cssi/SciSciGPT-SciSciNet huggingface dataset:
The Kauffman bracket ideal for genus-1 tangles

Given a compact oriented 3-manifold M in S3 with boundary, an (M, 2n)-tangle $\mathcal{T}$ is a 1-manifold with 2n boundary components properly embedded in M. We say that $\mathcal{T}$ embeds in a link L in S3 if $\mathcal{T}$ can be completed to L by a 1-manifold with 2n boundary components exterior to M. The link L is called a closure of $\mathcal{T}$. We define the Kauffman bracket ideal of $\mathcal{T}$ to be the ideal $I_\mathcal{T}$ of ℤ[A, A-1] generated by the reduced Kauffman bracket polynomials of all closures of $\mathcal{T}$. If this ideal is non-trivial, then $\mathcal{T}$ does not embed in the unknot. We give an algorithm for computing a finite list of generators for the Kauffman bracket ideal of any (S1 × D2, 2)-tangle, also called a genus-1 tangle, and give an example of a genus-1 tangle with non-trivial Kauffman bracket ideal. Furthermore, we show that if a single-component genus-1 tangle $\hat{\mathcal{T}}$ can be obtained as the partial closure of a (B3, 4)-tangle $\mathcal{T}$, then $I...
Hypothesis testing for automated community detection in networks

Summary Community detection in networks is a key exploratory tool with applications in a diverse set of areas, ranging from finding communities in social and biological networks to identifying link farms in the World Wide Web. The problem of finding communities or clusters in a network has received much attention from statistics, physics and computer science. However, most clustering algorithms assume knowledge of the number of clusters k. We propose to determine k automatically in a graph generated from a stochastic block model by using a hypothesis test of independent interest. Our main contribution is twofold; first, we theoretically establish the limiting distribution of the principal eigenvalue of the suitably centred and scaled adjacency matrix and use that distribution for our test of the hypothesis that a random graph is of Erdős–Renyi (noise) type. Secondly, we use this test to design a recursive bipartitioning algorithm, which naturally uncovers nested community structure. Using simulations and quantifiable classification tasks on real world networks with ground truth, we show that our algorithm outperforms state of the art methods.
Distributed frequency control in power grids under limited communication

In this paper, we analyze the impact of communication failures on the performance of optimal distributed frequency control. We consider a consensus-based control scheme, and show that it does not converge to the optimal solution when the communication network is disconnected. We propose a new control scheme that uses the dynamics of power grid to replicate the information not received from the communication network, and prove that it achieves the optimal solution under any single communication link failure. In addition, we show that this control improves cost under multiple communication link failures. Next, we analyze the impact of discrete-time communication on the performance of distributed frequency control. In particular, we will show that the convergence time increases as the time interval between two messages increases. We propose a new algorithm that uses the dynamics of the power grid, and show through simulation that it improves the convergence time of the control scheme significantly.
Appropriate and inappropriate estimation techniques

Mode (also called MAP) estimation, mean estimation and median estimation are examined here to determine when they can be safely used to derive (posterior) cost minimizing estimates. (These are all Bayes procedures, using the mode, mean, or median of the posterior distribution.) It is found that modal estimation only returns cost minimizing estimates when the cost function is squared distance from the true value and median estimation only returns cost minimizing estimates when the cost function is the distance from the true value. Results are presented on the goodness of modal estimation with non 0-1 cost functions.
Non-demolition dispersive measurement of a superconducting qubit with a microstrip SQUID amplifier

We have studied the possibility of a single-shot non-demolition measurement of a superconducting qubit using a microstrip SQUID amplifier (MSA). The Johnson noise generated by all resistors in the MSA is taken into consideration. We show that a single-shot non-demolition measurement is possible with six photons in the measurement resonator.
Virasoro Constraints and Descendant Hurwitz–Hodge Integrals

Virasoro constraints are applied to degree zero Gromov-Witten theory of weighted projective stacks ℙ(1, N) and ℙ(1, 1, N) to obtain formulas of descendant cyclic Hurwitz- Hodge integrals in higher genera.
On the Parameterized Computation of Minimum Volume Outer Ellipsoid of Minkowski Sum of Ellipsoids

We consider the problem of computing certain parameterized minimum volume outer ellipsoidal (MVOE) approximation of the Minkowski sum of a finite number of ellipsoids. We clarify connections among several parameterizations available in the literature, obtain novel analysis results regarding the conditions of optimality, and based on the same, propose two new algorithms for computing the parameterized MVOE. Numerical results reveal faster runtime for the proposed algorithms than the standard semidefinite programming approach for computing the same.
ACTOR-CRITIC DEEP REINFORCEMENT LEARNING FOR DYNAMIC MULTICHANNEL ACCESS

We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP). We first propose a model-free actor-critic deep reinforcement learning based framework to explore the sensing policy. To evaluate the performance of the proposed sensing policy and the framework’s tolerance against uncertainty, we test the framework in scenarios with different channel switching patterns and consider different switching probabilities. Then, we consider a time-varying environment to identify the adaptive ability of the proposed framework. Additionally, we provide comparisons with the Deep-Q network (DQN) based framework proposed in [1], in terms of both average reward and the time efficiency.
Refined Cyclic Sieving on Words for the Major Index Statistic

Abstract Reiner–Stanton–White (Reiner et al., 2004) defined the cyclic sieving phenomenon (CSP) associated to a finite cyclic group action and a polynomial. A key example arises from the length generating function for minimal length coset representatives of a parabolic quotient of a finite Coxeter group. In type A , this result can be phrased in terms of the natural cyclic action on words of fixed content. There is a natural notion of refinement for many CSP’s. We formulate and prove a refinement, with respect to the major index statistic, of this CSP on words of fixed content by also fixing the cyclic descent type. The argument presented is completely different from Reiner–Stanton–White’s representation-theoretic approach. It is combinatorial and largely, though not entirely, bijective in a sense we make precise with a “universal” sieving statistic on words, flex. A building block of our argument involves cyclic sieving for shifted subset sums, which also appeared in Reiner–Stanton–White. We give an alternate, largely bijective proof of a refinement of this result by extending some ideas of Wagon–Wilf (Wagon and Wilf, 1994).
Hilbert–Kunz theory for nodal cubics, via sheaves

Abstract Suppose B = F [ x , y , z ] / h is the homogeneous coordinate ring of a characteristic p degree 3 irreducible plane curve C with a node. Let J be a homogeneous ( x , y , z ) -primary ideal and n → e n be the Hilbert–Kunz function of B with respect to J. Let q = p n . When J = ( x , y , z ) , it is known that e n = 7 3 q 2 − 1 3 q − R where R = 5 3 if q ≡ 2 ( 3 ) , and is 1 otherwise. We generalize this, showing that e n = μ q 2 + α q − R where R only depends on q mod 3 . We describe α and R in terms of classification data for a vector bundle on C.
Kinematics of the Interstellar Vagabond 1I/‘Oumuamua (A/2017 U1)

The initial Galactic velocity vector for the recently discovered hyperbolic asteroid 1I/'Oumuamua (A/2017 U1) is calculated for before its encounter with our solar system. The latest orbit (JPL-13) shows that 'Oumuamua has eccentricity > 1 at 944\sigma\, significance (1.19936 +- 0.00021), i.e. clearly unbound. Assuming no non-gravitational forces, the object's inbound Galactic velocity was U, V, W = -11.457, -22.395, -7.746 (+-0.009, +-0.009, +-0.011) km/s (U towards Galactic center), with total heliocentric speed 26.32 +- 0.01 km/s. When the velocity is compared to the local stars, 'Oumuamua can be ruled out as co-moving with any of the dozen nearest systems, i.e. it does not appear to be associated with any local exo-Oort clouds (most notably that of the Alpha Centauri triple system). 'Oumuamua's velocity is within 5 km/s of the median Galactic velocity of the stars in the solar neighborhood (<25 pc), and within 2 km/s of the mean velocity of the local M dwarfs. Its velocity appears to be statistically "too" typical for a body whose velocity was drawn from the Galactic velocity distribution of the local stars (i.e. less than 1 in 500 field stars in the solar neighborhood would have a velocity so close to the median UVW velocity). In the Local Standard of Rest frame (circular Galactic motion), 'Oumuamua is remarkable for showing both negligible radial (U) and vertical (W) motion, while having a slightly sub-Keplerian circular velocity (V; by ~11 km/s). These calculations strengthen the interpretation that A/2017 U1 has a distant extrasolar origin, but not among the very nearest stars. Any formation mechanism for this interstellar asteroid should account for the coincidence of 'Oumuamua's velocity being so close to the LSR.
Blind interference alignment for private information retrieval

Blind interference alignment (BIA) refers to interference alignment schemes that are designed only based on channel coherence pattern knowledge at the transmitters (the “blind” transmitters do not know the exact channel values). Private information retrieval (PIR) refers to the problem where a user retrieves one out of K messages from N non-communicating databases (each holds all K messages) without revealing anything about the identity of the desired message index to any individual database. In this paper, we identify an intriguing connection between PIR and BIA. Inspired by this connection, we characterize the information theoretic optimal download cost of PIR, when we have K = 2 messages and the number of databases, N, is arbitrary.
Memory-sample tradeoffs for linear regression with small error

We consider the problem of performing linear regression over a stream of d-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. Specifically, consider a sequence of labeled examples (a1,b1), (a2,b2)…, with ai drawn independently from a d-dimensional isotropic Gaussian, and where bi = ⟨ ai, x⟩ + ηi, for a fixed x ∈ ℝd with ||x||2 = 1 and with independent noise ηi drawn uniformly from the interval [−2−d/5,2−d/5]. We show that any algorithm with at most d2/4 bits of memory requires at least Ω(d loglog1/є) samples to approximate x to l2 error є with probability of success at least 2/3, for є sufficiently small as a function of d. In contrast, for such є, x can be recovered to error є with probability 1−o(1) with memory O(d2 log(1/є)) using d examples. This represents the first nontrivial lower bounds for regression with super-linear memory, and may open the door for strong memory/sample tradeoffs for continuous optimization.
A computationally optimal randomized proper orthogonal decomposition technique

In this paper, we consider the model reduction problem of large-scale systems, such as systems obtained through the discretization of partial differential equations. We propose a computationally optimal randomized proper orthogonal decomposition (RPOD*) technique to obtain the reduced order model by perturbing the primal and adjoint system using Gaussian white noise. We show that the computations required by the RPOD* algorithm is orders of magnitude cheaper when compared to the balanced proper orthogonal decomposition (BPOD) algorithm while the performance of the RPOD* algorithm is better than BPOD. It is optimal in the sense that a minimal number of snapshots is needed. We also relate the RPOD* algorithm to random projection algorithms. One numerical example is given to illustrate the procedure.
Data-driven root-cause analysis for distributed system anomalies

Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems. In this regard, root-cause analysis becomes highly intractable due to complex fault propagation mechanisms in combination with diverse operating modes. This paper presents a new data-driven framework for root-cause analysis for addressing such issues. The framework is based on a spatiotemporal feature extraction scheme for distributed cyber-physical systems built on the concept of symbolic dynamics for discovering and representing causal interactions among subsystems of a complex system. We present two approaches for root-cause analysis, namely the sequential state switching (S3, based on free energy concept of a Restricted Boltzmann Machine, RBM) and artificial anomaly association (A3, a multi-class classification framework using deep neural networks, DNN). Synthetic data from cases with failed pattern(s) and anomalous node are simulated to validate the proposed approaches, then compared with the performance of vector autoregressive (VAR) model-based root-cause analysis. Real dataset based on Tennessee Eastman process (TEP) is also used for validation. The results show that: (1) S3 and A3 approaches can obtain high accuracy in root-cause analysis and successfully handle multiple nominal operation modes, and (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy.
k-Protected Vertices in Unlabeled Rooted Plane Trees

We find a simple, closed formula for the proportion of vertices which are k-protected in all unlabeled rooted plane trees on n vertices. We also find that, as n goes to infinity, the average rank of a random vertex in a tree of size n approaches 0.727649, and the average rank of the root of a tree of size n approaches 1.62297.
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories

A central challenge in neuroscience is understanding how neural system implements computation through its dynamics. We propose a nonlinear time series model aimed at characterizing interpretable dynamics from neural trajectories. Our model assumes low-dimensional continuous dynamics in a finite volume. It incorporates a prior assumption about globally contractional dynamics to avoid overly enthusiastic extrapolation outside of the support of observed trajectories. We show that our model can recover qualitative features of the phase portrait such as attractors, slow points, and bifurcations, while also producing reliable long-term future predictions in a variety of dynamical models and in real neural data.
Almost optimal phaseless compressed sensing with sublinear decoding time

In the problem of compressive phase retrieval, one wants to recover an approximately k-sparse signal x ∊ Cn, given the magnitudes of the entries of ϕ x , where ϕ ∊ Cm×n This problem has received a fair amount of attention, with sublinear time algorithms appearing in [CBJC14], [PLR14], [YLPR15]. In this paper we further investigate the direction of sublinear decoding for real signals by giving a recovery scheme under the l 2 / l 2 guarantee, with almost optimal, O(k log n), number of measurements. Our result outperforms all previous sublinear-time algorithms in the case of real signals. Moreover, we give a very simple deterministic scheme that recovers all k-sparse vectors in O(k3) time, using 4k − 1 measurements.
Birational contractions of $\overline{\mathrm{M}}_{0,n}$ and combinatorics of extremal assignments

From Smyth’s classification, modular compactifications of the moduli space of pointed smooth rational curves are indexed by combinatorial data, the so-called extremal assignments. We explore their combinatorial structures and show that any extremal assignment is a finite union of atomic extremal assignments. We discuss a connection with the birational geometry of the moduli space of stable pointed rational curves. As applications, we study three special classes of extremal assignments: smooth, toric, and invariant with respect to the symmetric group action. We identify them with three combinatorial objects: simple intersecting families, complete multipartite graphs, and special families of integer partitions, respectively.
When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?

The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases – providing gains of up to 20 BLEU points in the most favorable setting.
Iterated antiderivative extensions

Abstract Let F be a characteristic zero differential field with an algebraically closed field of constants and let E be a no new constants extension of F . We say that E is an iterated antiderivative extension of F if E is a liouvillian extension of F obtained by adjoining antiderivatives alone. In this article, we will show that if E is an iterated antiderivative extension of F and K is a differential subfield of E that contains F then K is an iterated antiderivative extension of F .
Duality and de Rham cohomology for graded D-modules

Abstract We consider the (graded) Matlis dual D ( M ) of a graded D -module M over the polynomial ring R = k [ x 1 , … , x n ] (k is a field of characteristic zero), and show that it can be given a structure of D -module in such a way that, whenever dim k ⁡ H d R i ( M ) is finite, then H d R i ( M ) is k-dual to H d R n − i ( D ( M ) ) . As a consequence, we show that if M is a graded D -module such that H d R n ( M ) is a finite-dimensional k-space, then dim k ⁡ ( H d R n ( M ) ) is the maximal integer s for which there exists a surjective D -linear homomorphism M → E s , where E is the top local cohomology module H ( x 1 , … , x n ) n ( R ) . This extends a recent result of Hartshorne and Polini on formal power series rings to the case of polynomial rings; we also apply the same circle of ideas to provide an alternate proof of their result. When M is a finitely generated graded D -module such that dim k ⁡ H d R i ( M ) is finite, we generalize the above result further, showing that H d R n − i ( M ) is k-dual to Ext D i ( M , E ) .
On the finite linear independence of lattice Gabor systems

In the restricted setting of product phase space lattices, we give an alternate proof of P. Linnell's theorem on the finite linear independence of lattice Gabor systems in $L^2(\mathbb R^d)$. Our proof is based on a simple argument from the spectral theory of random Schr\"odinger operators; in the one-dimensional setting, we recover the full strength of Linnell's result for general lattices.
MCUIUC — A new framework for metagenomic read compression

Metagenomics is an emerging field of molecular biology concerned with analyzing the genomes of environmental samples comprising many different diverse organisms. Given the nature of metagenomic data, one usually has to sequence the genomic material of all organisms in a batch, leading to a mix of reads coming from different DNA sequences. In deep high-throughput sequencing experiments, the volume of the raw reads is extremely high, frequently exceeding 600 Gb. With an ever increasing demand for storing such reads for future studies, the issue of efficient metagenomic compression becomes of paramount importance. We present the first known approach to metagenome read compression, termed MCUIUC (Metagenomic Compression at UIUC). The gist of the proposed algorithm is to perform classification of reads based on unique organism identifiers, followed by reference-based alignment of reads for individually identified organisms, and metagenomic assembly of unclassified reads. Once assembly and classification are completed, lossless reference based compression is performed via positional encoding. We evaluate the performance of the algorithm on moderate sized synthetic metagenomic samples involving 15 randomly selected organisms and describe future directions for improving the proposed compression method.
Simple isotherm equations to fit type I adsorption data

Abstract We propose that the Hill model of adsorption can be used to fit type I adsorption isotherms in general. The main assumption that allows us to apply this model empirically is that the adsorbate phase can be divided into identical and non-interacting effective subsystems. This gives rise to a simple multiparametric isotherm based on the grand canonical ensemble statistics, whose functional form is a ratio of two polynomial functions. The parameters are interpreted as adsorption equilibrium constants. A simple recurrence relation for the equilibrium constants is proposed for systems that show an apparent variation in the coverage limit with temperature. This relation avoids overparametrization and improves fitting deviations. We revisit a simplified isotherm derived by Ruthven and show that it can also be applied to heterogeneous systems. We also show how to use the isotherms of Hill and Ruthven, along with the recurrence relation shown in this work, to fit the adsorption data to obtain parameters with statistical significance. Due to their high accuracy, both isotherm equations can be used to estimate thermodynamic properties like isosteric and differential heats of adsorption. Finally, several applications to fitting data, taken from literature, of adsorption of some gases on activated carbon, molecular sieving carbon, silica gel, and pillared clays are presented.
Communication-Efficient Distributed Statistical Inference

We present a communication-efficient surrogate likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global like...
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels

In this paper, we establish a general framework on the reduced dimensional channel state information (CSI) estimation and pre-beamformer design for frequency-selective massive multiple-input multiple-output (MIMO) systems employing single- carrier (SC) modulation in time division duplex (TDD) mode by exploiting the joint angle-delay domain channel sparsity in millimeter (mm) wave frequencies. First, based on a generic subspace projection taking the joint angle-delay power profile and user-grouping into account, the reduced-rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived for spatially correlated wideband MIMO channels. Second, the statistical pre-beamformer design is considered for frequency-selective SC massive MIMO channels. We examine the dimension reduction problem and subspace (beamspace) construction on which the RR-MMSE estimation can be realized as accurately as possible. It is observed that the proposed techniques show remarkable robustness to the pilot interference (or contamination) with a significant reduction in pilot overhead.
Wiener–Hermite polynomial expansion for multivariate Gaussian probability measures ☆

Abstract This paper introduces a new, transformation-free, generalized polynomial chaos expansion (PCE) comprising multivariate Hermite orthogonal polynomials in dependent Gaussian random variables. The second-moment properties of Hermite polynomials reveal a weakly orthogonal system when obtained for a general Gaussian probability measure. Still, the exponential integrability of norm allows the Hermite polynomials to constitute a complete set and hence a basis in a Hilbert space. The completeness is vitally important for the convergence of the generalized PCE to the correct limit. The optimality of the generalized PCE and the approximation quality due to truncation are discussed. New analytical formulae are proposed to calculate the mean and variance of a generalized PCE approximation of a general output variable in terms of the expansion coefficients and statistical properties of Hermite polynomials. However, unlike in the classical PCE, calculating the coefficients of the generalized PCE requires solving a coupled system of linear equations. Besides, the variance formula of the generalized PCE contains additional terms due to statistical dependence among Gaussian variables. The additional terms vanish when the Gaussian variables are statistically independent, reverting the generalized PCE to the classical PCE. Numerical examples illustrate the generalized PCE approximation in estimating the statistical properties of various output variables.
Submodular hamming metrics

We show that there is a largely unexplored class of functions (positive polymatroids) that can define proper discrete metrics over pairs of binary vectors and that are fairly tractable to optimize over. By exploiting submodularity, we are able to give hardness results and approximation algorithms for optimizing over such metrics. Additionally, we demonstrate empirically the effectiveness of these metrics and associated algorithms on both a metric minimization task (a form of clustering) and also a metric maximization task (generating diverse k-best lists).
Capacity of Locally Recoverable Codes

Motivated by applications in distributed storage, the notion of a locally recoverable code (LRC) was introduced a few years back. In an LRC, any coordinate of a codeword is recoverable by accessing only a small number of other coordinates. While different properties of LRCs have been well-studied, their performance on channels with random erasures or errors has been mostly unexplored. In this note, we analyze the performance of LRCs over such stochastic channels. In particular, for input-symmetric discrete memoryless channels, we give a tight characterization of the gap to Shannon capacity when LRCs are used over the channel.
Theory of complex fluids in the warm-dense-matter regime, and application to an unusual phase-transitions in liquid carbon

Data from recent laser-shock experiments, density-functional theory (DFT) with molecular-dynamics (MD), and path-integral Monte Carlo (PIMC) simulations on carbon are compared with predictions from the neutral-pseudo-atom (NPA)+ hyper-netted-chain (HNC) approach for carbon, a complex liquid in the warm-dense matter regime. The NPA results are in good agreement, not only with high-density regimes that have been studies via PIMC, but even at low densities and low temperatures where transient covalent bonding dominates ionic correlations. Thus the `pre-peak' due to the C-C bond at $\sim$1.4-1.6 A$\,$ and other features found in the pair-distribution function from DFT+MD simulations at 0.86 eV and 3.7 g/cm$^3$ etc., are recovered accurately in the NPA+HNC calculations. Such C-C bonding peaks have not been captured via average-atom ion-sphere (IS) models. Evidence for an unusual liquid $\to$ vapor and metal$\to$ semi-metal transition occurring simultaneously is presented. Here a strongly correlated metallic-liquid with transient C-C bonds, i.e., carbon at density $\sim$ 1.0 g/cm$^3$ and mean ionization $Z=4$ transits abruptly to a disordered mono-atomic vapour at 7 eV, with $Z\simeq$ 3. Other cases where $Z$ drops abruptly are also noted. The nature of $Z$, its discontinuities, and the role of exchange correlation, are reviewed. The limitations of IS models in capturing the physics of transient covalent bonding in warm dense matter are discussed.
Is the Multigrid Method Fault Tolerant? The Two-Grid Case

The predicted reduced resiliency of next-generation high performance computers means that it will become necessary to take into account the effects of randomly occurring faults on numerical methods. Further, in the event of a hard fault occurring, a decision has to be made as to what remedial action should be taken in order to resume the execution of the algorithm. The action that is chosen can have a dramatic effect on the performance and characteristics of the scheme. Ideally, the resulting algorithm should be subjected to the same kind of mathematical analysis that was applied to the original, deterministic variant. The purpose of this work is to provide the first rigorous analysis of the behavior of the multigrid algorithm in the presence of faults. Specifically, we prove estimates on the behavior of the Two Grid Method similar to the classical asymptotic results. Multigrid is arguably the method of choice for the solution of large-scale linear algebra problems arising from discretization of partial d...
Centralizers in the group of interval exchange transformations

We study the group of interval exchange transformations. Let T be an m-interval exchange transformation. By the rank of T we mean the dimension of the ℚ-vector space spanned by the lengths of the exchanged subintervals. We prove that if T satisfies Keane’s infinite distinct orbit condition and rank(T) > 1 + [m/2], then the only interval exchange transformations which commute with T are its powers. In the case that T is a minimal 3-interval exchange transformation, we prove a more precise result: T has a trivial centralizer in the group of interval exchange transformations if and only if T satisfies the infinite distinct orbit condition.
Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things

The effective operation of time-critical Internet of things (IoT) applications requires real-time reporting of fresh status information of underlying physical processes. In this paper, a real-time IoT monitoring system is considered, in which the IoT devices sample a physical process with a sampling cost and send the status packet to a given destination with an updating cost. This joint status sampling and updating process is designed to minimize the average age of information (AoI) at the destination node under an average energy cost constraint at each device. This stochastic problem is formulated as an infinite horizon average cost constrained Markov decision process (CMDP) and transformed into an unconstrained Markov decision process (MDP) using a Lagrangian method. For the single IoT device case, the optimal policy for the CMDP is shown to be a randomized mixture of two deterministic policies for the unconstrained MDP, which is of threshold type. This reveals a fundamental tradeoff between the average AoI at the destination and the sampling and updating costs. Then, a structure-aware optimal algorithm to obtain the optimal policy of the CMDP is proposed and the impact of the wireless channel dynamics is studied while demonstrating that channels having a larger mean channel gain and less scattering can achieve better AoI performance. For the case of multiple IoT devices, a low-complexity semi-distributed suboptimal policy is proposed with the updating control at the destination and the sampling control at each IoT device. Then, an online learning algorithm is developed to obtain this policy, which can be implemented at each IoT device and requires only the local knowledge and small signaling from the destination. The proposed learning algorithm is shown to converge almost surely to the suboptimal policy. Simulation results show the structural properties of the optimal policy for the single IoT device case; and show that the proposed policy for multiple IoT devices outperforms a zero-wait baseline policy, with average AoI reductions reaching up to 33%.
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-armed Bandits

Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning. In this paper we study the distributed version of this problem where we have multiple agents, and they want to learn the best arm collaboratively. We want to quantify the power of collaboration under limited interaction (or, communication steps), as interaction is expensive in many settings. We measure the running time of a distributed algorithm as the speedup over the best centralized algorithm where there is only one agent. We give almost tight round-speedup tradeoffs for this problem, along which we develop several new techniques for proving lower bounds on the number of communication steps under time or confidence constraints.
Formal Specification of Continuum Deformation Coordination

Continuum deformation is a leader-follower multiagent cooperative control approach. Previous work showed a desired continuum deformation can be uniquely defined based on trajectories of $d$ +1 leaders in a d-dimensional motion space and acquired by followers through local inter-agent communication. This paper formally specifies continuum deformation coordination in an obstacle-laden environment. Using linear temporal logic (LTL), continuum deformation liveness and safety requirements are defined. Safety is prescribed by providing conditions on (i) agent deviation bound, (ii) inter-agent collision avoidance, (iii) agent containment, (iv) motion space containment, and (v) obstacle collision avoidance. Liveness specifies a reachability condition on the desired final formation.
Ergodic MIMO Mutual Information: Twenty Years After Emre Telatar

In the celebrated work of Emre Telatar in the year 1999 (14274 citations to date), it was shown that the expected value of the mutual information\begin{equation*}{\text{I}} = \ln \det \left( {{{\mathbf{I}}_m} + \frac{1}{t}{\mathbf{H}}{{\mathbf{H}}^{\dagger} }} \right)\end{equation*}of an m × n MIMO Rayleigh channel matrix H with a SNR 1/t can be represented as an integral involving Laguerre polynomials. We show, in this work, that Telatar’s integral representation can be explicitly evaluated to a finite sum of the form\begin{equation*}\mathbb{E}[{\text{I}}] = \sum\limits_{k = 0}^{n + m - 3} {{a_k}} {t^k} + {{\text{e}}^t}{\text{Ei}}( - t)\sum\limits_{k = 0}^{n + m - 2} {{b_k}} {t^k},\end{equation*}where Ei(−t) is the exponential integral and a k , b k are known constants that do not dependent on t. The renewed interest in this classical information theory problem came from, quite surprisingly, the recent development in quantum information theory.
Theory and applications of orthogonal polynomials for Gaussian input

Power amplifiers are the major source of nonlinearity in communications systems. Such nonlinearity causes adjacent channel interference and increases bit error rate. Polynomials are often used to model the nonlinear power amplifier or its predistortion linearizer. In this paper, we present a novel set of orthogonal polynomials for baseband Gaussian inputs to replace the conventional polynomials, and show how they alleviate the numerical instability problem associated with the conventional polynomials. The orthogonal polynomials also provide an intuitive means of spectral re-growth analysis.
Convexity properties of presymplectic moment maps

The convexity and Morse-theoretic properties of moment maps in symplectic geometry typically fail for presymplectic manifolds. We find a condition on presymplectic moment maps that prevents these failures. Our result applies for instance to Prato's quasifolds and to Hamiltonian actions on contact manifolds and cosymplectic manifolds.
Evasive Path Planning Under Surveillance Uncertainty

The classical setting of optimal control theory assumes full knowledge of the process dynamics and the costs associated with every control strategy. The problem becomes much harder if the controller only knows a finite set of possible running cost functions, but has no way of checking which of these running costs is actually in place. In this paper we address this challenge for a class of evasive path planning problems on a continuous domain, in which an evader needs to reach a target while minimizing his exposure to an enemy observer, who is in turn selecting from a finite set of known surveillance plans. Our key assumption is that both the evader and the observer need to commit to their (possibly probabilistic) strategies in advance and cannot immediately change their actions based on any newly discovered information about the opponent’s current position. We consider two types of evader behavior: in the first one, a completely risk-averse evader seeks a trajectory minimizing his worst-case cumulative observability, and in the second, the evader is concerned with minimizing the average-case cumulative observability. The latter version is naturally interpreted as a semi-infinite strategic game, and we provide an efficient method for approximating its Nash equilibrium. The proposed approach draws on methods from game theory, convex optimization, optimal control, and multiobjective dynamic programming. We illustrate our algorithm using numerical examples and discuss the computational complexity, including for the generalized version with multiple evaders.
Propagation of financial shocks in an input-output economy with trade and financial linkages of firms

Abstract Firms are connected through the production network, in which the production linkages coincide with financial linkages owing to delays in input payments that amount to a form of trade credit. In this paper, I investigate the roles of these interconnected production and financial linkages in the propagation of financial shocks. Empirically, I find, based on U.S. input-output matrix and loan data, that the upstream propagation of financial shocks is stronger than the downstream propagation. Theoretically, I elaborate a model that can capture this pattern of shocks, of which trade credit is an important component. Moreover, the model reflects the fact that trade credit attenuates the propagation of financial shocks when shocks are relatively small through the sharing of liquidity and amplifies their propagation when shocks are relatively large through illiquidity contagion.
WTO’ing a Resolution to the China Subsidy Problem

The USA, European Union, and Japan have begun a trilateral process to confront the Chinese economic model, including its use of industrial subsidies and deployment of state-owned enterprises. This paper seeks to identify the main areas of tension and to assess the legal-economic challenges to constructing new rules to address the underlying conflict. It begins by providing a brief history of subsidy disciplines in the General Agreement on Tariffs and Trade and World Trade Organization predating any concerns introduced by China. It then describes contemporary economic problems with Chinaâ€™s approach to subsidies, their impact, and the apparent ineffectiveness of the World Trade Organizationâ€™s Agreement on Subsidies and Countervailing Measures to address them. Finally, it calls for increased efforts to measure and pinpoint the source of the problemsâ€”in a manner analogous to how the Organization for Economic Cooperation and Development took on agricultural subsidies in the 1980sâ€”before providing a legal-economic assessment of proposals for reforms to notifications, evidence, remedies, enforcement, and the definition of a subsidy.
SI-ADMM: A Stochastic Inexact ADMM Framework for Stochastic Convex Programs

We consider the structured stochastic convex program requiring the minimization of $\mathbb {E}_\xi [\tilde{f}(x,\xi)]+\mathbb {E}_\xi [\tilde{g}(y,\xi)]$ subject to the constraint $Ax + By = b$ . Motivated by the need for decentralized schemes, we propose a stochastic inexact alternating direction method of multiplier (SI-ADMM) framework where subproblems are solved inexactly via stochastic approximation schemes. we propose a stochastic inexact alternating direction method of multiplier (SI-ADMM) framework where subproblems are solved inexactly via stochastic approximation schemes. Based on this framework, we prove the following: 1) under suitable assumptions on the associated batch-size of samples utilized at each iteration, the SI-ADMM scheme produces a sequence that converges to the unique solution almost surely; 2) if the number of gradient steps (or equivalently, the number of sampled gradients) utilized for solving the subproblems in each iteration increases at a geometric rate, the mean-squared error diminishes to zero at a prescribed geometric rate; and 3) the overall iteration complexity in terms of gradient steps (or equivalently samples) is found to be consistent with the canonical level of $\mathcal {O}(1/\epsilon)$ . Preliminary applications on LASSO and distributed regression suggest that the scheme performs well compared to its competitors.
A Composite Analysis of Snowfall Modes from Four Winter Seasons in Marquette, Michigan

AbstractPresented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annu...
Interventions Addressing Injury Among Agricultural Workers: A Systematic Review

Date Presented 04/05/19 Agricultural injuries lead to loss of work time and resultant negative effects for physical and mental health. Limited published research exists regarding the efficacy of OT intervention to mitigate or prevent such injuries. A systematic review exploring such interventions for management or prevention of agriculture-related injuries reveals that musculoskeletal interventions along with environmental modifications and client education are supported by evidence. Primary Author and Speaker: Katrin Volkmer Additional Authors and Speakers: Whitney Lucas Molitor
EIPR1 controls dense-core vesicle cargo retention and EARP complex localization in insulin-secreting cells

Dense-core vesicles (DCVs) are secretory vesicles found in neurons and endocrine cells. DCVs package and release cargoes including neuropeptides, biogenic amines, and peptide hormones. We recently identified the endosome-associated recycling protein (EARP) complex and the EARP-interacting-protein EIPR-1 as proteins important for controlling levels of DCV cargoes in Caenorhabditis elegans neurons. Here we determine the role of mammalian EIPR1 in insulinoma cells. We find that in Eipr1 KO cells, there is reduced insulin secretion, and mature DCV cargoes such as insulin and carboxypeptidase E (CPE) accumulate near the trans-Golgi network and are not retained in mature DCVs in the cell periphery. In addition, we find that EIPR1 is required for the stability of the EARP complex subunits and for the localization of EARP and its association with membranes, but EIPR1 does not affect localization or function of the related Golgi-associated retrograde protein (GARP) complex. EARP is localized to two distinct compartments related to its function: an endosomal compartment and a DCV biogenesis-related compartment. We propose that EIPR1 functions with EARP to control both endocytic recycling and DCV maturation.
The ASCCR Frame for Learning Essential Collaboration Skills.

Statistics and data science are especially collaborative disciplines that typically require practitioners to interact with many different people or groups. Consequently, interdisciplinary collabora...
Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles. In this article, we propose a data-driven approach for estimating the driver's take-over readiness based purely on observable cues from in-vehicle vision sensors. We present an extensive naturalistic driving dataset of drivers in a conditionally autonomous vehicle running on Californian freeways. We collect subjective ratings for the driver's take-over readiness from multiple human observers viewing the sensor feed. Analysis of the ratings in terms of intra-class correlation coefficients (ICCs) shows a high degree of consistency in the ratings across raters. We define a metric for the driver's take-over readiness termed the ‘Observable Readiness Index (ORI)’ based on the ratings. Finally, we propose an LSTM model for continuous estimation of the driver's ORI based on a holistic representation of the driver's state, capturing gaze, hand, pose and foot activity. Our model estimates the ORI with a mean absolute error of 0.449 on a 5 point scale.
Generic matter representations in 6D supergravity theories

In six-dimensional supergravity, there is a natural sense in which matter lying in certain representations of the gauge group is “generic,” in that other “exotic” matter representations require more fine tuning. From considerations of the dimensionality of the moduli space and anomaly cancellation conditions, we find that the generic sets of matter representations are well-defined for 6D supergravity theories with gauge groups containing arbitrary numbers of nonabelian factors and U(1) factors. These generic matter representations also match with those that arise in the most generic F-theory constructions, both in 6D and in 4D, with non-generic matter representations requiring more exotic singularity types. The analysis of generic versus exotic matter illuminates long-standing puzzles regarding F-theory models with multiple U(1) factors and provides a useful framework for analyzing the 6D “swampland” of apparently consistent low-energy theories that cannot be realized through known string constructions. We note also that the matter content of the standard model is generic by the criteria used here only if the global structure is SU(3)c × SU(2)L × U(1)Y/ℤ6.
Atomistic oxidation mechanism of a carbon nanotube in nitric acid.

Motivated by recent experiments, we investigate how ${\mathrm{NO}}_{3}\mathrm{\text{\ensuremath{-}}}\mathrm{SWNT}$ interactions become energetically favorable with varying oxidation state of a single-walled carbon nanotube (SWNT) using first-principles calculations. Chemisorption becomes less endothermic with respect to physisorption when the SWNT oxidation state is elevated. Importantly, the dissociative incorporation of an oxygen atom into the SWNT sidewall becomes highly favorable when the SWNT oxidation state is elevated from electron density depletion in the vicinity, as caused experimentally using electrochemical potential. The elevation of the SWNT oxidation state through accumulating local charge transfer from the surrounding molecules does not have the same effect. Our investigation reveals the crucial effects of the SWNT oxidation state in understanding the molecule-SWNT interaction.
Anticipating and addressing the ethical implications of deepfakes in the context of elections

New media synthesis technologies are rapidly advancing and becoming more accessible, allowing users to make video and audio clips (i.e. deepfakes) of individuals doing and saying things they never ...
Differential Equation Units: Learning Functional Forms of Activation Functions from Data

Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential equation units (DEUs), an improvement to modern neural networks, which enables each neuron to learn a particular nonlinear activation function from a family of solutions to an ordinary differential equation. Specifically, each neuron may change its functional form during training based on the behavior of the other parts of the network. We show that using neurons with DEU activation functions results in a more compact network capable of achieving comparable, if not superior, performance when compared to much larger networks.