arXiv subjects

S. Muthukrishnan

Publications and source records attributed to S. Muthukrishnan.

At least 19 recordsLinked to original sources

arXiv2026

Mixed Organic Cation in Chiral Two-Dimensional Organic-Inorganic Hybrid Metal Halides: An ab-initio Study of Nonlinear Optical (NLO) Properties

The mixing of organic cations represents yet another direction to explore in the field of chiral organic-inorganic hybrid metal halides (OIHMH). Here, we perform structural optimizations, electronic structures, and non-linear optical (NLO) studies using the density functional theory of two recently synthesized chiral OIHMHs, [R-MePEA][C3A]PbBr4, and [R-MePEA][C4A]PbBr4, with mixed chiral arylammonium and achiral alkylammonium cations. We find that the noncovalent weak interactions (e.g. Br...NH interactions) play an important role in the formation of these OIHMHs. Our study further indicates that the two non-centrosymmetric compounds exhibit relative wide bandgaps (~3.5 eV), strong second harmonic generation (SHG) responses (~0.5-1.5*KDP), and moderate birefringence (~0.088), indicating possible applications NLO materials. Atom response theory analysis reveals that the SHG responses are determined mainly by the occupied Br 3p non-bonding orbitals as well as by the unoccupied Pb 5p orbitals which shows the important contribution of the inorganic PbBr4 layer to the nonlinear optical properties.

cond-mat.mtrl-sci↗

arXiv2021

Bulk photovoltaic effect in BaTiO$_3$-based ferroelectric oxides: An experimental and theoretical study

The bulk photovoltaic effect exhibited by the non-centrosymmetric system gains research interest due to the observed large open-circuit voltage. The ferroelectric systems exhibiting anomalous photovoltaic effect are mostly crystallized with multiphase coexistence. Hence, the computational difficulty in building a multi-phase system restricts the detailed photovoltaic studies through phenomenological and shift current theory. In this work, ferroelectric Ba$_{1- x}$(Bi$_{0.5}$K$_{0.5}$)$_x$TiO$_3$ (BBKT) oxide is designed to crystallize in single-phase tetragonal symmetry with improved polarization characteristics, and it is found to exhibits large PV response. Both experimental and theoretical studies on BBKT samples reveal ~18% reduction in bandgap compared to the parent BaTiO$_3$. Short-circuit current measured as a function of light intensity and light polarization angle reveal linear and sinusoidal response, respectively. The observed features are in accordance with phenomenological theory. Remarkably, x = 0.125 sample displays ~8 times higher open-circuit voltage (7.39 V) than the parent compound. The enhanced PV effect is attributed to the large shift current along z-direction as evidenced from the additional charge-center shift of valence band occupied by O-2p orbital and conduction band occupied by Bi-6p orbital. Notably, the degenerate Bi-p$_z$ state at conduction band minimum in BBKT favours the large shift current response in the z-direction.

cond-mat.mtrl-sci↗

arXiv2020

CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by explicit KG reasoning, where a model starts from a user node, sequentially determines the next step, and walks towards an item node of potential interest to the user. However, this is challenging due to the huge search space, unknown destination, and sparse signals over the KG, so informative and effective guidance is needed to achieve a satisfactory recommendation quality. To this end, we propose a CoArse-to-FinE neural symbolic reasoning approach (CAFE). It first generates user profiles as coarse sketches of user behaviors, which subsequently guide a path-finding process to derive reasoning paths for recommendations as fine-grained predictions. User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user. To better exploit the user profiles, an improved path-finding algorithm called Profile-guided Path Reasoning (PPR) is also developed, which leverages an inventory of neural symbolic reasoning modules to effectively and efficiently find a batch of paths over a large-scale KG. We extensively experiment on four real-world benchmarks and observe substantial gains in the recommendation performance compared with state-of-the-art methods.

cs.IR↗

arXiv2020

Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation

Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide explanations of why the recommended items were chosen. Pure rule-based symbolic methods provide a transparent reasoning process over knowledge graph but lack generalization ability to unseen examples, while deep learning models enhance powerful feature representation ability but are hard to interpret. Moreover, direct reasoning over large-scale knowledge graph can be costly due to the huge search space of pathfinding. We approach the problem through a novel coarse-to-fine neural symbolic reasoning method called NSER. It first generates a coarse-grained explanation to capture abstract user behavioral pattern, followed by a fined-grained explanation accompanying with explicit reasoning paths and recommendations inferred from knowledge graph. We extensively experiment on four real-world datasets and observe substantial gains of recommendation performance compared with state-of-the-art methods as well as more diversified explanations in different granularity.

cs.IR↗

arXiv2019

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.

cs.IR↗

arXiv2019

Waterfall Bandits: Learning to Sell Ads Online

A popular approach to selling online advertising is by a waterfall, where a publisher makes sequential price offers to ad networks for an inventory, and chooses the winner in that order. The publisher picks the order and prices to maximize her revenue. A traditional solution is to learn the demand model and then subsequently solve the optimization problem for the given demand model. This will incur a linear regret. We design an online learning algorithm for solving this problem, which interleaves learning and optimization, and prove that this algorithm has sublinear regret. We evaluate the algorithm on both synthetic and real-world data, and show that it quickly learns high quality pricing strategies. This is the first principled study of learning a waterfall design online by sequential experimentation.

cs.LG↗

arXiv2018

Offline Evaluation of Ranking Policies with Click Models

Many web systems rank and present a list of items to users, from recommender systems to search and advertising. An important problem in practice is to evaluate new ranking policies offline and optimize them before they are deployed. We address this problem by proposing evaluation algorithms for estimating the expected number of clicks on ranked lists from historical logged data. The existing algorithms are not guaranteed to be statistically efficient in our problem because the number of recommended lists can grow exponentially with their length. To overcome this challenge, we use models of user interaction with the list of items, the so-called click models, to construct estimators that learn statistically efficiently. We analyze our estimators and prove that they are more efficient than the estimators that do not use the structure of the click model, under the assumption that the click model holds. We evaluate our estimators in a series of experiments on a real-world dataset and show that they consistently outperform prior estimators.

cs.LG↗

arXiv2018

Finding Subcube Heavy Hitters in Analytics Data Streams

Data streams typically have items of large number of dimensions. We study the fundamental heavy-hitters problem in this setting. Formally, the data stream consists of $d$-dimensional items $x_1,\ldots,x_m \in [n]^d$. A $k$-dimensional subcube $T$ is a subset of distinct coordinates $\{ T_1,\cdots,T_k \} \subseteq [d]$. A subcube heavy hitter query ${\rm Query}(T,v)$, $v \in [n]^k$, outputs YES if $f_T(v) \geq γ$ and NO if $f_T(v) < γ/4$, where $f_T$ is the ratio of number of stream items whose coordinates $T$ have joint values $v$. The all subcube heavy hitters query ${\rm AllQuery}(T)$ outputs all joint values $v$ that return YES to ${\rm Query}(T,v)$. The one dimensional version of this problem where $d=1$ was heavily studied in data stream theory, databases, networking and signal processing. The subcube heavy hitters problem is applicable in all these cases. We present a simple reservoir sampling based one-pass streaming algorithm to solve the subcube heavy hitters problem in $\tilde{O}(kd/γ)$ space. This is optimal up to poly-logarithmic factors given the established lower bound. In the worst case, this is $Θ(d^2/γ)$ which is prohibitive for large $d$, and our goal is to circumvent this quadratic bottleneck. Our main contribution is a model-based approach to the subcube heavy hitters problem. In particular, we assume that the dimensions are related to each other via the Naive Bayes model, with or without a latent dimension. Under this assumption, we present a new two-pass, $\tilde{O}(d/γ)$-space algorithm for our problem, and a fast algorithm for answering ${\rm AllQuery}(T)$ in $O(k/γ^2)$ time. Our work develops the direction of model-based data stream analysis, with much that remains to be explored.

cs.DS↗

arXiv2017

Stochastic Low-Rank Bandits

Many problems in computer vision and recommender systems involve low-rank matrices. In this work, we study the problem of finding the maximum entry of a stochastic low-rank matrix from sequential observations. At each step, a learning agent chooses pairs of row and column arms, and receives the noisy product of their latent values as a reward. The main challenge is that the latent values are unobserved. We identify a class of non-negative matrices whose maximum entry can be found statistically efficiently and propose an algorithm for finding them, which we call LowRankElim. We derive a $\DeclareMathOperator{\poly}{poly} O((K + L) \poly(d) Δ^{-1} \log n)$ upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the number of columns, $d$ is the rank of the matrix, and $Δ$ is the minimum gap. The bound depends on other problem-specific constants that clearly do not depend $K L$. To the best of our knowledge, this is the first such result in the literature.

cs.LG↗

arXiv2017

Testable Bounded Degree Graph Properties Are Random Order Streamable

We study which property testing and sublinear time algorithms can be transformed into graph streaming algorithms for random order streams. Our main result is that for bounded degree graphs, any property that is constant-query testable in the adjacency list model can be tested with constant space in a single-pass in random order streams. Our result is obtained by estimating the distribution of local neighborhoods of the vertices on a random order graph stream using constant space. We then show that our approach can also be applied to constant time approximation algorithms for bounded degree graphs in the adjacency list model: As an example, we obtain a constant-space single-pass random order streaming algorithms for approximating the size of a maximum matching with additive error $εn$ ($n$ is the number of nodes). Our result establishes for the first time that a large class of sublinear algorithms can be simulated in random order streams, while $Ω(n)$ space is needed for many graph streaming problems for adversarial orders.

cs.DS↗

arXiv2017

Heavy-Hitter Detection Entirely in the Data Plane

Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for line-rate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.

cs.NI↗

arXiv2016

The Sparse Awakens: Streaming Algorithms for Matching Size Estimation in Sparse Graphs

Estimating the size of the maximum matching is a canonical problem in graph algorithms, and one that has attracted extensive study over a range of different computational models. We present improved streaming algorithms for approximating the size of maximum matching with sparse (bounded arboricity) graphs. * Insert-Only Streams: We present a one-pass algorithm that takes O(c log^2 n) space and approximates the size of the maximum matching in graphs with arboricity c within a factor of O(c). This improves significantly on the state-of-the-art O~(cn^{2/3})-space streaming algorithms. * Dynamic Streams: Given a dynamic graph stream (i.e., inserts and deletes) of edges of an underlying c-bounded arboricity graph, we present a one-pass algorithm that uses space O~(c^{10/3}n^{2/3}) and returns an O(c)-estimator for the size of the maximum matching. This algorithm improves the state-of-the-art O~(cn^{4/5})-space algorithms, where the O~(.) notation hides logarithmic in $n$ dependencies. In contrast to the previous works, our results take more advantage of the streaming access to the input and characterize the matching size based on the ordering of the edges in the stream in addition to the degree distributions and structural properties of the sparse graphs.

cs.DS↗

arXiv2016

Graphical Model Sketch

Structured high-cardinality data arises in many domains, and poses a major challenge for both modeling and inference. Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables. The count-min (CM) sketch is a popular approach to estimating probabilities in high-cardinality data but it does not scale well beyond a few variables. In this work, we bring together the ideas of graphical models and count sketches; and propose and analyze several approaches to estimating probabilities in structured high-cardinality streams of data. The key idea of our approximations is to use the structure of a graphical model and approximately estimate its factors by "sketches", which hash high-cardinality variables using random projections. Our approximations are computationally efficient and their space complexity is independent of the cardinality of variables. Our error bounds are multiplicative and significantly improve upon those of the CM sketch, a state-of-the-art approach to estimating probabilities in streams. We evaluate our approximations on synthetic and real-world problems, and report an order of magnitude improvements over the CM sketch.

cs.DS↗

arXiv2014

The Shapley Value in Knapsack Budgeted Games

We propose the study of computing the Shapley value for a new class of cooperative games that we call budgeted games, and investigate in particular knapsack budgeted games, a version modeled after the classical knapsack problem. In these games, the "value" of a set $S$ of agents is determined only by a critical subset $T\subseteq S$ of the agents and not the entirety of $S$ due to a budget constraint that limits how large $T$ can be. We show that the Shapley value can be computed in time faster than by the naïve exponential time algorithm when there are sufficiently many agents, and also provide an algorithm that approximates the Shapley value within an additive error. For a related budgeted game associated with a greedy heuristic, we show that the Shapley value can be computed in pseudo-polynomial time. Furthermore, we generalize our proof techniques and propose what we term algorithmic representation framework that captures a broad class of cooperative games with the property of efficient computation of the Shapley value. The main idea is that the problem of determining the efficient computation can be reduced to that of finding an alternative representation of the games and an associated algorithm for computing the underlying value function with small time and space complexities in the representation size.

cs.GT↗

arXiv2014

Modeling Collaboration in Academia: A Game Theoretic Approach

In this work, we aim to understand the mechanisms driving academic collaboration. We begin by building a model for how researchers split their effort between multiple papers, and how collaboration affects the number of citations a paper receives, supported by observations from a large real-world publication and citation dataset, which we call the h-Reinvestment model. Using tools from the field of Game Theory, we study researchers' collaborative behavior over time under this model, with the premise that each researcher wants to maximize his or her academic success. We find analytically that there is a strong incentive to collaborate rather than work in isolation, and that studying collaborative behavior through a game-theoretic lens is a promising approach to help us better understand the nature and dynamics of academic collaboration.

cs.SI↗

arXiv2014

A Time and Space Efficient Algorithm for Contextual Linear Bandits

We consider a multi-armed bandit problem where payoffs are a linear function of an observed stochastic contextual variable. In the scenario where there exists a gap between optimal and suboptimal rewards, several algorithms have been proposed that achieve $O(\log T)$ regret after $T$ time steps. However, proposed methods either have a computation complexity per iteration that scales linearly with $T$ or achieve regrets that grow linearly with the number of contexts $|\myset{X}|$. We propose an $ε$-greedy type of algorithm that solves both limitations. In particular, when contexts are variables in $\reals^d$, we prove that our algorithm has a constant computation complexity per iteration of $O(poly(d))$ and can achieve a regret of $O(poly(d) \log T)$ even when $|\myset{X}| = Ω(2^d) $. In addition, unlike previous algorithms, its space complexity scales like $O(Kd^2)$ and does not grow with $T$.

cs.DS↗

arXiv2014

People Like Us: Mining Scholarly Data for Comparable Researchers

We present the problem of finding comparable researchers for any given researcher. This problem has many motivations. Firstly, know thyself. The answers of where we stand among research community and who we are most alike may not be easily found by existing evaluations of ones' research mainly based on citation counts. Secondly, there are many situations where one needs to find comparable researchers e.g., for reviewing peers, constructing programming committees or compiling teams for grants. It is often done through an ad hoc and informal basis. Utilizing the large scale scholarly data accessible on the web, we address the problem of automatically finding comparable researchers. We propose a standard to quantify the quality of research output, via the quality of publishing venues. We represent a researcher as a sequence of her publication records, and develop a framework of comparison of researchers by sequence matching. Several variations of comparisons are considered including matching by quality of publication venue and research topics, and performing prefix matching. We evaluate our methods on a large corpus and demonstrate the effectiveness of our methods through examples. In the end, we identify several promising directions for further work.

cs.DL↗

arXiv2014

Adscape: Harvesting and Analyzing Online Display Ads

Over the past decade, advertising has emerged as the primary source of revenue for many web sites and apps. In this paper we report a first-of-its-kind study that seeks to broadly understand the features, mechanisms and dynamics of display advertising on the web - i.e., the Adscape. Our study takes the perspective of users who are the targets of display ads shown on web sites. We develop a scalable crawling capability that enables us to gather the details of display ads including creatives and landing pages. Our crawling strategy is focused on maximizing the number of unique ads harvested. Of critical importance to our study is the recognition that a user's profile (i.e. browser profile and cookies) can have a significant impact on which ads are shown. We deploy our crawler over a variety of websites and profiles and this yields over 175K distinct display ads. We find that while targeting is widely used, there remain many instances in which delivered ads do not depend on user profile; further, ads vary more over user profiles than over websites. We also assess the population of advertisers seen and identify over 3.7K distinct entities from a variety of business segments. Finally, we find that when targeting is used, the specific types of ads delivered generally correspond with the details of user profiles, and also on users' patterns of visit.

cs.CY↗