Abstract

The homodimeric catabolite activator protein (CAP) regulates the transcription of several bacterial genes based on the cellular concentration of cyclic adenosine monophosphate (cAMP). The binding of cAMP to CAP triggers allosteric communication between the cAMP binding domains (CBD) and DNA binding domains (DBD) of CAP, which entails repositioning of DNA recognition helices (F-helices) in the DBD to dock favorably to the target DNA. Despite considerable progress, much remains to be understood about the mechanistic details of DNA recognition by CAP and about the map of allosteric pathways involved in CAP-mediated gene transcription. The present study uses molecular dynamics and umbrella sampling simulations to investigate the mechanism of cAMP- and DNA-induced changes in the conformation and energetics of F-helices observed during the allosteric regulation of CAP by cAMP and the …

Abstract

Aqueous urea stabilizes the unfolded states of protein due to their ability to solvate both hydrophilic and hydrophobic residues favorably. The nature of interactions that stabilize different types of amino acid side chains in their solvent exposed state is still not understood. To gain insights into the molecular level details of urea interactions with proteins in their unfolded states, we have performed atomistic molecular dynamics simulations and free energy calculations using the thermodynamic integration method on model systems representing side chains of all amino acids in different solvent environments (water and varying concentrations of aqueous urea). A systematic analysis of structural, energetic and dynamic parameters has been done to understand the detailed atomistic mechanism. The main aim of the current study is to unravel the nature of urea-amino acid interactions by emphasizing on the chemical nature …

Abstract

In many real-life classification problems, we may not get exact class labels for training samples. One such example is bandit feedback in multiclass classification. In this setting, we only get to know whether our predicted label is correct or not. Due to which, we are left in uncertainty about the actual class label when we predict the wrong class. This paper proposes exact passive-aggressive online algorithms for multiclass classification under bandit feedback (EPABF). The proposed approach uses an exploration-exploitation strategy to guess the class label in every trial. To update the weights, we solve a quadratic optimization problem under multiple class separability constraints and find the exact solution. We do this by finding active constraints using the KKT conditions of the optimization problem. These constraints form a support set that determines the classes for which the weight vector needs to be updated. We propose three different variants of the weight update rule, which vary based on the aggressiveness to correct the mistake. These are called EPABF, EPABF-I, and EPABF-II. We also provide mistake bounds for the proposed EPABF, EPABF-I, and EPABF-II. Experiments demonstrated that our proposed algorithms perform better than other bandit feedback-based approaches and comparably to the full information approaches.

Abstract

Density functional theory (DFT) based calculations are carried out to study effect of molecular charge on both the binding energy as well as chemical Raman enhancement of deprotonated 4-Mercaptopyridine (ligand) bound to the semiconducting nanocluster, Zn3Se3 and metal substituted nanoclusters, Zn2MSe3 (M: Ag, Cu). Change of molecular charge from 0 to −1 increases binding energy of the ligand for Zn3Se3 cluster and decreases Raman activity of vibrational modes. On the other hand, the ligand bound to the metal substituted nanoclusters, Zn2MSe3 shows minimal decrease in binding energy and a noteworthy enhancement in Raman activity of the vibrational modes, on varying the molecular charge from 0 to −1. Static Raman activities of vibrational modes are analyzed using change of molecular polarizability components with normal coordinates of the modes. An APT (Atomic Polar Tensor) charge based descriptor is used for characterization of the chemical Raman enhancement. The descriptor measures substrate induced charge concentration per unit displacement of an atom involved in a mode of vibration. We also show that significantly enhanced intensity of vibrational modes is associated with atoms of relatively greater magnitudes of the APT charge based descriptor

Abstract

Information acquisition through crowdsourcing with mobile agents is a popular way to collect data, especially in the context of smart cities where the deployment of dedicated data collectors is expensive and ineffective. It requires trustworthy and effective mechanisms to guarantee that the collected data are accurately acquired and reported. Trustworthy and transparent frameworks can be implemented via smart contracts on blockchain to enable robust information elicitation mechanisms. In this work we develop Orthos, a blockchain-based trustworthy framework for spontaneous location-based information aggregation queries without assuming any prior knowledge about them. We employ game-theoretic mechanisms to incentivize agents to report truthfully and ensure that the information is collected at the desired location while ensuring the privacy of the agents. We identify six necessary characteristics for information aggregation mechanisms to be applicable in spontaneous location-based settings. Orthos implements the function of robust peer truth serum for crowdsourcing (RPTSC), the state-of-the-art information aggregation mechanism using smart contracts. Additionally, as location information is exogenous to RPTSC, we design the Proof-of-Location protocol to ensure that agents gather the data at the desired locations. We examine the performance of Orthos on Rinkeby Ethereum testnet and conduct experiments with live audience.

Abstract

Demand response is a crucial tool to maintain the stability of the smart grids. With the upcoming research trends in the area of electricity markets, it has become a possibility to design a dynamic pricing system, and consumers are made aware of what they are going to pay. Though the dynamic pricing system (pricing based on the total demand a distributor company is facing) seems to be one possible solution, the current dynamic pricing approaches are either too complex for a consumer to understand or are too naive leading to inefficiencies in the system (either consumer side or distributor side). Due to these limitations, the recent literature is focusing on the approach to provide incentives to the consumers to reduce the electricity, especially in peak hours. For each round, the goal is to select a subset of consumers to whom the distributor should offer incentives so as to minimize the loss which comprises of cost of buying the electricity from the market, uncertainties at consumer end, and cost incurred to the consumers to reduce the electricity which is a private information to the consumers. Due to the uncertainties in the loss function (arising from renewable energy resources as well as consumption needs), traditional auction theory-based incentives face manipulation challenges. Towards this, we propose a novel combinatorial multi-armed bandit (MAB) algorithm, which we refer to as GLS-MAB to learn the uncertainties along with an auction to elicit true costs incurred by the consumers. We prove that our mechanism is regret optimal and is incentive compatible. We further demonstrate efficacy of our algorithms via simulations.

Abstract

In classification models fairness can be ensured by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimization frameworks to obtain fair classifiers. Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging. We propose a neural network-based framework, FNNC, to achieve fairness while maintaining high accuracy in classification. The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero. The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better. The experimental evidence supplements our theoretical guarantees. In summary, we have an automated solution to achieve fairness in classification, which is easily extendable to many fairness constraints.

Abstract

The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. In our work, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, a guessing task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketchstroke sequences as visual data. Sketch-QA involves asking a fixed question (“What object is being drawn?”) and gathering open-ended guess-words from human guessers. We analyze the resulting dataset and present many interesting findings therein. Tomimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn object sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor.We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. Wealso conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.

Abstract

As the communications among the vehicles, theRoad-Side Units(RSU)and the Edge Servers(ES)take placevia wireless communication and the Internet, an adversary maytake the opportunity to tamper with the data communicatedamong various entities in an Internet of Vehicles (IoV) envi-ronment. Therefore, it demands secure communication amongthe involved entities in an IoV-based Intelligent TransportationSystem (ITS) deployment. In this work, we design a newblockchain-enabled certificate-based authentication scheme forvehicle accident detection and notification in ITS, called BCAS-VADN. In BCAS-VADN, through the authentication process, eachvehicle can securely notify accident related transactions to itsnearby Cluster Head(CH), if an accident is detected on roadseither by its own or neighbor vehicle(s). TheCHthen securelysends the transactions received from the vehicles to itsRSUand subsequently, these transactions are also received secretlyby theESs. TheESis responsible for preparing partial blockcontaining transactions and Merkle tree root, and a digital signa-ture on those information, and then forwarding to its associatedCloud Server(CS)in the Blockchain Center(BC)for completeblock creation, verification and addition of the block using thedesigned consensus process. Due to blockchain technology usage,it is shown that BCAS-VADN is not only secure against variouspotential attacks, but also maintains transparency, immutabilityand decentralization of the information. Furthermore, a compre-hensive comparative analysis reveals that BCAS-VADN achievesbetter security and more functionality attributes, and has lowcommunication and computational overheads as compared toother competitive authentication schemes in IoV. In addition,the practical demonstration using the blockchain technology hasbeen also provided.

Abstract

Agriculture is a vital area for the sustenance ofmankind engulfing manufacturing, security, traceability, andsustainable resource management. With the resources recedingexpeditiously, it is of utmost significance to innovate techniquesthat help in the subsistence of agriculture. The growth of Internetof Things (IoT) and Blockchain technology as two rapidlyemerging fields can ameliorate the state of food chain today. Thispaper provides a rigorous literature review to inspect the state-of-the-art development of the schemes that provide informationsecurity using blockchain technology. After identifying the corerequirements in smart agriculture, a generalized blockchain-based security architecture has been proposed. A detailed costanalysis has been conducted on the studied schemes. A meticu-lous comparative analysis uncovered the drawbacks in existingresearch. Furthermore, detailed analysis of the literature has alsorevealed the security goals towards which the research has beendirected and helped to identify new avenues for future researchusing artificial intelligence.