Abstract

With increasing computing power, capacity and internet connectivity, the ability to learn has crossed classroom boundaries in the form of online learning. The adoption rate and effectiveness of online learning continues to be an area of research. Collaborative and Cooperative (CoCo) learning involves a joint effort by students to understand and solve problems or tasks. Adoption of open source software has also increased with technology-aided collaboration and cooperation. Leveraging the practices of crowdsourced software development, we propose CoCo Learning approach for technical education using virtual labs. About 145 undergraduate engineering students from various institutes participated in our study for a period of one full semester. About 92% of the students conducted an average of 24 virtual experiments with this approach. Nearly 90% of the faculty from these institutes also confirmed that their students' understanding of courses has improved while delivering tangible software artifacts as outcomes.

Abstract

The Standard Model (SM), when extended with a leptoquark (LQ) and right-handed neutrinos, can have interesting new implications for Higgs physics. We show that sterile neutrinos can induce a boost to the down-type quark Yukawa interactions through a diagonal coupling associated with the quarks and a scalar LQ of electromagnetic charge 1/3. The relative change is moderately larger in the case of the first two generations of quarks, as they have vanishingly small Yukawa couplings in the SM. The enhancement in the couplings would also lead to a non-negligible contribution from the quark fusion process to the production of the 125 GeV Higgs scalar in the SM, though the gluon fusion always dominates. However, this may not be true for a general scalar. As an example, we consider a scenario with a SM-gauge-singlet scalar ϕ where an O (1) coupling between ϕ and the LQ is allowed. The ϕ q q Yukawa …

Abstract

Upon nitrogen starvation, Schizosaccharomyces pombe exit the mitotic cell cycle and become irreversibly committed to the completion of meiosis program. Meiotic cell divisions are coordinated with sporulation events to produce haploid spores. In the last few decades, experiments on fission yeast have revealed different molecular players involved in two meiotic cell divisions, meiosis I (MI) and meiosis II (MII). How the MI entry, MI-to-MII transition, and MII exit occur because of the dynamics of the regulatory network is not well understood. In this work, we developed a comprehensive mathematical model of the network that describes the temporal dynamics of meiotic progression. The model accounts for the phenotypes of several experimental data (single and multiple mutations). We demonstrate the control strategy involving multiple feedback loops to yield two successive division cycles. The differential regulation of …

Abstract

Patterns of DNA methylation are significantly altered in cancers. Interpreting the functional consequences of DNA methylation requires the integration of multiple forms of data. The recent advancement in the next-generation sequencing can help to decode this relationship and in biomarker discovery. In this study, we investigated the methylation patterns of papillary renal cell carcinoma (PRCC) and its relationship with the gene expression using The Cancer Genome Atlas (TCGA) multi-omics data. We found that the promoter and body of tumor suppressor genes, microRNAs and gene clusters and families, including cadherins, protocadherins, claudins and collagens, are hypermethylated in PRCC. Hypomethylated genes in PRCC are associated with the immune function. The gene expression of several novel candidate genes, including interleukin receptor IL17RE and immune checkpoint genes HHLA2, SIRPA and …

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.