Fast Differentially Private Matrix Factorization

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm lends itself to efficient implementation. By careful systems design and by exploiting the power law behavior of the data to maximize CPU cache bandwidth we are able to generate 1024 dimensional models at a rate of 8.5 million recommendations per second on a single PC

Self-supervised Outdoor Scene Relighting

Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, requiring high quality synthetic renderings to train a solution. Such renderings are synthesized using priors learned from limited data. In contrast, we propose a self-supervised approach for relighting. Our approach is trained only on corpora of images collected from the internet without any user-supervision. This virtually endless source of training data allows training a general relighting solution. Our approach first decomposes an image into its albedo, geometry and illumination. A novel relighting is then produced by modifying the illumination parameters. Our solution capture shadow using a dedicated shadow prediction map, and does not rely on accurate geometry estimation. We evaluate our technique subjectively and objectively using a new dataset with ground-truth relighting. Results show the ability of our technique to produce photo-realistic and physically plausible results, that generalizes to unseen scenes.Comment: Published in ECCV '20, http://gvv.mpi-inf.mpg.de/projects/SelfRelight

Online discrepancy minimization for stochastic arrivals

In the stochastic online vector balancing problem, vectors v1,v2,…,vT chosen independently from an arbitrary distribution in Rn arrive one-by-one and must be immediately given a ± sign. The goal is to keep the norm of the discrepancy vector, i.e., the signed prefix-sum, as small as possible for a given target norm. We consider some of the most well-known problems in discrepancy theory in the above online stochastic setting, and give algorithms that match the known offline bounds up to polylog(nT) factors. This substantially generalizes and improves upon the previous results of Bansal, Jiang, Singla, and Sinha (STOC' 20). In particular, for the Komlos problem where ∥v_t∥_2≤1 for each t, our algorithm achieves ˜O(1) discrepancy with high probability, improving upon the previous ˜O(n3/2) bound. For Tusnády's problem of minimizing the discrepancy of axis-aligned boxes, we obtain an O(log^{d+4}T) bound for arbitrary distribution over points. Previous techniques only worked for product distributions and gave a weaker O(log^{2d+1}T) bound. We also consider the Banaszczyk setting, where given a symmetric convex body K with Gaussian measure at least 1/2, our algorithm achieves \tilde{O}(1) discrepancy with respect to the norm given by K for input distributions with sub-exponential tails. Our results are based on a new potential function approach. Previous techniques consider a potential that penalizes large discrepancy, and greedily chooses the next color to minimize the increase in potential. Our key idea is to introduce a potential that also enforces constraints on how the discrepancy vector evolves, allowing us to maintain certain anti-concentration properties. We believe that our techniques to control the evolution of states could find other applications in stochastic processes and online algorithms. For the Banaszczyk setting, we further enhance this potential by combining it with ideas from generic chaining. Finally, we also extend these results to the setting of online multi-color discrepancy.</p

Online discrepancy minimization for stochastic arrivals

In the stochastic online vector balancing problem, vectors v1, v2,..., vT chosen independently from an arbitrary distribution in Rn arrive one-by-one and must be immediately given a ± sign. The goal is to keep the norm of the discrepancy vector, i.e., the signed prefix-sum, as small as possible for a given target norm. We consider some of the most well-known problems in discrepancy theory in the above online stochastic setting, and give algorithms that match the known offline bounds up to polylog(nT) factors. This substantially generalizes and improves upon the previous results of Bansal, Jiang, Singla, and Sinha (STOC' 20). In particular, for the Komlós problem where kvtk2 ≤ 1 for each t, our algorithm achieves Oe(1) discrepancy with high probability, improving upon the previous Oe(n3/2) bound. For Tusnády's problem of minimizing the discrepancy of axis-aligned boxes, we obtain an O(logd+4 T) bound for arbitrary distribution over points. Previous techniques only worked for product distributions and gave a weaker O(log2d+1 T) bound. We also consider the Banaszczyk setting, where given a symmetric convex body K with Gaussian measure at least 1/2, our algorithm achieves Oe(1) discrepancy with respect to the norm given by K for input distributions with sub-exponential tails. Our results are based on a new potential function approach. Previous techniques consider a potential that penalizes large discrepancy, and greedily chooses the next color to minimize the increase in potential. Our key idea is to introduce a potential that also enforces constraints on how the discrepancy vector evolves, allowing us to maintain certain anti-concentration properties. We believe that our techniques to control the evolution of states could find other applications in stochastic processes and online algorithms. For the Banaszczyk setting, we further enhance this potential by combining it with ideas from generic chaining. Finally, we also extend these results to the setting of online multicolor discrepancy

Role of Androgen Receptor CAG Repeat Polymorphism and X-Inactivation in the Manifestation of Recurrent Spontaneous Abortions in Indian Women

The aim of the present study was to investigate the role of CAG repeat polymorphism and X-chromosome Inactivation (XCI) pattern in Recurrent Spontaneous Abortions among Indian women which has not been hitherto explored. 117 RSA cases and 224 Controls were included in the study. Cases were recruited from two different hospitals - Lakshmi Fertility Clinic, Nellore and Fernandez Maternity Hospital, Hyderabad. Controls were roughly matched for age, ethnicity and socioeconomic status. The CAG repeats of the Androgen Receptor gene were genotyped using a PCR-based assay and were analysed using the GeneMapper software to determine the CAG repeat length. XCI analysis was also carried out to assess the inactivation percentages. RSA cases had a significantly greater frequency of allele sizes in the polymorphic range above 19 repeats (p = 0.006), which is the median value of the controls, and in the biallelic mean range above 21 repeats (p = 0.002). We found no evidence of abnormal incidence of skewed X-inactivation. We conclude that longer CAG repeat lengths are associated with increased odds for RSA with statistical power estimated to be ∼90%

A Computational Framework for Influenza Antigenic Cartography

Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap

Liposomi rivastigmina za isporuku u mozak intranazalnim putem

The present study is mainly aimed at delivering a drug into the brain via the intranasal route using a liposomal formulation. For this purpose, rivastigmine, which is used in the management of Alzheimer’s disease, was selectd as a model drug. Conventional liposomes were formulated by lipid layer hydration method using cholesterol and soya lecithin as lipid components. The concentration of rivastigmine in brain and plasma was studied in rat models after intranasal and oral administration of liposomes and free drug. A significantly higher level of drug was found in the brain with intranasal liposomes of rivastigmine compared to the intranasal free drug and the oral route. Intranasal liposomes had a longer half-life in the brain than intranasally or orally administered free drug. Delivering rivastigmine liposomes through the intranasal route for the treatment of Alzheimer’s disease might be a new approach to the management of this condition.Glavni cilj rada je razvoj liposoma za intranazalnu primjenu za isporuku lijeka u mozak. U tu svrhu izabran je rivastigmin kao modelni lijek koji se upotrebljava u terapiji Alzheimerove bolesti. Liposomi su pripravljeni metodom hidratacije lipidnog sloja koristeći kolesterol i lecitin iz soje kao lipidne komponente. Praćena je koncentracija rivastigmina u mozgu i plazmi nakon intranazalne i peroralne primjene liposoma i slobodnog lijeka. S intranazalnim liposomima rivastigmina postignuta je značajno veća koncentracija lijeka u mozgu. Osim toga intranazalni liposomi imaju dulje vrijeme poluživota u mozgu. Intranazalna primjena liposoma rivastigmina mogla bi predstavljati novi pristup terapiji Alzheimerove bolesti

Amorphous 1-propanol interstellar ice beyond its melting point

The recent discovery of 1-propanol (CH3CH2CH2OH) in the interstellar medium (ISM) is of tremendous interest since fatty alcohols have been proposed as constituents of proto-cell membranes. Motivated by this discovery, we present the laboratory midinfrared (MIR) and vacuum ultra-violet (VUV) absorption spectra of 1-propanol ice under astrochemical conditions, mimicking an icy mantle on cold dust in the ISM. Both MIR and VUV spectra were recorded at ultra-high vacuum (UHV) of ∼ 10-9 mbar and at temperatures ranging from 10 K to sublimation. The morphology of the 1-propanol ice deposited at 10 K was amorphous. By warming the ice to temperatures of 140 K and above, with subsequent recording of IR spectra, we observe complete sublimation of 1-propanol molecules from the substrate around 170 K. No amorphous-to-crystalline phase change was observed upon warming to higher temperatures. Additionally, We observe the IR and VUV signatures of 1-propanol ice on the substrate well beyond its melting point (147 K). To the best of our knowledge, this is the first reported observation of a molecular ice staying well beyond its melting point under such conditions. This result shows that the morphology of icy mantles on ISM cold dust grains is more complex than previously thought. Our atomistic molecular dynamics (MD) simulations capture the experimental trends and shed light on the microscopic origin of this unusual phase behaviour of 1-propanol

Demarcation of the Boundaries of the Central Asian Desert Natural Focus of Plague of Kazakhstan and Monitoring the Areal of the Main Carrier, <I>Rhombomys opimus</I>

The aim of the study was to clarify the boundaries of the Central Asian natural plague focus of Kazakhstan and the modern boundaries of the areal of the great gerbil (Rhombomys opimus) in order to improve epizootiological monitoring and increase the effectiveness of preventive (anti-epidemic) measures.Materials and methods. Data from the epizootiological monitoring of the great gerbil populations in 14 autonomous foci of the Central Asian desert natural plague focus in the Republic of Kazakhstan between 2010 and 2020 were used for the analysis. An epizootiologic survey of an area of 875350 km2 was carried out. When processing the data, epidemiological, epizootiological, statistical research methods, as well as GIS technologies were used.Results and discussion. An increase in the total area of the Central Asian desert natural plague focus of the Republic of Kazakhstan by 79710 km2 (9.98 %) has been established for the period of 1990–2020. It is noted that the change in the area of plague-enzootic territory was a consequence of the ever changing areal of the main carrier of plague pathogen – the great gerbil – under the influence of climatic and anthropogenic factors. The most significant changes were found in the southeastern part of the plague-enzootic territory, including those for the Betpakdala (50 %), Balkhash (34.3 %), Taukum (13.3 %) and Mojynkum (0.32 %) autonomous foci. The area of the Aryskum-Dariyalyktakyr autonomous focus decreased by 2100 km2 (4 %). In 2000–2002, new Alakol’sky and Ili intermountain autonomous foci with a total area of 26759 km2 were discovered. It is shown that due to the regression of the Aral Sea, the areal of the great girbil expanded and the area of the North Aral and Kyzylkum natural plague foci increased by 10500 km2 (29.2 %) and 560 km2 (0.4%), respectively. The areas of the Aral-Karakum and UralEmba desert autonomous foci, on the contrary, decreased by 2000 km2 (2.6 %) and 12300 km2 (17.6 %), respectively. Passportization and landscape-epizootiologic zoning of the territory of the Central Asian desert natural plague focus of the Republic of Kazakhstan has been completed

Capture, Reconstruction, and Representation of the Visual Real World for Virtual Reality

We provide an overview of the concerns, current practice, and limitations for capturing, reconstructing, and representing the real world visually within virtual reality. Given that our goals are to capture, transmit, and depict complex real-world phenomena to humans, these challenges cover the opto-electro-mechanical, computational, informational, and perceptual fields. Practically producing a system for real-world VR capture requires navigating a complex design space and pushing the state of the art in each of these areas. As such, we outline several promising directions for future work to improve the quality and flexibility of real-world VR capture systems