cssi/SciSciGPT-SciSciNet huggingface dataset:
Online Learning of Power Transmission Dynamics

We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not assume any knowledge of system parameters. It can be implemented in near real-time and requires a small amount of data. Our learning algorithms can be used for model validation and calibration, and can also be applied to related problems of system stability, detection of forced oscillations, generation re-dispatch, as well as to the estimation of the system state.
Distributed semi-stochastic optimization with quantization refinement

We consider the problem of regularized regression in a network of communication-constrained devices. Each node has local data and objectives, and the goal is for the nodes to optimize a global objective. We develop a distributed optimization algorithm that is based on recent work on semi-stochastic proximal gradient methods. Our algorithm employs iteratively refined quantization to limit message size. We present theoretical analysis and conditions for the algorithm to achieve a linear convergence rate. Finally, we demonstrate the performance of our algorithm through numerical simulations.
Construction of Arbitrary Order Conformally Invariant Operators in Higher Spin Spaces

This paper completes the construction of arbitrary order conformally invariant differential operators in higher spin spaces. Jan Slovak has classified all conformally invariant differential operators on locally conformally flat manifolds. We complete his results in higher spin theory in Euclidean space by giving explicit expressions for arbitrary order conformally invariant differential operators, where by conformally invariant we mean equivariant with respect to the conformal group of $$S^m$$ acting in Euclidean space $${\mathbb {R}}^m$$ . We name these the fermionic operators when the order is odd and the bosonic operators when the order is even. Our approach explicitly uses convolution-type operators to construct conformally invariant differential operators. These convolution-type operators are examples of Knapp–Stein operators and they can be considered as the inverses of the corresponding differential operators. Intertwining operators of these convolution-type operators are provided and intertwining operators of differential operators follow immediately. This reveals that our convolution-type operators and differential operators are all conformally invariant. This also gives us a class of conformally invariant convolution-type operators in higher spin spaces. Their inverses, when they exist, are conformally invariant pseudo-differential operators. Further we use Stein Weiss gradient operators and representation theory for the Spin group to naturally motivate the construction of Rarita–Schwinger operators.
X-ray scattering from random rough surfaces

This paper presents a new method to model X-ray scattering on random rough surfaces. It combines the approaches we presented in two previous papers – PZ&LVS1& PZ.2 An actual rough surface is (incompletely) described by its Power Spectral Density (PSD). For a given PSD, model surfaces with the same roughness as the actual surface are constructed by preserving the PSD amplitudes and assigning a random phase to each spectral component. Rays representing the incident wave are reflected from the model surface and projected onto a flat plane, which is the first order approximation of the model surface, as outgoing rays and corrected for phase delays. The projected outgoing rays are then corrected for wave densities and redistributed onto an uniform grid where the model surface is constructed. The scattering is then calculated using the Fourier Transform of the resulting distribution. This method provides the exact solutions for scattering in all directions, without small angle approximation. It is generally applicable to any wave scatterings on random rough surfaces and is not limited to small scattering angles. Examples are given for the Chandra X-ray Observatory optics. This method is also useful for the future generation X-ray astronomy missions.
On Fujita's spectrum conjecture

Abstract We prove Fujita's spectrum conjecture on the discreteness of pseudo-effective thresholds for polarized varieties.
Linked Hom spaces

Author(s): Osserman, Brian | Abstract: In this note, we describe a theory of linked Hom spaces which complements that of linked Grassmannians. Given two chains of vector bundles linked by maps in both directions, we give conditions for the space of homomorphisms from one chain to the other to be itself represented by a vector bundle. We apply this to present a more transparent version of an earlier construction of limit linear series spaces out of linked Grassmannians.
Hypertoric Varieties and Zonotopal Tilings

We give an abstract definition of a hypertoric variety, generalizing the existing constructive definition. We construct a hypertoric variety associated with any zonotopal tiling, and we show that the previously known examples are exactly those varieties associated with regular tilings. In particular, the examples that we construct from irregular tilings have not appeared before. We conjecture that our construction gives a complete classification of hypertoric varieties, analogous to the classification of toric varieties by fans.
Early MFCC and HPCP Fusion for Robust Cover Song Identification.

While most schemes for automatic cover song identification have focused on note-based features such as HPCP and chord profiles, a few recent papers surprisingly showed that local self-similarities of MFCC-based features also have classification power for this task. Since MFCC and HPCP capture complementary information, we design an unsupervised algorithm that combines normalized, beat-synchronous blocks of these features using cross-similarity fusion before attempting to locally align a pair of songs. As an added bonus, our scheme naturally incorporates structural information in each song to fill in alignment gaps where both feature sets fail. We show a striking jump in performance over MFCC and HPCP alone, achieving a state of the art mean reciprocal rank of 0.87 on the Covers80 dataset. We also introduce a new medium-sized hand designed benchmark dataset called "Covers 1000," which consists of 395 cliques of cover songs for a total of 1000 songs, and we show that our algorithm achieves an MRR of 0.9 on this dataset for the first correctly identified song in a clique. We provide the precomputed HPCP and MFCC features, as well as beat intervals, for all songs in the Covers 1000 dataset for use in further research.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks

One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods.
Numerical integration for fractal measures

We find estimates for the error in replacing an integral $\int f d\mu$ with respect to a fractal measure $\mu$ with a discrete sum $\sum_{x \in E} w(x) f(x)$ over a given sample set $E$ with weights $w$. Our model is the classical Koksma-Hlawka theorem for integrals over rectangles, where the error is estimated by a product of a discrepancy that depends only on the geometry of the sample set and weights, and variance that depends only on the smoothness of $f$. We deal with p.c.f self-similar fractals, on which Kigami has constructed notions of energy and Laplacian. We develop generic results where we take the variance to be either the energy of $f$ or the $L^1$ norm of $\Delta f$, and we show how to find the corresponding discrepancies for each variance. We work out the details for a number of interesting examples of sample sets for the Sierpinski gasket, both for the standard self-similar measure and energy measures, and for other fractals.
Decomposition of a Complete Bipartite Multigraph into Arbitrary Cycle Sizes

In a graph G, let $$\mu _G(xy)$$ denote the number of edges between x and y in G. Let $$\lambda K_{v,u}$$ be the graph $$(V\cup U,E)$$ with $$|V|=v$$ , $$|U|=u$$ , and $$\begin{aligned} \mu _G{(xy)}={\left\{ \begin{array}{ll} \lambda &{}\text{ if }\,\, x\in U \text{ and }\,\, y\in V \text{ or } \text{ if }\,\, x\in V \text{ and }\,\, y\in U\\ 0 &{}\text{ otherwise. } \\ \end{array}\right. } \end{aligned}$$ Let M be the sequence of non-negative integers $$m_1,m_2,\ldots ,m_n$$ . An (M)-cycle decomposition of a graph G is a partition of the edge set into cycles of lengths $$m_1,m_2,\ldots ,m_n$$ . In this paper, we establish some necessary and some sufficient conditions for the existence of an (M)-cycle decomposition of $$\lambda K_{v,u}$$ .
Refined Goldbach conjectures with primes in progressions

We consider binary Goldbach-type conjectures with primes in arithmetic progressions. Namely, we make conjectures about the size of exceptional sets based on heuristic arguments and numerical data. ...
CER: Complementary entity recognition via knowledge expansion on large unlabeled product reviews

Product reviews contain a lot of useful information about product features and customer opinions. One important product feature is the complementary entity (products) that may potentially work together with the reviewed product. Knowing complementary entities of the reviewed product is very important because customers want to buy compatible products and avoid incompatible ones. In this paper, we address the problem of Complementary Entity Recognition (CER). Since no existing method can solve this problem, we first propose a novel unsupervised method to utilize syntactic dependency paths to recognize complementary entities. Then we expand category-level domain knowledge about complementary entities using only a few general seed verbs on a large amount of unlabeled reviews. The domain knowledge helps the unsupervised method to adapt to different products and greatly improves the precision of the CER task. The advantage of the proposed method is that it does not require any labeled data for training. We conducted experiments on 7 popular products with about 1200 reviews in total to demonstrate that the proposed approach is effective.
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization

Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constraints. Existing compression methods are either lossy or introduce significant latency. We propose a compression method that leverages low rank matrix factorization during training, to compress the word embedding layer which represents the size bottleneck for most NLP models. Our models are trained, compressed and then further re-trained on the downstream task to recover accuracy while maintaining the reduced size. Empirically, we show that the proposed method can achieve 90% compression with minimal impact in accuracy for sentence classification tasks, and outperforms alternative methods like fixed-point quantization or offline word embedding compression. We also analyze the inference time and storage space for our method through FLOP calculations, showing that we can compress DNN models by a configurable ratio and regain accuracy loss without introducing additional latency compared to fixed point quantization. Finally, we introduce a novel learning rate schedule, the Cyclically Annealed Learning Rate (CALR), which we empirically demonstrate to outperform other popular adaptive learning rate algorithms on a sentence classification benchmark.
Efficient Byzantine Sequential Change Detection

In the multisensor sequential change detection problem, a disruption occurs in an environment monitored by multiple sensors. This disruption induces a change in the observations of an unknown subset of sensors. In the Byzantine version of this problem, which is the focus of this work, it is further assumed that the postulated change-point model may be misspecified for an unknown subset of sensors. The problem then is to detect the change quickly and reliably, for any possible subset of affected sensors, even if the misspecified sensors are controlled by an adversary. Given a user-specified upper bound on the number of compromised sensors, we propose and study three families of sequential change-detection rules for this problem. These are designed and evaluated under a generalization of Lorden’s criterion, where conditional expected detection delay and expected time to false alarm are both computed in the worst-case scenario for the compromised sensors. The first-order asymptotic performance of these procedures is characterized as the worst-case false alarm rate goes to 0. The insights from these theoretical results are corroborated by a simulation study.
Learning causal graphs with small interventions

We consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (SEM-IE). The objective is to minimize the number of experiments to discover the causal directions of all the edges in a causal graph. Previous work has focused on the use of separating systems for complete graphs for this task. We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in the worst case. In addition, we present a novel separating system construction, whose size is close to optimal and is arguably simpler than previous work in combinatorics. We also develop a novel information theoretic lower bound on the number of interventions that applies in full generality, including for randomized adaptive learning algorithms. For general chordal graphs, we derive worst case lower bounds on the number of interventions. Building on observations about induced trees, we give a new deterministic adaptive algorithm to learn directions on any chordal skeleton completely. In the worst case, our achievable scheme is an α-approximation algorithm where α is the independence number of the graph. We also show that there exist graph classes for which the sufficient number of experiments is close to the lower bound. In the other extreme, there are graph classes for which the required number of experiments is multiplicatively α away from our lower bound. In simulations, our algorithm almost always performs very close to the lower bound, while the approach based on separating systems for complete graphs is significantly worse for random chordal graphs.
Order Cancellations, Fees, and Execution Quality in U.S. Equity Options

We examine the effects of an order cancellation fee on limit order flow and execution quality in the PHLX options market. The cancellation fee on professional order flow is effective in reducing the rate at which limit orders are canceled. While the cancellation fee discourages the submission of non-marketable orders, it encourages the submission of marketable orders. Consequently, non-marketable order fill rates increase, marketable order fill speeds decrease, and bid-ask spreads widen. We also find slight increases in both dollar volume and market share. We explore whether a change in pick-off risk is a potential explanation for these new findings and policy implications.
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time

We give a polynomial-time algorithm for learning neural networks with one layer of sigmoids feeding into any Lipschitz, monotone activation function (e.g., sigmoid or ReLU). We make no assumptions on the structure of the network, and the algorithm succeeds with respect to {\em any} distribution on the unit ball in $n$ dimensions (hidden weight vectors also have unit norm). This is the first assumption-free, provably efficient algorithm for learning neural networks with two nonlinear layers. Our algorithm-- {\em Alphatron}-- is a simple, iterative update rule that combines isotonic regression with kernel methods. It outputs a hypothesis that yields efficient oracle access to interpretable features. It also suggests a new approach to Boolean learning problems via real-valued conditional-mean functions, sidestepping traditional hardness results from computational learning theory. Along these lines, we subsume and improve many longstanding results for PAC learning Boolean functions to the more general, real-valued setting of {\em probabilistic concepts}, a model that (unlike PAC learning) requires non-i.i.d. noise-tolerance.
Adversarial Exploitation of Policy Imitation.

This paper investigates a class of attacks targeting the confidentiality aspect of security in Deep Reinforcement Learning (DRL) policies. Recent research have established the vulnerability of supervised machine learning models (e.g., classifiers) to model extraction attacks. Such attacks leverage the loosely-restricted ability of the attacker to iteratively query the model for labels, thereby allowing for the forging of a labeled dataset which can be used to train a replica of the original model. In this work, we demonstrate the feasibility of exploiting imitation learning techniques in launching model extraction attacks on DRL agents. Furthermore, we develop proof-of-concept attacks that leverage such techniques for black-box attacks against the integrity of DRL policies. We also present a discussion on potential solution concepts for mitigation techniques.
Ductal carcinoma in situ of breast: update 2019.

Ductal carcinoma in situ is a non-invasive form of breast cancer. Its incidence is increasing due to widespread use of mammographic screening. It presents several diagnostic and management challenges in part due to its relatively indolent behaviour. Most series analysing biomarkers in these lesions are small (<100 patients) and large clinical trials have not been frequent. Herein, we review the recent progress made in understanding the biology of this entity and the tools available for prognostication.
Structural state recognition facilitates tip tracking of EB1 at growing microtubule ends

The microtubule binding protein EB1 specifically targets the growing ends of microtubules in cells, where EB1 facilitates the interactions of cellular proteins with microtubule plus-ends. Microtubule end targeting of EB1 has been attributed to high-affinity binding of EB1 to GTP-tubulin that is present at growing microtubule ends. However, our 3D single-molecule diffusion simulations predicted a ~ 6000% increase in EB1 arrivals to open, tapered microtubule tip structures relative to closed lattice conformations. Using quantitative fluorescence, single-molecule, and electron microscopy experiments, we found that the binding of EB1 onto opened, structurally disrupted microtubules was dramatically increased relative to closed, intact microtubules, regardless of hydrolysis state. Correspondingly, in cells, the blunting of growing microtubule plus-ends by Vinblastine was correlated with reduced EB1 targeting. Together, our results suggest that microtubule structural recognition, based on a fundamental diffusion-limited binding model, facilitates the tip tracking of EB1 at growing microtubule ends.
An Agent-Based Model of Ethnocentrism and the Unintended Consequences of Violence

We repurpose an agent-based model of ethnocentrism to show how violence affects people’s willingness to cooperate with members of other groups. We account for extra benefits which arise from interacting with a member of the same culture (‘cultural boosts’) and for mutual gains from cooperative activities (‘public goods’). In environments where one person gains at another’s expense, violence decreases ethnocentrism. However, violence increases ethnocentric behavior when cooperation produces shared benefits. These results point to new empirical questions and contribute to policy discussions regarding the use and reduction of violence.
Contingency Model Predictive Control for Automated Vehicles

We present Contingency Model Predictive Control (CMPC), a novel and implementable control framework which tracks a desired path while simultaneously maintaining a contingency plan - an alternate trajectory to avert an identified potential emergency. In this way, CMPC anticipates events that might take place, instead of reacting when emergencies occur. We accomplish this by adding an additional prediction horizon in parallel to the classical receding MPC horizon. The contingency horizon is constrained to maintain a feasible avoidance solution; as such, CMPC is selectively robust to this emergency while tracking the desired path as closely as possible. After defining the framework mathematically, we demonstrate its effectiveness experimentally by comparing its performance to a state-of-the-art deterministic MPC. The controllers drive an automated research platform through a left-hand turn which may be covered by ice. Contingency MPC prepares for the potential loss of friction by purposefully and intuitively deviating from the prescribed path to approach the turn more conservatively; this deviation significantly mitigates the consequence of encountering ice.
Fast Nonseparable Gaussian Stochastic Process With Application to Methylation Level Interpolation

Gaussian stochastic process (GaSP) has been widely used as a prior over functions due to its flexibility and tractability in modeling. However, the computational cost in evaluating the likelihood i...
Targeted testing for bias in order assignment, with an application to Texas election ballots

Abstract Statistical methods are developed for assessing the likelihood of prejudicial bias in agent-assigned permutations, such as the ordering of candidates on an election ballot. The null hypothesis of an unbiased order assignment is represented by several forms of probabilistic exchangeability of the random orderings, while bias is represented either by compatibility with an assumed ranking of the items with respect to a hypothesized preference criterion (PC) or by linear concordance with assumed scores of the items on a PC scale. A power analysis indicates the superiority of these methods to a neutral alternative when appropriate a priori information is available; their usefulness is affirmed in an application to the ordering of candidates on 2014 Texas Republican primary election ballots. Significant evidence of bias is found in three of the five races studied, a finding that does not obtain using currently available tests.
Have You Seen the Standard Deviation

Background: Sarkar and Rashid (2016a) introduced a geometric way to visualize the mean based on either the empirical cumulative distribution function of raw data, or the cumulative histogram of tabular data. Objective: Here, we extend the geometric method to visualize measures of spread such as the mean deviation, the root mean squared deviation and the standard deviation of similar data. Materials and Methods: We utilized elementary high school geometric method and the graph of a quadratic transformation. Results: We obtain concrete depictions of various measures of spread. Conclusion: We anticipate such visualizations will help readers understand, distinguish and remember these concepts.
Self-localization of three-dimensional sensor networks

Summary form only given. We consider the problem of locating and orienting a heterogeneous network of sensors that deployed in a three-dimensional scene at unknown locations and orientations. The self-localization problem is solved by placing a number of source signals, which in general also have unknown locations, in the scene. A subset of sensors in the network measures the time-of-arrival and local direction-of-arrival of the signal emitted from each source. From these noisy measurements and a measurement uncertainty model we compute maximum likelihood (ML) sensor locations and orientation estimates. We also compute the Cramer-Rao bound for localization accuracy. We present numerical examples using a mix of acoustic and imaging sensors. The acoustic sensors measure TDOAs of acoustic calibration sources, along with DOA with relatively high uncertainty. The imaging sensors measure DOA only, but with high accuracy.
Demonstrating Context-Aware Services in the Mobility First Future Internet Architecture.

As the amount of mobile devices populating the Internet keeps growing at tremendous pace, context-aware services have gained a lot of traction thanks to the wide set of potential use cases they can be applied to. Environmental sensing applications, emergency services, and location-aware messaging are just a few examples of applications that are expected to increase in popularity in the next few years. The MobilityFirst future Internet architecture, a clean-slate Internet architecture design, provides the necessary abstractions for creating and managing context-aware services. Starting from these abstractions we design a context services framework, which is based on a set of three fundamental mechanisms: an easy way to specify context based on human understandable techniques, i.e. use of names, an architecture supported management mechanism that allows both to conveniently deploy the service and efficiently provide management capabilities, and a native delivery system that reduces the tax on the network components and on the overhead cost of deploying such applications. In this paper, we present an emergency alert system for vehicles assisting first responders that exploits users location awareness to support quick and reliable alert messages for interested vehicles. By deploying a demo of the system on a nationwide testbed, we aim to provide better understanding of the dynamics involved in our designed framework.
Computational Experience with the Reich-Moore Resolved-Resonance Equations in the AMPX Cross-Section Processing System

The Reich-Moore (RM) formulation is used extensively in many isotope/nuclide evaluations to represent neutron cross-section data for the resolved-resonance region. The RM equations require the eval...
Persistent Homology and the Upper Box Dimension

We introduce a fractal dimension for a metric space defined in terms of the persistent homology of extremal subsets of that space. We exhibit hypotheses under which this dimension is comparable to the upper box dimension; in particular, the dimensions coincide for subsets of $${\mathbb {R}}^2$$ whose upper box dimension exceeds 1.5. These results are related to extremal questions about the number of persistent homology intervals of a set of n points in a metric space.
Synthesis of microporous silica nanoparticles to study water phase transitions by vibrational spectroscopy

Silica can take many forms, and its interaction with water can change dramatically at the interface. Silica based systems offer a rich tapestry to probe the confinement of water as size and volume can be controlled by various templating strategies and synthetic procedures. To this end, microporous silica nanoparticles have been developed by a reverse microemulsion method utilizing zinc nanoclusters encapsulated in hydroxyl-terminated polyamidoamine (PAMAM-OH) dendrimers as a soft template. These nanoparticles were made tunable within the outer diameter range of 20–50 nm with a core mesopore of 2–15 nm. Synthesized nanoparticles were used to study the effects of surface area and microporous volumes on the vibrational spectroscopy of water. These spectra reveal contributions from bulk interfacial/interparticle water, ice-like surface water, liquid-like water, and hydrated silica surfaces suggesting that microporous silica nanoparticles allow a way to probe silica water interactions at the molecular scale.
Quasilinear elliptic equations with sub-natural growth terms and nonlinear potential theory

We discuss recent advances in the theory of quasilinear equations of the type $ -\Delta_{p} u = \sigma u^{q} \; \; \text{in} \;\; \mathbb{R}^n, $ in the case $0<q< p-1$, where $\sigma$ is a nonnegative measurable function, or measure, for the $p$-Laplacian $\Delta_{p}u= \text{div}(|\nabla u|^{p-2}\nabla u)$, as well as more general quasilinear, fractional Laplacian, and Hessian operators. Within this context, we obtain some new results, in particular, necessary and sufficient conditions for the existence of solutions $u \in \text{BMO}(\mathbb{R}^n)$, $u \in L^r_{{\rm loc}}(\mathbb{R}^n)$, etc., and prove an enhanced version of Wolff's inequality for intrinsic nonlinear potentials associated with such problems.
Climate-Water Adaptation for Future US Electricity Infrastructure

Future climate-water conditions are anticipated to increase electricity demand, reduce transmission capacity, and limit power production. Yet, typical electricity capacity expansion planning does not consider climate-water constraints. We project four alternative U.S. power system configurations using an iterative modeling and data exchange platform that integrates climate-driven hydrological, thermal power plant, and capacity expansion models. Through a comparison with traditional modeling approaches, we show that this novel approach provides greater confidence in electricity capacity projections by incorporating feasibility checks that adjust infrastructure development to reach grid reliability thresholds under climate-water constraints. Initial projections without climate-water impacts on electricity generation show future power systems become less vulnerable, independent of climate-water adaptation, as economic drivers increase renewable and natural gas-based capacity, while water-intensive coal and nuclear plants retire. However, power systems may face reliability challenges without climate-water adaptation, revealing the significance of incorporating climate-water impacts into power system planning. Climate-adjusted (Iterative approach) projections require a 5.3-12.0% increase in national-level capacity, relative to Initial projections, leading to an additional $125-143 billion (5.0-7.0%) in infrastructure costs. Variable renewable and natural gas technologies account for nearly all the additional capacity and, together with regional trade-offs in electricity generation, enhance grid performance to reach reliability thresholds. These adaptation transitions also lower water use and emissions, contributing to climate change mitigation, and highlight the trade-offs and impacts of both near and long-term electricity generation planning decisions.
Catalytic, Enantioselective syn-Diamination of Alkenes

The enantioselective, vicinal diamination of alkenes represents one of the stereocontrolled additions that remains an outstanding challenge in organic synthesis. A general solution to this problem would enable the efficient and selective preparation of widely useful, enantioenriched diamines for applications in medicinal chemistry and catalysis. In this article, we describe the first enantioselective, syn-diamination of simple alkenes mediated by a chiral, enantioenriched organoselenium catalyst together with a N,N'-bistosyl urea as the bifunctional nucleophile and N-fluorocollidinium tetrafluoroborate as the stoichiometric oxidant. Diaryl, aryl-alkyl, and alkyl-alkyl olefins bearing a variety of substituents are all diaminated in consistently high enantioselectivities but variable yields. The reaction likely proceeds through a Se(II)/Se(IV) redox catalytic cycle reminiscent of the syn-dichlorination reported previously. Furthermore, the syn-stereospecificity of the transformation shows promise for highly enantioselective diaminations of alkenes with no strong steric or electronic bias.
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points

Detecting 3D objects from a single RGB image is intrinsically ambiguous, thus requiring appropriate prior knowledge and intermediate representations as constraints to reduce the uncertainties and improve the consistencies between the 2D image plane and the 3D world coordinate. To address this challenge, we propose to adopt perspective points as a new intermediate representation for 3D object detection, defined as the 2D projections of local Manhattan 3D keypoints to locate an object; these perspective points satisfy geometric constraints imposed by the perspective projection. We further devise PerspectiveNet, an end-to-end trainable model that simultaneously detects the 2D bounding box, 2D perspective points, and 3D object bounding box for each object from a single RGB image. PerspectiveNet yields three unique advantages: (i) 3D object bounding boxes are estimated based on perspective points, bridging the gap between 2D and 3D bounding boxes without the need of category-specific 3D shape priors. (ii) It predicts the perspective points by a template-based method, and a perspective loss is formulated to maintain the perspective constraints. (iii) It maintains the consistency between the 2D perspective points and 3D bounding boxes via a differentiable projective function. Experiments on SUN RGB-D dataset show that the proposed method significantly outperforms existing RGB-based approaches for 3D object detection.
Learning Warm-Start Points For Ac Optimal Power Flow

A large amount of data has been generated by grid operators solving AC optimal power flow (ACOPF) throughout the years, and we explore how leveraging this data can be used to help solve future ACOPF problems. We use this data to train a Random Forest to predict solutions of future ACOPF problems. To preserve correlations and relationships between predicted variables, we utilize a multi-target approach to learn approximate voltage and generation solutions to ACOPF problems directly by only using network loads, without the knowledge of other network parameters or the system topology. We explore the benefits of using the learned solution as an intelligent warm start point for solving the ACOPF, and the proposed framework is evaluated numerically using multiple IEEE test networks. The benefit of using learned ACOPF solutions is shown to be solver and network dependent, but shows promise for quickly finding approximate solutions to the ACOPF problem.
Local Fiscal Multipliers and Fiscal Spillovers in the USA

We estimate local fiscal multipliers and spillovers for the USA using a rich dataset based on the US Department of Defense contracts and a variety of outcome variables relating to income and employment. We find strong positive spillovers across locations and industries. Both backward linkages and general equilibrium effects (e.g., income multipliers) contribute to the positive spillovers. Geographical spillovers appear to dissipate fairly quickly with distance. Our evidence points to the relevance of Keynesian-type models that feature excess capacity.
High Energy Colliding Beams; What Is Their Future?

The success of the first few years of LHC operations at CERN, and the expectation of more to come as the LHC's performance improves, are already leading to discussions of what should be next for both proton–proton and electron–positron colliders. In this discussion I see too much theoretical desperation caused by the so-far-unsuccessful hunt for what is beyond the Standard Model, and too little of the necessary interaction of the accelerator, experimenter, and theory communities necessary for a scientific and engineering success. Here, I give my impressions of the problem, its possible solution, and what is needed to have both a scientifically productive and financially viable future.
Determination of Space‐Time Structures from Gravitational Perturbations

We study inverse problems for the Einstein equations with source fields in a general form. Under a microlocal linearization stability condition, we show that by generating small gravitational perturbations and measuring the responses near a freely falling observer, one can uniquely determine the background Lorentzian metric up to isometries in a region where the gravitational perturbations can travel to and return. We apply the result to two concrete examples when the source fields are scalar fields (i.e. Einstein-scalar field equations) and electromagnetic fields (i.e. Einstein-Maxwell equations).
On the Chiral Ring of Calabi–Yau Hypersurfaces in Toric Varieties

This paper is devoted to the calculation of the B-model chiral ring, used in physics, for semiample Calabi–Yau hypersurfaces. We also study the cohomology of semiample hypersurfaces.
A classification of star log symplectic structures on a compact oriented surface

Given a compact oriented surface, we classify log Poisson bi-vectors whose degeneracy loci are locally modeled by a finite set of lines in the plane intersecting at a point. Further, we compute the Poisson cohomology of such structures and discuss the relationship between our classification and the second Poisson cohomology.
Early conservation benefits of a de facto marine protected area at San Clemente Island, California.

De facto marine protected areas (DFMPAs) are regions of the ocean where human activity is restricted for reasons other than conservation. Although DFMPAs are widespread globally, their potential role in the protection of marine habitats, species, and ecosystems has not been well studied. In 2012 and 2013, we conducted remotely operated vehicle (ROV) surveys of marine communities at a military DFMPA closed to all civilian access since 2010 and an adjacent fished reference site at San Clemente Island, the southernmost of California’s Channel Islands. We used data extracted from ROV imagery to compare density and biomass of focal species, as well as biodiversity and community composition, between the two sites. Generalized linear modeling indicated that both density and biomass of California sheephead (Semicossyphus pulcher) were significantly higher inside the DFMPA. Biomass of ocean whitefish (Caulolatilus princeps) was also significantly higher inside the DFMPA. However, species richness and Shannon-Weaver diversity were not significantly higher inside the DFMPA, and overall fish community composition did not differ significantly between sites. Demonstrable differences between the DFMPA and fished site for two highly sought-after species hint at early potential benefits of protection, though the lack of differences in the broader community suggests that a longer trajectory of recovery may be required for other species. A more comprehensive understanding of the potential conservation benefits of DFMPAs is important in the context of marine spatial planning and global marine conservation objectives.
Effects of curative-intent lung cancer therapy on functional exercise capacity and patient-reported outcomes.

Lung cancer treatment can lead to negative health consequences. We analyzed the effects of curative-intent lung cancer treatment on functional exercise capacity (EC) and patient-reported outcomes (PROs). We performed a prospective, observational cohort study of consecutive patients with stage I–IIIA lung cancer undergoing curative-intent therapy and assessed functional EC (primary outcome, six-minute walk distance (6MWD)), cancer-specific quality of life (QoL) (secondary outcome, European Organization for Research and Treatment of Cancer QoL Questionnaire Core 30 (EORTC-QLQ-C30) summary score), and exploratory outcomes including dyspnea (University of California San Diego Shortness of Breath Questionnaire (UCSD SOBQ)) and fatigue Brief Fatigue Inventory (BFI)) symptoms before and at 1 to 3 months post-treatment. We analyzed the time effect of treatment on outcomes using multivariable generalized estimating equations. In 35 enrolled participants, treatment was associated with a clinically meaningful and borderline-significant decline in functional EC ((mean change, 95% CI) 6MWD = − 25.4 m (− 55.3, + 4.47), p = 0.10), clinically meaningful and statistically significant higher dyspnea (UCSD SOBQ = + 13.1 (+ 5.7, + 20.6), p = 0.001) and fatigue (BFI = + 10.0 (+ 2.9, + 17.0), p = 0.006), but no clinically meaningful or statistically significant change in cancer-specific QoL (EORTC-QLQ-C30 summary score = − 3.4 (− 9.8, + 3.0), p = 0.30). Among the first prospective analysis of the effect of curative-intent lung cancer treatment on functional EC and PROs, we observed worsening dyspnea and fatigue, and possibly a decline in functional EC but not cancer-specific QoL at 1 to 3 months post-treatment. Interventions to reduce treatment-related morbidities and improve lung cancer survivorship may need to focus on reducing dyspnea, fatigue, and/or improving functional EC.
Entrepreneurship in a theory of capital and finance—Illustrating the use of subjective quantification

In order to identify an entrepreneurial opportunity, calculation is required. The entrepreneur must do more than simply perceive an opportunity to do something new to create value in the marketplace. Insofar as alternative possible actions exist, the entrepreneur must choose between them. And even as the chosen opportunity is pursued, he must choose between alternative modes of production and organization in the ongoing combination of resources. The details of the kind of calculation required have been left mostly implicit in the literature. This article borrows from the finance literature to suggest a general and universal framework for such calculation and to offer one possible more specific method for using that framework for ongoing entrepreneurial judgment calls. We describe the familiar context in which entrepreneurial decisions occur and reiterate the importance of understanding the nature of capital in such a context of subjective value, uncertainty, and entrepreneurial imagination. We describe the familiar universal present value arithmetic and how it applies to entrepreneurial calculation. Finally, we take a deeper look at this framework, providing an example of one method of analysis used in financial management.
Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning

In this paper, a biologically-inspired adaptive intelligent secondary controller is developed for microgrids to tackle system dynamics uncertainties, faults, and/or disturbances. The developed adaptive biologically-inspired controller adopts a novel computational model of emotional learning in mammalian limbic system. The learning capability of the proposed biologically-inspired intelligent controller makes it a promising approach to deal with the power system non-linear and volatile dynamics without increasing the controller complexity, and maintain the voltage and frequency stabilities by using an efficient reference tracking mechanism. The performance of the proposed intelligent secondary controller is validated in terms of the voltage and frequency absolute errors in the simulated microgrid. Simulation results highlight the efficiency and robustness of the proposed intelligent controller under the fault conditions and different system uncertainties compared to other benchmark controllers.
The Design of Personal Privacy and Security Risk Scores for Minimizing Consumers' Cognitive Gaps in IoT Settings.

The advent of Internet of Things (IoT) technology exponentially increases the collection of new information types in consumers’ lives from various sensors. However, many consumers do not fully recognize the potential privacy and security risks (PSR) associated with IoT. Those who are aware rarely take action to protect their personal information because of a cognitive gap between PSR and its impact. To address this problem, we propose a design framework for evaluating and quantifying IoT PSRs related to IoT adoption. Grounded in the cognitive dissonance theory (CDT) and information processing theory (IPT), the proposed framework defines IoT PSR scores and proposes a visual representation for improving consumers’ awareness of PSRs. Furthermore, we suggest a PSR control balance theory (PSR-CBT) to explicate the consumers’ two internal power conflicts. The proposed PSR scores can reduce consumers’ cognitive gaps, and thus, help them make informed purchase decisions toward IoT devices and services.
A variational principle for a non-integrable model

We show the existence of a variational principle for graph homomorphisms from $$\mathbb {Z}^m$$ to a d-regular tree. The technique is based on a discrete Kirszbraun theorem and a concentration inequality obtained through the dynamics of the model. As another consequence of the concentration inequality we also obtain the existence of a continuum of translation-invariant ergodic gradient Gibbs measures for graph homomorphisms from $$\mathbb {Z}^m$$ to a regular tree. The method is sufficiently robust such that it could be applied to other discrete models with a quite general target graphs.
Fiber Invariants of Projective Morphisms and Regularity of Powers of Ideals

We introduce an invariant, associated to a coherent sheaf of graded modules over a projective morphism of schemes, which controls when sheaf cohomology can be passed through the given morphism. We then use this invariant to estimate the stability indexes of the regularity and a∗-invariant of powers of homogeneous ideals. Specifically, for an equigenerated homogeneous ideal I in a standard graded algebra over a Noetherian ring, we give bounds for the smallest values of power q starting from which a∗(Iq) and reg(Iq) become linear functions.
Theory of mind mediates the relations of language and domain-general cognitions to discourse comprehension

Theory of mind has received intensive attention in research as an important skill to develop. Furthermore, recent evidence indicates its role in discourse comprehension. In the current study, we examined the mediating role of theory of mind in the relations of foundational language and cognitive skills (working memory, attentional control, vocabulary, and grammatical knowledge) to discourse comprehension using the direct and indirect effects model of text comprehension and production (Kim, 2016) as a theoretical framework, and using longitudinal data from kindergarten to Grade 2. Structural equation model results showed that theory of mind partially mediated the relations in both grades, and the effects (standardized regression weights) were similar in kindergarten and Grade 2. Interestingly, the relations of language and cognitive skills to theory of mind differed in kindergarten versus Grade 2. Language and cognitive skills had moderate to strong longitudinal stability, and these skills in kindergarten were indirectly related to discourse comprehension in Grade 2 via the language and cognitive skills in Grade 2. These results support the mediating role of theory of mind as well as the nature of structural and longitudinal relations among language and cognitive skills and to discourse comprehension.