MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference

Sistema de medición basado en los modelos de Visuales y Malca para lograr eficacia en la responsabilidad social empresarial ambiental del Hiper Mercado Plaza Vea de Chiclayo 2009-2010

A lo largo de las últimas décadas ante los cambios económicos y globales observados dentro del proceso de globalización es que se visualiza un nuevo concepto de negocio que actualmente la sociedad exige, por eso el presente trabajo aborda las relaciones existentes entre la empresa, sus públicos interesados (stakeholders) y el instinto de desarrollo sustentable y social, en dicho proceso se incorporan valores y nuevos programas, haciendo un enfoque nuevo de una perspectiva a largo plazo que evidencie no solo la formación de valor para los accionistas y la empresa sino también para el conjunto de redes sociales que están unidos directa o indirectamente. En este trabajo de investigación estudiaremos los problemas que envuelven los stakeholders, que son colaboradores, proveedores, socios, los productos y servicios que se ofertan, si son nocivos o no, la creación de empleos, capacitación ambiental, salud, seguridad laboral y programas de promoción a través de los medios de comunicación, partiendo de este punto para llegar al objetivo que es medir y diagnosticar la responsabilidad social empresarial en la empresa Plaza Vea Chiclayo, basándonos en las teorías de Malca y Visuales. Es decisión de la empresa introducir en su gestión empresarial nuestro enfoque presentado en la investigación, como parte de su filosofía, visión y al transmitirla a la sociedad, la empresa presentará una ventaja competitiva sobre las otras que operan en el mercado.Tesi

Whitening Black-Box Neural Networks

Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models

The effects of creatine supplementation combined with resistance training on regional measures of muscle hypertrophy: a systematic review with meta-analysis.

The purpose of this paper was to carry out a systematic review with meta-analysis of randomized controlled trials that examined the combined effects of resistance training (RT) and creatine supplementation on regional changes in muscle mass with direct imaging measures of hypertrophy. Moreover, we performed regression analyses to determine the potential influence of covariates. We included trials of at least 6 weeks in duration that examined the combined effects of creatine supplementation and RT on site-specific direct measures of hypertrophy (magnetic resonance imaging [MRI], computed tomography [CT] or ultrasound) in healthy adults. A total of 44 outcomes were analyzed across 10 studies that met inclusion criteria. Univariate analysis of all standardized outcomes showed a pooled mean estimate of 0.11 (95% Credible Interval [CrI]: -0.02 to 0.25) providing evidence of a very small effect favoring creatine supplementation when combined with RT, compared to RT and placebo. Multivariate analyses found similar small benefits for the combination of creatine supplementation and RT on changes in upper and lower body muscle thickness (0.10-0.16 cm). Analyses of moderating effects indicated a small superior benefit for creatine supplementation on younger compared to older adults (0.17 [95% CrI: -0.09 to 0.45]). In conclusion, results suggest that creatine supplementation combined with RT promotes a small increase in direct measures of skeletal muscle hypertrophy in both the upper and lower body

Gaining more from doing less? The effects of a one-week deload period during supervised resistance training on muscular adaptations.

Based on emerging evidence that brief periods of cessation from resistance training (RT) may re-sensitize muscle to anabolic stimuli, we aimed to investigate how a 1-week deload interval at the midpoint of a 9-week RT program affected muscular adaptations in resistance-trained individuals. Thirty-nine young men (n=29) and women (n=10) were randomly assigned to one of two experimental, parallel groups: An experimental group that abstained from RT for 1 week at the midpoint of a 9-week, high-volume RT program (DELOAD) or a traditional training group that performed the same RT program continuously over the study period (TRAD). The lower body routines were directly supervised by the research staff while upper body training was carried out in an unsupervised fashion. Muscle growth outcomes included assessments of muscle thickness along proximal, mid and distal regions of the middle and lateral quadriceps femoris as well as the mid-region of the triceps surae. Adaptations in lower body isometric and dynamic strength, local muscular endurance of the quadriceps, and lower body muscle power were also assessed. Results indicated no appreciable differences in increases of lower body muscle size, local endurance, and power between groups. Alternatively, TRAD showed greater improvements in both isometric and dynamic lower body strength compared to DELOAD. Additionally, TRAD showed some slight psychological benefits as assessed by the readiness to train questionnaire over DELOAD. In conclusion, our findings suggest that a 1-week deload period at the midpoint of a 9-week RT program appears to negatively influence measures of lower body muscle strength but has no effect on lower body hypertrophy, power or local muscular endurance

Mechanism of selective benzene hydroxylation catalyzed by iron-containing zeolites

A direct, catalytic conversion of benzene to phenol would have wide-reaching economic impacts. Fe zeolites exhibit a remarkable combination of high activity and selectivity in this conversion, leading to their past implementation at the pilot plant level. There were, however, issues related to catalyst deactivation for this process. Mechanistic insight could resolve these issues, and also provide a blueprint for achieving high performance in selective oxidation catalysis. Recently, we demonstrated that the active site of selective hydrocarbon oxidation in Fe zeolites, named α-O, is an unusually reactive Fe(IV)=O species. Here, we apply advanced spectroscopic techniques to determine that the reaction of this Fe(IV)=O intermediate with benzene in fact regenerates the reduced Fe(II) active site, enabling catalytic turnover. At the same time, a small fraction of Fe(III)-phenolate poisoned active sites form, defining a mechanism for catalyst deactivation. Density-functional theory calculations provide further insight into the experimentally defined mechanism. The extreme reactivity of α-O significantly tunes down (eliminates) the rate-limiting barrier for aromatic hydroxylation, leading to a diffusion-limited reaction coordinate. This favors hydroxylation of the rapidly diffusing benzene substrate over the slowly diffusing (but more reactive) oxygenated product, thereby enhancing selectivity. This defines a mechanism to simultaneously attain high activity (conversion) and selectivity, enabling the efficient oxidative upgrading of inert hydrocarbon substrates

On the evolution of the size of Lyman alpha halos across cosmic time: no change in the circumgalactic gas distribution when probed by line emission

Lyman $\alpha$ (Ly$\alpha$) is now routinely used as a tool for studying high-redshift galaxies and its resonant nature means it can trace neutral hydrogen around star-forming galaxies. Integral field spectrograph measurements of high-redshift Ly$\alpha$ emitters indicate that significant extended Ly$\alpha$ halo emission is ubiquitous around such objects. We present a sample of redshift 0.23 to 0.31 galaxies observed with the Hubble Space Telescope selected to match the star formation properties of high-$z$ samples while optimizing the observations for detection of low surface brightness Ly$\alpha$ emission. The Ly$\alpha$ escape fractions range between 0.7\% and 37\%, and we detect extended Ly$\alpha$ emission around six out of seven targets. We find Ly$\alpha$ halo to UV scale length ratios around 6:1 which is marginally lower than high-redshift observations, and halo flux fractions between 60\% and 85\% -- consistent with high-redshift observations -- when using comparable methods. However, our targets show additional extended stellar UV emission: we parametrize this with a new double exponential model. We find that this parametrization does not strongly affect the observed Ly$\alpha$ halo fractions. We find that deeper H$\alpha$ data would be required to firmly determine the origin of Ly$\alpha$ halo emission, however, there are indications that H$\alpha$ is more extended than the central FUV profile, potentially indicating conditions favorable for the escape of ionizing radiation. We discuss our results in the context of high-redshift galaxies, cosmological simulations, evolutionary studies of the circumgalactic medium in emission, and the emission of ionizing radiation.Comment: 20 page, 14 figures, 6 tables. Accepted for publication in MNRA

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

Immunological fingerprint in coronavirus disease-19 convalescents with and without post-COVID syndrome

BackgroundSymptoms lasting longer than 12  weeks after severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection are called post-coronavirus disease (COVID) syndrome (PCS). The identification of new biomarkers that predict the occurrence or course of PCS in terms of a post-viral syndrome is vital. T-cell dysfunction, cytokine imbalance, and impaired autoimmunity have been reported in PCS. Nevertheless, there is still a lack of conclusive information on the underlying mechanisms due to, among other things, a lack of controlled study designs.MethodsHere, we conducted a prospective, controlled study to characterize the humoral and cellular immune response in unvaccinated patients with and without PCS following SARS-CoV-2 infection over 7 months and unexposed donors.ResultsPatients with PCS showed as early as 6 weeks and 7 months after symptom onset significantly increased frequencies of SARS-CoV-2-specific CD4+ and CD8+ T-cells secreting IFNγ, TNF, and expressing CD40L, as well as plasmacytoid dendritic cells (pDC) with an activated phenotype. Remarkably, the immunosuppressive counterparts type 1 regulatory T-cells (TR1: CD49b/LAG-3+) and IL-4 were more abundant in PCS+.ConclusionThis work describes immunological alterations between inflammation and immunosuppression in COVID-19 convalescents with and without PCS, which may provide potential directions for future epidemiological investigations and targeted treatments

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine