cssi/SciSciGPT-SciSciNet huggingface dataset:
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions

The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks. In the chemistry domain, recent advances have also led to the development of similar CNN models, such as Chemception, that is trained to predict chemical properties using images of molecular drawings. In this work, we investigate the effects of systematically removing and adding localized domain-specific information to the image channels of the training data. By augmenting images with only 3 additional basic information, and without introducing any architectural changes, we demonstrate that an augmented Chemception (AugChemception) outperforms the original model in the prediction of toxicity, activity, and solvation free energy. Then, by altering the information content in the images, and examining the resulting model's performance, we also identify two distinct learning patterns in predicting toxicity/activity as compared to solvation free energy. These patterns suggest that Chemception is learning about its tasks in the manner that is consistent with established knowledge. Thus, our work demonstrates that advanced chemical knowledge is not a pre-requisite for deep learning models to accurately predict complex chemical properties.
Galaxy morphology — An unsupervised machine learning approach ☆

Abstract Structural properties poses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement with the De Vaucouleurs system, demonstrating the ability of computer vision and machine learning methods to automatically profile galaxy morphological sequences. The unsupervised analysis method is based on comprehensive computer vision techniques that compute the visual similarities between the different morphological types. Rather than relying on human cognition, the proposed system deduces the similarities between sets of galaxy images in an automatic manner, and is therefore not limited by the number of galaxies being analyzed. The source code of the method is publicly available, and the protocol of the experiment is included in the paper so that the experiment can be replicated, and the method can be used to analyze user-defined datasets of galaxy images.
Involutions and their progenies

Any permutation has a disjoint cycle decomposition and concept generates an equivalence class on the symmetry group called the cycle-type. The main focus of this work is on permutations of restricted cycle-types, with particular emphasis on the special class of involutions and their partial sums. The paper provides generating functions, determinantal expressions, asymptotic estimates as well as arithmetic and combinatorial properties.
Energy Disaggregation via Deep Temporal Dictionary Learning

This paper presents a novel nonlinear dictionary learning (DL) model to address the energy disaggregation (ED) problem, i.e., decomposing the electricity signal of a home to its operating devices. First, ED is modeled as a new temporal DL problem where a set of dictionary atoms is learned to capture the most representative temporal features of electricity signals. The sparse codes corresponding to these atoms show the contribution of each device in the total electricity consumption. To learn powerful atoms, a novel deep temporal DL (DTDL) model is proposed that computes complex nonlinear dictionaries in the latent space of a long short-term memory autoencoder (LSTM-AE). While the LSTM-AE captures the deep temporal manifold of electricity signals, the DTDL model finds the most representative atoms inside this manifold. To simultaneously optimize the dictionary and the deep temporal manifold, a new optimization algorithm is proposed that alternates between finding the optimal LSTM-AE and the optimal dictionary. To the best of authors’ knowledge, DTDL is the only DL model that understands the deep temporal structures of the data. Experiments on the Reference ED Data Set show an outstanding performance compared with the recent state-of-the-art algorithms in terms of precision, recall, accuracy, and F-score.
Global spherical tropicalization via toric embeddings

The first steps in defining tropicalization for spherical varieties have been taken in the last few years. There are two parts to this theory: tropicalizing subvarieties of homogeneous spaces and tropicalizing their closures in spherical embeddings. In this paper, we obtain a new description of spherical tropicalization that is equivalent to the other theories. This works by embedding in a toric variety, tropicalizing there, and then applying a particular piecewise projection map. We use this theory to prove that taking closures commutes with the spherical tropicalization operation.
Hybrid Riemannian metrics for diffeomorphic shape registration

We consider the results of combining two approaches developed for the design of Riemannian metrics on curves and surfaces, namely parametrization-invariant metrics of the Sobolev type on spaces of immersions, and metrics derived through Riemannian submersions from right-invariant Sobolev metrics on groups of diffeomorphisms (the latter leading to the "large deformation diffeomorphic metric mapping" framework). We show that this quite simple approach inherits the advantages of both methods, both on the theoretical and experimental levels, and provide additional flexibility and modeling power, especially when dealing with complex configurations of shapes. Experimental results illustrating the method are provided for curve and surface registration.
Exceptional knot homology

The goal of this paper is twofold. First, we find a natural home for the double affine Hecke algebras (DAHA) in the physics of BPS states. Second, we introduce new invariants of torus knots and links called hyperpolynomials that address the “problem of negative coefficients” often encountered in DAHA-based approaches to homological invariants of torus knots and links. Furthermore, from the physics of BPS states and the spectra of singularities associated with Landau–Ginzburg potentials, we also describe a rich structure of differentials that act on homological knot invariants for exceptional groups and uniquely determine the latter for torus knots.
Robust Photometric Stereo via Dictionary Learning

Photometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources. While effective in some situations, classical photometric stereo relies on a diffuse surface model that cannot handle objects with complex reflectance patterns, and it is sensitive to non-idealities in the images. In this paper, we propose a novel approach to photometric stereo that relies on dictionary learning to produce robust normal vector reconstructions. Specifically, we develop two formulations for applying dictionary learning to photometric stereo. We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertian reflectance model. We then generalize this model to explicitly model non-Lambertian objects. We investigate both approaches using extensive experimentation on synthetic and real benchmark datasets and observe state-of-the-art performance compared to the existing robust photometric stereo methods.
Admissibility for Quasiregular Representations of Exponential Solvable Lie Groups

Let $N$ be a simply connected, connected non-commutative nilpotent Lie group with Lie algebra $\mathfrak{n}$ of dimension $n.$ Let $H$ be a subgroup of the automorphism group of $N.$ Assume that $H$ is a commutative, simply connected, connected Lie group with Lie algebra $\mathfrak{h}.$ Furthermore, let us assume that the linear adjoint action of $\mathfrak{h}$ on $\mathfrak{n}$ is diagonalizable with non-purely imaginary eigenvalues. Let $\tau=\mathrm{Ind}%_{H}^{N\rtimes H} 1$. We obtain an explicit direct integral decomposition for $\tau$, including a description of the spectrum as a sub-manifold of $(\mathfrak{n}+\mathfrak{h})^{\ast}$, a formula for the multiplicity function of the unitary irreducible representations occurring in the direct integral, and a precise intertwining operator. Finally, we completely settle the admissibility question of $\tau$. In fact, we show that if $G=N\rtimes H$ is unimodular, then $\tau$ is never admissible, and if $G$ is nonunimodular, $\tau$ is admissible if and only if the intersection of $H$ and the center of $G$ is equal to the identity of the group.
PUlasso: High-Dimensional Variable Selection With Presence-Only Data

In various real-world problems, we are presented with classification problems with positive and unlabeled data, referred to as presence-only responses. In this article we study variable selection i...
A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks

Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency of information exchange. However, malware and a virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use a susceptible–infected–susceptible epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus resistance can be achieved by a distributed weight adaptation scheme.
Impact of Free-Certificate Coupons on Learner Behavior in Online Courses: Results from Two Case Studies

The relationship between pricing and learning behavior is an increasingly important topic in MOOC (massive open online course) research. We report on two case studies where cohorts of learners were offered coupons for free-certificates to explore price reductions might influence user behavior in MOOC-based online learning settings. In Case Study #1, we compare participation and certification rates between courses with and without coupons for free-certificates. In the courses with a free-certificate track, participants signed up for the verified certificate track at higher rates and completion rates among verified students were higher than in the paid-certificate track courses. In Case Study #2, we compare the behaviors of learners within the same courses based on whether they received access to a free-certificate track. Access to free-certificates was associated with somewhat lower certification rates, but overall certification rates remained high particularly among those who viewed the courses. These findings suggests that some other incentives, other than simply the sunk-cost of paying for a verified certificate-track, may motivate learners to complete MOOC courses.
Homomorphisms of matrix algebras and constructions of Butson–Hadamard matrices

Abstract An n × n matrix H is Butson–Hadamard if its entries are k th roots of unity and it satisfies H H ∗ = n I n . Write BH ( n , k ) for the set of such matrices. Suppose that k = p α q β where p and q are primes and α ≥ 1 . A recent result of Ostergard and Paavola uses a matrix H ∈ BH ( n , p k ) to construct H ′ ∈ BH ( p n , k ) . We simplify the proof of this result and remove the restriction on the number of prime divisors of k . More precisely, we prove that if k = m t , and each prime divisor of k divides t , then we can construct a matrix H ′ ∈ BH ( m n , t ) from any H ∈ BH ( n , k ) .
Acceleration of the Power Method with Dynamic Mode Decomposition

An algorithm based on dynamic mode decomposition (DMD) for acceleration of the power method (PM) is presented. The PM is a simple technique for determining the dominant eigenmode of an operator A, ...
Output and Attribute-Based Carbon Regulation Under Uncertainty

Abstract Output-based carbon regulations—such as fuel economy standards and the rate-based standards in the Clean Power Plan—create well-known incentives to inefficiently increase output. Similar distortions are created by attribute-based regulations. This paper demonstrates that, despite these distortions, output and attribute-based standards can always yield strictly greater expected welfare than “flat” emission standards given uncertainty in demand for output (or attributes), assuming locally constant marginal damages. For fuel economy standards, the welfare-maximizing amount of attribute or mileage-basing is likely small relative to current policy. For the electricity sector, however, an intensity standard may yield greater expected welfare than a flat standard.
Prevention and Reversion of Pancreatic Tumorigenesis through a Differentiation-Based Mechanism.

Summary Activating mutations in Kras are nearly ubiquitous in human pancreatic cancer and initiate precancerous pancreatic intraepithelial neoplasia (PanINs) when induced in mouse acinar cells. PanINs normally take months to form but are accelerated by deletion of acinar cell differentiation factors such as Ptf1a, suggesting that loss of cell identity is rate limiting for pancreatic tumor initiation. Using a genetic mouse model that allows for independent control of oncogenic Kras and Ptf1a expression, we demonstrate that sustained Ptf1a is sufficient to prevent Kras-driven tumorigenesis, even in the presence of tumor-promoting inflammation. Furthermore, reintroducing Ptf1a into established PanINs reverts them to quiescent acinar cells in vivo. Similarly, Ptf1a re-expression in human pancreatic cancer cells inhibits their growth and colony-forming ability. Our results suggest that reactivation of an endogenous differentiation program can prevent and reverse oncogene-driven transformation in cells harboring tumor-driving mutations, introducing a potential paradigm for solid tumor prevention and treatment.
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator

We study the sample complexity of approximate policy iteration (PI) for the Linear Quadratic Regulator (LQR), building on a recent line of work using LQR as a testbed to understand the limits of reinforcement learning (RL) algorithms on continuous control tasks. Our analysis quantifies the tension between policy improvement and policy evaluation, and suggests that policy evaluation is the dominant factor in terms of sample complexity. Specifically, we show that to obtain a controller that is within $\varepsilon$ of the optimal LQR controller, each step of policy evaluation requires at most $(n+d)^3/\varepsilon^2$ samples, where $n$ is the dimension of the state vector and $d$ is the dimension of the input vector. On the other hand, only $\log(1/\varepsilon)$ policy improvement steps suffice, resulting in an overall sample complexity of $(n+d)^3 \varepsilon^{-2} \log(1/\varepsilon)$. We furthermore build on our analysis and construct a simple adaptive procedure based on $\varepsilon$-greedy exploration which relies on approximate PI as a sub-routine and obtains $T^{2/3}$ regret, improving upon a recent result of Abbasi-Yadkori et al. 2019.
Design of a prototype ANSI/SPARC three-schema data base system *

This paper has three objectives. First, it describes briefly how an ANSI/SPARC three-schema data base system prototype could be constructed, using wherever possible available data models, system software, and research results. Second, it lists data models selected for each of the three levels; it also explains these selections. We find no existing proposal for an external schema facility to be adequate; therefore, our third objective is to develop specifications for the external level. A user interface based upon simple hierarchical user records is proposed, the necessary theory is described and a mapping language for the definition of external schemata is proposed.
Bleomycin use in the treatment of Hodgkin lymphoma (HL): toxicity and outcomes in the modern era

One-in-five Hodgkin Lymphoma (HL) patients treated with bleomycin develop bleomycin pulmonary toxicity (BPT). Given bleomycin-omission data with negative interim-PET, we assessed changes in BPT sta...
Reduced continental weathering and marine calcification linked to late Neogene decline in atmospheric CO 2

The globally averaged calcite compensation depth has deepened by several hundred metres in the past 15 Myr. This deepening has previously been interpreted to reflect increased alkalinity supply to the ocean driven by enhanced continental weathering due to the Himalayan orogeny during the late Neogene period. Here we examine mass accumulation rates of the main marine calcifying groups and show that global accumulation of pelagic carbonates has decreased from the late Miocene epoch to the late Pleistocene epoch even though CaCO3 preservation has improved, suggesting a decrease in weathering alkalinity input to the ocean, thus opposing expectations from the Himalayan uplift hypothesis. Instead, changes in relative contributions of coccoliths and planktonic foraminifera to the pelagic carbonates in relative shallow sites, where dissolution has not taken its toll, suggest that coccolith production in the euphotic zone decreased concomitantly with the reduction in weathering alkalinity inputs as registered by the decline in pelagic carbonate accumulation. Our work highlights a mechanism whereby, in addition to deep-sea dissolution, changes in marine calcification acted to modulate carbonate compensation in response to reduced weathering linked to the late Neogene cooling and decline in atmospheric partial pressure of carbon dioxide. A redistribution of marine calcifiers along with a reduction in weathering led to increased seafloor carbonate deposition during the late Neogene, according to a global compilation of carbonate mass accumulation rate records from sediment cores.
Design and Analysis of a Proportional-Integral-Derivative Controller with Biological Molecules.

Summary The capability to engineer de novo feedback control with biological molecules is ushering in an era of robust functionality for many applications in biotechnology and medicine. To fulfill their potential, these control strategies need to be generalizable, modular, and operationally predictable. Proportional-integral-derivative (PID) control fulfills this role for technological systems. Integral feedback control allows a system to return to an invariant steady-state value after step disturbances. Proportional and derivative feedback control used with integral control modulate the dynamics of the return to steady state following perturbation. Recently, a biomolecular implementation of integral control was proposed based on an antithetic motif in which two molecules interact stoichiometrically to annihilate each other’s function. In this work, we report how proportional and derivative implementations can be layered on top of this integral architecture to achieve a biochemical PID control design. We investigate computationally and analytically their properties and ability to improve performance.
Sensor Placement for Outage Identifiability in Power Distribution Networks

Accurate topology information is critical for effective operation of power distribution networks. Line outages change the operational topology of a distribution network. Hence, outage detection is an important task. Power distribution networks are operated as radial trees and are recently adopting the integration of advanced sensors to monitor the network in real time. In this paper, a dynamic-programming-based minimum cost sensor placement solution is proposed for outage identifiability. A novel formulation of the sensor placement as a cost optimization problem involving binary placement decisions is proposed, along with a dynamic programming algorithm to solve it in polynomial time. Our sensor placement solution provides a set of locations in a distribution network where sensor placement is mandatory for outage identifiability. We also extend our sensor placement optimization problem and dynamic programming algorithm to the case in which the number of measurement channels of sensors are limited. The advantage of placement strategies are that they incorporate various types of sensors, have a polynomial execution time, are cost effective and ensure outage identifiability for any arbitrary values of loads. Numerical results illustrating the proposed sensor placement solutions are presented under different scenarios for multiple feeder models including standard IEEE test feeders.
Multi-itinerary optimization as cloud service

In this paper, we describe multi-itinerary optimization (MIO)---a novel Bing Maps service that automates the process of building itineraries for multiple agents while optimizing their routes to minimize travel time or distance. MIO can be used by organizations with a fleet of vehicles and drivers, mobile salesforce, or a team of personnel in the field, to maximize workforce efficiency. It supports a variety of constraints, such as service time windows, duration, priority, pickup and delivery dependencies, and vehicle capacity. MIO also considers traffic conditions between locations, resulting in algorithmic challenges at multiple levels (e.g., calculating time-dependent travel-time distance matrices at scale and scheduling services for multiple agents). To support an end-to-end cloud service with turnaround times of a few seconds, our algorithm design targets a sweet spot between accuracy and performance. Toward that end, we build a scalable approach based on the ALNS metaheuristic. Our experiments show that accounting for traffic significantly improves solution quality: MIO finds efficient routes that avoid late arrivals, whereas traffic-agnostic approaches result in a 15% increase in the combined travel time and the lateness of an arrival. Furthermore, our approach generates itineraries with substantially higher quality than a cutting-edge heuristic (LKH), with faster running times for large instances.
Energy Scavenging for Inductively Coupled Passive RFID Systems

Deployment of passive radio-frequency identification (RFID) systems or RFID-enhanced sensor networks requires good understanding of the energy scavenging principles. This paper focuses on the energy scavenging design considerations of inductively coupled passive HF RFID systems. The theoretical estimation of the power by a loop antenna is derived, and the effect of the design parameters on the harvested power is investigated. It is shown that the power delivery performance is largely affected by the tag load at the reader. An adaptive matching circuit at the reader is proposed for achieving optimum power delivery performance when the reader has a variable load. Experimental studies confirm analytical derivations
Proceedings of the 2008 workshop on Testing, analysis, and verification of web services and applications

The last decade has seen the explosive growth of interactive Web software applications in a diverse set of domains including retail, electronic commerce, wikis, blogs, social network services, etc. This explosive growth is likely to accelerate further by adoption of the service oriented computing paradigm that enables interactions among Web accessible software components. Challenges in developing Web accessible software have inspired a new wave of languages, standards, and tools that have not yet become part of the mainstream software engineering research and education. On the other hand, an increasingly large number of software developers work exclusively on Web software development. TAV-WEB 2008 is the third in a series of workshops that focus on testing, analysis and verification of web software. The scope of TAV-WEB 2008 has been extended (from the web service scope of previous workshops TAV-WEB 2004 and TAV-WEB 2006) to include all Web software. The goal of these workshops has been to bring together researchers from academic, research, and industrial communities interested in the emerging area of dependable Web software development, to present and discuss their recent research results. The TAV-WEB 2008 program consists of seven technical papers and an invited tool demonstration reporting recent research results on testing, analysis and verification of web software.
The solar EUV-emitting plasma

The extreme ultraviolet (EUV), covering a decade in wavelength from about 300 A to about 3000 A, is considered. The general characteristics of the quiet solar atmosphere are reviewed, with emphasis on how the observed emission may be used as a tool to infer this structure. Many of the same techniques are applied to the study of overall properties of active regions.
Pain management and psychosocial issues in HIV and AIDS

My goal for this presentation is to provide an overview of the pain experience of patients with AIDS, including focuses on prevalence, common syndromes, and factors that contribute to undertreatment. As a means to dramatize the impact of pain on quality of life, we'll discuss excerpts from a videotape of one of my patients who eloquently expresses the difficulty he had in getting effective pain treatment, and the impact if finally made. Finally, we'll discuss basic management principles, largely adapted from those developed over the past decades for cancer pain, with a special emphasis on issues related to the management of pain in AIDS patients who have a history of substance abuse.
Knowledge Discovery in Nanophotonics Using Geometric Deep Learning

We present here a new approach for using the intelligence aspects of artificial intelligence for knowledge discovery rather than device optimization in electromagnetic (EM) nanostructures. This approach uses training data obtained through full-wave EM simulations of a series of nanostructures to train geometric deep learning algorithms to assess the range of feasible responses as well as the feasibility of a desired response from a class of EM nanostructures. To facilitate the knowledge discovery and reduce the computation complexity, our approach combines the dimensionality reduction technique (using an autoencoder) with convex-hull and one-class support-vector-machine (SVM) algorithms to find the range of the feasible responses in the latent (or the reduced) response space of the EM nanostructure. We show that by using a small set of training instances (compared to all possible structures), our approach can provide better than 95% accuracy in assessing the feasibility of a given response. More importantly, the one-class SVM algorithm can be trained to provide the degree of feasibility (or unfeasibility) of a response from a given nanostructure. This important information can be used to modify the initial structure to an alternative one that can enable an initially unfeasible response. To show the applicability of our approach, we apply it to two important classes of binary metasurfaces (MSs), formed by array of plasmonic nanostructures, and periodic MSs formed by an array of dielectric nanopillars. In addition to theoretical results, we show the experimental results obtained by fabricating several MSs of the second class. Our theoretical and experimental results confirm the unique features of this approach for knowledge discovery in EM nanostructures.
Experimental measurement of structural intensity on an aircraft fuselage

An experimental technique was used to measure structural intensity through an aircraft fuselage with an excitation load applied near one of the wing attachment locations. The fuselage was relatively large, requiring several measurement locations to analyze the intensity flow through the whole of the structure. For the measurement of structural intensity, the use of a transducer array was necessary at every location of interest. A trade-off was therefore required between the number of measurement transducers, the mounting of these transducers, and the accuracy of the measurements. Using four accelerometers mounted on a bakelite platform, structural intensity vectors were measured at locations distributed throughout the fuselage. The results of these measurements, together with a discussion on the suitability of the approach for measuring structural intensity on a real structure, are presented in this paper.
International Business Cycles in Emerging Markets

I document cyclical behavior of real exchange rates (RERs) in emerging and developed economies: stronger RER procyclicality coincides with larger relative volatility of consumption and more countercyclical trade balance. I then reevaluate the sources of fluctuations in emerging economies using an international business cycle model estimated to match the behavior of the RERs. Interest rate shocks, without any frictions, account for most of output fluctuations. This result is driven by imperfect substitution between domestic and foreign goods, which dampens the impact of trend shocks and accentuates the impact of interest rate shocks on output and consumption.
Transition-metal phthalocyanine monolayers as new Chern insulators

To explore new materials for the realization of the quantum anomalous Hall effect (QAHE), we studied the electronic, magnetic and topological properties of transition-metal phthalocyanine (TMPc) monolayers in a square lattice. Many of them have large topologically nontrivial band gaps and integer Chern numbers. In particular, the Fermi level of MoPc lies right in the band gap (Eg = 8.1 meV) and has large magnetic anisotropy energy (MAE = 2.0 meV per Mo atom) and a Curie temperature of 16 K, showing its usefulness for applications. The presence of the topologically protected edge state in a MoPc nanoribbon further confirms it as a new two-dimensional topological insulator. This much widens the search for QAHE materials for the design of quantum devices.
How holistic processing of faces relates to cognitive control and intelligence

The Vanderbilt Holistic Processing Test for faces (VHPT-F) is the first standard test designed to measure individual differences in holistic processing. The test measures failures of selective attention to face parts through congruency effects, an operational definition of holistic processing. However, this conception of holistic processing has been challenged by the suggestion that it may tap into the same selective attention or cognitive control mechanisms that yield congruency effects in Stroop and Flanker paradigms. Here, we report data from 130 subjects on the VHPT-F, several versions of Stroop and Flanker tasks, as well as fluid IQ. Results suggested a small degree of shared variance in Stroop and Flanker congruency effects, which did not relate to congruency effects on the VHPT-F. Variability on the VHPT-F was also not correlated with Fluid IQ. In sum, we find no evidence that holistic face processing as measured by congruency in the VHPT-F is accounted for by domain-general control mechanisms.
A Novel Explicit Disturbance Model-Based Robust Damping of Interarea Oscillations Through MTDC Grids Embedded in AC Systems

This paper presents a novel approach to damp inter-area oscillations by designing a robust multi-input multi-output (MIMO) supplementary controller for Multiterminal DC (MTDC) systems embedded in AC grids. The key idea to achieve robustness lies in explicitly modeling the MTDC current injection as disturbances using an H-infinity mixed-sensitivity formulation in the Linear Matrix Inequality (LMI) framework. Control directions are established by selecting wide-area feedback signals and the Relative Gain Array computation. Robustness is assessed through dynamic simulations for scenarios including: (a) disturbances on the AC side, (b) disturbances on the DC-side such as loss of a converter pole including actuator, (c) partial loss of feedback signal, and (d) communication latencies. The performance of the proposed controller is compared against the conventional H-infinity based design, using a 4-terminal DC grid embedded within the New England-New York test system. The results suggest that the proposed approach demonstrates superior performance following DC-side disturbances, actuator outages, and latency.
Linear temporal logic for hybrid dynamical systems: Characterizations and sufficient conditions

Abstract This paper introduces operators, semantics, characterizations, and solution-independent conditions to guarantee temporal logic specifications for hybrid dynamical systems. Hybrid dynamical systems are given in terms of differential inclusions – capturing the continuous dynamics – and difference inclusions – capturing the discrete dynamics or events – with constraints. State trajectories (or solutions) to such systems are parameterized by a hybrid notion of time. For such broad class of solutions, the operators and semantics needed to reason about temporal logic are introduced. Characterizations of temporal logic formulas in terms of dynamical properties of hybrid systems are presented – in particular, forward invariance and finite time attractivity. These characterizations are exploited to formulate sufficient conditions assuring the satisfaction of temporal logic formulas — when possible, these conditions do not involve solution information. Combining the results for formulas with a single operator, ways to certify more complex formulas are pointed out, in particular, via a decomposition using a finite state automaton. Academic examples illustrate the results throughout the paper.
The 2020 Presidential election: A race against mortality

A year from the inauguration, four of the top five Democratic 2020 U S Presidential election candidates in the polls are in their seventies Using actuarial data and the history of Presidential assassinations, the top two contenders, Former Vice President Joe Biden and Vermont Senator Bernie Sanders, have a 24–29% chance of not surviving to the end of a hypothetical first term The 77 and 78-year-old men's chances of dying before the end of a second term as President of the United States are between 46 and 56% © 2020 The American Risk and Insurance Association
Modulation and filtering control of soliton transmission

Two methods of controlling the temporal diffusion of solitons in long-distance transmission are investigated theoretically. One, pursued experimentally by Nakazawa et al. [ Electron. Lett.27, 1270 ( 1991)] uses amplitude modulation synchronized with the bit rate. The other method, first proposed recently, uses filtering. The latter is better adapted to wavelength division multiplexing. It is shown that the so-called Gordon-Haus limit of soliton propagation can be extended significantly.
Application of information-percolation method to reconstruction problems on graphs

In this paper we propose a method of proving impossibility results based on applying strong data-processing inequalities to estimate mutual information between sets of variables forming certain Markov random fields. The end result is that mutual information between two "far away" (as measured by the graph distance) variables is bounded by the probability of the existence of an open path in a bond-percolation problem on the same graph. Furthermore, stronger bounds can be obtained by establishing mutual information comparison results with an erasure model on the same graph, with erasure probabilities given by the contraction coefficients. As applications, we show that our method gives sharp threshold for partially recovering a rank-one perturbation of a random Gaussian matrix (spiked Wigner model), yields the best known upper bound on the noise level for group synchronization (obtained concurrently by Abbe and Boix), and establishes new impossibility result for community detection on the stochastic block model with $k$ communities.
Meta Distribution for Downlink NOMA in Cellular Networks with 3GPP-Inspired User Ranking

This paper presents the meta distribution analysis of the downlink two-user non-orthogonal multiple access (NOMA) in cellular networks. We propose a novel user ranking technique wherein the users from the cell center (CC) and cell edge (CE) regions are paired for the non-orthogonal transmission. Inspired by how users are partitioned in 3GPP cellular models, the CC and CE users are characterized based on the mean powers received from the serving and the dominant interfering BSs. We demonstrate that the proposed technique ranks users in an accurate order with distinct link qualities, which is imperative for the performance of NOMA system. The exact moments of the meta distributions for the CC and CE users under NOMA and orthogonal multiple access (OMA) are derived. In addition, we provide tight beta distribution approximations for the meta distributions and exact expressions of the mean local delays and the cell throughputs for the NOMA and OMA cases. To the best of our knowledge, this is the first comprehensive analysis of NOMA using stochastic geometry with 3GPP-inspired user ranking scheme that depends upon both of the link qualities from the serving and dominant interfering BSs.
An Expanded Local Variance Gamma Model

The paper proposes an expanded version of the Local Variance Gamma model of Carr and Nadtochiy by adding drift to the governing underlying process. Still in this new model it is possible to derive an ordinary differential equation for the option price which plays a role of Dupire’s equation for the standard local volatility model. It is shown how calibration of multiple smiles (the whole local volatility surface) can be done in such a case. Further, assuming the local variance to be a piecewise linear function of strike and piecewise constant function of time this ODE is solved in closed form in terms of Confluent hypergeometric functions. Calibration of the model to market smiles does not require solving any optimization problem and, in contrast, can be done term-by-term by solving a system of non-linear algebraic equations for each maturity. This is much faster as compared with calibration which requires solving the Dupire PDE.
Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes

This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a polic...
Trustworthy Misinformation Mitigation with Soft Information Nudging

Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.
Flux expulsion in niobium superconducting radio-frequency cavities of different purity and essential contributions to the flux sensitivity

Magnetic flux trapped during the cooldown of superconducting radio-frequency cavities through the transition temperature due to incomplete Meissner state is known to be a significant source of radio-frequency losses. The sensitivity of flux trapping depends on the distribution and the type of defects and impurities which pin vortices, as well as the cooldown dynamics when the cavity transitions from a normal to superconducting state. Here we present the results of measurements of the flux trapping sensitivity on 1.3 GHz elliptical cavities made from large-grain niobium with different purity for different cooldown dynamics and surface treatments. The results show that lower purity material results in a higher fraction of trapped flux and that the trapped flux sensitivity parameter $S$ is significantly affected by surface treatments but without much change in the mean free path $l$. We discuss our results within an overview of published data on the dependencies of $S(l,f)$ on $l$ and frequency $f$ using theoretical models of rf losses of elastic vortex lines driven by weak rf currents in the cases of sparse strong pinning defects and collective pinning by many weak defects. Our analysis shows how multiscale pinning mechanisms in cavities can result in a maximum in $S(l)$ similar to that observed by the FNAL and Cornell groups and how pinning characteristics can be extracted from the experimental data. Here the main contribution to $S$ come from weak pinning regions at the cavity surface, where dissipative oscillations along trapped vortices perpendicular to the surface propagate into the bulk well beyond the layer of rf screening current. However, the analysis of $S$ as a function of only the mean free path is incomplete since cavity treatments change not only $l$ but pinning characteristics as well. The effect of cavity treatments on pinning is primarily responsible for the change of $S$ without much effect on $l$ observed in this work. It also manifests itself in different magnitudes and peak positions in $S(l)$, and scatter of the $S$-data coming from the measurements on different cavities which have undergone different treatments affecting both $l$ and pinning. Optimizations of flux pinning to reduce flux sensitivity at low rf fields is discussed.
Control Barrier Functions for Systems with High Relative Degree

This paper extends control barrier functions (CBFs) to high order control barrier functions (HOCBFs) that can be used for high relative degree constraints. The proposed HOCBFs are more general than recently proposed (exponential) HOCBFs. We introduce high order barrier functions (HOBFs), and show that their satisfaction of Lyapunov-like conditions implies the forward invariance of the intersection of a series of sets. We then introduce HOCBF, and show that any control input that satisfies the HOCBF constraint renders the intersection of a series of sets forward invariant. We formulate optimal control problems with constraints given by HOCBF and control Lyapunov functions (CLF), and provide a promising method to address the conflict between HOCBF constraints and control limitations by penalizing the class $\mathcal{K}$ functions. We illustrate the proposed method on an adaptive cruise control problem.
Near-Perfect Recovery in the One-Dimensional Latent Space Model

Suppose a graph G is stochastically created by uniformly sampling vertices along a line segment and connecting each pair of vertices with a probability that is a known decreasing function of their distance. We ask if it is possible to reconstruct the actual positions of the vertices in G by only observing the generated unlabeled graph. We study this question for two natural edge probability functions — one where the probability of an edge decays exponentially with the distance and another where this probability decays only linearly. We initiate our study with the weaker goal of recovering only the order in which vertices appear on the line segment. For a segment of length n and a precision parameter δ, we show that for both exponential and linear decay edge probability functions, there is an efficient algorithm that correctly recovers (up to reflection symmetry) the order of all vertices that are at least δ apart, using only samples (vertices). Building on this result, we then show that vertices (samples) are sufficient to additionally recover the location of each vertex on the line to within a precision of δ. We complement this result with an lower bound on samples needed for reconstructing positions (even by a computationally unbounded algorithm), showing that the task of recovering positions is information-theoretically harder than recovering the order. We give experimental results showing that our algorithm recovers the positions of almost all points with high accuracy.
A revolution in biochemistry and molecular biology education informed by basic research to meet the demands of 21st century career paths

The National Science Foundation estimates that 80% of the jobs available during the next decade will require math and science skills, dictating that programs in biochemistry and molecular biology must be transformative and use new pedagogical approaches and experiential learning for careers in industry, research, education, engineering, health-care professions, and other interdisciplinary fields. These efforts require an environment that values the individual student and integrates recent advances from the primary literature in the discipline, experimentally directed research, data collection and analysis, and scientific writing. Current trends shaping these efforts must include critical thinking, experimental testing, computational modeling, and inferential logic. In essence, modern biochemistry and molecular biology education must be informed by, and integrated with, cutting-edge research. This environment relies on sustained research support, commitment to providing the requisite mentoring, access to instrumentation, and state-of-the-art facilities. The academic environment must establish a culture of excellence and faculty engagement, leading to innovation in the classroom and laboratory. These efforts must not lose sight of the importance of multidimensional programs that enrich science literacy in all facets of the population, students and teachers in K-12 schools, nonbiochemistry and molecular biology students, and other stakeholders. As biochemistry and molecular biology educators, we have an obligation to provide students with the skills that allow them to be innovative and self-reliant. The next generation of biochemistry and molecular biology students must be taught proficiencies in scientific and technological literacy, the importance of the scientific discourse, and skills required for problem solvers of the 21st century.