low nutrients. Although different phytoplankton assemblages analyzed by the two methods showed various relationships with environmental factors, a common feature was that the dinoflagellate proportion showed a significantly negative correlation with low nutrients and a positive correlation with Chl a

Energy loss enhancement of very intense proton beams in dense matter due to the beam-density effect

Thoroughly understanding the transport and energy loss of intense ion beams in dense matter is essential for high-energy-density physics and inertial confinement fusion. Here, we report a stopping power experiment with a high-intensity laser-driven proton beam in cold, dense matter. The measured energy loss is one order of magnitude higher than the expectation of individual particle stopping models. We attribute this finding to the proximity of beam ions to each other, which is usually insignificant for relatively-low-current beams from classical accelerators. The ionization of the cold target by the intense ion beam is important for the stopping power calculation and has been considered using proper ionization cross section data. Final theoretical values agree well with the experimental results. Additionally, we extend the stopping power calculation for intense ion beams to plasma scenario based on Ohm's law. Both the proximity- and the Ohmic effect can enhance the energy loss of intense beams in dense matter, which are also summarized as the beam-density effect. This finding is useful for the stopping power estimation of intense beams and significant to fast ignition fusion driven by intense ion beams

Target density effects on charge tansfer of laser-accelerated carbon ions in dense plasma

We report on charge state measurements of laser-accelerated carbon ions in the energy range of several MeV penetrating a dense partially ionized plasma. The plasma was generated by irradiation of a foam target with laser-induced hohlraum radiation in the soft X-ray regime. We used the tri-cellulose acetate (C$_{9}$H$_{16}$O$_{8}$) foam of 2 mg/cm$^{-3}$ density, and $1$-mm interaction length as target material. This kind of plasma is advantageous for high-precision measurements, due to good uniformity and long lifetime compared to the ion pulse length and the interaction duration. The plasma parameters were diagnosed to be T$_{e}$=17 eV and n$_{e}$=4 $\times$ 10$^{20}$ cm$^{-3}$. The average charge states passing through the plasma were observed to be higher than those predicted by the commonly-used semiempirical formula. Through solving the rate equations, we attribute the enhancement to the target density effects which will increase the ionization rates on one hand and reduce the electron capture rates on the other hand. In previsous measurement with partially ionized plasma from gas discharge and z-pinch to laser direct irradiation, no target density effects were ever demonstrated. For the first time, we were able to experimentally prove that target density effects start to play a significant role in plasma near the critical density of Nd-Glass laser radiation. The finding is important for heavy ion beam driven high energy density physics and fast ignitions.Comment: 7 pages, 4 figures, 35 conference

A Design Strategy to Improve Machine Learning Resiliency for Ring Oscillator Physically Unclonable Function

Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity of PUFs to provide resilience against ML attacks. However, an increase in complexity often results in an increase in area and/or a decrease in reliability. This study proposes a lightweight ring oscillator (RO)-based PUFs using an additional modulus process to improve ML resiliency. The idea was to increase the complexity of the RO-PUF without significant hardware overhead by applying a modulus process to the outcomes from the RO frequency counter. We also present a thorough investigation of the design space to balance ML resiliency and other performance metrics such as reliability, uniqueness, and uniformity

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed

The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land Extraction from High-Resolution Remote Sensing Image

Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This report introduces our solution to the iFLYTEK challenge 2021 cultivated land extraction from high-resolution remote sensing image. The challenge requires segmenting cultivated land objects in very high-resolution multispectral remote sensing images. We established a highly effective and efficient pipeline to solve this problem. We first divided the original images into small tiles and separately performed instance segmentation on each tile. We explored several instance segmentation algorithms that work well on natural images and developed a set of effective methods that are applicable to remote sensing images. Then we merged the prediction results of all small tiles into seamless, continuous segmentation results through our proposed overlap-tile fusion strategy. We achieved the first place among 486 teams in the challenge