cssi/SciSciGPT-SciSciNet huggingface dataset:
Correlation of Large Longitudinal Deformations with Different Strain Histories

Abstract In 1963 Bernstein, Kearsley, and Zapas1 presented a theory of an elastic fluid which gave the correct stress-relaxation response for a large variety of elastomeric materials, including vulcanized rubbers. A principle attractiveness of this theory is its relative simplicity; with a single integral in time, it describes the stress-strain behavior for all types of deformation histories. In the case of simple extension, it predicts the behavior in any uniaxial strain history from the results of single step stress-relaxation experiments which cover the same range of extension and time. We designed a series of experiments to check the validity of this theory and found, as is shown in this paper, excellent agreement with experiment in all cases. We are aware that experiments cannot prove a theory. From our results, however, we feel strongly that a single integral expression with a nonlinear integrand such as the BKZ elastic fluid equation is sufficient to describe the stress-strain behavior of elastomer...
Fortified LEWICE with viscous effects

A method for computing the prediction of ice shapes on airfoils and their effects on the airfoil lift and drag coefficients are described. The previously developed LEWICE code has been modified to avoid problems due to multiple stagnation points. The interactive boundary-layer method developed by Cebeci has been incorporated into LEWICE to improve the accuracy of predicting ice shapes as well as to compute the performance characteristics of iced airfoils. The paper also presents ice shapes calculated without and with viscous effects, the consequences for aerodynamic properties, particulary lift and drag, and evaluation of the time steps used in the ice accretion process.
Regulation of Myofilament Contractile Function in Human Donor and Failing Hearts.

Heart failure (HF) often includes changes in myocardial contractile function. This study addressed the myofibrillar basis for contractile dysfunction in failing human myocardium. Regulation of contractile properties was measured in cardiac myocyte preparations isolated from frozen, left ventricular mid-wall biopsies of donor (n = 7) and failing human hearts (n = 8). Permeabilized cardiac myocyte preparations were attached between a force transducer and a position motor, and both the Ca2+ dependence and sarcomere length (SL) dependence of force, rate of force, loaded shortening, and power output were measured at 15 ± 1°C. The myocyte preparation size was similar between groups (donor: length 148 ± 10 μm, width 21 ± 2 μm, n = 13; HF: length 131 ± 9 μm, width 23 ± 1 μm, n = 16). The maximal Ca2+-activated isometric force was also similar between groups (donor: 47 ± 4 kN⋅m-2; HF: 44 ± 5 kN⋅m-2), which implicates that previously reported force declines in multi-cellular preparations reflect, at least in part, tissue remodeling. Maximal force development rates were also similar between groups (donor: k tr = 0.60 ± 0.05 s-1; HF: k tr = 0.55 ± 0.04 s-1), and both groups exhibited similar Ca2+ activation dependence of k tr values. Human cardiac myocyte preparations exhibited a Ca2+ activation dependence of loaded shortening and power output. The peak power output normalized to isometric force (PNPO) decreased by ∼12% from maximal Ca2+ to half-maximal Ca2+ activations in both groups. Interestingly, the SL dependence of PNPO was diminished in failing myocyte preparations. During sub-maximal Ca2+ activation, a reduction in SL from ∼2.25 to ∼1.95 μm caused a ∼26% decline in PNPO in donor myocytes but only an ∼11% change in failing myocytes. These results suggest that altered length-dependent regulation of myofilament function impairs ventricular performance in failing human hearts.
Dynamic Weighted Fairness with Minimal Disruptions

In this paper, we consider the following dynamic fair allocation problem: Given a sequence of job arrivals and departures, the goal is to maintain an approximately fair allocation of the resource against a target fair allocation policy, while minimizing the total number of \em disruptions, which is the number of times the allocation of any job is changed. We consider a rich class of fair allocation policies that significantly generalize those considered in previous work. We first consider the models where jobs only arrive, or jobs only depart. We present tight upper and lower bounds for the number of disruptions required to maintain a constant approximate fair allocation every time step. In particular, for the canonical case where jobs have weights and the resource allocation is proportional to the job's weight, we show that maintaining a constant approximate fair allocation requires Θ(log^* n) disruptions per job, almost matching the bounds in prior work for the unit weight case. For the more general setting where the allocation policy only decreases the allocation to a job when new jobs arrive, we show that maintaining a constant approximate fair allocation requires Θ(log n) disruptions per job. We then consider the model where jobs can both arrive and depart. We first show strong lower bounds on the number of disruptions required to maintain constant approximate fairness for arbitrary instances. In contrast we then show that there there is an algorithm that can maintain constant approximate fairness with $O(1)$ expected disruptions per job if the weights of the jobs are independent of the jobs arrival and departure order. We finally show how our results can be extended to the setting with multiple resources.
Genetic regulation of the bacterial omega-3 polyunsaturated fatty acid biosynthesis pathway

A characteristic among many marine Gammaproteobacteria is the biosynthesis and incorporation of omega-3 polyunsaturated fatty acids into membrane phospholipids. The biosynthesis of eicosapentaenoic (EPA) and/or docosahexaenoic (DHA) acids is mediated by a polyketide/fatty acid synthase mechanism encoded by a set of five genes, pfaABCDE. This unique fatty acid synthesis pathway co-exists with the principal type II dissociated fatty acid synthesis pathway, which is responsible for the biosynthesis of core saturated, monounsaturated, and hydroxylated fatty acids used in phospholipid and lipid A biosynthesis. In this work, a genetic approach was undertaken to elucidate genetic regulation of the pfa genes in the model marine bacterium Photobacterium profundum SS9. Using a reporter gene fusion, we showed that expression of the pfa operon is down regulated in response to exogenous fatty acids, particularly long chain monounsaturated fatty acids. This regulation occurs independently of the canonical fatty acid regulators, FabR and FadR, present in P. profundum SS9. Transposon mutagenesis and screening of a library of mutants identified a novel transcriptional regulator, which we have designated pfaF, to be responsible for the observed regulation of the pfa operon in P. profundum SS9. Gel mobility shift and DNase I footprinting assays confirmed that PfaF binds the pfaA promoter and identified the PfaF binding site.Importance The production of long-chain omega-3 polyunsaturated fatty acids (PUFA) by marine Gammaproteobacteria, particularly those from deep-sea environments, has been known for decades. These unique fatty acids are produced by a polyketide-type mechanism and subsequently incorporated into the phospholipid membrane. While much research has focused on the biosynthesis genes, their products and the phylogenetic distribution of these gene clusters, no prior studies have detailed the genetic regulation of this pathway. This study describes how this pathway is regulated under various culture conditions and has identified and characterized a fatty acid responsive transcriptional regulator specific to PUFA biosynthesis.
Notch Inhibition via GSI Treatment Elevates Protein Synthesis in C2C12 Myotubes

The role of Notch signaling is widely studied in skeletal muscle regeneration but little is known about its influences on muscle protein synthesis (MPS). The purpose of this study was to investigate whether Notch signaling is involved in the regulation of MPS. C2C12 cells were treated with a γ-secretase inhibitor (GSI), to determine the effect of reduced Notch signaling on MPS and anabolic signaling markers. GSI treatment increased myotube hypertrophy by increasing myonuclear accretion (nuclei/myotube: p = 0.01) and myonuclear domain (myotube area per fusing nuclei: p < 0.001) in differentiating C2C12 cells. GSI treatment also elevated myotube hypertrophy in differentiated C2C12s (area/myotube; p = 0.01). In concert, GSI treatment augmented pmTOR Ser2448 (p = 0.01) and protein synthesis (using SUnSET method) in myotubes (p < 0.001). Examining protein expression upstream of mTOR revealed reductions in PTEN (p = 0.04), with subsequent elevations in pAKT Thr308 (p < 0.001) and pAKT Ser473 (p = 0.05). These findings reveal that GSI treatment elevates myotube hypertrophy through both augmentation of fusion and MPS. This study sheds light on the potential multifaceted roles of Notch within skeletal muscle. Furthermore, we have demonstrated that Notch may modulate the PTEN/AKT/mTOR pathway.
Two paths forward for Austrian macroeconomics

Twenty years ago, two scholars published important books on the subject of Austrian macroeconomic theory. Each offered a somewhat distinct approach. And, as much as it was the case when these two works were published twenty years ago, Austrian macroeconomists find themselves with two potential paths forward today. On the one hand, they might stress the temporal structure of production, while incorporating insights from finance. On the other, they might take a more general approach to capital specificity, while deviating from more mainstream economic models only insofar as is essential to preserve a uniquely Austrian view.
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

Weak supervision is a popular method for building machine learning models without relying on ground truth annotations. Instead, it generates probabilistic training labels by estimating the accuracies of multiple noisy labeling sources (e.g., heuristics, crowd workers). Existing approaches use latent variable estimation to model the noisy sources, but these methods can be computationally expensive, scaling superlinearly in the data. In this work, we show that, for a class of latent variable models highly applicable to weak supervision, we can find a closed-form solution to model parameters, obviating the need for iterative solutions like stochastic gradient descent (SGD). We use this insight to build FlyingSquid, a weak supervision framework that runs orders of magnitude faster than previous weak supervision approaches and requires fewer assumptions. In particular, we prove bounds on generalization error without assuming that the latent variable model can exactly parameterize the underlying data distribution. Empirically, we validate FlyingSquid on benchmark weak supervision datasets and find that it achieves the same or higher quality compared to previous approaches without the need to tune an SGD procedure, recovers model parameters 170 times faster on average, and enables new video analysis and online learning applications.
From Importance Sampling to Doubly Robust Policy Gradient

We show that on-policy policy gradient (PG) and its variance reduction variants can be derived by taking finite difference of function evaluations supplied by estimators from the importance sampling (IS) family for off-policy evaluation (OPE). Starting from the doubly robust (DR) estimator (Jiang & Li, 2016), we provide a simple derivation of a very general and flexible form of PG, which subsumes the state-of-the-art variance reduction technique (Cheng et al., 2019) as its special case and immediately hints at further variance reduction opportunities overlooked by existing literature. We analyze the variance of the new DR-PG estimator, compare it to existing methods as well as the Cramer-Rao lower bound of policy gradient, and empirically show its effectiveness.
The Limit of Stationary Distributions of Many-Server Queues in the Halfin–Whitt Regime

We consider the so-called GI/GI/N queue, in which a stream of jobs with independent and identically distributed service times arrive as a renewal process to a common queue that is served by N ident...
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D depth estimation with those for 3D object detection by converting 2D depth map outputs to 3D point cloud inputs. However, so far these two networks have to be trained separately. In this paper, we introduce a new framework based on differentiable Change of Representation (CoR) modules that allow the entire PL pipeline to be trained end-to-end. The resulting framework is compatible with most state-of-the-art networks for both tasks and in combination with PointRCNN improves over PL consistently across all benchmarks --- yielding the highest entry on the KITTI image-based 3D object detection leaderboard at the time of submission. Our code will be made available at https://github.com/mileyan/pseudo-LiDAR_e2e.
Conditional Lpar1 gene targeting identifies cell types mediating neuropathic pain

LPA1 is one of six known receptors (LPA1-6) for lysophosphatidic acid (LPA). Constitutive Lpar1 null mutant mice have been instrumental in identifying roles for LPA-LPA1 signaling in neurobiological processes, brain development, and behavior, as well as modeling human neurological diseases like neuropathic pain. Constitutive Lpar1 null mutant mice are protected from partial sciatic nerve ligation (PSNL)-induced neuropathic pain, however, the cell types that are functionally responsible for mediating this protective effect are unknown. Here, we report the generation of an Lpar1flox/flox conditional null mutant mouse that allows for cre-mediated conditional deletion, combined with a PSNL pain model. Lpar1flox/flox mice were crossed with cre transgenic lines driven by neural gene promoters for nestin (all neural cells), synapsin (neurons), or P0 (Schwann cells). CD11b-cre transgenic mice were also used to delete Lpar1 in microglia. PSNL-initiated pain responses were reduced following cre-mediated Lpar1 deletion with all three neural promoters as well as the CD11b promoter, supporting involvement of Schwann cells, central and/or peripheral neurons, and microglia in mediating pain. Interestingly, rescue responses were nonidentical, implicating distinct roles for Lpar1-expressing cell types. Our results with a new Lpar1 conditional mouse mutant expand an understanding of LPA1 signaling in the PSNL model of neuropathic pain.
On Recoverability of Randomly Compressed Tensors With Low CP Rank

Our interest lies in the recoverability properties of compressed tensors under the canonical polyadic decomposition (CPD) model. The considered problem is well-motivated in many applications, e.g., hyperspectral image and video compression. Prior work studied this problem under a variety of assumptions, e.g., that the latent factors of the tensor are sparse and that the compressing matrix follows a joint absolutely continuous distribution. These results leverage analytical tools such as CPD uniqueness and algebraic geometry—which are elegant. In this work, we offer an alternative result: We show that if the tensor is compressed by a subgaussian linear mapping, then the tensor is recoverable if the number of measurements is on the same order of magnitude as that of the model parameters. Unlike existing results, our proof is based on deriving a restricted isometry property (R.I.P.) under the CPD model via set covering techniques, and thus exhibits a flavor of classic compressive sensing. The new recoverability result enriches the understanding to the compressed CP tensor recovery problem. It offers theoretical guarantees for recovering tensors whose elements are not necessarily sparse; the compressing matrix is also not restricted to continuous matrices under our framework. The newly derived covering number for tensors with low CP rank may also benefit future research, e.g., sketching based tensor compression for reducing computational burden.
Minimal Parabolic $k$-subgroups acting on Symmetric $k$-varieties Corresponding to $k$-split Groups

Symmetric k-varieties are a natural generalization of symmetric spaces to general fields k. We study the action of minimal parabolic k-subgroups on symmetric k-varieties and define a map that embed...
Thresholding Bandit Problem with Both Duels and Pulls.

The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold. We consider a new setting of TBP, where in addition to pulling arms, one can also \emph{duel} two arms and get the arm with a greater mean. In our motivating application from crowdsourcing, dueling two arms can be more cost-effective and time-efficient than direct pulls. We refer to this problem as TBP with Dueling Choices (TBP-DC). This paper provides an algorithm called Rank-Search (RS) for solving TBP-DC by alternating between ranking and binary search. We prove theoretical guarantees for RS, and also give lower bounds to show the optimality of it. Experiments show that RS outperforms previous baseline algorithms that only use pulls or duels.
Delta Oscillations Are a Robust Biomarker of Dopamine Depletion Severity and Motor Dysfunction in Awake Mice

Delta oscillations (0.5-4 Hz) are a robust feature of basal ganglia pathophysiology in patients with Parkinson's disease (PD) in relationship to tremor, but their relationship to other parkinsonian symptoms has not been investigated. While delta oscillations have been observed in mouse models of PD, they have only been investigated in anesthetized animals, suggesting that the oscillations may be an anesthesia artifact and limiting the ability to relate them to motor symptoms. Here, we establish a novel approach to detect spike oscillations embedded in noise to provide the first study of delta oscillations in awake, dopamine-depleted mice. We find that approximately half of neurons in the substantia nigra pars reticulata (SNr) exhibit delta oscillations in dopamine depletion and that these oscillations are a strong indicator of dopamine loss and akinesia, outperforming measures such as changes in firing rate, irregularity, bursting, and synchrony. These oscillations are typically weakened, but not ablated, during movement. We further establish that these oscillations are caused by the loss of D2-receptor activation and do not originate from motor cortex, contrary to previous findings in anesthetized animals. Instead, SNr oscillations precede those in M1 at a 100- to 300-ms lag, and these neurons' relationship to M1 oscillations can be used as the basis for a novel classification of SNr into two subpopulations. These results give insight into how dopamine loss leads to motor dysfunction and suggest a reappraisal of delta oscillations as a marker of akinetic symptoms in PD.NEW & NOTEWORTHY This work introduces a novel method to detect spike oscillations amidst neural noise. Using this method, we demonstrate that delta oscillations in the basal ganglia are a defining feature of awake, dopamine-depleted mice and are strongly correlated with dopamine loss and parkinsonian motor symptoms. These oscillations arise from a loss of D2-receptor activation and do not require motor cortex. Similar oscillations in human patients may be an underappreciated marker and target for Parkinson's disease (PD) treatment.
Optimal Rendezvous in the Neighborhood of a Circular Orbit

The minimum velocity-change rendezvous solutions, when the motion may be linearized about a circular orbit, fall into two separate regions; the phase-for-free region and the general region. Phase-for-free solutions are derived from the optimum transfer solutions, require the same velocity-change expenditure, but may not be unique. Analytic solutions are presented in two of the three subregions. An algorithm is presented for determining the unique solutions in the general region. Various sources of initial conditions are discussed and three examples are presented.
The containment poset of type \(A\) Hessenberg varieties

Flag varieties are well-known algebraic varieties with many important geometric, combinatorial, and representation theoretic properties. A Hessenberg variety is a subvariety of a flag variety identified by two parameters: an element \(X\) of the Lie algebra \(\mathfrak{g}\) and a Hessenberg subspace \(H\subseteq \mathfrak{g}\). This paper considers when two Hessenberg spaces define the same Hessenberg variety when paired with \(X\). To answer this question we present the containment poset \(\mathcal{P}_X\) of type \(A\) Hessenberg varieties with a fixed first parameter \(X\) and give a simple and elegant proof that if \(X\) is not a multiple of the element \(\bf 1\) then the Hessenberg spaces containing the Borel subalgebra determine distinct Hessenberg varieties. Lastly we give a natural involution on \(\mathcal{P}_X\) that induces a homeomorphism of varieties and prove additional properties of \(\mathcal{P}_X\) when \(X\) is a regular nilpotent element.
Differentially Private Call Auctions and Market Impact

We propose and analyze differentially private (DP) mechanisms for call auctions as an alternative to the complex and ad-hoc privacy efforts that are common in modern electronic markets. We prove that the number of shares cleared in the DP mechanisms compares favorably to the non-private optimal and provide a matching lower bound. We analyze the incentive properties of our mechanisms and their behavior under natural no-regret learning dynamics by market participants. We include simulation results and connections to the finance literature on market impact.
Reductivity of the automorphism group of K-polystable Fano varieties

We prove that K-polystable log Fano pairs have reductive automorphism groups. In fact, we deduce this statement by establishing more general results concerning the S-completeness and $$\Theta $$ -reductivity of the moduli of K-semistable log Fano pairs. Assuming the conjecture that K-semistability is an open condition, we prove that the Artin stack parametrizing K-semistable Fano varieties admits a separated good moduli space.
Different paths for institutional theory: foundational dichotomies and theoretical framing

It is common for scholars to describe institutions as “rules of the game.” This description entails a separation between a society and its rules. Social change thus results as societies amend their framing rules. This paper explores that common treatment of institutions as rules with a treatment wherein societies and institutions as images of one another. If there were no rules governing interactions among some set of people, you would have a mass of people but that mass would not constitute what we recognize as society. This simple distinction between institutions as rules by which a society is governed and institutions as society itself creates divergent paths for institutional theory, which this paper explores.
Algebraic stability of meromorphic maps descended from Thurston’s pullback maps

Let $\phi:S^2 \to S^2$ be an orientation-preserving branched covering whose post-critical set has finite cardinality $n$. If $\phi$ has a fully ramified periodic point $p_{\infty}$ and satisfies certain additional conditions, then, by work of Koch, $\phi$ induces a meromorphic self-map $R_{\phi}$ on the moduli space $\mathcal{M}_{0,n}$; $R_{\phi}$ descends from Thurston's pullback map on Teichmuller space. Here, we relate the dynamics of $R_{\phi}$ on $\mathcal{M}_{0,n}$ to the dynamics of $\phi$ on $S^2$. Let $\ell$ be the length of the periodic cycle in which the fully ramified point $p_{\infty}$ lies; we show that $R_{\phi}$ is algebraically stable on the heavy-light Hassett space corresponding to $\ell$ heavy marked points and $(n-\ell)$ light points.
The COVID-19 Gene and Drug Set Library

The coronavirus (CoV) severe acute respiratory syndrome (SARS)-CoV-2 (COVID-19) pandemic has received rapid response by the research community to offer suggestions for repurposing of approved drugs as well as to improve our understanding of the COVID-19 viral life cycle molecular mechanisms. In a short period, tens of thousands of research preprints and other publications have emerged including those that report lists of experimentally validated drugs and compounds as potential COVID-19 therapies. In addition, gene sets from interacting COVID-19 virus-host proteins and differentially expressed genes when comparing infected to uninfected cells are being published at a fast rate. To organize this rapidly accumulating knowledge, we developed the COVID-19 Gene and Drug Set Library (https://amp.pharm.mssm.edu/covid19/), a collection of gene and drug sets related to COVID-19 research from multiple sources. The COVID-19 Gene and Drug Set Library is delivered as a web-based interface that enables users to view, download, analyze, visualize, and contribute gene and drug sets related to COVID-19 research. To evaluate the content of the library, we performed several analyses including comparing the results from 6 in-vitro drug screens for COVID-19 repurposing candidates. Surprisingly, we observe little overlap across these initial screens. The most common and unique hit across these screen is mefloquine, a malaria drug that should receive more attention as a potential therapeutic for COVID-19. Overall, the library of gene and drug sets can be used to identify community consensus, make researchers and clinicians aware of the development of new potential therapies, as well as allow the research community to work together towards a cure for COVID-19.
Vibration Suppression for Coupled Wave PDEs in Deep-Sea Construction

A deep-sea construction vessel (DCV) is used to install underwater parts of an offshore oil drilling platform at the designated locations on the seafloor. By using extended Hamilton’s principle, a nonlinear partial differential equation (PDE) system governing the lateral-longitudinal coupled vibration dynamics of the DCV consisting of a time-varying-length cable with an attached item is derived, and it is linearized at the steady state generating a linear PDE model, which is extended to a more general system including two coupled wave PDEs connected with two interacting ordinary differential equations (ODEs) at the uncontrolled boundaries. Through a preliminary transformation, an equivalent reformulated plant is generated as a $4\times 4$ coupled heterodirectional hyperbolic PDE-ODE system characterized by spatially varying coefficients on a time-varying domain. To stabilize such a system, an observer-based output-feedback control design is proposed, where the measurements are only placed at the actuated boundary of the PDE, namely, at the platform at the sea surface. The exponential stability of the closed-loop system, boundedness and exponential convergence of the control inputs, are proved via Lyapunov analysis. The obtained theoretical result is tested on a nonlinear model with ocean disturbances, even though the design is developed in the absence of such real-world effects.
Lifelong Machine Learning With Deep Streaming Linear Discriminant Analysis

When an agent acquires new information, ideally it would immediately be capable of using that information to understand its environment. This is not possible using conventional deep neural networks, which suffer from catastrophic forgetting when they are incrementally updated, with new knowledge overwriting established representations. A variety of approaches have been developed that attempt to mitigate catastrophic forgetting in the incremental batch learning scenario, where a model learns from a series of large collections of labeled samples. However, in this setting, inference is only possible after a batch has been accumulated, which prohibits many applications. An alternative paradigm is online learning in a single pass through the training dataset on a resource constrained budget, which is known as streaming learning. Streaming learning has been much less studied in the deep learning community. In streaming learning, an agent learns instances one-by-one and can be tested at any time, rather than only after learning a large batch. Here, we revisit streaming linear discriminant analysis, which has been widely used in the data mining research community. By combining streaming linear discriminant analysis with deep learning, we are able to outperform both incremental batch learning and streaming learning algorithms on both ImageNet ILSVRC-2012 and CORe50, a dataset that involves learning to classify from temporally ordered samples.
Selection of hydrocarbon gas for huff-n-puff IOR in shale oil reservoirs

Abstract In shale oil reservoirs, cyclic CO2 injection has proven to be effective at recovering significant amounts of oil, however, certain downsides to the use of CO2 exist owing to its corrosive nature and its higher miscibility pressure compared to hydrocarbon gases such as ethane and propane. This paper presents simulation results focused on the impact of phase behavior on the injection of single component gases such as; N2, CO2, C1 and C2, and a combination of hydrocarbon gases with varying C1, C2 and C3 compositions, as the basis for selecting an injectant, that is more effective than CO2 for cyclic gas injection. The simulations presented were carried out with rock and fluid properties from the Eagle Ford reservoir using Multi-Frac-Comp, an integrated Equation-of-State geomechanical compositional hydraulic fracturing and reservoir simulator (Zheng et al., 2019a, 2019b). Oil recovery results obtained for single component gas injection shows that C2 injection yields comparable oil recovery to CO2 and lower gas-oil ratio. Of all the injected gases, N2 yielded the lowest oil recovery and highest GOR because of its poor miscibility with the reservoir fluid. Simulations carried out using hydrocarbon gases with varying compositions of C1, C2 and C3 show recovery factors that are higher than C2 injection. The results show that hydrocarbon gases with a higher C1 content over C2, results in faster oil recovery during the initial cycles of huff-n-puff because the high compressibility of C1 enhances the volume expansion obtained upon depressurization during the production phase. In contrast, HC gases with higher C2 and C3 content yield higher recoveries during the later cycles because the high density of these gases improves miscibility with the reservoir fluid during the soak phase. The results from this study provide operators with information required to select a hydrocarbon gas with an optimum composition of C1, C2, C3 and C4. This strategy of tuning the injected fluid composition during different cycles of gas injection can play an important role in improving oil recovery from huff-n-puff field pilots.
Implied and realized volatility: A study of distributions and the distribution of difference

We study distributions of realized variance (squared realized volatility) and squared implied volatility, as represented by VIX and VXO indices. We find that generalized beta distribution provide the best fits. These fits are much more accurate for realized variance than for squared VIX and VXO—possibly another indicator that the latter have deficiencies in predicting the former. We also show that there are noticeable differences between the distributions of the 1970–2017 realized variance and its 1990–2017 portion, for which VIX and VXO became available. This may be indicative of a feedback effect that implied volatility has on realized volatility. We also discuss the distribution of the difference between squared implied volatility and realized variance and show that, at the basic level, it is consistent with Pearson's correlations obtained from linear regression.
Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors

Network-distributed optimization has attracted sig-nificant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (DGD) algorithm is communication-inefficient for large-scale and high-dimensional network-distributed optimization problems. To address this challenge, many compressed DGD-based algorithms have been proposed. However, most of the existing works have high complexity and assume compressors with bounded noise power. To overcome these limitations, in this paper, we propose a new differential-coded compressed DGD (DC-DGD) algorithm. The key features of DC-DGD include: i) DC-DGD works with general SNR-constrained compressors, relaxing the bounded noise power assumption; ii) The differential-coded design entails the same convergence rate as the original DGD algorithm; and iii) DC-DGD has the same low-complexity structure as the original DGD due to a self-noise-reduction effect. Moreover, the above features inspire us to develop a hybrid compression scheme that offers a systematic mechanism to minimize the communication cost. Finally, we conduct extensive experiments to verify the efficacy of the proposed DC-DGD and hybrid compressor.
Heating of the solar corona by the resonant absorption of Alfven waves

An improved method for calculating the resonance absorption heating rate is discussed and the results are compared with observations in the solar corona. To accomplish this, the wave equation for a dissipative, compressible plasma is derived from the linearized magnetohydrodynamic equations for a plasma with transverse Alfven speed gradients. For parameters representative of the solar corona, it is found that a two-scale description of the wave motion is appropriate. The large-scale motion, which can be approximated as nearly ideal, has a scale which is on the order of the width of the loop. The small-scale wave, however, has a transverse scale much smaller than the width of the loop, with a width of about 0.3-250 km, and is highly dissipative. These two wave motions are coupled in a narrow resonance region in the loop where the global wave frequency equals the local Alfven wave frequency. Formally, this coupling comes about from using the method of matched asymptotic expansions to match the inner and outer (small and large scale) solutions. The resultant heating rate can be calculated from either of these solutions. A formula derived using the outer (ideal) solution is presented, and shown to be consistent with observations of heatingmore » and line broadening in the solar corona. 34 references.« less
Simple High-Level Code For Cryptographic Arithmetic: With Proofs, Without Compromises

We introduce an unusual approach for implementing cryptographic arithmetic in short high-level code with machinechecked proofs of functional correctness. We further demonstrate that simple partial evaluation is sufficient to transform such initial code into highly competitive C code, breaking the decades-old pattern that the only fast implementations are those whose instruction-level steps were written out by hand. These techniques were used to build an elliptic-curve library that achieves competitive performance for a wide range of prime fields and multiple CPU architectures, showing that implementation and proof effort scales with the number and complexity of conceptually different algorithms, not their use cases. As one outcome, we present the first verified highperformance implementation of P-256, the most widely used elliptic curve. Implementations from our library were included in BoringSSL to replace existing specialized code, for inclusion in several large deployments for Chrome, Android, and CloudFlare. This is an abridged version of the full paper originally presented in IEEE S&P 2019 [10]. We have omitted most proof-engineering details in favor of a focus on the system's functional capabilities.
Scaled recoupling of chemical shift anisotropies at high magnetic fields under MAS with interspersed C-elements.

The power of chemical shift anisotropy (CSA) measurements for probing structure and dynamics of molecules has been long recognized. NMR pulse sequences that allow measurement of CSA values in an indirect dimension of a protein correlation spectrum have been employed for aliphatic groups, but for practical reasons, carbonyl functional groups have been little studied, despite the fact that carbonyls are expected to give particularly varied and informative CSA values. Specifically, the wide spectral widths of carbonyl tensors make their measurements difficult with typically attainable spectrometer settings. We present here an extended family of experiments that enable the recovery of static CSA lineshapes in an indirect dimension of magic angle spinning (MAS) solid-state NMR experiments, except for various real valued scaling factors. The experiment is suitable for uniformly labeled material, at moderate MAS rates (10 kHz–30 kHz) and at higher magnetic fields (ν0H > 600 MHz). Specifically, the experiments are based on pulse sequence elements from a previous commonly used pulse sequence for CSA measurement, recoupling of chemical shift anisotropy (ROCSA), while modification of scaling factors is achieved by interspersing different blocks of C-elements of the same Cnn1 cycle. Using experimental conditions similar to the parent ROCSA sequence, a CSA scaling factor between 0 and 0.272 can be obtained, thus allowing a useful practical range of possibilities in experimental conditions for measurement of larger CSA values. Using these blocks, it is also possible to make a constant-time CSA recoupling sequence. The effectiveness of this approach, fROCSA, is shown on model compounds 1-13C-Gly, U–13C,15N-l-His, and microcrystalline U–13C,15N-Ubiquitin.
Bearing-Only Navigation With Field of View Constraints

This letter addresses the problem of navigation using only relative directional measurements (i.e., relative distances are unknown) with field of view constraints. We present a novel navigation vector field for the bearing-based visual homing problem with respect to static landmarks in 2-D and 3-D environments. Our method employs two control fields that are tangent and normal to ellipsoids having landmarks as their foci. The tangent field steers the robot to a set of points where the average of observed bearing vectors is parallel to the average of the desired bearings. The normal field uses the angle between different pairs of bearings as a proxy to adjust the robot’s distance from the landmarks and to satisfy the field of view constraints. Both fields are blended together to construct an almost globally stable control law. Our method is straightforward to implement, as it requires only comparisons between average bearings, and angles of pairs of vectors. We provide simulations that demonstrate the robustness of our approach on two systems: a double integrator, and a unicycle.
Analysis of synchronization in load ensembles

Abstract While coordinated control of a large population of electric loads can provide important services to the electric grid, situations have been observed where control of load ensembles may lead to highly nonlinear behavior such as synchronization, sustained oscillations and bifurcations. Synchronization of thermostatically controlled loads (TCLs) is undesirable since it can lead to increased short-cycling, sudden changes in power demand and network voltage issues. In this paper, we investigate the synchronizing tendency of TCLs under control strategies where updates are broadcast periodically for coordinating TCLs. We study the problem using a hybrid dynamical systems framework to model both the continuous and discrete dynamics of load ensembles. Analysis of eigenmodes of the underlying discrete-time system provide insights into synchronizing tendencies and rate of convergence to the synchronized state. Simulations are provided to illustrate the theory.
An experimental investigation of the transverse pressure gradient associated with curved free jets in the proximity of solid boundaries

An experiment has been performed to determine the mass flow and thrust augmentation associated with the flow of a two-dimensional incompressible jet issued parallel to an adjacent offset wall. A theoretical analysis based on a linear static pressure profile across the jet exit reveals that thrust augmentation is possible only for a wall offset of less than one half of the nozzle exit width. Mass flow augmentation measurements are in reasonable agreement with the proposed theory. Augmented mass flow of over three percent is observed directly, while thrust augmentation in excess of three percent is predicted when measured values of the pressure difference across the exiting jet are supplied to the theory. Measurements of the separation cavity length are presented which complement those given by previous investigators by extending the range to small values of the ratio of the wall offset to the slot height. For large offset ratios it is found that the cavity length approaches 1.25 times the wall offset distance. The initial radius of curvature of the jet centerline is found to be substantially greater than the mean radius of curvature over the cavity length, thereby indicating that the trajectory of the jet does not follow the arc of a circle as has been previously assumed.
Exploring the effects of California's COVID-19 shelter-in-place order on household energy practices and intention to adopt smart home technologies

Abstract To contain the spread of the novel coronavirus (COVID-19), local and state governments in the U.S. have imposed restrictions on daily life, resulting in dramatic changes to how and where people interact, travel, socialize, and work. Using a social practice perspective, we explore how California's Shelter-in-Place (SIP) order impacted household energy activities. To do so, we conducted an online survey of California residents (n = 804) during active SIP restrictions (May 5–18, 2020). We asked respondents about changes to home occupancy patterns and household energy activities (e.g., cooking, electronics usage) due to SIP restrictions, as well as perspectives toward smart energy technologies. Households reported increased midday (10am–3pm) occupancy during SIP, and this increase is related to respondent and household characteristics, such as education and the presence of minors in the home. Examining change in the frequency of household activities during SIP, presence of minors and increased midday occupancy proved important. Finally, we considered relationships to intention to purchase smart home technologies, with the presence of minors and increased activity frequency relating to greater intention to purchase. These findings demonstrate how household activities and occupancy changed under COVID restrictions, how these changes may be related to energy use in the home, and how such COVID-related changes could be shaping perspectives toward smart home technology, potentially providing insight into future impacts on household practices and electricity demand.
Real-time suppression and amplification of frequency-specific neural activity using stimulation evoked oscillations.

Abstract Background Approaches to predictably control neural oscillations are needed to understand their causal role in brain function in healthy or diseased states and to advance the development of neuromodulation therapies. Objective We present a closed-loop neural control and optimization framework to actively suppress or amplify low-frequency neural oscillations observed in local field potentials in real-time by using electrical stimulation. The rationale behind this control approach and our working hypothesis is that neural oscillatory activity evoked by electrical pulses can suppress or amplify spontaneous oscillations via destructive or constructive interference when the pulses are continuously delivered with appropriate amplitudes and at precise phases of the modulated oscillations in a closed-loop scheme. Methods We tested our hypothesis in two nonhuman primates that exhibited a robust increase in low-frequency (8–30 Hz) oscillatory power in the subthalamic nucleus (STN) following administration of the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). To test our neural control approach, we targeted 8–17 Hz oscillations and used electrode arrays and electrical stimulation waveforms similar to those used in humans chronically implanted with brain stimulation systems. Stimulation parameters that maximize the suppression or amplification of neural oscillations were predicted using mathematical models of the stimulation evoked oscillations. Results Our neural control and optimization approach was capable of actively and robustly suppressing or amplifying oscillations in the targeted frequency band (8–17 Hz) in real-time in the studied subjects. Conclusions The results from this study support our hypothesis and suggest that the proposed neural control framework allows one to characterize in controlled experiments the functional role of frequency-specific neural oscillations by using electrodes and stimulation waveforms currently being employed in humans.
Rural Windfall or a New Resource Curse? Coca, Income, and Civil Conflict in Colombia *

Natural and agricultural resources for which there is a substantial black market, such as coca, opium, and diamonds, appear especially likely to be exploited by the parties to a civil conflict. Even legally traded commodities such as oil and timber have been linked to civil war. On the other hand, these resources may also provide one of the few reliable sources of income in the countryside. In this paper, we study the economic and social consequences of a major exogenous shift in the production of one such resource – coca paste – into Colombia, where most coca leaf is now harvested. Our analysis shows that this shift generated only modest economic gains in rural areas, primarily in the form of increased selfemployment earnings and increased labor supply by teenage boys. The results also suggest that the rural areas which saw accelerated coca production subsequently became more violent, while urban areas were affected little. The acceleration in violence is greater in departments (provinces) where there was a pre-coca guerilla presence. Taken together, these findings are consistent with the view that the Colombian civil conflict is fueled by the financial opportunities that coca provides, and that the consequent rent-seeking activity by combatants limits the economic gains from coca cultivation.