Non-Gaussianity and direction dependent systematics in HST key project data

Two new statistics, namely $\Delta_\chi^2$ and $\Delta_\chi$, based on extreme value theory, were derived in \cite{gupta08,gupta10}. We use these statistics to study direction dependence in the HST key project data which provides the most precise measurement of the Hubble constant. We also study the non-Gaussianity in this data set using these statistics. Our results for $\Delta_\chi^2$ show that the significance of direction dependent systematics is restricted to well below one $\sigma$ confidence limit, however, presence of non-Gaussian features is subtle. On the other hand $\Delta_\chi$ statistic, which is more sensitive to direction dependence, shows direction dependence systematics to be at slightly higher confidence level, and the presence of non-Gaussian features at a level similar to the $\Delta_\chi^2$ statistic.Comment: 6 pages, 4 figures; accepted for publication in MNRA

Ultra low power adiabatic logic using diode connected DC biased PFAL logic

With the continuous scaling down of technology in the field of integrated circuit design, low power dissipation has become one of the primary focuses of the research. With the increasing demand for low power devices, adiabatic logic gates prove to be an effective solution. This paper briefs on different adiabatic logic families such as ECRL (Efficient Charge Recovery Logic), 2N-2N2P and PFAL (Positive Feedback Adiabatic Logic), and presents a new proposed circuit based on the PFAL logic circuit. The aim of this paper is to simulate various logic gates using PFAL logic circuits and with the proposed logic circuit, and hence to compare the effectiveness in terms of average power dissipation and delay at different frequencies. This paper further presents implementation of C17 and C432 benchmark circuits, using the proposed logic circuit and the conventional PFAL logic circuit to compare effectiveness of the proposed logic circuit in terms of average power dissipation at different frequencies. All simulations are carried out by using HSPICE Simulator at 65 nm technology at different frequency ranges. Finally, average power dissipation characteristics are plotted with the help of graphs, and comparisons are made between PFAL logic family and new proposed PFAL logic family

Cosmology with decaying tachyon matter

We investigate the case of a homogeneous tachyon field coupled to gravity in a spatially flat Friedman-Robertson-Walker spacetime. Assuming the field evolution to be exponentially decaying with time we solve the field equations and show that, under certain conditions, the scale factor represents an accelerating universe, following a phase of decelerated expansion. We make use of a model of dark energy (with p=-\rho) and dark matter (p=0) where a single scalar field (tachyon) governs the dynamics of both the dark components. We show that this model fits the current supernova data as well as the canonical \LambdaCDM model. We give the bounds on the parameters allowed by the current data.Comment: 14 pages, 6 figures, v2, Discussions and references addede

Evaluation of surface roughness of enamel after various bonding and clean-up procedures on enamel bonded with three different bonding agents : an in-vitro study

The purpose of this study was to analyze and compare the enamel surface roughness before bonding and after debonding, to find correlation between the adhesive remnant index and its effect on enamel surface roughness and to evaluate which clean-up method is most efficient to provide a smoother enamel surface. 135 premolars were divided into 3 groups containing 45 premolars in each group. Group I was bonded by using moisture insensitive primer, Group II by using conventional orthodontic adhesive and Group III by using self-etching primer. Each group was divided into 3 sub-groups on the basis of type of clean-up method applied i,e scaling followed by polishing, tungsten carbide bur and Sof-Lex disc. Enamel surface roughness was measured and compared before bonding and after clean-up. Evaluation of pre bonding and post clean-up enamel surface roughness (Ra value) with the t test showed that Post clean-up Ra values were greater than Pre bonding Ra values in all the groups except in teeth bonded with self-etching primer cleaned with Sof-Lex disc. Reliability of ARI score taken at different time interval tested with Kruskal Wallis test suggested that all the readings were reliable. No clean-up procedure was able to restore the enamel to its original smoothness. Self-etching primer and Sof-Lex disc clean-up method combination restored the enamel surface roughness (Ra value) closest to its pre-treatment value

Ultra low power high speed domino logic circuit by using FinFET technology

Scaling of the MOSFET face greater challenge by extreme power density due to leakage current in ultra deep sub-micron (UDSM) technology. To overcome from this situation double gate device like FinFET is used which has excellent control over the thin silicon fins with two electrically coupled gate, which mitigate shorter channel effect and exponentially reduces the leakage current. In this research paper utilize the property of FinFET in domino logic, for high speed operation and reduction of power consumption in wide fan-in OR gate. Proposed circuit is simulated in FinFET technology by BISM4 model using HSPICE at 32nm process technology at 250C with CL=1pF at 100MHz frequency. For 8 and 16 input OR gate we save average power 11.5%,11.39% in SFLD, 22.97%, 18.12% in HSD, 30.90%, 34.57% in CKD in SG mode and for LP mode 11.26%, 15.78% in SFLD, 19.74%, 17.94% in HSD, 45.23%, 34.69% in CKD respectivel

METHYL-CPG BINDING PROTEINS MEDIATE OCTOPAMINERGIC REGULATION OF COMPLEX BEHAVIORAL TRAITS

An organism’s survivability in the natural world is contingent to its ability to respond rapidly and appropriately to various cues and challenges in its physical and social environment. The dynamicity of various environmental and social factors necessitates plasticity in morphological, physiological and behavioral systems – both at the level of an individual organism and that of a species. For more than century, natural selection of existing genetic variation in populations has helped us understand such plasticity across generations. However, recent years have seen a re-emergence of somewhat contentious quasi-Lamarckian framework with which organisms can reliably transmit acquired traits to subsequent generations in response to changes in external conditions. Whether or not it can be categorized as such, a stable transgenerational transmission of acquired alterations in epigenetic code, including methylation patterns and small RNA molecules, associated with behavioral and physiological, and I use the term here loosely, ‘adaptations’ for up to three generations has indeed been demonstrated in a number of species. The focus on methyl-binding proteins in this dissertation is guided by a motivation to advance our understanding of such epigenetic systems in one of the most extensively used model systems in biological and biomedical research – Drosophila. In contrast to the vast body of literature on the genetics, physiology, ecology, and neurobiology of Drosophila, methylation and methylation-associated processes represent one of the few relatively unexplored territories in this system. This certainly hasn’t been for the lack of trying (see section 1.8). Consistent with their role in other species, Drosophila MBD proteins have been implicated in dynamic regulation of chromatin architecture and spatiotemporal regulation of gene expression. However, methylationdependence of their functions and their contribution to the overall organismal behavior remains equivocal. In this dissertation, I explore the role of the conserved methyl-CpG binding (MBD) proteins in the regulation of octopaminergic (OA) systems that are associated with a number of critical behaviors such as aggression, courtship, feeding, locomotion, sleep, and learning and memory. In chapter II, I, along with my colleagues, demonstrate functional conservation of human and Drosophila MBD-containing proteins. We show – (a) that a well-characterized human protein – MeCP2 – can regulate amine neuron output in Drosophila through MBD domain, (b) that endogenous MBD proteins in Drosophila regulate OA sleep circuitry in a manner similar to human MeCP2, and (c) that human and Drosophila MBD proteins may share a select few genomic binding sites on larval polytene chromosomes. In chapter III, we describe a novel function of these chromatin modifiers in the regulation of social behaviors, including aggression and courtship. Returning to the issue of methylation, we demonstrate an interaction effect between induced-DNA hypermethylation and MBD-function in context of aggression and intermale courtship. Species – and sex–specific behaviors such as courtship and aggression rely on an organism’s ability to reliably discriminate between species, sexes and social hierarchy of interacting partners, and adjust to the dynamic shifts in sensory and behavioral feedback cues. At the level of an individual organism, such behavioral flexibility is often achieved by modulating the strength and directionality of neural network outputs which endows a limited biological circuit the capacity to generate variable outputs and adds richness to the repertoire of behaviors it can display (Marder, 2012). The role of MBD proteins discussed in this dissertation highlights a mechanism that couples chromatin remodeling and OA neuromodulation in context-dependent decision-making processes

Improving single and multi-agent deep reinforcement learning methods

Reinforcement Learning (RL) is a framework where an agent learns to make decisions using data-driven feedback from interactions with the environment in the form of rewards or penalties for actions. Deep RL integrates deep learning with RL, harnessing the power of deep neural networks to process complex, high-dimensional data. Using the framework of deep RL, our machine learning research community has achieved tremendous progress in enabling machines to make sequential decisions over long time horizons. These advances include attaining super-human performance in Atari [Mnih et al., 2015], mastering the game of Go, beating the human world champion [Silver et al., 2017], providing robust recommendation systems [GomezUribe and Hunt, 2015, Singh et al., 2021]. This thesis focuses on identifying some key challenges that impede the learning of RL agents within their specific environments and improving the methods leading to better performance of agents, improved sample efficiency, and generalizability of learned agent policies. In Part I of the thesis, we focus on exploration in single-agent RL settings where an agent must interact with a complex environment to pursue a goal. An agent that fails to explore its environment is unlikely to achieve high performance, as it will miss critical rewards and, as a result, cannot learn optimal behavior. One key challenge arises from sparse reward environments where the agent only receives feedback once the task is completed, making exploration more challenging. We propose a novel method that enables semantic exploration, resulting in higher sample efficiency and performance on sparse reward tasks. In Part II of the thesis, we focus on cooperative Multi-Agent Reinforcement Learning (MARL), an extension of the usual RL setting, where we consider multiple agents interacting in the same environment toward a shared task. In multi-agent tasks requiring significant coordination among agents with strict penalties for miscoordination, state-of-the-art MARL methods often fail to learn useful behaviors as agents get stuck in a sub-optimal equilibrium. Another challenge is exploration in the joint action space of all agents, which grows exponentially with the number of agents. To address these challenges, we propose innovative approaches like universal value exploration and scalable role-based learning. These methods facilitate improved coordination among agents, faster exploration, and enhance the agents’ ability to adapt to new environments and tasks, showcasing zero-shot generalization capabilities and resulting in higher sample efficiency. Lastly, we investigate independent policybased methods in cooperative MARL, where each agent considers other agents as part of the environment. We show that such methods can perform better than state-of-the-art joint learning approaches on a popular multi-agent benchmark. In summary, the contributions of this thesis significantly improve the stateof-the-art in deep (multi agent) reinforcement learning. The agents developed in his thesis can explore their environments efficiently to improve sample efficiency, learn tasks that require significant multi-agent coordination, and enable zero-shot generalization across various tasks