Miracle-dz/UniFAR-FTUs huggingface dataset:
Quadratic coupling between a classical nanomechanical oscillator and a single spin

Though the motions of macroscopic objects must ultimately be governed by quantum mechanics, the distinctive features of quantum mechanics can be hidden or washed out by thermal excitations and coupling to the environment. For the work of this thesis, we tried to develop a hybrid system consisting a classical and a quantum component, which can be used to probe the quantum nature of both these components. This hybrid system quadratically coupled a nanomechanical oscillator (NMO) with a single spin in presence of a uniform external magnetic field. The NMO was fabricated out of single-layer graphene, grown using Chemical Vapor Deposition (CVD) and patterned using various lithography and etching techniques. The NMO was driven electrically and detected optically. The NMO's resonant frequencies, and their stabilities were studied. The spin originated from a nitrogen vacancy (NV) center in a diamond nanocrystal which is positioned on the NMO. In presence of an external magnetic field, we show that the NV centers are excellent $\theta^{2}$ sensors. Their sensitivity is shown to increase much faster than linearly with the external magnetic field and diverges as the external field approaches an internally-defined limit. Both these components of the hybrid system get coupled by physical placement of NV-containing diamond nanocrystals on top of NMO undergoing torsional mode of oscillation, in presence of an external magnetic field. The capability of the NV centers to detect the quadratic behavior of the oscillation angle of the NMO with excellent sensitivity, ensures quantum non-demolition (QND) measurement of both components of the hybrid system. This enables a bridge between the quantum and classical worlds for a simple readout of the NV center spin and observation of the discrete states of the NMO. This system could become the building block for a wide range of quantum nanomechanical devices.
Light-enabled identification of the neuronal substrates of alkylphenol anesthetics

General anesthetics are a critical class of drugs in modern medicine; however, the precise mechanisms by which they cause unconsciousness and unwanted side effects are largely undefined. In order to understand pharmacologic mechanisms of anesthetic action, drug interactions with macromolecular substrates and the subsequent functional consequences must be characterized. Analogs of general anesthetics that function as photolabels have been developed to assist in the identification of molecular targets. One such photolabel, meta-azi-propofol (AziPm), is an analog of the clinically used alkylphenol anesthetic propofol. In this work, AziPm is employed in a variety of experiments that aim to identify molecular substrates of propofol. Two proteins identified as propofol targets are more thoroughly examined: (1) the sirtuin deacetylase SIRT2 and (2) the mitochondrial voltage-dependent anion channel (VDAC). The binding sites of propofol on these proteins, and the in vitro functional consequences of propofol binding, are determined. Also described are the molecular interactions of VDAC with a separate ligand, cholesterol, which shares a binding site with propofol. In addition to molecular studies, a novel in vivo photolabeling technique, called optoanesthesia, that utilizes AziPm is introduced, and the behavioral phenotype induced by optoanesthesia in Xenopus laevis tadpoles is characterized. Finally, optoanesthesia is demonstrated with other ligands, including a photoactive analog of an anthracene anesthetic, and mechanistic insight into the pharmacology of this anthracene is revealed. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Pharmacology First Advisor Roderic G. Eckenhoff Subject Categories Pharmacology This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/1496 LIGHT-ENABLED IDENTIFICATION OF THE NEURONAL SUBSTRATES OF ALKYLPHENOL ANESTHETICS Brian Patrick Weiser A DISSERTATION in Pharmacology Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy 2014 Supervisor of Dissertation: _________________________ Roderic G. Eckenhoff, MD, Austin Lamont Professor of Anesthesiology & Critical Care Graduate Group Chairperson: _________________________ Julie A. Blendy, PhD, Professor of Pharmacology Dissertation Committee: (chair) Harry Ischiropoulos, PhD, Research Professor of Pediatrics Max B. Kelz, MD PhD, Associate Professor of Anesthesiology & Critical Care Ivan J. Dmochowski, PhD, Associate Professor of Chemistry A. Joshua Wand, PhD, Benjamin Rush Professor of Biochemistry
Density functional study of roles of porphyrin ring in electronic structures of heme

Heme is a prosthetic group that consists of an iron ion contained in the center of a porphyrin, and a widely distributed cofactor in biology. It serves electron transfer and enzyme catalysis. There are many types of heme, which have several heme side chains such as propionyl, vinyl, and hydroxyfarnesylethyl groups. The differences in heme side chains yield many important functions. In this study, we have investigated the roles of porphyrin ring: heme porphyrin ring distortion, heme peripheral substitution, and protonation of propionyl group of heme, by using density functional theory with the polarizable continuum model. The effects of porphyrin ring of heme were discussed from the viewpoint of the charge density difference in the redox reaction and the redox potential. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009
Rectification of the Current in α-Hemolysin Pore Depends on the Cation Type: The Alkali Series Probed by Molecular Dynamics Simulations and Experiments

A striking feature of the α-hemolysin channel—a prime candidate for biotechnological applications—is the dependence of its ionic conductance on the magnitude and direction of the applied bias. Through a combination of lipid bilayer single-channel recording and molecular dynamics (MD) simulations, we characterized the current−voltage relationship of α-hemolysin for all alkali chloride salts at neutral pH. The rectification of the ionic current was found to depend on the type of cations and increase from Li+ to Cs+. Analysis of the MD trajectories yielded a simple quantitative model that related the ionic current to the electrostatic potential, the concentration and effective mobility of ions in the channel. MD simulations reveal that the major contribution to the current asymmetry and rectification properties originates from the cationic contribution to the current that is significantly reduced in a cationic-dependent way when the membrane polarity is reversed. The variation of chloride current was found t.
Absorption and translocation of nitrogen in cucumber (Cucumis sativus L.) plants using the 15N tracer technique

Abstract Cucumber (Cucumis sativus L.) plants were grown with hydroponics in a greenhouse and the absorption and translocation of nitrogen in the plants were investigated after the first fruit harvest using 15N-labeled nitrogen. The 15N-labeled nutrient solution, which contained 13 mmol L−1 nitrate, was supplied for 7 days from first fruit harvest on 21 September, 2005. Following 7 days of 15N treatment, these plants received the same composition of non-labeled nitrogen for 16 days (chase period). Commercial-sized fruits, approximately 20 cm in length, were harvested every day. The whole plants were harvested three times at the beginning (21 September) and at the end (28 September) of the 15N treatment period, and at the end of the chase period on 14 October. The average nitrogen absorption rate was 196 mg plant−1 per day during the 15N treatment period and 181 mg plant−1 per day during the chase period. The labeled nitrogen was detected in the fruits within 1 day of the 15N treatment, and the percentage of nitrogen from labeled nitrogen in the harvested fruits increased up to approximately 50% at the end of the 15N treatment period. The percentage of labeled nitrogen in the fruits quickly decreased after changing from 15N solution to the non-labeled conditions during the chase period. At the end of the 15N treatment period on 28 September, the percentage distribution of labeled nitrogen was 46% in the leaves, 24% in the harvested fruits, 13% in the stems, 9% in the roots and 9% in the small fruits. After 16 days of chase period, the distribution of labeled nitrogen decreased in the leaves (33%), stems (6.5%), roots (3.6%) and small fruits (1%), but 56% of labeled nitrogen was distributed in the harvested fruits. Based on the result obtained, the nitrate absorbed from the medium is rapidly transported to young fruits as well as to leaves and stems. The nitrogen once assimilated or stored in the leaves, stems and roots is gradually redistributed to the fruits for supporting their rapid growth. To maximize the yield of cucumber fruits, it is necessary to supply sufficient levels of nitrogen continuously after the first harvest.
The influence of diaphragm flexibility on the performance of an earth pressure cell

A theoretical analysis of the effect of a flexible diaphragm on the calibration of a pressure cell subjected to one dimensional compression is presented together with the results predicted for a range of cell thickness to diameter ratios, in soils of different moduli. The likely over or under registration of a cell in a given soil can be readily obtained and, more important, the probable sensitivity of the calibration of the cell to the soil modulus is easily seen. The results are restricted to a cell having an active diameter to overall diameter ratio of 0.6, a fairly typical value.
Improved Drude-Lorentz dielectric function for gold nanospheres.

There is an ever-increasing body of research in applying the Localized Surface Plasmon Resonance (LSPR) of gold nanospheres for applications in the visual part of the electromagnetic spectrum. An important aspect of this work is the accurate representation of the dielectric function for nanospheres with diameters less than the mean free path of the conduction electrons in the bulk material. This paper describes the development of a modified Drude-Lorentz dielectric functions for bulk gold, and for nanosphere. The models are validated against previously published experimental work and are shown to be valid for spheres of diameters of greater than 10 nm.
Quantitative characterization of chemorepulsive alignment-induced interactions in active emulsions.

The constituent elements of active matter in nature often communicate with their counterparts or the environment by chemical signaling which is central to many biological processes. Examples range from bacteria or sperm that bias their motion in response to an external chemical gradient, to collective cell migration in response to a self-generated gradient. Here, in a purely physicochemical system based on self-propelling oil droplets, we report a novel mechanism of dynamical arrest in active emulsions: swimmers are caged between each other's trails of secreted chemicals. We explore this mechanism quantitatively both on the scale of individual agent-trail collisions as well as on the collective scale where the transition to caging happens as a result of autochemotactic interactions.
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics

Predicting the dynamics of neural network parameters during training is one of the key challenges in building a theoretical foundation for deep learning. A central obstacle is that the motion of a network in high-dimensional parameter space undergoes discrete finite steps along complex stochastic gradients derived from real-world datasets. We circumvent this obstacle through a unifying theoretical framework based on intrinsic symmetries embedded in a network's architecture that are present for any dataset. We show that any such symmetry imposes stringent geometric constraints on gradients and Hessians, leading to an associated conservation law in the continuous-time limit of stochastic gradient descent (SGD), akin to Noether's theorem in physics. We further show that finite learning rates used in practice can actually break these symmetry induced conservation laws. We apply tools from finite difference methods to derive modified gradient flow, a differential equation that better approximates the numerical trajectory taken by SGD at finite learning rates. We combine modified gradient flow with our framework of symmetries to derive exact integral expressions for the dynamics of certain parameter combinations. We empirically validate our analytic predictions for learning dynamics on VGG-16 trained on Tiny ImageNet. Overall, by exploiting symmetry, our work demonstrates that we can analytically describe the learning dynamics of various parameter combinations at finite learning rates and batch sizes for state of the art architectures trained on any dataset.
Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction

Relation extraction (RE) aims to predict a relation between a subject and an object in a sentence, while knowledge graph link prediction (KGLP) aims to predict a set of objects, O, given a subject and a relation from a knowledge graph. These two problems are closely related as their respective objectives are intertwined: given a sentence containing a subject and an object o, a RE model predicts a relation that can then be used by a KGLP model together with the subject, to predict a set of objects O. Thus, we expect object o to be in set O. In this paper, we leverage this insight by proposing a multi-task learning approach that improves the performance of RE models by jointly training on RE and KGLP tasks. We illustrate the generality of our approach by applying it on several existing RE models and empirically demonstrate how it helps them achieve consistent performance gains.
Silver Acetate–Triphenylphosphine Complexes. Acetatobis(triphenylphosphine)silver(I) and its Sesquihydrate, and Bis[acetato(triphenylphosphine)silver(I)] Hydrate and its Hemihydrate

The Ag atom in both independent molecules of acetatobis(triphenylphosphine)silver(I), [Ag(C2H3O2)(C18H15P)2], is bonded to the triphenylphospine and chelating acetato ligands in a distorted tetrahedral environment. Acetatobis(triphenylphosphine)silver(I) sesquihydrate, [Ag(C2H3O2)(C18H15P)2]. 1.5H2O, also crystallizes as two independent molecules whose Ag atoms adopt a similar geometry. One [Ag(C2H3O2)(C18H15P)2] molecule is linked through a water molecule to another complex molecule, which in turn is linked to the symmetry-related first molecule through the other two water molecules, one of which is disordered affording a chain structure parallel to the b axis. Bis(μ-acetato)-O,O':O;O:O-bis[(triphenylphosphine)silver(I)] hydrate, [Ag2(C2H3O2)2(C18H15P)2]. H2O comprises two [Ag(C2H3O2)(C18H15P)] moieties; the acetato ligand behaves as a bidentate chelating anion in one complex moiety but as a monodentate anion in the other. The two moieties link through the singly bonded ester O atom into a dinuclear entity. Neighboring dinuclear entities are linked by hydrogen bonds involving their doubly bonded carboxyl ends with the disordered water molecule into a zigzag chain along the c axis. In bis(μ-acetato-O:O)bis[(triphenylphosphine)silver(I)] hemihydrate, [Ag2(C2H3O2)2(C18H15P)2]. 0.5H2O, the two Ag—Ocarboxyl distances are long enough for the geometry of the Ag atoms to be regarded as being essentially trigonal planar in the two complex units. The dinuclear molecule uses its doubly bonded O atoms to link with the disordered water molecule to afford a hydrogen-bonded tetranuclear entity.
Streaming Algorithms for Stochastic Multi-armed Bandits

We study the Stochastic Multi-armed Bandit problem under bounded arm-memory. In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory at any time, is bounded. The decision-maker can only pull arms that are present in the memory. We address the problem from the perspective of two standard objectives: 1) regret minimization, and 2) best-arm identification. For regret minimization, we settle an important open question by showing an almost tight hardness. We show {\Omega}(T^{2/3}) cumulative regret in expectation for arm-memory size of (n-1), where n is the number of arms. For best-arm identification, we study two algorithms. First, we present an O(r) arm-memory r-round adaptive streaming algorithm to find an {\epsilon}-best arm. In r-round adaptive streaming algorithm for best-arm identification, the arm pulls in each round are decided based on the observed outcomes in the earlier rounds. The best-arm is the output at the end of r rounds. The upper bound on the sample complexity of our algorithm matches with the lower bound for any r-round adaptive streaming algorithm. Secondly, we present a heuristic to find the {\epsilon}-best arm with optimal sample complexity, by storing only one extra arm in the memory.
Iterative Collision Resolution for Slotted ALOHA With NOMA for Heterogeneous Devices

In this paper, the problem of using uncoordinated multiple access (UMA) to serve a massive amount of heterogeneous users is investigated. Leveraging the heterogeneity, we propose a novel UMA protocol, called iterative collision resolution for slotted ALOHA (IRSA) with non-orthogonal multiple access (NOMA), to improve the conventional IRSA. In addition to the inter-slot successive interference cancellation (SIC) technique used in existing IRSA-based schemes, the proposed protocol further employs the intra-slot SIC technique that enables collision resolution for certain configurations of collided packets. A novel multi-dimensional density evolution is then proposed to analyze and to optimize the proposed protocol. Simulation results show that the proposed IRSA with NOMA protocol can efficiently exploit the heterogeneity among users and the multi-dimensional density evolution can accurately predict the throughput performance. Last, an extension of the proposed IRSA with NOMA protocol to the frame-asynchronous setting is investigated, where a boundary effect similar to that in spatially-coupled low-density parity check codes can be observed to bootstrap the decoding process.
On the theory and applications of mechanism design and coalitional games in electricity markets

Although the specific structures of electricity markets are diverse around the world, they were all conceived on the premise of predictable, controllable generation with nonnegligible marginal costs. Recent changes, specifically, the increasing renewable integration, have challenged such assumptions. In light of this shift, this thesis intends to devise new frameworks and advance our understanding of the future markets. The first part focuses on mechanism design when the model fully reflects the physics of the grid and the participants. We consider a market that involves continuous goods, general nonconvex constraints, and second stage costs. We then design the payments and conditions under which coalitions cannot influence the outcome. Under the incentive-compatible VCG mechanism, we prove that coalition-proof outcomes are achieved if bids are convex and constraints are polymatroids. By relaxing incentive-compatibility, we investigate core-selecting mechanisms that are coalition-proof without conditions. We show that they generalize the economic rationale of the LMP mechanism, and can approximate truthfulness without the price-taking assumption. Finally, they are budget-balanced. The second part coordinates regional markets to exploit the geographic diversification of renewables. In Europe, reserves remain an exclusive responsibility of regional operators. This limited coordination and the sequential structure hinder the utilization of generation and transmission. To promote reserve exchange, a preemptive model can optimally withdraw inter-area transmission capacity from day-ahead energy for reserves. This bilevel program however does not suggest costs that guarantee coordination. We formulate a new preemptive model that allows us to obtain stable benefits immune to deviations. Our proposal, least-core benefits, achieves minimal stability violation with a tractable computation.
Plasma-photon interaction in curved spacetime: Formalism and quasibound states around nonspinning black holes

We investigate the linear dynamics of an electromagnetic field propagating in curved spacetime in the presence of plasma. The dynamical equations are generically more involved and richer than the effective Proca equation adopted as a model in previous work. We discuss the general equations and focus on the case of a cold plasma in the background of a spherically-symmetric black hole, showing that the system admits plasma-driven, quasibound electromagnetic states that are prone to become superradiantly unstable when the black hole rotates. The quasibound states are different from those of the Proca equation and have some similarities with the case of a massive scalar field, suggesting that the linear instability can be strongly suppressed compared to previous estimates. Our framework provides the first step towards a full understanding of the plasma-photon interactions around astrophysical black holes.
Positional Encoding as Spatial Inductive Bias in GANs

SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translationinvariant convolutional generator could capture the global structure with just a spatially i.i. d. input. In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators. Such positional encoding is indispensable for generating images with high fidelity. The same phenomenon is observed in other generative architectures such as DCGAN and PGGAN. We further show that zero padding leads to an unbalanced spatial bias with a vague relation between locations. To offer a better spatial inductive bias, we investigate alternative positional encodings and analyze their effects. Based on a more flexible positional encoding explicitly, we propose a new multi-scale training strategy and demonstrate its effectiveness in the state-of-the-art unconditional generator StyleGAN2. Besides, the explicit spatial inductive bias substantially improves SinGAN for more versatile image manipulation. 1
Infinite use of finite means: Zero-Shot Generalization using Compositional Emergent Protocols

Human language has been described as a system that makes use of finite means to express an unlimited array of thoughts. Of particular interest is the aspect of compositionality, whereby, the meaning of a complex, compound language expression can be deduced from the meaning of its constituent parts. If artificial agents can develop compositional communication protocols akin to human language, they can be made to seamlessly generalize to unseen combinations. However, the real question is, how do we induce compositionality in emergent communication? Studies have recognized the role of curiosity in enabling linguistic development in children. It is this same intrinsic urge that drives us to master complex tasks with decreasing amounts of explicit reward. In this paper, we seek to use this intrinsic feedback in inducing a systematic and unambiguous protolanguage in artificial agents. We show in our experiments, how these rewards can be leveraged in training agents to induce compositionality in absence of any external feedback. Additionally, we introduce Comm-gSCAN, a platform for investigating grounded language acquisition in 2D-grid environments. Using this, we demonstrate how compositionality can enable agents to not only interact with unseen objects, but also transfer skills from one task to other in zero-shot (Can an agent, trained to pull and push twice, pull twice? )