DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework

Hyperparameter optimization, also known as hyperparameter tuning, is a widely recognized technique for improving model performance. Regrettably, when training private ML models, many practitioners often overlook the privacy risks associated with hyperparameter optimization, which could potentially expose sensitive information about the underlying dataset. Currently, the sole existing approach to allow privacy-preserving hyperparameter optimization is to uniformly and randomly select hyperparameters for a number of runs, subsequently reporting the best-performing hyperparameter. In contrast, in non-private settings, practitioners commonly utilize "adaptive" hyperparameter optimization methods such as Gaussian process-based optimization, which select the next candidate based on information gathered from previous outputs. This substantial contrast between private and non-private hyperparameter optimization underscores a critical concern. In our paper, we introduce DP-HyPO, a pioneering framework for "adaptive" private hyperparameter optimization, aiming to bridge the gap between private and non-private hyperparameter optimization. To accomplish this, we provide a comprehensive differential privacy analysis of our framework. Furthermore, we empirically demonstrate the effectiveness of DP-HyPO on a diverse set of real-world and synthetic datasets

Review on the Conflicts between Offshore Wind Power and Fishery Rights: Marine Spatial Planning in Taiwan

In recent years, Taiwan has firmly committed itself to pursue the green energy transition and a nuclear-free homeland by 2025, with an increase in renewable energy from 5% in 2016 to 20% in 2025. Offshore wind power (OWP) has become a sustainable and scalable renewable energy source in Taiwan. Maritime Spatial Planning (MSP) is a fundamental tool to organize the use of the ocean space by different and often conflicting multi-users within ecologically sustainable boundaries in the marine environment. MSP is capable of definitively driving the use of offshore renewable energy. Lessons from Germany and the UK revealed that MSP was crucial to the development of OWP. This paper aims to evaluate how MSP is able to accommodate the exploitation of OWP in Taiwan and contribute to the achievement of marine policy by proposing a set of recommendations. It concludes that MSP is emerging as a solution to be considered by government institutions to optimize the multiple use of the ocean space, reduce conflicts and make use of the environmental and economic synergies generated by the joint deployment of OWP facilities and fishing or aquaculture activities for the conservation and protection of marine environments.Peer Reviewe

Hexa-μ2-acetato-1:2κ4 O:O′;1:2κ2 O:O;2:3κ4 O:O′;2:3κ2 O:O-bis­(2-amino-7-chloro-5-methyl-1,8-naphthyridine)-1κN 1,3κN 1-trizinc(II)

The title complex, [Zn3(C2H3O2)6(C9H8ClN3)2], contains three ZnII atoms bridged by six acetate ligands. The central ZnII ion, located on an inversion centre, is surrounded by six O atoms from acetate ligands in a distorted octa­hedral geometry [Zn—O = 1.9588 (12)–2.1237 (12) Å]. The terminal ZnII ions are coordinated by one N atom of 2-amino-7-chloro-5-methyl-1,8-naphthyridine and three O atoms of three acetate ligands in a distorted tetra­hedral geometry. The separation between the central and terminal ZnII ions is 3.245 (3) Å

An Accelerating 3D Image Reconstruction System Based on the Level-of-Detail Algorithm

This paper proposes a research of An Accelerating 3D Image Reconstruction System Based on the Level-of-Detail Algorithm and combines 3D graphic application interfaces, such as DirectX3D and OpenCV to reconstruct the 3D imaging system for Magnetic Resonance Imaging (MRI), and adds Level of Detail (LOD) algorithm to the system. The system uses the volume rendering method to perform 3D reconstruction for brain imaging. The process, which is based on the level of detail algorithm that converts and formulates functions from differing levels of detail and scope, significantly reduces the complexity of required processing and computation, under the premises of maintaining drawing quality. To validate the system's efficiency enhancement on brain imaging reconstruction, this study operates the system on various computer platforms, and uses multiple sets of data to perform rendering and 3D object imaging reconstruction, the results of which are then verified and compared

PP-012 Novel blaCTX-M-79 gene from community isolates in association with ISEcp1 in Shenyang, China

Digitalitzat per Artypla

Protein subcellular localization prediction based on compartment-specific features and structure conservation

BACKGROUND: Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. RESULTS: We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. CONCLUSION: Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes

Neural-Network Based Adaptive Proxemics-Costmap for Human-Aware Autonomous Robot Navigation

In the revolution of Industry 4.0, autonomous robot navigation plays a vital role in ensuring intelligent cooperation with human workers to increase manufacturing efficiency. Human prefers to maintain a proxemic distance with other subjects for safety and comfort purposes, where the human personal-space can be represented by a costmap. Current proxemic costmaps perform well in defining the proxemic boundary to maintain the human-robot proxemic distance. However, these approaches generate static costmaps that are not adaptive towards different human states (linear position, angular position and velocity). This problem impacts the robot navigation efficiency, reduces human safety and comfort as the autonomous robot failed to prioritize avoiding certain humans over the other. To overcome this drawback, this paper proposed a neural-network based adaptive proxemic-costmap, named as NNPC, that can generate different sized personal-spaces at different human state encounters. The proposed proxemic-costmap was developed by learning a neural-network model using real human state data. A total of three human scenarios were used for data collection. The data were collected by tracking the humans in video recordings. After the model was trained, the proposed NNPC costmap was evaluated against two other state-of-art proxemic costmaps in five simulated human scenarios with various human states. Results show that NNPC outperformed the compared costmaps by ensuring human-aware robot manoeuvres that have higher robot efficiency and increased human safety and comfort. &nbsp

[μ-Bis(5,7-dimethyl-1,8-naphthyridin-2-yl)diazene]bis­[difluoridoboron(III)]

In the title compound, C20H18B2F4N6, the bis­(5,7-dimethyl-1,8-naphthyridin-2-yl)diazene molecule is bis­ected by a symmetry centre midway between the central N atoms of the diazene group. Each of the symmetry-related halves of the molecule binds to a B atom through an N,N′-bite. Two terminal F ions complete the distorted BN2F2 tetra­hedral geometry around each B atom. The BF2 plane is almost perpendicular to the boron–naphthyridine ring plane, with a dihedral angle of 87.8 (2)°. The main inter­actions in the crystal structure are some C—H⋯F hydrogen bonds and π–π contacts between 1,8-naphthyridine rings [centroid–centroid distance = 4.005 (1) Å]

Optimized antimicrobial and antiproliferative activities of titanate nanofibers containing silver

Titanate nanofibers containing silver have been demonstrated through the experiments reported herein to have effective antifungal and antiproliferative activities in the presence of UV light. The titanate nanofibers containing silver can be fabricated by means of ion exchange followed by a topochemical process in an environment suitable for reductive reactions. Excellent antibacterial, antifungal, and antiproliferative activities could be demonstrated by both Ag2Ti5O11 · xH2O and Ag/titanate (UV light irradiation) due to their unique structures and compositions, which have photocatalytic activities to generate reactive oxygen species and capabilities to continuously release the silver ions. Therefore these materials have the potential to produce a membrane for the treatment of superficial malignant tumor, esophageal cancer, or cervical carcinoma. They may also hold utility if incorporated into a coating on stents in moderate and advanced stage esophageal carcinoma or for endoscopic retrograde biliary drainage. These approaches may significantly reduce infections, inhibit tumor growth, and importantly, improve quality of life and prolong survival time for patients with tumors