Miracle-dz/UniFAR-FTUs huggingface dataset:
A secure Dynamic Searchable Symmetric Encryption Framework for Privacy-critical cloud storage services

Searchable encryption permit encryption while quietly authorize searching for keywords. Its instant application is cloud storage where a client deploy its files while the cloud provider should search and selectively restore those. Searchable symmetric encryption has received a serious consideration from the research community with distinct constructions being proposed, each obtain asymptotically optimal complexity for distinct metrics (e.g., search, update). However their dignity, the practical attacks deployment have shown that the optimal asymptotic complexity might not always intend practical performance, specifically if the application demands a high privacy. In order to protect sensitive information from being leaked by the service providers, documents are usually encrypted by data owners before being outsource to the cloud. We proposed a novel Dynamic Searchable Symmetric Encryption (DSSE) framework called Incidence Matrix (IM)-DSSE, which attain a high level of privacy, efficient search/update, and low client storage with actual deployments on real cloud settings. We tackle an incidence matrix along with two hash tables to create an encrypted index, on which both search and update operations can be performed effectively with less information leakage. IM-DSSE achieves forward-privacy, backward-privacy and size-obliviousness simultaneously. We also create several distinct DSSE frameworks each offering different trade-offs that are suitable for different cloud applications and SaaS infrastructures. We fully implemented our framework and rigorously evaluated its performance on a real cloud system (Amazon EC2). Our framework IM-DSSE is highly practical on mobile networks with large datasets . We have released IM-DSSE as an open-source library for wide development and adaptation.
BIOMECHANICAL PROPERTIES OF HYBRID KEVLAR/LINEN/EPOXY COMPOSITE FOR BONE PLATE APPLICATIONS

Vishnu.G, Chandrasenan.P, Anoop Thankachan 1,2 M Tech Student, Dept of Mechanical Engineering,Sree Narayana Institute of Technology,Adoor, Kerala, India 3 Assistant Professor, Dept of Mechanical Engineering, Sree Narayana Institute of Technology, Adoor, Kerala ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Tibia fracture is mainly treated using rigid metallic bone plates which may result in “stress shielding” leading to bone resorption and implant loosening. This work aims to develop a Kevlar/Linen/Epoxy hybrid composite material for use as an orthopaedic Tibia fracture plate, instead of the common steel and titanium metal plates. The Kevlar/Linen/Epoxy composite would have properties similar to that of human cortical bone, there by having potential application as a Tibia fracture plate. The composite material used here is a “sandwich structure” in which mats of Kevlar are attached to both outer surface of the linen core, which results in a unique structure compared to other composite plates for bone plate applications. This study was conducted to evaluate the mechanical properties of the newly developed material. Mechanical properties were evaluated using experimental analysis of the proposed material which included tensile, flexural, and hardness tests. The specimens are prepared by means of hand lay-up process considering ASTM D 3039, ASTM 790 and ASTM D785 standards.
03 18-81 (1995) Ortiz et al.indd

*Department of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico; †Department of Biology, Faculty of Natural Sciences, University of Puerto Rico, Río Piedras Campus, Puerto Rico; ‡Division of High-Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA, USA; ¶University of Puerto Rico Cancer Control and Population Sciences Program, University of Puerto Rico Comprehensive Cancer Center
Theoretical study of vibrationally resolved photoionization of diatomic and polyatomic molecules

Con el advenimiento reciente de las fuentes de radiacion de sincrotron de tercera generacion, ha sido posible resolver vibracionalmente y a altas energias los espectros asociados con la emision de un electron por la absorcion de un foton. El comportamiento resultante de la seccion eficaz resuelta vibracionalmente se explica generalmente por el principio de Franck-Condon, que establece que las intensidades relativas de los picos de vibracion vienen dadas por el solapamiento entre las funciones de onda vibracional inicial y final. Una consecuencia es que el ratio entre dos secciones eficaces resueltas vibracionalmente (llamado "v-ratio") se espera que sea constante con la energia del foton. Sin embargo, se han observado muchas desviaciones respecto a la distribucion de Franck-Condon a bajas energias de los fotones (debido a resonancias de forma, autoionizaciones, etc.) y, mas recientemente, por fotones de altas energias (debido al efecto de retroceso, interferencias). En este estudio, hemos utilizado un metodo numerico basado en el formalismo de la teoria del funcional de la densidad y dentro de la aproximacion ``static exchange'' con el fin de entender dichas desviaciones, en particular, las oscilaciones que aparecen a altas energias. El metodo para calcular la estructura electronica, desarrollado por Piero Decleva y colaboradores, hace uso de funciones B-spline en base multicentrica con el fin de describir con precision los estados del continuo a altas energias del fotoelectron. En el presente trabajo, el movimiento nuclear esta incluido en la aproximacion de Born-Oppenheimer mediante la integracion de las funciones de onda vibracional, calculadas utilizando el metodo variacional en una base de B-splines.
Influence of Human Resource Processes on the Performance of Selected Manufacturing SMEs in Nairobi City County, Kenya

Manufacturing firms in Sub-Saharan Africa are not optimally managed which substantially lowers their productivity. The informal approach to human resource management is attributable to poor management practices with a consequent effect on performance. Due to these challenges, this study sought to examine the effect of human resource processes on the performance of manufacturing SMEs in Nairobi City County, Kenya. The study was explanatory and was based on 136 manufacturing SMEs which was drawn using proportionate stratified sampling. Data collection was achieved through the use of a self–administered questionnaire which was subjected to an inter-consistency test using the Cronbach's coefficient, α ≥ 0.70, which indicated that the research instrument was reliable. Descriptive and inferential statistics (at 0.05 significance levels) was used for the analysis of data. Diagnostic tests were conducted before regression analysis with the data was presented in tabular format. The results show that human resource processes cumulatively explain 23% of the variations in firm performance, therefore, the study concludes that the human resource processes have a positive influence on firm performance. The study recommends that manufacturing firms should seek to entrench an HR philosophy with commensurate improvements in the HR practises. The study limitations include a relatively small sample and geographical scope.
Crystal chemistry of natural Μn3+-bearing calderite-andradite garnets from Otjosondu, SWA/Namibia

Five calderite-andradite garnets from Otjosondu, Namibia, were studied chemically and by optical absorption and Mossbauer spectrosco- py. The combined results of chemical analyses and Mossbauer and opti- cal absorption spectroscopy make small amounts of octahedral Mn3+ likely, corresponding to up to 4.8 mol% of the blythite endmember, Mn~+Mn~+Si3012.
Morpho-chemical fingerprinting and salt tolerance of sweet basil (Ocimum basilicum L.) genotypes grown in sodic soil.

A field experiment was conducted in sodic soil (pH2 9.3) during rainy seasons for three years (2005-2008) to evaluate diverse accessions of sweet basil (Ocimum basilicum L.) for 15 plant traits, herb and oil yields, oil quality and to know physiological limits of salt tolerance. The chemical profiling of oil for about 50 compounds revealed that the accession CSLT5 was of the linalool chemotype yielding more than 60 % linalool in its oil whereas CSLT13 and CSLT9 can be classified as methyl chavicol chemotypes yielding 68 % and 56 % of methyl chavicol respectively. CSLT 12 and CSLT 12-1 were found as Bulgarian types which yielded more than 41 % of methyl chavicol and 29 % of linalool with appreciable content of methyl cinnamate. Pronounced inter- and intra-genotypic differences for other important mono- and sesqui- terpenes as well as oil yields in these genotypes were noticed. LC50 values of Na + ions in the selected genotypes indicated that CSLT12 is physiologically more salt tolerant. The paper presents morpho-chemical characterization, herb and oil yields obtained from different plant parts of basil genotypes and discusses potential for harnessing genotypic attributes in this promising crop for oil production and quality for enhanced productivity in sodic soils.
Effects of mercury and selenium on glutathione metabolism and oxidative stress in mallard ducks

Earlier studies have reported on the toxicity and related oxidative stress of different forms of Se, including seleno‐ D,L‐methionine, in mallards (Anas platyrhynchos). This study compares the effects of Se (seleno‐D,L‐methionine) and Hg (methylmercury chloride) separately and in combination. Mallard drakes received one of the following diets: untreated feed (controls), or feed containing 10 ppm Se, 10 ppm Hg, or 10 ppm Se in combination with 10 ppm Hg. After 10 weeks, blood, liver, and brain samples were collected for biochemical assays. The following clinical and biochemical alterations occurred in response to Hg exposure: hematocrit and hemoglobin concentrations decreased; activities of the enzymes glutathione (GSH) peroxidase (plasma and liver), glutathione‐S‐transferase (liver), and glucose‐6‐phosphate dehydrogenase (G‐6‐PDH) (liver and brain) decreased; hepatic oxidized glutathione (GSSG) concentration increased relative to reduced glutathione (GSH); and lipid peroxidation in the brain was detected by increased thiobarbituric reactive substances (TBARS). Effects of Se alone included increased hepatic GSSG reductase activity and brain TBARS concentration. Selenium in combination with Hg partially or totally alleviated effects of Hg on GSH peroxidase, G‐6‐PDH, and GSSG. These findings are compared in relation to field observations for diving ducks and other aquatic birds. It is concluded that since both Hg and excess Se can affect thiol status, measurement of associated enzymes in conjunction with thiol status may be a useful bioindicator to discriminate between Hg and Se effects. The ability of Se to restore the activities of G‐6‐PDH, GSH peroxidase, and glutathione status involved in antioxidative defense mechanisms may be crucial to biological protection from the toxic effects of methylmercury.
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning

Federated distillation (FD) is a popular novel algorithmic paradigm for Federated learning (FL), which achieves training performance competitive to prior parameter averaging-based methods, while additionally allowing the clients to train different model architectures, by distilling the client predictions on an unlabeled auxiliary set of data into a student model. In this work, we propose FedAUX, an extension to FD, which, under the same set of assumptions, drastically improves the performance by deriving maximum utility from the unlabeled auxiliary data. FedAUX modifies the FD training procedure in two ways: First, unsupervised pre-training on the auxiliary data is performed to find a suitable model initialization for the distributed training. Second, $(\varepsilon, \delta)$ -differentially private certainty scoring is used to weight the ensemble predictions on the auxiliary data according to the certainty of each client model. Experiments on large-scale convolutional neural networks (CNNs) and transformer models demonstrate that our proposed method achieves remarkable performance improvements over state-of-the-art FL methods, without adding appreciable computation, communication, or privacy cost. For instance, when training ResNet8 on non-independent identically distributed (i.i.d.) subsets of CIFAR10, FedAUX raises the maximum achieved validation accuracy from 30.4% to 78.1%, further closing the gap to centralized training performance. Code is available at https://github.com/fedl-repo/fedaux.
Key parameters for droplet evaporation and mixing at the cloud edge

The distribution of liquid water in ice‐free clouds determines their radiative properties, a significant source of uncertainty in weather and climate models. Evaporation and turbulent mixing cause a cloud to display large variations in droplet number density, but quite small variations in droplet size (Beals et al., Science, 2015, vol. 350, pp. 87–90). However, direct numerical simulations of the joint effect of evaporation and mixing near the cloud edge predict quite different behaviours, and how to reconcile these results with the experimental findings remains an open question. To infer the history of mixing and evaporation from observational snapshots of droplets in clouds is challenging, because clouds are transient systems. We formulated a statistical model that provides a reliable description of the evaporation–mixing process as seen in direct numerical simulations and allows us to infer important aspects of the history of observed droplet populations, highlighting the key mechanisms at work and explaining the differences between observations and simulations.
Tachyonic preheating in Palatini R 2 inflation

We study preheating in the Palatini formalism with a quadratic inflaton potential and an added αR 2 term. In such models, the oscillating inflaton field repeatedly returns to the plateau of the Einstein frame potential, on which the tachyonic instability fragments the inflaton condensate within less than an e-fold. We find that tachyonic preheating takes place when α ≳ 1013 and that the energy density of the fragmented field grows with the rate Γ/H ≈ 0.011 × α0. 31. The model extends the family of plateau models with similar preheating behaviour. Although it contains non-canonical quartic kinetic terms in the Einstein frame, we show that, in the first approximation, these can be neglected during both preheating and inflation.
On the Approximation Power of Two-Layer Networks of Random ReLUs

This paper considers the following question: how well can depth-two ReLU networks with randomly initialized bottom-level weights represent smooth functions? We give near-matching upper- and lower-bounds for $L_2$-approximation in terms of the Lipschitz constant, the desired accuracy, and the dimension of the problem, as well as similar results in terms of Sobolev norms. Our positive results employ tools from harmonic analysis and ridgelet representation theory, while our lower-bounds are based on (robust versions of) dimensionality arguments.
DIFFnet: Diffusion Parameter Mapping Network Generalized for Input Diffusion Gradient Schemes and b-Value

In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. For generalization, diffusion signals are normalized in a q-space and then projected and quantized, producing a matrix (Qmatrix) as an input for the network. To demonstrate the validity of this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnetDTI) and for neurite orientation dispersion and density imaging (DIFFnetNODDI). In each model, two datasets with different gradient schemes and b-values are tested. The results demonstrate accurate reconstruction of the diffusion parameters at substantially reduced processing time (approximately 8.7 times and 2240 times faster processing time than conventional methods in DTI and NODDI, respectively; less than 4% mean normalized root-mean-square errors (NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the networks was further validated using reduced numbers of diffusion signals from the datasets and a public dataset from Human Connection Project. Different from previously proposed deep neural networks, DIFFnet does not require any specific gradient scheme and b-value for its input. As a result, it can be adopted as an online reconstruction tool for various complex diffusion imaging.
Dynamical properties across different coarse-grained models for ionic liquids

Room-temperature ionic liquids (RTILs) stand out among molecular liquids for their rich physicochemical characteristics, including structural and dynamic heterogeneity. The significance of electrostatic interactions in RTILs results in long characteristic length- and timescales, and has motivated the development of a number of coarse-grained (CG) simulation models. In this study, we aim to better understand the connection between certain CG parameterization strategies and the dynamical properties and transferability of the resulting models. We systematically compare five CG models: a model largely parameterized from experimental thermodynamic observables; a refinement of this model to increase its structural accuracy; and three models that reproduce a given set of structural distribution functions by construction, with varying intramolecular parameterizations and reference temperatures. All five CG models display limited structural transferability over temperature, and also result in various effective dynamical speedup factors, relative to a reference atomistic model. On the other hand, the structure-based CG models tend to result in more consistent cation–anion relative diffusion than the thermodynamic-based models, for a single thermodynamic state point. By linking short- and long-timescale dynamical behaviors, we demonstrate that the varying dynamical properties of the different CG models can be largely collapsed onto a single curve, which provides evidence for a route to constructing dynamically-consistent CG models of RTILs.
EUCA: A Practical Prototyping Framework towards End-User-Centered Explainable Artificial Intelligence

Fig. 1. End-user-friendly explanatory forms in the EUCA framework. The explanatory forms are shown on the right grids, each accompanied by a prototyping card example across four tasks used in the user study. The forms are a familiar language to both AI designers and end-users, thus overcoming the technical communication barriers between the two. The 12 explanatory forms are grouped into four categories to explain AI’s prediction on a new data point (the red dot in the leftmost 2D feature-space plot), or the model’s overall behavior: Explaining using features, examples, rules, and supplementary information. These categories correspond to the different aspects of showing AI’s learned representations at the feature, instance, and decision boundary level, indicated in the plot.
Substructure at High Speed. II. The Local Escape Velocity and Milky Way Mass with Gaia eDR3

Measuring the escape velocity of the Milky Way is critical in obtaining the mass of the Milky Way, understanding the dark matter velocity distribution, and building the dark matter density profile. In Necib & Lin, we introduced a strategy to robustly measure the escape velocity. Our approach takes into account the presence of kinematic substructures by modeling the tail of the stellar distribution with multiple components, including the stellar halo and the debris flow called the Gaia Sausage (Enceladus). In doing so, we can test the robustness of the escape velocity measurement for different definitions of the “tail” of the velocity distribution and the consistency of the data with different underlying models. In this paper, we apply this method to the Gaia eDR3 data release and find that a model with two components is preferred, although results from a single-component fit are also consistent. Based on a fit to retrograde data with two bound components to account for the relaxed halo and the Gaia Sausage, we find the escape velocity of the Milky Way at the solar position to be vesc=445−8+25 km s−1. A fit with a single component to the same data gives vesc=472−12+17 km s−1. Assuming a Navarro−Frenck−White dark matter profile, we find a Milky Way concentration of c200=19−7+11 and a mass of M200=4.6−0.8+1.5×1011M⊙ , which is considerably lighter than previous measurements.
App Developer Centric Trusted Execution Environment

—ARM TrustZone is the de-facto hardware TEE implementation on mobile devices like smartphones. As a vendor-centric TEE, TrustZone greatly overlooks the strong protection demands and requirements from the App developers. Several security solutions have been proposed to enable the TEE-assisted isolation in the Normal World of ARM, attempting to balance the security and usability. However, they are still not full-ﬂedged in serving Apps’ needs. In this paper, we introduce LEAP, which is a lightweight App developer Centric TEE solution in the Normal World. LEAP offers the auto DevOps tool to help developers to prepare the codes running on it, enables isolated codes to execute in parallel and access peripheral (e.g. mobile GPUs) with ease, and dynamically manage system resources upon Apps’ requests. We implement the LEAP prototype on the off-the-shelf ARM platform without any hardware change. We perform the comprehensive analyses and experiments to demonstrate that LEAP is efﬁcient in design, comprehensive in support, and convenient in adoption.
A Deeper Look into Convolutions via Pruning

Convolutional neural networks (CNNs) are able to attain better visual recognition performance than fully connected neural networks despite having much less parameters due to their parameter sharing principle. Hence,modernarchitecturesaredesignedtocontainaverysmallnumberoffully-connected layers, often at the end, after multiple layers of convolutions. It is interesting to observe that we can replace large fully-connected layers with relatively small groups of tiny matrices applied on the entire image. Moreover, although this strategy already reduces the number of parameters, most of the convolutions can be eliminated as well, without suﬀering any loss in recognition performance. However, there is no solid recipe to detect this hidden subset of convolutional neurons that is responsible for the majority of the recognition work. Hence, in this work, we use the matrix characteristics based on eigenvalues in addition to the classical weight-based importance assignment approach for pruning to shed light on the internal mechanisms of a widely used family of CNNs, namely residual neural networks (ResNets), for the image classiﬁcation problem using CIFAR-10, CIFAR-100 and Tiny ImageNet datasets. The codes are available at https://github.com/cuguilke/psykedelic .