Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Matching-Based Privacy-Preserving Statistics and Its Applications in Digital Publishing Industry

With the rapid development of digital media technology, many people prefer to read e-books over article versions. The digital publishing platform can collect and analyze massive amounts of readers’ reading information. The statistical analysis results can be regarded as the platform's digital assets based on which it provides paid services for its users. However, three privacy issues are related to readers’ reading information, users’ statistical preferences, and the platform's digital assets. This article proposes a data-matching-based privacy-preserving statistic scheme. The proposed solution combines bloom filters, secret sharing, and perturbing technologies to realize an efficient match between users’ statistical preferences and massive readers’ corresponding reading information and statistical analysis of the matching results without compromising the privacy of different parties. Besides, the proposed solution adopts an edge computing paradigm to realize the process of massive data in a divide-and-conquer parallel way. It introduces the concepts of Mirror Secret Shares and Buddy Edge Devices to virtualize the <inline-formula><tex-math notation="LaTeX">$(m+1, m+1)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>m</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="shen-ieq1-3326497.gif"/></alternatives></inline-formula>-threshold secret sharing scheme to an <inline-formula><tex-math notation="LaTeX">$(m+1, m+\lfloor m/2 \rfloor +2)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>m</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>+</mml:mo><mml:mo>⌊</mml:mo><mml:mi>m</mml:mi><mml:mo>/</mml:mo><mml:mn>2</mml:mn><mml:mo>⌋</mml:mo><mml:mo>+</mml:mo><mml:mn>2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="shen-ieq2-3326497.gif"/></alternatives></inline-formula>-threshold secret sharing scheme for achieving good robustness without adding hardware devices. The detailed analyses show that our solution meets the defined design goals. Furthermore, the experimental results demonstrate the efficiency of the proposed work.
Routing Innate and Adaptive Immune response against M. tuberculosis and boosting Mycobacterium bovis Bacillus Calmette Güerin (BCG) vaccine immunity through prime boost protocols

Tuberculosis (Tb) is still a global health problem, especially in developing countries. Several factors contribute to this, among them the increasing multidrug resistance strains, the dangerous liaisons with other intracellular pathogens, such as HIV, and more recently, SARS-CoV2 pandemics. There are many aspects that remain to understand the bacterial molecular mechanism of pathogenicity and the immune response induced by the interaction of M. tuberculosis (MTb) with the host. The official and current vaccine based on the attenuated Mycobacterium bovis Bacillus Calmette Guerin (BCG) is protective against several forms of Tb meningitis and Miliary TB or disseminated disease in young children. However, it fails to protect young and adult individuals. There are several new promising candidates for vaccines to replace or boost BCG-induced immunity. Several evidences exist from humans and mice on the role of the trained innate memory of monocytes and NK cells, on the second encounter with the same mycobacterial pathogen or other respiratory pathogens. This type of immune response is nonspecific and independent of the T and B cells. Thus, BCG vaccination is a double immunogen that activate specific immune responses and is also able to stimulate nonspecific immune responses. Here, it is outlined the host immunity against MTb, the potential of BCG vaccination and prime boost protocols for routing innate and adaptive immune responses in TB.
Gl 86B: a white dwarf orbits an exoplanet host star

In this Letter we present our first high-contrast observations of the exoplanet host star Gl 86 using NAOS-CONICA (NACO) and its new Simultaneous Differential Imager (SDI) as well as results from NACO spectroscopy. Els et al. found a faint comoving companion located only ∼2 arcsec east of the exoplanet host star Gl 86A. Our high-contrast SDI observations rule out additional stellar companions from 1 au up to 23 au, and are sensitive for faint T-dwarf companions down to 35 MJup. We present evidence for orbital motion of Gl 86B around the exoplanet host star Gl 86A, which finally confirms that this is a bound binary system. With the given photometry from Els et al. and the NACO spectroscopy obtained, we prove that the companion Gl 86B is a cool white dwarf with an effective temperature of 5000 ± 500 K. This is the first confirmed white dwarf companion to an exoplanet host star and the first observational confirmation that planets survive the post-main-sequence evolution (giant phase and planetary nebula) of a star from which they are separated by only one to two dozen astronomical units, as expected from theory.
Complaints about police misconduct have adverse effects for Black civilians

Existing literature examines the effectiveness of civilian oversight in reducing police misconduct. However, little-to-no quantitative research explores possible adverse consequences of this accountability mechanism. Utilizing time series analysis of administrative data on aggregate monthly civilian complaints and police behavior in the largest American city, this article offers evidence of racial inequality in police response to civilian complaints. For White civilians, complaint against the police abates subsequent police stops. For Black civilians, complaint is associated with subsequent intensification of police stops. This intensification only follows complaints against White officers, is conditional upon officer knowledge of the complaint, is confined to stops involving greater officer discretion to perform the stop, and is only observed in police precincts with large Black populations.
The Study on Wind Speed Change with Height Under Different Terrain Conditions in Hubei Province

The vertical wind shear characteristics under the different terrain conditions are explored by using the observed data of whole year in Longganhu of Huanggang,Xianjuding of Dawu and Huashanguan of Zhongxiang,and the change characteristics such as daily and seasonal variations are analyzed with methods of linear analysis and least-squares fitting.The results showed that,the variability of wind speed in Huashanguan is the biggest as the sea level increases,and followed by Longganhu,and the smallest is Xianjuding.Moreover,the trends in wind shear index are different with increasing heights,the curve of daily change is near the sinusoidal waveform;the seasonal variations in different terrain conditions are shown obviously.Compared with the normal circumstances,the vertical wind shear index is significantly reduced in windy conditions.Finally,a conclusion may be drawn that there is a significant impact of terrain and the underlying surface on the vertical change of wind.
Edinburgh Research Explorer Drivers of atmospheric methane uptake by montane forest soils in the southern Peruvian Andes

The soils of tropical montane forests can act as sources or sinks of atmospheric methane (CH 4 ) . Understanding this activity is important in regional atmospheric CH 4 budgets given that these ecosystems account for substantial portions of the landscape in mountainous areas like the Andes. We investigated the drivers of net CH 4 ﬂuxes from premontane, lower and upper montane forests, experiencing a seasonal climate, in south-eastern Peru. Between February 2011 and June 2013, these soils all functioned as net sinks for atmospheric CH 4 . Mean (standard error) net CH 4 ﬂuxes for the dry and wet season were − 1.6 (0.1) and − 1.1 (0.1) mg CH 4 -C m − 2 d − 1 in the upper montane forest, − 1.1 (0.1) and − 1.0 (0.1) mg CH 4 -C m − 2 d − 1 in the lower montane forest, and − 0.2 (0.1) and − 0.1
Tree-based Models for Inductive Classiﬁcation on the Web Of Data

The Web of Data, which is one of the dimensions of the Semantic Web (SW), represents a tremendous source of information, which motivates the increasing attention to the formalization and application of machine learning methods for solving tasks such as concept learning, link prediction, inductive instance retrieval in this context. However, the Web of Data is also characterized by various forms of uncertainty, owing to its inherent incompleteness (missing information, uneven data distributions) and noise, which may affect open and distributed architectures. In this paper, we focus on the inductive instance retrieval task regarded as a classiﬁcation problem. The proposed solution is a framework for learning Terminological Decision Trees from examples described in an ontological knowledge base, to be used for performing instance classiﬁcations. For the purpose, suitable pruning strategies and a new prediction procedure are proposed. Furthermore, in order to tackle the class-imbalance distribution problem, the framework is extended to ensembles of Terminological Decision Trees called Terminological Random Forest s. The proposed framework has been evaluated, in comparative experiments, with the main state of the art solutions grounded on a similar approach, showing that: 1) the employment of the formalized pruning strategies can improve the model predictiveness; 2) Terminological Random Forest s outperform the usage of a single Terminological Decision Tree , particularly when the knowledge base is endowed with a large number of concepts and roles; 3) the framework can be exploited for solving related problems, such as predicting the values of given properties with ﬁnite ranges.
Study on the Application of Oversized Horn Antenna in Array

To obtain high equivalent isotropically radiated power (EIRP) with limited transmission power for an 8 × 30 antenna array with large dimensions relative to the operating wavelength of the antenna element, the dielectric lens is designed as well as a matching layer which is used to reduce the reflection caused by the dielectric lens. 3-D electromagnetic computer code CST is used to verify the design. The simulation results show that the proposed antenna element used in the array has improved 1.8 dB of the gain, and decreased the side lode level from -5.2 dB to -13.4 dB.
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity

Offline reinforcement learning (RL) can in principle synthesize more optimal behavior from a dataset consisting only of suboptimal trials. One way that this can happen is by"stitching"together the best parts of otherwise suboptimal trajectories that overlap on similar states, to create new behaviors where each individual state is in-distribution, but the overall returns are higher. However, in many interesting and complex applications, such as autonomous navigation and dialogue systems, the state is partially observed. Even worse, the state representation is unknown or not easy to define. In such cases, policies and value functions are often conditioned on observation histories instead of states. In these cases, it is not clear if the same kind of"stitching"is feasible at the level of observation histories, since two different trajectories would always have different histories, and thus"similar states"that might lead to effective stitching cannot be leveraged. Theoretically, we show that standard offline RL algorithms conditioned on observation histories suffer from poor sample complexity, in accordance with the above intuition. We then identify sufficient conditions under which offline RL can still be efficient -- intuitively, it needs to learn a compact representation of history comprising only features relevant for action selection. We introduce a bisimulation loss that captures the extent to which this happens, and propose that offline RL can explicitly optimize this loss to aid worst-case sample complexity. Empirically, we show that across a variety of tasks either our proposed loss improves performance, or the value of this loss is already minimized as a consequence of standard offline RL, indicating that it correlates well with good performance.
Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm

The growing size of available data has attracted increasing interest in solving minimax problems in a decentralized manner for various machine learning tasks. Previous theoretical research has primarily focused on the convergence rate and communication complexity of decentralized minimax algorithms, with little attention given to their generalization. In this paper, we investigate the primal-dual generalization bound of the decentralized stochastic gradient descent ascent (D-SGDA) algorithm using the approach of algorithmic stability under both convex-concave and nonconvex-nonconcave settings. Our theory refines the algorithmic stability in a decentralized manner and demonstrates that the decentralized structure does not destroy the stability and generalization of D-SGDA, implying that it can generalize as well as the vanilla SGDA in certain situations. Our results analyze the impact of different topologies on the generalization bound of the D-SGDA algorithm beyond trivial factors such as sample sizes, learning rates, and iterations. We also evaluate the optimization error and balance it with the generalization gap to obtain the optimal population risk of D-SGDA in the convex-concave setting. Additionally, we perform several numerical experiments which validate our theoretical findings.
Reflective Amplification of Powerful Terahertz Pulse by Relativistic Electron Bunch

We describe an amplifier based on a principally non-linear effect of reflection of a short photo-injector electron bunch from the powerful wave pulse. This is effective mechanism of energy extraction by the wave from particles, when parameters of the electron bunch (initial energy spread, bunch length and emittance) haven't significant influence on the efficiency of the electron-wave interaction. Two concepts of this process (non-resonant and resonant electron-wave interaction) are described.
Competing interests

A competing interest exists when professional judgment concerning a primary interest (such as patients' welfare or the validity of research) may be influenced by a secondary interest (such as financial gain or personal rivalry). It may arise for the authors of a JRTPH article when they have a financial interest that may influence probably without their knowing their interpretation of their results or those of others.
Research on passive anti islanding protection method for distributed photovoltaic power generation based on deep learning

Distributed photovoltaics are mainly connected to low-voltage distribution networks of 35kV and below. When the power grid malfunctions, conventional distributed photovoltaics island protection methods have issues of island discrimination failure and unreliability, posing serious challenges to the safe and stable operation of the power grid. This paper proposes a new passive island recognition method based on deep learning to address the problem of complex and redundant feature selection and large blind spots in distributed photovoltaic island events. And compared with traditional anti islanding protection methods, simulation results show that this method can improve the accuracy of distributed power islanding detection and reduce detection blind spots.
Assessment of Osteoarthritis Candidate Genes in a

ABSTRACT Objectives: To assess osteoarthritis (OA) candidate genes for identification of promising genetic factors and, secondarily, to assess the candidate gene approach in OA. Methods: 199 published candidate genes for OA were obtained from the HuGe Navigator. All their SNPs with allele frequency >5% were assessed with fixed effect meta-analysis of 9 genome-wide association studies (GWAS) including 5 636 knee OA patients and 16 972 controls, and 4 349 hip OA patients and 17 836 controls of European ancestry. Additional 5 921 individuals were studied for top SNPs in the meta-analysis. Significance was corrected for the number of independent tests at p < 1.58 x 10 -5 . Results: SNPs at only two of the 199 candidate genes were associated with OA in the meta-analysis. They were associated with hip OA, COL11A1 showing two independent associations in the combined analysis (rs4907986, p = 1.29 x 10 -5 , OR = 1.12; 95 % CI = 1.06-1.17; and rs1241164, p = 1.47 x 10 -5 , OR = 0.82, CI = 0.74-0.89) and a SNP in linkage disequilibrium with rs4907986 in the female-specific analysis (rs4908291, p = 1.29 x 10
Spanlastics: a novel elastic drug delivery system with potential applications via multifarious routes of administration

Abstract Drug delivery systems (DDS) based on nanocarriers are designed to transport therapeutic agents to specific areas of the body where they are required to exhibit pharmacodynamic effect. These agents rely on an appropriate carrier to protect them from rapid degradation or clearance and enhance their concentration in target tissues. Spanlastics, an elastic, deformable surfactant-based nanovesicles have the potential to be used as a drug delivery vehicle for wide array of drug molecules. Spanlastics are formed by the self-association of non-ionic surfactants and edge activators in an aqueous phase and have gained attention as promising drug carriers due to their biodegradable, biocompatible, and non-immunogenic structure. In recent years, numerous scientific journals have published research articles exploring the potential of spanlastics to serve as a DDS for various types of drugs as they offer targeted delivery and regulated release of the drugs. Following brief introduction to spanlastics, their structure and methods of preparation, this review focuses on the delivery of various drugs using spanlastics as a carrier via various routes viz. topical, transdermal, ototopical, ocular, oral and nasal. Work carried out by various researchers by employing spanlastics as a carrier for enhancing therapeutic activity of different moieties has been discussed in detail. Graphical Abstract
Improved deep learning image classification algorithm based on Swin Transformer V2

While convolutional operation effectively extracts local features, their limited receptive fields make it challenging to capture global dependencies. Transformer, on the other hand, excels at global modeling and effectively captures global dependencies. However, the self-attention mechanism used in Transformers lacks a local mechanism for information exchange within specific regions. This article attempts to leverage the strengths of both Transformers and convolutional neural networks (CNNs) to enhance the Swin Transformer V2 model. By incorporating both convolutional operation and self-attention mechanism, the enhanced model combines the local information-capturing capability of CNNs and the long-range dependency-capturing ability of Transformers. The improved model enhances the extraction of local information through the introduction of the Swin Transformer Stem, inverted residual feed-forward network, and Dual-Branch Downsampling structure. Subsequently, it models global dependencies using the improved self-attention mechanism. Additionally, downsampling is applied to the attention mechanism’s Q and K to reduce computational and memory overhead. Under identical training conditions, the proposed method significantly improves classification accuracy on multiple image classification datasets, showcasing more robust generalization capabilities.
Analysis of CT Numbers in Thoracic CT Scan Image Segmentation and Spirometry on Pulmonary Vital Capacity

This research aims to analyze the correct CT Number to calculate the capacity volume of vital lungs using process segmentation image CT Scans. Compare results from capacity volume vital lungs on segmentation with results measurement volume capacity vital from spirometry. The research method used is an applied experiment that compares the results of lung vital capacity volume in segmentation with the results of vital capacity volume measurements from spirometry. Testing is carried out by calculating each volume on the CT Number used in segmentation and then comparing it with results from spirometry. Analysis data use test Correlation Pearson And Test paired Q Test Results study show that application segmentation image CT Scans with CT Numbers -850 HU up to -950 HU is quite good in calculating the volume of vital lung capacity. There is no significant difference between the results of lung vital capacity volume in segmentation and the results of vital capacity volume measurements in spirometry with a value (p > 0.05) of 0.06. The conclusion of this research is that segmentation image CT Scans of Thorax with the use of CT Numbers -850 HU until -950 HU can considered as an alternative in calculating the volume of vital lung capacity in patients with COPD.
Exploring Geometry of Blind Spots in Vision Models

Despite the remarkable success of deep neural networks in a myriad of settings, several works have demonstrated their overwhelming sensitivity to near-imperceptible perturbations, known as adversarial attacks. On the other hand, prior works have also observed that deep networks can be under-sensitive, wherein large-magnitude perturbations in input space do not induce appreciable changes to network activations. In this work, we study in detail the phenomenon of under-sensitivity in vision models such as CNNs and Transformers, and present techniques to study the geometry and extent of"equi-confidence"level sets of such networks. We propose a Level Set Traversal algorithm that iteratively explores regions of high confidence with respect to the input space using orthogonal components of the local gradients. Given a source image, we use this algorithm to identify inputs that lie in the same equi-confidence level set as the source image despite being perceptually similar to arbitrary images from other classes. We further observe that the source image is linearly connected by a high-confidence path to these inputs, uncovering a star-like structure for level sets of deep networks. Furthermore, we attempt to identify and estimate the extent of these connected higher-dimensional regions over which the model maintains a high degree of confidence. The code for this project is publicly available at https://github.com/SriramB-98/blindspots-neurips-sub