Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation

Stein Variational Gradient Descent (SVGD) is a popular variational inference algorithm which simulates an interacting particle system to approximately sample from a target distribution, with impressive empirical performance across various domains. Theoretically, its population (i.e, infinite-particle) limit dynamics is well studied but the behavior of SVGD in the finite-particle regime is much less understood. In this work, we design two computationally efficient variants of SVGD, namely VP-SVGD (which is conceptually elegant) and GB-SVGD (which is empirically effective), with provably fast finite-particle convergence rates. We introduce the notion of \emph{virtual particles} and develop novel stochastic approximations of population-limit SVGD dynamics in the space of probability measures, which are exactly implementable using a finite number of particles. Our algorithms can be viewed as specific random-batch approximations of SVGD, which are computationally more efficient than ordinary SVGD. We show that the $n$ particles output by VP-SVGD and GB-SVGD, run for $T$ steps with batch-size $K$, are at-least as good as i.i.d samples from a distribution whose Kernel Stein Discrepancy to the target is at most $O\left(\tfrac{d^{1/3}}{(KT)^{1/6}}\right)$ under standard assumptions. Our results also hold under a mild growth condition on the potential function, which is much weaker than the isoperimetric (e.g. Poincare Inequality) or information-transport conditions (e.g. Talagrand's Inequality $\mathsf{T}_1$) generally considered in prior works. As a corollary, we consider the convergence of the empirical measure (of the particles output by VP-SVGD and GB-SVGD) to the target distribution and demonstrate a \emph{double exponential improvement} over the best known finite-particle analysis of SVGD.Comment: 34 Pages, 2 Figure

Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms

We consider stochastic approximations of sampling algorithms, such as Stochastic Gradient Langevin Dynamics (SGLD) and the Random Batch Method (RBM) for Interacting Particle Dynamcs (IPD). We observe that the noise introduced by the stochastic approximation is nearly Gaussian due to the Central Limit Theorem (CLT) while the driving Brownian motion is exactly Gaussian. We harness this structure to absorb the stochastic approximation error inside the diffusion process, and obtain improved convergence guarantees for these algorithms. For SGLD, we prove the first stable convergence rate in KL divergence without requiring uniform warm start, assuming the target density satisfies a Log-Sobolev Inequality. Our result implies superior first-order oracle complexity compared to prior works, under significantly milder assumptions. We also prove the first guarantees for SGLD under even weaker conditions such as H\"{o}lder smoothness and Poincare Inequality, thus bridging the gap between the state-of-the-art guarantees for LMC and SGLD. Our analysis motivates a new algorithm called covariance correction, which corrects for the additional noise introduced by the stochastic approximation by rescaling the strength of the diffusion. Finally, we apply our techniques to analyze RBM, and significantly improve upon the guarantees in prior works (such as removing exponential dependence on horizon), under minimal assumptions.Comment: Version 2 considers more results, including those for stochastic gradient lagevin dynamics and the random batch method for interacting particle dynamics, along with the results in the previous version. This also contains 2 additional author

Development of Starch-Polyvinyl Alcohol (PVA) Biodegradable Film: Effect of Cross-Linking Agent and Antimicrobials on Film Characteristics

To satisfy the need of developing eco-friendly flexible antimicrobial packaging film with minimum use of synthetic chemical ingredients, the present study examined the efficacy of citric acid (CA) as cross-linking agent and essential oils (EOs), viz., cinnamon essential oil (CEO) and oregano essential oil (OEO) as natural antimicrobials in corn starch-polyvinyl alcohol (CS-PVA) film. Compared to film prepared from filmogenic solution (FS) containing 75 kg CS+8.75 kg PVA+24.6 kg glycerol per m3 FS, film additionally containing CA at 0.07 kg/kg CS indicated 95% higher ultimate tensile strength (UTS) and 27% lower water vapor permeability (WVP). Film developed with incorporation of CEO and OEO at 1.875 m3 in 100 m3 FS (CS:PVA= 8.5:1) containing CA at 0.07 kg/kg CS exhibited antimicrobial action against Staphylococcus aureus. Added advantage was, both EOs could reduce WVP of film with no EO by about 50%, though CEO exhibited better antimicrobial action. Structural alteration in film matrix due to incorporation of EOs was evident from FTIR and SEM analyses. Thus, from the overall results, CEO (at 1.875 m3 /100 m3 FS) incorporated CS-PVA film cross-linked with CA, in prescribed amounts, was found to be the suitable antimicrobial film with appreciable mechanical properties (UTS ≈4 MPa, Elongation ≈50%) and water vapor permeability (≈0.5×10-6 kg.m.m-2.kPa-1.h-1)

Jointly trained image and video generation using residual vectors

In this work, we propose a modeling technique for jointly training image and video generation models by simultaneously learning to map latent variables with a fixed prior onto real images and interpolate over images to generate videos. The proposed approach models the variations in representations using residual vectors encoding the change at each time step over a summary vector for the entire video. We utilize the technique to jointly train an image generation model with a fixed prior along with a video generation model lacking constraints such as disentanglement. The joint training enables the image generator to exploit temporal information while the video generation model learns to flexibly share information across frames. Moreover, experimental results verify our approach's compatibility with pre-training on videos or images and training on datasets containing a mixture of both. A comprehensive set of quantitative and qualitative evaluations reveal the improvements in sample quality and diversity over both video generation and image generation baselines. We further demonstrate the technique's capabilities of exploiting similarity in features across frames by applying it to a model based on decomposing the video into motion and content. The proposed model allows minor variations in content across frames while maintaining the temporal dependence through latent vectors encoding the pose or motion features.Comment: Accepted in 2020 Winter Conference on Applications of Computer Vision (WACV '20

OrgAn: Organizational Anonymity with Low Latency

There is a growing demand for network-level anonymity for delegates at global organizations such as the UN and Red Cross. Numerous anonymous communication (AC) systems have been proposed over the last few decades to provide anonymity over the internet; however, they either introduce high latency overhead, provide weaker anonymity guarantees, or are difficult to be deployed at the organizational networks. Recently, the PriFi system introduced a client/relay/server model that suitably utilizes the organizational network topology and proposes a low-latency, strong-anonymity AC protocol. Using an efficient lattice-based (almost) key-homomorphic pseudorandom function and Netwon\u27s power sums, we present a novel AC protocol OrgAn in this client/relay/server model that provides strong anonymity against a global adversary controlling the majority of the network. OrgAn\u27s cryptographic design allows it to overcome several major problems with any realistic PriFi instantiation: (a) unlike PriFi, OrgAn avoids frequent, interactive, slot-agreement protocol among the servers; (b) a PriFi relay has to receive frequent communication from the servers which can not only become a latency bottleneck but also reveal the access pattern to the servers and increases the chance of server collusion/coercion, while OrgAn servers are absent from any real-time process. We demonstrate how to make this public-key cryptographic solution scale equally well as the symmetric-cryptographic PriFi with practical pre-computation and storage requirements. Through a prototype implementation we show that OrgAn provides similar throughput and end-to-end latency guarantees as PriFi, while still discounting the setup challenges in PriFi

Anonymity Trilemma: Strong Anonymity, Low Bandwidth Overhead, Low Latency---Choose Two

This work investigates the fundamental constraints of anonymous communication (AC) protocols. We analyze the relationship between bandwidth overhead, latency overhead, and sender anonymity or recipient anonymity against a global passive (network-level) adversary. We confirm the trilemma that an AC protocol can only achieve two out of the following three properties: strong anonymity (i.e., anonymity up to a negligible chance), low bandwidth overhead, and low latency overhead. We further study anonymity against a stronger global passive adversary that can additionally passively compromise some of the AC protocol nodes. For a given number of compromised nodes, we derive as a necessary constraint a relationship between bandwidth and latency overhead whose violation make it impossible for an AC protocol to achieve strong anonymity. We analyze prominent AC protocols from the literature and depict to which extent those satisfy our necessary constraints. Our fundamental necessary constraints offer a guideline not only for improving existing AC systems but also for designing novel AC protocols with non-traditional bandwidth and latency overhead choices

Divide and Funnel: a Scaling Technique for Mix-Networks

While many anonymous communication (AC) protocols have been proposed to provide anonymity over the internet, scaling to a large number of users while remaining provably secure is challenging. We tackle this challenge by proposing a new scaling technique to improve the scalability/anonymity of AC protocols that distributes the computational load over many nodes without completely disconnecting the paths different messages take through the network. We demonstrate that our scaling technique is useful and practical through a core sample AC protocol, Streams, that offers provable security guarantees and scales for a million messages. The scaling technique ensures that each node in the system does the computation-heavy public key operation only for a tiny fraction of the total messages routed through the Streams network while maximizing the mixing/shuffling in every round. We demonstrate Streams\u27 performance through a prototype implementation. Our results show that Streams can scale well even if the system has a load of one million messages at any point in time. Streams maintains a latency of $16$ seconds while offering provable ``one-in-a-billion\u27\u27 unlinkability, and can be leveraged for applications such as anonymous microblogging and network-level anonymity for blockchains. We also illustrate by examples that our scaling technique can be useful to many other AC protocols to improve their scalability and privacy, and can be interesting to protocol developers

DeepTMH: Multimodal Semi-supervised framework leveraging Affective and Cognitive engagement for Telemental Health

To aid existing telemental health services, we propose DeepTMH, a novel framework that models telemental health session videos by extracting latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature. Our approach leverages advances in semi-supervised learning to tackle the data scarcity in the telemental health session video domain and consists of a multimodal semi-supervised GAN to detect important mental health indicators during telemental health sessions. We demonstrate the usefulness of our framework and contrast against existing works in two tasks: Engagement regression and Valence-Arousal regression, both of which are important to psychologists during a telemental health session. Our framework reports 40% improvement in RMSE over SOTA method in Engagement Regression and 50% improvement in RMSE over SOTA method in Valence-Arousal Regression. To tackle the scarcity of publicly available datasets in telemental health space, we release a new dataset, MEDICA, for mental health patient engagement detection. Our dataset, MEDICA consists of 1299 videos, each 3 seconds long. To the best of our knowledge, our approach is the first method to model telemental health session data based on psychology-driven Affective and Cognitive features, which also accounts for data sparsity by leveraging a semi-supervised setup

Fungal diversity notes 929–1035: taxonomic and phylogenetic contributions on genera and species of fungi

This article is the ninth in the series of Fungal Diversity Notes, where 107 taxa distributed in three phyla, nine classes, 31 orders and 57 families are described and illustrated. Taxa described in the present study include 12 new genera, 74 new species, three new combinations, two reference specimens, a re-circumscription of the epitype, and 15 records of sexualasexual morph connections, new hosts and new geographical distributions. Twelve new genera comprise Brunneofusispora, Brunneomurispora, Liua, Lonicericola, Neoeutypella, Paratrimmatostroma, Parazalerion, Proliferophorum, Pseudoastrosphaeriellopsis, Septomelanconiella, Velebitea and Vicosamyces. Seventy-four new species are Agaricus memnonius, A. langensis, Aleurodiscus patagonicus, Amanita flavoalba, A. subtropicana, Amphisphaeria mangrovei, Baorangia major, Bartalinia kunmingensis, Brunneofusispora sinensis, Brunneomurispora lonicerae, Capronia camelliaeyunnanensis, Clavulina thindii, Coniochaeta simbalensis, Conlarium thailandense, Coprinus trigonosporus, Liua muriformis, Cyphellophora filicis, Cytospora ulmicola, Dacrymyces invisibilis, Dictyocheirospora metroxylonis, Distoseptispora thysanolaenae, Emericellopsis koreana, Galiicola baoshanensis, Hygrocybe lucida, Hypoxylon teeravasati, Hyweljonesia indica, Keissleriella caraganae, Lactarius olivaceopallidus, Lactifluus midnapurensis, Lembosia brigadeirensis, Leptosphaeria urticae, Lonicericola hyaloseptispora, Lophiotrema mucilaginosis, Marasmiellus bicoloripes, Marasmius indojasminodorus, Micropeltis phetchaburiensis, Mucor orantomantidis, Murilentithecium lonicerae, Neobambusicola brunnea, Neoeutypella baoshanensis, Neoroussoella heveae, Neosetophoma lonicerae, Ophiobolus malleolus, Parabambusicola thysanolaenae, Paratrimmatostroma kunmingensis, Parazalerion indica, Penicillium dokdoense, Peroneutypa mangrovei, Phaeosphaeria cycadis, Phanerochaete australosanguinea, Plectosphaerella kunmingensis, Plenodomus artemisiae, P. lijiangensis, Proliferophorum thailandicum, Pseudoastrosphaeriellopsis kaveriana, Pseudohelicomyces menglunicus, Pseudoplagiostoma mangiferae, Robillarda mangiferae, Roussoella elaeicola, Russula choptae, R. uttarakhandia, Septomelanconiella thailandica, Spencermartinsia acericola, Sphaerellopsis isthmospora, Thozetella lithocarpi, Trechispora echinospora, Tremellochaete atlantica, Trichoderma koreanum, T. pinicola, T. rugulosum, Velebitea chrysotexta, Vicosamyces venturisporus, Wojnowiciella kunmingensis and Zopfiella indica. Three new combinations are Baorangia rufomaculata, Lanmaoa pallidorosea and Wojnowiciella rosicola. The reference specimens of Canalisporium kenyense and Tamsiniella labiosa are designated. The epitype of Sarcopeziza sicula is re-circumscribed based on cyto- and histochemical analyses. The sexual-asexual morph connection of Plenodomus sinensis is reported from ferns and Cirsium for the first time. In addition, the new host records and country records are Amanita altipes, A. melleialba, Amarenomyces dactylidis, Chaetosphaeria panamensis, Coniella vitis, Coprinopsis kubickae, Dothiorella sarmentorum, Leptobacillium leptobactrum var. calidus, Muyocopron lithocarpi, Neoroussoella solani, Periconia cortaderiae, Phragmocamarosporium hederae, Sphaerellopsis paraphysata and Sphaeropsis eucalypticola

An exploration of biodiesel for application in aviation and automobile sector

In recent times, the greenhouse gas emission became one of the key controlling factor behind environmental pollution and its origin can be traced to the fossil fuel exhausts from the transportation sector. Apart from that, fluctuating economy is prone to destabilise the crude oil price and can directly affect the transportation industry. Therefore in search of alternative, low cost, and renewable resources, the biodiesel comes as the saviour in terms of its cost and emission friendly characteristics. Various automobile and aviation brands have already started the use of biodiesel in their engines. Numerous reports have been published citing the effects of biodiesel in the aviation and automobile sector. But the efficiency of biodiesel as an alternative fuel is attributed to several key factors such as raw material, composition, viscosity, pour point, flash point etc. Although several literature reports on the composition and structure property relationship of biodiesel are available, a comprehensive review accommodating all key factors of biodiesel efficiency is scarce. In this regard, the manuscript represents an effort towards exploring the relation of the properties of biodiesel e.g., density, viscosity etc., towards the engine performance separately for automobile and aviation industry