cssi/SciSciGPT-SciSciNet huggingface dataset:
When face-to-face interactions become an occupational hazard: Jobs in the time of COVID-19

There is a crisis of demand brewing around the globe as social distancing becomes the norm to counter the COVID-19 outbreak. So, which parts of the economy are most in the line of fire? Looking at jobs that can be done at home or that require a high degree of face-to-face interactions with consumers can capture complementary but distinct mechanisms to assess this vulnerability. This paper uses data on 900 job titles from the Occupational Information Network (O*NET) database for the United States to demonstrate that there is substantial heterogeneity in vulnerability across industries, income groups, and gender. First, industries vary in whether they emphasize face-to-face interactions and home-based work and the two do not always go hand-in-hand. Second, occupations that are less amenable to home-based work are largely concentrated among the lower wage deciles. Third, a larger share of women's employment is accounted for by occupations that are intensive in face-to-face interactions.
How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach

Proving ground has been a critical component in testing and validation for Connected and Automated Vehicles (CAV). Although quite a few world-class testing facilities have been under construction over the years, the evaluation of proving grounds themselves as testing approaches has rarely been studied. In this paper, we present the first attempt to systematically evaluate CAV proving grounds and contribute to a generative sample-based approach to assessing the representation of traffic scenarios in proving grounds. Leveraging typical use cases extracted from naturalistic driving events, we establish a strong link between proving ground testing results of CAVs and their anticipated public street performance. We present benchmark results of our approach on three world-class CAV testing facilities: Mcity, Almono (Uber ATG), and Kcity. We successfully show the overall evaluation of these proving grounds in terms of their capability to accommodate real-world traffic scenarios. We believe that when the effectiveness of a testing ground itself is validated, the testing results would grant more confidence for CAV public deployment.
Structured Encryption and Controlled Disclosure.

We consider the problem of encrypting structured data (e.g., a web graph or a social network) in such a way that it can be efficiently and privately queried. For this purpose, we introduce the notion of structured encryption which generalizes previous work on symmetric searchable encryption (SSE) to the setting of arbitrarily-structured data. In the context of cloud storage, structured encryption allows a client to encrypt data without losing the ability to query and retrieve it efficiently. Another application, which we introduce in this work, is to the problem of controlled disclosure, where a data owner wishes to grant access to only part of a massive dataset. We propose a model for structured encryption, a formal security definition and several efficient constructions. We present schemes for performing queries on two simple types of structured data, specifically lookup queries on matrix-structured data, and search queries on labeled data. We then show how these can be used to construct efficient schemes for encrypting graph data while allowing for efficient neighbor and adjacency queries. Finally, we consider data that exhibits a more complex structure such as labeled graph data (e.g., web graphs). We show how to encrypt this type of data in order to perform focused subgraph queries, which are used in several web search algorithms. Our construction is based on our labeled data and basic graph encryption schemes and provides insight into how several simpler algorithms can be combined to generate an efficient scheme for more complex queries.
Edge-SLAM: edge-assisted visual simultaneous localization and mapping

Localization in urban environments is becoming increasingly important and used in tools such as ARCore [11], ARKit [27] and others. One popular mechanism to achieve accurate indoor localization as well as a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Further, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading of some tasks without the large latencies seen when offloading to the cloud. In this paper, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closure, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant which would allow for deployment of other end applications that use Visual-SLAM.
Weakly mixing smooth planar vector field without asymptotic directions

We construct a planar smooth weakly mixing stationary random vector field with nonnegative components such that, with probability 1, the flow generated by this vector field does not have an asymptotic direction. Moreover, for all individual trajectories, the set of partial limiting directions coincides with those spanning the positive quadrant. A modified example shows that a particle in space-time weakly mixing positive velocity field does not necessarily have an asymptotic average velocity.
Learning to Encode Position for Transformer with Continuous Dynamical Model

We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, which contain inductive bias by loading the input tokens sequentially, non-recurrent models are less sensitive to position. The main reason is that position information among input units is not inherently encoded, i.e., the models are permutation equivalent; this problem justifies why all of the existing models are accompanied by a sinusoidal encoding/embedding layer at the input. However, this solution has clear limitations: the sinusoidal encoding is not flexible enough as it is manually designed and does not contain any learnable parameters, whereas the position embedding restricts the maximum length of input sequences. It is thus desirable to design a new position layer that contains learnable parameters to adjust to different datasets and different architectures. At the same time, we would also like the encodings to extrapolate in accordance with the variable length of inputs. In our proposed solution, we borrow from the recent Neural ODE approach, which may be viewed as a versatile continuous version of a ResNet. This model is capable of modeling many kinds of dynamical systems. We model the evolution of encoded results along position index by such a dynamical system, thereby overcoming the above limitations of existing methods. We evaluate our new position layers on a variety of neural machine translation and language understanding tasks, the experimental results show consistent improvements over the baselines.
Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (Rt ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the {\color{red}transmission} rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (two to three weeks after the outbreak). A fast rate of decline in $R_t$ was observed and adopting mitigation strategies early in the epidemic was effective in reducing the {\color{red}transmission} rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the {\color{red}transmission} rate decreases. In the United States, Rt significantly decreased during a 2-week period after the declaration of national emergency, but declines at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid November with less than 100 daily cases) and a total of more than 2 million cases.
Quasi-isometric embeddings of symmetric spaces and lattices: reducible case

We study quasi-isometric embeddings of symmetric spaces and non-uniform irreducible lattices in semi-simple higher rank Lie groups. We show that any quasi-isometric embedding between symmetric spaces of the same rank can be decomposed into a product of quasi-isometric embeddings into irreducible symmetric spaces. We thus extend earlier rigidity results about quasi-isometric embeddings to the setting of semi-simple Lie groups. We also present some examples when the rigidity does not hold, including first examples in which every flat is mapped into multiple flats.
A High-Resolution Full-Earth Disk Model for Evaluating Synthetic Aperture Passive Microwave Observations From GEO

A proposed instrument for deployment on next-generation Geostationary Operational Environmental Satellite (GOES) platforms is the Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR). A high-resolution full-Earth disk model has been developed to aid in the design of the instrument and to characterize sensor performance. A number of ancillary geophysical data fields are used as inputs into a radiative-transfer model that also accounts for the propagation and viewing geometries from a geostationary Earth orbit (GEO). The model produces high-resolution (10 km x 10 km) simulated full-Earth disk microwave images from GEO. The model is used as a tool to examine several critical aspects of GeoSTAR performance and design. Differential image processing is assessed as a means of mitigating the effects of the Gibbs phenomenon; its performance is found to be excellent, even with nonideal a priori information. The spatial resolution and precision of images generated at 50 GHz are evaluated. The magnitude of the highest spatial-frequency components sampled by GeoSTAR is found to be well above its minimum detectable signal. However, the differential image processing removes most of the high-frequency content, which is due to static high-contrast boundaries in the scene. Most of the residual high-frequency content lies at or below the instrument noise floor.
The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables

Empirical researchers often combine multiple instrumental variables (IVs) for a single treatment using two-stage least squares (2SLS). When treatment effects are heterogeneous, a common justification for including multiple IVs is that the 2SLS estimand can be given a causal interpretation as a positively weighted average of local average treatment effects (LATEs). This justification requires the well-known monotonicity condition. However, we show that with more than one instrument, this condition can only be satisfied if choice behavior is effectively homogeneous. Based on this finding, we consider the use of multiple IVs under a weaker, partial monotonicity condition. We characterize empirically verifiable sufficient and necessary conditions for the 2SLS estimand to be a positively weighted average of LATEs under partial monotonicity. We apply these results to an empirical analysis of the returns to college with multiple instruments. We show that the standard monotonicity condition is at odds with the data. Nevertheless, our empirical checks reveal that the 2SLS estimate retains a causal interpretation as a positively weighted average of the effects of college attendance among complier groups.
Semi-Parallel logistic regression for GWAS on encrypted data

The sharing of biomedical data is crucial to enable scientific discoveries across institutions and improve health care. For example, genome-wide association studies (GWAS) based on a large number of samples can identify disease-causing genetic variants. The privacy concern, however, has become a major hurdle for data management and utilization. Homomorphic encryption is one of the most powerful cryptographic primitives which can address the privacy and security issues. It supports the computation on encrypted data, so that we can aggregate data and perform an arbitrary computation on an untrusted cloud environment without the leakage of sensitive information. This paper presents a secure outsourcing solution to assess logistic regression models for quantitative traits to test their associations with genotypes. We adapt the semi-parallel training method by Sikorska et al., which builds a logistic regression model for covariates, followed by one-step parallelizable regressions on all individual single nucleotide polymorphisms (SNPs). In addition, we modify our underlying approximate homomorphic encryption scheme for performance improvement. We evaluated the performance of our solution through experiments on real-world dataset. It achieves the best performance of homomorphic encryption system for GWAS analysis in terms of both complexity and accuracy. For example, given a dataset consisting of 245 samples, each of which has 10643 SNPs and 3 covariates, our algorithm takes about 43 seconds to perform logistic regression based genome wide association analysis over encryption. We demonstrate the feasibility and scalability of our solution.
A Distributed Algorithm for Solving Linear Algebraic Equations Over Random Networks

This article considers the problem of solving linear algebraic equations of the form $Ax=b$ among multiagents, which seek a solution by using local information in presence of random communication topologies. The equation is solved by $m$ agents where each agent only knows a subset of rows of the partitioned matrix $[A,b]$ . The problem is formulated such that this formulation does not need the distribution of random interconnection graphs. Therefore, this framework includes asynchronous updates and/or unreliable communication protocols. The random Krasnoselskii–Mann iterative algorithm is applied that converges almost surely and in mean square to a solution of the problem for any matrices $A$ and $b$ and any initial conditions of agents’ states. The algorithm is a totally asynchronous algorithm without requiring a priori B-connectivity and distribution dependency assumptions. The algorithm is able to solve the problem even if the weighted matrix of the graph is periodic and irreducible for synchronous protocol. It is demonstrated that the limit point to which the agents’ states converge is determined by the unique solution of a convex optimization problem regardless of the distribution of random communication graphs. Finally, some numerical examples are given to show the results.
Balancing Gaussian vectors in high dimension

Motivated by problems in controlled experiments, we study the discrepancy of random matrices with continuous entries where the number of columns $n$ is much larger than the number of rows $m$. Our first result shows that if $\omega(1) = m = o(n)$, a matrix with i.i.d. standard Gaussian entries has discrepancy $\Theta(\sqrt{n} \, 2^{-n/m})$ with high probability. This provides sharp guarantees for Gaussian discrepancy in a regime that had not been considered before in the existing literature. Our results also apply to a more general family of random matrices with continuous i.i.d entries, assuming that $m = O(n/\log{n})$. The proof is non-constructive and is an application of the second moment method. Our second result is algorithmic and applies to random matrices whose entries are i.i.d. and have a Lipschitz density. We present a randomized polynomial-time algorithm that achieves discrepancy $e^{-\Omega(\log^2(n)/m)}$ with high probability, provided that $m = O(\sqrt{\log{n}})$. In the one-dimensional case, this matches the best known algorithmic guarantees due to Karmarkar--Karp. For higher dimensions $2 \leq m = O(\sqrt{\log{n}})$, this establishes the first efficient algorithm achieving discrepancy smaller than $O( \sqrt{m} )$.
On completion of graded $D$-modules

Let $R = k[x_1, \ldots, x_n]$ be a polynomial ring over a field $k$ of characteristic zero and $\cR$ be the formal power series ring $k[[x_1, \ldots, x_n]]$. If $M$ is a $\D$-module over $R$, then $\cR \otimes_R M$ is naturally a $\D$-module over $\cR$. Hartshorne and Polini asked whether the natural maps $H^i_{\dR}(M)\to H^i_{\dR}(\cR \otimes_R M)$ (induced by $M\to \cR \otimes_R M$) are isomorphisms whenever $M$ is graded and holonomic. We give a positive answer to their question, as a corollary of the following stronger result. Let $M$ be a finitely generated graded $\D$-module: for each integer $i$ such that $\dim_kH^i_{\dR}(M)<\infty$, the natural map $H^i_{\dR}(M)\to H^i_{\dR}(\cR \otimes_R M)$ (induced by $M\to \cR \otimes_R M$) is an isomorphism.
The rigidity of the graphs of homology spheres minus one edge

Abstract We prove that for d − 1 ≥ 3 , the graph of any homology ( d − 1 ) -sphere Δ with g 2 ( Δ ) ≥ 1 and without missing ( d − 1 ) -faces is generically redundantly d -rigid. This confirms a conjecture of Nevo and Novinsky.
Adaptive Neural Trajectory Tracking Control for Flexible-Joint Robots with Online Learning

Collaborative robots and space manipulators contain significant joint flexibility. It complicates the control design, compromises the control bandwidth, and limits the tracking accuracy. The imprecise knowledge of the flexible joint dynamics compounds the challenge. In this paper, we present a new control architecture for controlling flexible-joint robots. Our approach uses a multi-layer neural network to approximate unknown dynamics needed for the feedforward control. The network may be viewed as a linear-in-parameter representation of the robot dynamics, with the nonlinear basis of the robot dynamics connected to the linear output layer. The output layer weights are updated based on the tracking error and the nonlinear basis. The internal weights of the nonlinear basis are updated by online backpropagation to further reduce the tracking error. To use time scale separation to reduce the coupling of the two steps - the update of the internal weights is at a lower rate compared to the update of the output layer weights. With the update of the output layer weights, our controller adapts quickly to the unknown dynamics change and disturbances (such as attaching a load). The update of the internal weights would continue to improve the converge of the nonlinear basis functions. We show the stability of the proposed scheme under the "outer loop" control, where the commanded joint position is considered as the control input. Simulation and physical experiments are conducted to demonstrate the performance of the proposed controller on a Baxter robot, which exhibits significant joint flexibility due to the series-elastic joint actuators.
Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling

Nanopore genome sequencing is the key to enabling personalized medicine, global food security, and virus surveillance. The state-of-the-art base-callers adopt deep neural networks (DNNs) to translate electrical signals generated by nanopore sequencers to digital DNA symbols. A DNN-based base-caller consumes 44.5% of total execution time of a nanopore sequencing pipeline. However, it is difficult to quantize a base-caller and build a power-efficient processing-in-memory (PIM) to run the quantized base-caller. Although conventional network quantization techniques reduce the computing overhead of a base-caller by replacing floating-point multiply-accumulations by cheaper fixed-point operations, it significantly increases the number of systematic errors that cannot be corrected by read votes. The power density of prior nonvolatile memory (NVM)-based PIMs has already exceeded memory thermal tolerance even with active heat sinks, because their power efficiency is severely limited by analog-to-digital converters (ADC). Finally, Connectionist Temporal Classification (CTC) decoding and read voting cost 53.7% of total execution time in a quantized base-caller, and thus became its new bottleneck. In this paper, we propose a novel algorithm/architecture co-designed PIM, Helix, to power-efficiently and accurately accelerate nanopore base-calling. From algorithm perspective, we present systematic error aware training to minimize the number of systematic errors in a quantized base-caller. From architecture perspective, we propose a low-power SOT-MRAM-based ADC array to process analog-to-digital conversion operations and improve power efficiency of prior DNN PIMs. Moreover, we revised a traditional NVM-based dot-product engine to accelerate CTC decoding operations, and create a SOT-MRAM binary comparator array to process read voting. Compared to state-of-the-art PIMs, Helix improves base-calling throughput by 6x, throughput per Watt by 11.9x and per mm2 by 7.5x without degrading base-calling accuracy.
Towards Mobile Multi-Task Manipulation in a Confined and Integrated Environment with Irregular Objects

The FetchIt! Mobile Manipulation Challenge, held at the IEEE International Conference on Robots and Automation (ICRA) in May 2019, offered an environment with complex and integrated task sets, irregular objects, confined space, and machining, introducing new challenges in the mobile manipulation domain. Here we describe our efforts to address these challenges by demonstrating the assembly of a kit of mechanical parts in a caddy. In addition to implementation details, we examine the issues in this task set extensively, and we discuss our software architecture in the hope of providing a base for other researchers. To evaluate performance and consistency, we conducted 20 full runs, then examined failure cases with possible solutions. We conclude by identifying future research directions to address the open challenges.
Teenage Driving, Mortality, and Risky Behaviors

We investigate the effect of teenage driving on mortality and risky behaviors in the United States using a regression discontinuity design. We estimate that motor vehicle fatalities rise by 40% at the minimum legal driving age cutoff, implying a mortality risk per additional mile driven 6-9 times higher than the risk faced by adult drivers. We also find a stark 80% increase in female deaths from drug overdoses and carbon monoxide poisoning at the cutoff, caused by changes in both suicides and accidental deaths. Our analysis suggests driving regulations could be an effective tool to improve teenage health.
Induced subgraphs of powers of oriented cycles

By using a q-analogue of the ‘magic’ matrix introduced by H.Huang in his elegant solution of the sensitivity conjecture, we give a direct generalization of his result, replacing a hypercube graph b...
Towards Non-interactive Witness Hiding.

Witness hiding proofs require that the verifier cannot find a witness after seeing a proof. The exact round complexity needed for witness hiding proofs has so far remained an open question. In this work, we provide compelling evidence that witness hiding proofs are achievable non-interactively for wide classes of languages. We use non-interactive witness indistinguishable proofs as the basis for all of our protocols. We give four schemes in different settings under different assumptions: A universal non-interactive proof that is witness hiding as long as any proof system, possibly an inefficient and/or non-uniform scheme, is witness hiding, has a known bound on verifier runtime, and has short proofs of soundness. A non-uniform non-interactive protocol justified under a worst-case complexity assumption that is witness hiding and efficient, but may not have short proofs of soundness. A new security analysis of the two-message argument of Pass [Crypto 2003], showing witness hiding for any non-uniformly hard distribution. We propose a heuristic approach to removing the first message, yielding a non-interactive argument. A witness hiding non-interactive proof system for languages with unique witnesses, assuming the non-existence of a weak form of witness encryption for any language in \(\textsf {NP}\,\cap \,\textsf {coNP}\).
LL/SC and Atomic Copy: Constant Time, Space Efficient Implementations Using Only Pointer-Width CAS

When designing concurrent algorithms, Load-Link/Store-Conditional (LL/SC) is a very useful primitive since it avoids ABA problems. The full semantics of LL/SC are not supported in hardware by any modern architecture, so there has been a significant amount of work on simulations of LL/SC using CAS. However, all previous algorithms that are constant time either use unbounded sequence numbers (and thus base objects of unbounded size), or require Ω(MP) space to implement M LL/SC objects for P processes. We present the first constant time implementation of LL/SC from bounded-sized CAS objects using only constant space overhead per LL/SC variable. In particular, our implementation uses Θ(M+kP²) space, where k is the number of outstanding LL operations per process, and only requires pointer-width CAS operations. In most algorithms that use LL/SC, k is a small constant which reduces our additive space overhead to Θ(P²). Our algorithm can also be extended to implement L word LL/SC objects in Θ(L) time for LL and SC, O(1) time for VL, and Θ((M+kP²)L) space. To achieve these bounds, our main technical contribution is implementing a new primitive called Single-Writer Copy which takes a pointer to a word sized memory location and atomically copies its contents into another object. The restriction is that only one process is allowed to write/copy into the destination object at a time. The ability to read from one memory location and write to another atomically, and in constant-time, is very powerful and we believe this primitive will be useful in designing other algorithms.
Energy-minimizing maps from manifolds with nonnegative Ricci curvature

The energy of any C1 representative of a homotopy class of maps from a compact and connected Riemannian manifold with nonnegative Ricci curvature into a complete Riemannian manifold with no conjuga...
RANDOMIZED CELLULAR AUTOMATA

We define and study a few properties of a class of random automata networks. While regular finite one-dimensional cellular automata are defined on periodic lattices, these automata networks, called randomized cellular automata, are defined on random directed graphs with constant out-degrees and evolve according to cellular automaton rules. For some families of rules, a few typical a priori unexpected results are presented.
Planets near Mean-motion Resonances

The multiple-planet systems discovered by the Kepler mission exhibit the following feature: planet pairs near first-order mean-motion resonances prefer orbits just outside the nominal resonance, while avoiding those just inside the resonance. We explore an extremely simple dynamical model for planet formation, in which planets grow in mass at a prescribed rate without orbital migration or dissipation. We develop an analytic version of this model for two-planet systems in two limiting cases: the planet mass grows quickly or slowly relative to the characteristic resonant libration time. In both cases, the distribution of systems in period ratio develops a characteristic asymmetric peak-trough structure around the resonance, qualitatively similar to that observed in the Kepler sample. We verify this result with numerical integrations of the three-body problem. We show that for the 3 : 2 resonance, where the observed peak-trough structure is strongest, our simple model is consistent with the observations for a range of mean planet masses 20-100 M ⊕. This predicted mass range is higher—by at least a factor of three—than the range expected from the few Kepler planets with measured masses, but part of this discrepancy could be due to oversimplifications in the dynamical model or uncertainties in the planetary mass-radius relation.
A New Two-fluid Radiation-hydrodynamical Model for X-Ray Pulsar Accretion Columns

Previous research centered on the hydrodynamics in X-ray pulsar accretion columns has largely focused on the single-fluid model, in which the super-Eddington luminosity inside the column decelerates the flow to rest at the stellar surface. This type of model has been relatively successful in describing the overall properties of the accretion flows, but it does not account for the possible dynamical effect of the gas pressure. On the other hand, the most successful radiative transport models for pulsars generally do not include a rigorous treatment of the dynamical structure of the column, instead assuming an ad hoc velocity profile. In this paper, we explore the structure of X-ray pulsar accretion columns using a new, self-consistent, "two-fluid" model, which incorporates the dynamical effect of the gas and radiation pressure, the dipole variation of the magnetic field, the thermodynamic effect of all of the relevant coupling and cooling processes, and a rigorous set of physical boundary conditions. The model has six free parameters, which we vary in order to approximately fit the phase-averaged spectra in Her X-1, Cen X-3, and LMC X-4. In this paper, we focus on the dynamical results, which shed new light on the surface magnetic field strength, the inclination of the magnetic field axis relative to the rotation axis, the relative importance of gas and radiation pressure, and the radial variation of the ion, electron, and inverse-Compton temperatures. The results obtained for the X-ray spectra are presented in a separate paper.
Next-level composite fermions

A rich pattern of fractional quantum Hall states in graphene double layers can be naturally explained in terms of two-component composite fermions carrying both intra- and interlayer vortices.
A Universal Hurricane Frequency Function

Evidence is provided that the global distribution of tropical hurricanes is principally determined by a universal function H of a single variable z that in turn is expressible in terms of the local sea surface temperature and latitude. The data-driven model presented here carries stark implications for the large increased numbers of hurricanes which it predicts for a warmer world. Moreover, the rise in recent decades in the numbers of hurricanes in the Atlantic, but not the Pacific basin, is shown to have a simple explanation in terms of the specific form of H(z), which yields larger percentage increases when a fixed increase in sea surface temperature occurs at higher latitudes and lower temperatures.
Synchronization of Coupled Limit Cycles

A unified approach to the analysis of synchronization in coupled systems of autonomous differential equations is presented in this work. Through a careful analysis of the variational equation of the coupled system we establish a sufficient condition for synchronization in terms of the geometric properties of the local limit cycles and the coupling operator. This result applies to a large class of differential equation models in physics and biology. The stability analysis is complemented by a discussion of numerical simulations of a compartmental model of a neuron.
Matching theorems for systems of a finitely generated Coxeter group

We study the relationship between two sets S and S 0 of Coxeter generators of a finitely generated Coxeter group W by proving a series of theorems that identify common features of S and S 0 . We describe an algorithm for constructing from any set of Coxeter generators S of W a set of Coxeter generators R of maximum rank for W . A subset C of S is called complete if any two elements of C generate a finite group. We prove that if S and S 0 have maximum rank, then there is a bijection between the complete subsets of S and the complete subsets of S 0 so that corresponding subsets generate isomorphic Coxeter systems. In particular, the Coxeter matrices of .W;S/ and .W;S 0 / have the same multiset of entries.
Hierarchical Probabilistic Inference of the Color–Magnitude Diagram and Shrinkage of Stellar Distance Uncertainties

We present a hierarchical probabilistic model for improving geometric stellar distance estimates using color--magnitude information. This is achieved with a data driven model of the color--magnitude diagram, not relying on stellar models but instead on the relative abundances of stars in color--magnitude cells, which are inferred from very noisy magnitudes and parallaxes. While the resulting noise-deconvolved color--magnitude diagram can be useful for a range of applications, we focus on deriving improved stellar distance estimates relying on both parallax and photometric information. We demonstrate the efficiency of this approach on the 1.4 million stars of the Gaia TGAS sample that also have APASS magnitudes. Our hierarchical model has 4~million parameters in total, most of which are marginalized out numerically or analytically. We find that distance estimates are significantly improved for the noisiest parallaxes and densest regions of the color--magnitude diagram. In particular, the average distance signal-to-noise ratio and uncertainty improve by 19~percent and 36~percent, respectively, with 8~percent of the objects improving in SNR by a factor greater than 2. This computationally efficient approach fully accounts for both parallax and photometric noise, and is a first step towards a full hierarchical probabilistic model of the Gaia data.
Symmetries of Cosmological Cauchy Horizons with Non-Closed Orbits

We consider analytic, vacuum spacetimes that admit compact, non-degenerate Cauchy horizons. Many years ago we proved that, if the null geodesic generators of such a horizon were all closed curves, then the enveloping spacetime would necessarily admit a non-trivial, horizon-generating Killing vector field. Using a slightly extended version of the Cauchy–Kowaleski theorem one could establish the existence of infinite dimensional, analytic families of such ‘generalized Taub-NUT’ spacetimes and show that, generically, they admitted only the single (horizon-generating) Killing field alluded to above. In this article we relax the closure assumption and analyze vacuum spacetimes in which the generic horizon generating null geodesic densely fills a 2-torus lying in the horizon. In particular we show that, aside from some highly exceptional cases that we refer to as ‘ergodic’, the non-closed generators always have this (densely 2-torus-filling) geometrical property in the analytic setting. By extending arguments we gave previously for the characterization of the Killing symmetries of higher dimensional, stationary black holes we prove that analytic, 4-dimensional, vacuum spacetimes with such (non-ergodic) compact Cauchy horizons always admit (at least) two independent, commuting Killing vector fields of which a special linear combination is horizon generating. We also discuss the conjectures that every such spacetime with an ergodic horizon is trivially constructable from the flat Kasner solution by making certain ‘irrational’ toroidal compactifications and that degenerate compact Cauchy horizons do not exist in the analytic case.
Enriching Word Embeddings with Temporal and Spatial Information

The meaning of a word is closely linked to sociocultural factors that can change over time and location, resulting in corresponding meaning changes. Taking a global view of words and their meanings in a widely used language, such as English, may require us to capture more refined semantics for use in time-specific or location-aware situations, such as the study of cultural trends or language use. However, popular vector representations for words do not adequately include temporal or spatial information. In this work, we present a model for learning word representation conditioned on time and location. In addition to capturing meaning changes over time and location, we require that the resulting word embeddings retain salient semantic and geometric properties. We train our model on time- and location-stamped corpora, and show using both quantitative and qualitative evaluations that it can capture semantics across time and locations. We note that our model compares favorably with the state-of-the-art for time-specific embedding, and serves as a new benchmark for location-specific embeddings.
Non-Gaussianity Consistency Relations, Initial States and Back-reaction

We explore the consistency relations for the three-point functions, in the squeezed limit, of scalar and tensor perturbations in single-eld ination with general initial conditions for the perturbations. For slow-roll ination, we nd that all the three- point functions of scalar and tensor perturbations with a coherent state as the initial state are identical to the three-point functions with the Bunch-Davies initial state. On the other hand, there is an apparent violation of some of the consistency relations for initial states that are related to the Bunch-Davies state by Bogoliubov transformations and we identify the reason for this violation. The back-reaction calculations indicate that the three-point functions for these states can be large enough to violate the consistency relations, however, they are too small to be observed in the near future.
Fine properties of the curvature of arbitrary closed sets

Given an arbitrary closed set A of $$ {\mathbf {R}}^{n} $$ , we establish the relation between the eigenvalues of the approximate differential of the spherical image map of A and the principal curvatures of A introduced by Hug–Last–Weil, thus extending a well-known relation for sets of positive reach by Federer and Zahle. Then, we provide for every $$ m = 1, \ldots , n-1 $$ an integral representation for the support measure $$ \mu _{m} $$ of A with respect to the m-dimensional Hausdorff measure. Moreover, a notion of second fundamental form $$Q_{A}$$ for an arbitrary closed set A is introduced so that the finite principal curvatures of A correspond to the eigenvalues of $$ Q_{A} $$ . Finally, we establish the relation between $$Q_A$$ and the approximate differential of order 2 for sets introduced in a previous work of the author, proving that in a certain sense the latter corresponds to the absolutely continuous part of $$Q_A$$ .
A Wearable General-Purpose Solution for Human-Swarm Interaction

Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. Controlling the motion and behavior of these swarms presents unique challenges for human operators, who cannot yet effectively convey their high-level intentions to a group of robots in application. This position paper proposes a new human-swarm interface based on novel wearable gesture-control and haptic-feedback devices. This position paper seeks to combine a wearable gesture recognition device that can detect high-level intentions, a portable device that can detect Cartesian information and finger movements, and a wearable advanced haptic device that can provide real-time feedback. This project is the first to envisage a wearable Human-Swarm Interaction (HSI) interface that separates the input and feedback components of the classical control loop (input, output, feedback), as well as being the first of its kind suitable for both indoor and outdoor environments.
Concerning the existence of Einstein and Ricci soliton metrics on solvable Lie groups

In this work we investigate solvable and nilpotent Lie groups with special metrics. The metrics of interest are left-invariant Einstein and algebraic Ricci soliton metrics. Our main result shows that one may determine the existence of a such a metric by analyzing algebraic properties of the Lie algebra and infinitesimal deformations of any initial metric. Our second main result concerns the isometry groups of such distinguished metrics. Among the completely solvable unimodular Lie groups (this includes nilpotent groups), if the Lie group admits such a metric, we show that the isometry group of this special metric is maximal among all isometry groups of left-invariant metrics.
Quantum Cohomology of Hypertoric Varieties

We give a complete description of the equivariant quantum cohomology ring of any smooth hypertoric variety, and find a mirror formula for the quantum differential equation.
Scalable constructions of fractional repetition codes in distributed storage systems

In distributed storage systems built using commodity hardware, it is necessary to have data redundancy in order to ensure system reliability. In such systems, it is also often desirable to be able to quickly repair storage nodes that fail. We consider a scheme — introduced by El Rouayheb and Ramchandran — which uses combinatorial block design in order to design storage systems that enable efficient (and exact) node repair. In this work, we investigate systems where node sizes may be much larger than replication degrees, and explicitly provide algorithms for constructing these storage designs. Our designs, which are related to projective geometries, are based on the construction of bipartite cage graphs (with girth 6) and the concept of mutually-orthogonal Latin squares. Via these constructions, we can guarantee that the resulting designs require the fewest number of storage nodes for the given parameters, and can further show that these systems can be easily expanded without need for frequent reconfiguration.
Self-similar Magnetocentrifugal Disk Winds with Cylindrical Asymptotics

We construct a two-parameter family of models for self-collimated, magnetized outflows from accretion disks. As in previous magnetocentrifugal wind solutions, a flow at zero initial poloidal speed leaves the surface of a disk in Kepler rotation about a central star, and it is accelerated and redirected toward the pole by rotating, helical magnetic fields that thread the disk. At large distances from the disk, the flow streamlines asymptote to wrap around the surfaces of nested cylinders, with velocity v${b v}$ --> and magnetic field B${b B}$ --> directed in the axial () and toroidal () directions. In the asymptotic regime, the velocity secularly decreases with cylindrical radius R from the inside to the outside of the flow because successive streamlines originate in the circumstellar disk in successively shallower portions of the stellar potential. In contrast to previous disk wind modeling, we have explicitly implemented the cylindrical asymptotic boundary condition to examine the consequences for flow dynamics. The present solutions are developed within the context of r-self-similar flows, such that v${b v}$ -->, the density ?, and B${b B}$ --> scale with spherical radius r as v${b v}$ --> r-1/2, ? r-q, and B${b B}$ --> r-(1+q)/2; q must be smaller than unity in order to achieve cylindrical collimation. We self-consistently obtain the shapes of magnetic field lines and the ?-dependence of all flow quantities. The solutions are characterized by q together with the ratios RA/R1 and R0/R1, where for a given streamline, R0 is the radius of its footpoint in the disk, RA is the cylindrical radius where the flow makes an Alfv?n transition, and R1 is its final asymptotic cylindrical radius. For given q and R0/R1, RA/R1 must be found as an eigenvalue such that the Alfv?n transition is made smoothly. In the solutions we have found, the asymptotic poloidal speed vz on any streamline is typically just a few tenths of the Kepler speed ?R0 at the corresponding disk footpoint, while the asymptotic rotation speed v may be a few tenths to several tenths of ?R0. The asymptotic toroidal Alfv?n speed vA, = B/(4??)1/2 is, however, a few times ?R0; thus the outflows remain magnetically dominated, never making a fast-MHD transition. We discuss the implications of these models for interpretations of observed optical jets and molecular outflows from young stellar systems, and we suggest that the difficulty of achieving strong collimation in vector velocity simultaneously with a final speed comparable to ?R0 argues against isolated jets and in favor of models with broader winds.
Unitarity Methods in AdS/CFT

We develop a systematic unitarity method for loop-level AdS scattering amplitudes, dual to non-planar CFT correlators, from both bulk and boundary perspectives. We identify cut operators acting on bulk amplitudes that put virtual lines on shell, and show how the conformal partial wave decomposition of the amplitudes may be efficiently computed by gluing lower-loop amplitudes. A central role is played by the double discontinuity of the amplitude, which has a direct relation to these cuts. We then exhibit a precise, intuitive map between the diagrammatic approach in the bulk using cutting and gluing, and the algebraic, holographic unitarity method of [1] that constructs the non-planar correlator from planar CFT data. Our analysis focuses mostly on four-point, one-loop diagrams — we compute cuts of the scalar bubble, triangle and box, as well as some one-particle reducible diagrams — in addition to the five-point tree and four-point double-ladder. Analogies with S-matrix unitarity methods are drawn throughout.
Phase sensitive imaging of 10 GHz vibrations in an AlN microdisk resonator

We demonstrate a high frequency phase-sensitive heterodyne vibrometer, operating up to 10 GHz. Using this heterodyne vibrometer, the amplitude and phase fields of the fundamental thickness mode, the radial fundamental, and the 2nd-order modes of an AlN optomechanical microdisk resonator are mapped with a displacement sensitivity of around 0.36pm/Hz. The simultaneous amplitude and phase measurement allow precise mode identification and characterization. The recorded modal frequencies and profiles are consistent with numerical simulations. This vibrometer will be of great significance for the development of high frequency mechanical devices.
Electronic Transport and Thermopower in 2D and 3D Heterostructures—A Theory Perspective

In this review, we discuss the impact of interfaces and heterojuctions on the electronic and thermoelectric transport properties of materials. We review recent progress in understanding electronic transport in two-dimensional (2D) materials ranging from graphene to transition metal dichalcogenides (TMDs), their homojunctions (grain boundaries), lateral heterojunctions (such as graphene/MoS$_2$ lateral interfaces), and vertical van der Waals (vdW) heterostructures. We also review work in thermoelectric properties of 2D heterojunctions, as well as their applications in creating devices such as resonant tunneling diodes (RTDs). Lastly, we turn our focus to work in three-dimensional (3D) heterostructures. While transport in 3D heterostructures has been researched for several decades, here we review recent progress in theory and simulation of quantum effects on transport via the Wigner and non-equilibrium Green's functions (NEGF) approaches. These simulation techniques have been successfully applied toward understanding the impact of heterojunctions on the thermoelectric properties, with applications in energy harvesting, and electron resonant tunneling, with applications in RTDs. We conclude that tremendous progress has been made in both simulation and experiments toward the goal of understanding transport in heterostructures and this progress will soon be parlayed into improved energy converters and quantum nanoelectronic devices.