cssi/SciSciGPT-SciSciNet huggingface dataset:
A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a practical method to provide appliance-level electricity consumption information. Event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This paper presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The proposed approach includes a base algorithm based on moving average change with time limit, and two auxiliary algorithms based on derivative analysis and filtering analysis. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets. Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches.
An Optimal Callback Policy for General Arrival Processes: A Pathwise Analysis

The callback option can be used as an instrument to effectively mitigate congestion due to temporary and unpredictable bursts of the arrivals in call centers. It works as follows: when the system i...
Towards optimal algorithms for prediction with expert advice

We study the classical problem of prediction with expert advice in the adversarial setting with a geometric stopping time. In 1965, Cover gave the optimal algorithm for the case of 2 experts. In this paper, we design the optimal algorithm, adversary and regret for the case of 3 experts. Further, we show that the optimal algorithm for 2 and 3 experts is a probability matching algorithm (analogous to Thompson sampling) against a particular randomized adversary. Remarkably, our proof shows that the probability matching algorithm is not only optimal against this particular randomized adversary, but also minimax optimal.Our analysis develops upper and lower bounds simultaneously, analogous to the primal-dual method. Our analysis of the optimal adversary goes through delicate asymptotics of the random walk of a particle between multiple walls. We use the connection we develop to random walks to derive an improved algorithm and regret bound for the case of 4 experts, and, provide a general framework for designing the optimal algorithm and adversary for an arbitrary number of experts.
Nondegenerate motion of singular points in obstacle problems with varying data

Abstract Recent work by Serfaty and Serra give a formula for the velocity of the free boundary of the obstacle problem at regular points ( [19] ), and much older work by King, Lacey, and Vazquez gives an example of a singular free boundary point (in the Hele-Shaw flow) that remains stationary for a positive amount of time ( [13] ). The authors show how singular free boundaries in the obstacle problem in some settings move immediately in response to varying data. Three applications of this result are given, and in particular, the authors show a uniqueness result: For sufficiently smooth elliptic divergence form operators on domains in R n and for the Laplace-Beltrami operator on a smooth manifold, the boundaries of distinct mean value sets (of the type found in [7] and [5] ) which are centered at the same point do not intersect.
Cooperative learning in multi-agent systems from intermittent measurements

Motivated by the problem of decentralized direction-tracking, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector μ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of inter-agent communication, and intermittent noisy measurements of μ. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) network connecting the nodes.
Natural properties of the trunk of a knot

The trunk of a knot in S3, defined by Makoto Ozawa, is a measure of geometric complexity similar to the bridge number or width of a knot. We prove that for any two knots K1 and K2, we have tr(K1#K2) =max{tr(K1),tr(K2)}, confirming a conjecture of Ozawa. Another conjecture of Ozawa asserts that any width-minimizing embedding of a knot K also minimizes the trunk of K. We produce several families of probable counterexamples to this conjecture.
Interlacing of zeros of weakly holomorphic modular forms

We prove that the zeros of a family of extremal modular forms interlace, settling a question of Nozaki. Additionally, we show that the zeros of almost all forms in a basis for the space of weakly holomorphic modular forms of weight k for SL2(Z) interlace on most of the lower boundary of the fundamental domain.
Kolmogorov complexity version of Slepian-Wolf coding

Alice and Bob are given two correlated n-bit strings x1 and, respectively, x2, which they want to losslessly compress and send to Zack. They can either collaborate by sharing their strings, or work separately. We show that there is no disadvantage in the second scenario: Alice and Bob, without knowing the other party's string, can compress their strings to almost minimal description length in the sense of Kolmogorov complexity. Furthermore, compression takes polynomial time and can be made at any combination of lengths that satisfy some necessary conditions (modulo additive polylogarithmic terms). More precisely, there exist probabilistic algorithms E1, E2, and D, with E1 and E2 running in polynomial time, having the following behavior: if n1, n2 are two integers satisfying n1 + n2 ≥ C(x1,x2), n1 ≥ C(x1 | x2), n2 ≥ C(x2 | x1), then for i ∈ {1,2}, Ei on input xi and ni outputs a string of length ni + O(log3 n) such that D on input E1(x1), E2(x2) reconstructs (x1,x2) with high probability (where C(x) denotes the plain Kolmogorov complexity of x, and C(x | y) is the complexity of x conditioned by y). Our main result is more general, as it deals with the compression of any constant number of correlated strings. It is an analog in the framework of algorithmic information theory of the classic Slepian-Wolf Theorem, a fundamental result in network information theory, in which x1 and x2 are realizations of two discrete random variables representing n independent draws from a joint distribution. In the classical result, the decompressor needs to know the joint distribution of the sources. In our result no type of independence is assumed and the decompressor does not have any prior information about the sources that are compressed.
Removability, rigidity of circle domains and Koebe's conjecture

Abstract A circle domain Ω in the Riemann sphere is conformally rigid if every conformal map of Ω onto another circle domain is the restriction of a Mobius transformation. We show that two rigidity conjectures of He and Schramm are in fact equivalent, at least for a large family of circle domains. The proof follows from a result on the removability of countable unions of certain conformally removable sets. We also introduce trans-quasiconformal deformation of Schottky groups to prove that a circle domain is conformally rigid if and only if it is quasiconformally rigid, thereby providing new evidence for the aforementioned conjectures.
Inviscid damping and the asymptotic stability of planar shear flows in the 2D Euler equations

We prove asymptotic stability of shear flows close to the planar Couette flow in the 2D inviscid Euler equations on T×R. That is, given an initial perturbation of the Couette flow small in a suitable regularity class, specifically Gevrey space of class smaller than 2, the velocity converges strongly in L 2 to a shear flow which is also close to the Couette flow. The vorticity is asymptotically driven to small scales by a linear evolution and weakly converges as t→±∞. The strong convergence of the velocity field is sometimes referred to as inviscid damping, due to the relationship with Landau damping in the Vlasov equations. This convergence was formally derived at the linear level by Kelvin in 1887 and it occurs at an algebraic rate first computed by Orr in 1907; our work appears to be the first rigorous confirmation of this behavior on the nonlinear level.
Bounding the maximum likelihood degree

Maximum likelihood estimation is a fundamental computational problem in statistics. In this note, we give a bound for the maximum likelihood degree of algebraic statistical models for discrete data. As usual, such models are identified with special very affine varieties. Using earlier work of Franecki and Kapranov, we prove that the maximum likelihood degree is always less or equal to the signed intersection-cohomology Euler characteristic. We construct counterexamples to a bound in terms of the usual Euler characteristic conjectured by Huh and Sturmfels.
Online Model Estimation for Predictive Thermal Control of Buildings

This brief proposes a general, scalable method to learn control-oriented thermal models of buildings that could enable wide-scale deployment of cost-effective predictive controls. An unscented Kalman filter (UKF) augmented for parameter and disturbance estimation is shown to accurately learn and predict a building’s thermal response. Recent studies of heating, ventilating, and air-conditioning systems have shown significant energy savings with advanced model predictive control. A scalable cost-effective method to readily acquire accurate, robust models of individual buildings’ unique thermal envelopes has historically been elusive and hindered the widespread deployment of prediction-based control systems. Continuous commissioning and lifetime performance of these thermal models requires the deployment of online data-driven system identification and parameter estimation routines. We propose a novel gray-box approach using an UKF based on a multizone thermal network and validate it with EnergyPlus simulation data. The filter quickly learns the parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. This brief extends our initial investigation by formalizing parameter and disturbance estimation routines and demonstrating results across a year-long study.
Mechanically-Assisted Current-Induced Switching of the Magnetic Moment in a Torsional Oscillator

It is well known that the torque from a spin-polarized current can be used to switch the magnetization of memory elements. This study shows that mechanical vibrations can also be used to either assist or inhibit switching through spin-transfer torque. This finding implies that the minimal current needed to switch magnetic memory elements can potentially be reduced, and by identifying vibrational parameters that provoke or impede magnetic flipping, the authors provide direction for the potential design of future nanoscale devices.
Algorithmic Randomness and Fourier Analysis

Suppose 1 < p < ∞. Carleson’s Theorem states that the Fourier series of any function in Lp[−π, π] converges almost everywhere. We show that the Schnorr random points are precisely those that satisfy this theorem for every f ∈ Lp[−π, π] given natural computability conditions on f and p.
A new equation for activity calculation in pulse irradiation: derivation, simulation, and experimental validation

To calculate the radioactivity of product nuclides generated in pulse irradiation, it is generally assumed that the irradiation is approximately continuous in the entire irradiation period ( $$t_{\text{i}}$$ ) and the flux of the incoming irradiation particle can be obtained by averaging their intensity in each pulse period ( $$T$$ ). However, this approximation fails to acknowledge the fact that the product nuclides are not created in each pulse period ( $$T$$ ) evenly: They are only produced in a very short pulse width ( $$t_{\text{p}}$$ ) and then decay in a relatively long rest time ( $$t_{\text{r}} = T - t_{\text{p}}$$ ). Given by the enormous number of pulses, the sum of these decays may not be negligible. To make the activity calculation in accordance with the real situation in pulse irradiation, we scrutinize the details of irradiation and decay processes in each pulse, apply the geometric series to obtain the activity superimposition of millions of pulses, and derive a novel activity equation particularly suitable for pulse irradiation. The experimental results, numerical simulations, and activity measurements from photon activation driven by a pulsed electron LINAC have confirmed the validity of this new equation. The comparison between the new and traditional equations indicates that their discrepancy could be significant under certain conditions. The limitations of the new activity equation for pulse irradiation are discussed as well.
Galileo Ionosphere Profile Coincident with Repeat Plume Detection Location at Europa

The location of a repeat plume detected at Europa is found to be coincident with the strongest ionosphere detection made by Galileo radio occultation in 1997.
Gaussian Approximation of Collective Graphical Models

The Collective Graphical Model (CGM) models a population of independent and identically distributed individuals when only collective statistics (i.e., counts of individuals) are observed. Exact inference in CGMs is intractable, and previous work has explored Markov Chain Monte Carlo (MCMC) and MAP approximations for learning and inference. This paper studies Gaussian approximations to the CGM. As the population grows large, we show that the CGM distribution converges to a multivariate Gaussian distribution (GCGM) that maintains the conditional independence properties of the original CGM. If the observations are exact marginals of the CGM or marginals that are corrupted by Gaussian noise, inference in the GCGM approximation can be computed efficiently in closed form. If the observations follow a different noise model (e.g., Poisson), then expectation propagation provides efficient and accurate approximate inference. The accuracy and speed of GCGM inference is compared to the MCMC and MAP methods on a simulated bird migration problem. The GCGM matches or exceeds the accuracy of the MAP method while being significantly faster.
Spectral transition line for the extended Harper's model in the positive Lyapunov exponent regime

Abstract We study the spectral transition line of the extended Harper's model in the positive Lyapunov exponent regime. We show that both pure point spectrum and purely singular continuous spectrum occur for dense subsets of frequencies on the transition line.
Word length perturbations in certain symmetric presentations of dihedral groups

Given a finite group with a generating subset there is a well-established notion of length for a group element given in terms of its minimal length expression as a product of elements from the generating set. Recently, certain quantities called 1 and 2 have been defined that allow for a precise measure of how stable a group is under certain types of small perturbations in the generating expressions for the elements of the group. These quantities provide a means to measure differences among all possible paths in a Cayley graph for a group, establish a group theoretic analog for the notion of stability in nonlinear dynamical systems, and play an important role in the application of groups to computational genomics. In this paper, we further expose the fundamental properties of 1 and 2 by establishing their bounds when the underlying group is a dihedral group. An essential step in our approach is to completely characterize so-called symmetric presentations of the dihedral groups, providing insight into the manner in which 1 and 2 interact with finite group presentations. This is of interest independent of the study of the quantities 1,2. Finally, we discuss several conjectures and open questions for future consideration.
On Mordell–Weil groups of Jacobians over function fields

We study the arithmetic of abelian varieties over $K=k(t)$ where $k$ is an arbitrary field. The main result relates Mordell-Weil groups of certain Jacobians over $K$ to homomorphisms of other Jacobians over $k$. Our methods also yield completely explicit points on elliptic curves with unbounded rank over $\Fpbar(t)$ and a new construction of elliptic curves with moderately high rank over $\C(t)$.
Q-polynomial distance-regular graphs and a double affine Hecke algebra of rank one

Abstract We study a relationship between Q -polynomial distance-regular graphs and the double affine Hecke algebra of type ( C 1 ∨ , C 1 ) . Let Γ denote a Q -polynomial distance-regular graph with vertex set X . We assume that Γ has q -Racah type and contains a Delsarte clique C . Fix a vertex x ∈ C . We partition X according to the path-length distance to both x and C . This is an equitable partition. For each cell in this partition, consider the corresponding characteristic vector. These characteristic vectors form a basis for a C -vector space W . The universal double affine Hecke algebra of type ( C 1 ∨ , C 1 ) is the C -algebra H ˆ q defined by generators { t n ± 1 } n = 0 3 and relations (i) t n t n − 1 = t n − 1 t n = 1 ; (ii) t n + t n − 1 is central; (iii) t 0 t 1 t 2 t 3 = q − 1 / 2 . In this paper, we display an H ˆ q -module structure for W . For this module and up to affine transformation, • t 0 t 1 + ( t 0 t 1 ) − 1 acts as the adjacency matrix of Γ ; • t 3 t 0 + ( t 3 t 0 ) − 1 acts as the dual adjacency matrix of Γ with respect to C ; • t 1 t 2 + ( t 1 t 2 ) − 1 acts as the dual adjacency matrix of Γ with respect to x . To obtain our results we use the theory of Leonard systems.
Two-sided vector spaces

Abstract We study the structure of two-sided vector spaces over a perfect field K . In particular, we give a complete characterization of isomorphism classes of simple two-sided vector spaces which are left finite-dimensional. Using this description, we compute the Quillen K -theory of the category of left finite-dimensional, two-sided vector spaces over K . We also consider the closely related problem of describing homomorphisms ϕ : K → M n ( K ).
Large Shafarevich–Tate groups over quadratic number fields

Abstract Let E be an elliptic curve over the rational field Q. Let K be a quadratic extension of Q. We show that (under mild conditions on E) for every r > 0 , there are infinitely many quadratic twists E d / Q of E / Q such that .
Adversarial Examples: Attacks and Defenses for Deep Learning

With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks (DNNs) have been recently found vulnerable to well-designed input samples called adversarial examples . Adversarial perturbations are imperceptible to human but can easily fool DNNs in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying DNNs in safety-critical environments. Therefore, attacks and defenses on adversarial examples draw great attention. In this paper, we review recent findings on adversarial examples for DNNs, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods. Under the taxonomy, applications for adversarial examples are investigated. We further elaborate on countermeasures for adversarial examples. In addition, three major challenges in adversarial examples and the potential solutions are discussed.
cpSGD: communication-efficient and differentially-private distributed SGD

Distributed stochastic gradient descent is an important subroutine in distributed learning. A setting of particular interest is when the clients are mobile devices, where two important concerns are communication efficiency and the privacy of the clients. Several recent works have focused on reducing the communication cost or introducing privacy guarantees, but none of the proposed communication efficient methods are known to be privacy preserving and none of the known privacy mechanisms are known to be communication efficient. To this end, we study algorithms that achieve both communication efficiency and differential privacy. For d variables and n ≈ d clients, the proposed method uses O(log log(nd)) bits of communication per client per coordinate and ensures constant privacy. We also improve previous analysis of the Binomial mechanism showing that it achieves nearly the same utility as the Gaussian mechanism, while requiring fewer representation bits, which can be of independent interest.
Enhancing the performance and robustness of the FEAST eigensolver

The FEAST algorithm is a subspace iteration method that uses a spectral projector as a rational filter in order to efficiently solve interior eigenvalue problems in parallel. Although the solutions from the FEAST algorithm converge rapidly in many cases, convergence can be slow in situations where the eigenvalues of a matrix are densely populated near the edges of the search interval of interest, which can be detrimental to parallel load balancing. This work introduces two methods that allow one to improve the convergence robustness of the FEAST algorithm in these situations without having to increase the amount of computation. Selected numerical examples are presented and discussed.
An incidence conjecture of Bourgain over fields of positive characteristic

In this note we generalize a recent theorem of Guth and Katz on incidences between points and lines in 3-space from characteristic 0 to characteristic , and we explain how some of the special features of algebraic geometry in characteristic manifest themselves in problems of incidence geometry.
The Schroedinger-equation presentation of any oscillatory classical linear system that is homogeneous and conservative

The time-dependent Schrodinger equation with time-independent Hamiltonian matrix is a homogeneous linear oscillatory system in canonical form. We investigate whether any classical system that itself is linear, homogeneous, oscillatory and conservative is guaranteed to linearly map into a Schrodinger equation. Such oscillatory classical systems can be analyzed into their normal modes, which are mutually independent, uncoupled simple harmonic oscillators, and the equation of motion of such a system linearly maps into a Schrodinger equation whose Hamiltonian matrix is diagonal, with h times the individual simple harmonic oscillator frequencies as its diagonal entries. Therefore if the coupling-strength matrix of such an oscillatory system is presented in symmetric, positive-definite form, the Hamiltonian matrix of the Schrodinger equation it maps into is h-bar times the square root of that coupling-strength matrix. We obtain a general expression for mapping this type of oscillatory classical equation of motion into a Schrodinger equation, and apply it to the real-valued classical Klein-Gordon equation and the source-free Maxwell equations, which results in relativistic Hamiltonian operators that are strictly compatible with the correspondence principle. Once such an oscillatory classical system has been mapped into a Schrodinger equation, it is automatically in canonical form, making second quantization of that Schrodinger equation a technically simple as well as a physically very interpretable way to quantize the original classical system.
The Number of Smooth 4-Manifolds with a Fixed Complexity

One can define the complexity of a smooth 4-manifold as the minimal sum of the number of disks, strands and crossings in a Kirby diagram. Martelli proved that the number of homeomorphism classes of complexity less than n grows as $n^2$. In this paper we prove that the number of diffeomorphism classes grows at least as fast as $n^{c\sqrt[3]{n}}$. Along the way we construct complete kirby diagrams for a large family of knot surgery manifolds.
Fine-Grained Entity Typing with High-Multiplicity Assignments

As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data sources such as Wikipedia that have semi-open type systems. We introduce a set-prediction approach to this problem and show that our model outperforms unstructured baselines on a new Wikipedia-based fine-grained typing corpus.
Numerical integration over implicitly defined domains for higher order unfitted finite element methods

The paper studies several approaches to numerical integration over a domain defined implicitly by an indicator function such as the level set function. The integration methods are based on subdivision, moment–fitting, local quasi-parametrization and Monte-Carlo techniques. As an application of these techniques, the paper addresses numerical solution of elliptic PDEs posed on domains and manifolds defined implicitly. A higher order unfitted finite element method (FEM) is assumed for the discretization. In such a method the underlying mesh is not fitted to the geometry, and hence the errors of numerical integration over curvilinear elements affect the accuracy of the finite element solution together with approximation errors. The paper studies the numerical complexity of the integration procedures and the performance of unfitted FEMs which employ these tools.
EQUIDISTRIBUTION OF ALGEBRAIC NUMBERS OF NORM ONE IN QUADRATIC NUMBER FIELDS

Given a fixed quadratic extension K of ℚ, we consider the distribution of elements in K of norm one (denoted $\mathscr{N}$). When K is an imaginary quadratic extension, $\mathscr{N}$ is naturally embedded in the unit circle in ℂ and we show that it is equidistributed with respect to inclusion as ordered by the absolute Weil height. By Hilbert's Theorem 90, an element in $\mathscr{N}$ can be written as $\alpha/\overline{\alpha}$ for some $\alpha \in \mathscr{O}_K$, which yields another ordering of $\mathscr{N}$ given by the minimal norm of the associated algebraic integers. When K is imaginary we also show that $\mathscr{N}$ is equidistributed in the unit circle under this norm ordering. When K is a real quadratic extension, we show that $\mathscr{N}$ is equidistributed with respect to norm, under the map β ↦ log|β|(mod log|ϵ2|) where ϵ is a fundamental unit of $\mathscr{O}_K$.
Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation

Defects such as dislocations impact materials properties and their response during external stimuli. Imaging these defects in their native operating conditions to establish the structure-function relationship and, ultimately, to improve performance via defect engineering has remained a considerable challenge for both electron-based and x-ray-based imaging techniques. While Bragg coherent x-ray diffractive imaging (BCDI) is successful in many cases, nuances in identifying the dislocations has left manual identification as the preferred method. Derivative-based methods are also used, but they can be inaccurate and are computationally inefficient. Here we demonstrate a derivative-free method that is both more accurate and more computationally efficient than either derivative- or human-based methods for identifying 3D dislocation lines in nanocrystal images produced by BCDI. We formulate the problem as a min-max optimization problem and show exceptional accuracy for experimental images. We demonstrate a 227x ...
Magnetism in the KBaRE(BO3)2 (RE = Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) series: materials with a triangular rare earth lattice

We report the magnetic properties of compounds in the KBaRE(BO3)2 family (RE = Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb), materials with a planar triangular lattice composed of rare earth ions. The samples were analyzed by x-ray diffraction and crystallize in the space group R-3m. Physical property measurements indicate the compounds display predominantly antiferromagnetic interactions between spins without any signs of magnetic ordering above 1.8 K. The ideal 2D rare earth triangular layers in this structure type make it a potential model system for investigating magnetic frustration in rare-earth-based materials.
A result of uniqueness of solutions of the Shigesada–Kawasaki–Teramoto equations

We derive the uniqueness of weak solutions to the Shigesada-Kawasaki-Teramoto (SKT) systems using the adjoint problem argument. Combining with [PT17] we then derive the well-posedness for the SKT systems in space dimension $d\le 4$
p-adic Mellin amplitudes

In this paper, we propose a p-adic analog of Mellin amplitudes for scalar operators, and present the computation of the general contact amplitude as well as arbitrary-point tree-level amplitudes for bulk diagrams involving up to three internal lines, and along the way obtain the p-adic version of the split representation formula. These amplitudes share noteworthy similarities with the usual (real) Mellin amplitudes for scalars, but are also significantly simpler, admitting closed-form expressions where none are available over the reals. The dramatic simplicity can be attributed to the absence of descendant fields in the p-adic formulation.
Measure preserving actions of affine semigroups and {x+y,xy} patterns

Ergodic and combinatorial results obtained in Bergelson and Moreira [Ergodic theorem involving additive and multiplicative groups of a field and patterns. Ergod. Th. & Dynam. Sys. to appear, published online 6 October 2015, doi:10.1017/etds.2015.68], involved measure preserving actions of the affine group of a countable field . In this paper, we develop a new approach, based on ultrafilter limits, which allows one to refine and extend the results obtained in Bergelson and Moreira, op. cit., to a more general situation involving measure preserving actions of the non-amenable affine semigroups of a large class of integral domains. (The results and methods in Bergelson and Moreira, op. cit., heavily depend on the amenability of the affine group of a field.) Among other things, we obtain, as a corollary of an ultrafilter ergodic theorem, the following result. Let be a number field and let be the ring of integers of . For any finite partition , there exists such that, for many and many , .
Determining the Dominant Acceleration Mechanism during Relativistic Magnetic Reconnection in Large-scale Systems

While a growing body of research indicates that relativistic magnetic reconnection is a prodigious source of particle acceleration in high-energy astrophysical systems, the dominant acceleration mechanism remains controversial. Using a combination of fully kinetic simulations and theoretical analysis, we demonstrate that Fermi-type acceleration within the large-scale motional electric fields dominates over direct acceleration from non-ideal electric fields within small-scale diffusion regions. This result has profound implications for modeling particle acceleration in large-scale astrophysical problems, since it opens up the possiblity of modeling the energetic spectra without resolving microscopic diffusion regions.
Probabilistic Threshold Indexing for Uncertain Strings.

Strings form a fundamental data type in computer systems. String searching has been extensively studied since the inception of computer science. Increasingly many applications have to deal with imprecise strings or strings with fuzzy information in them. String matching becomes a probabilistic event when a string contains uncertainty, i.e. each position of the string can have different probable characters with associated probability of occurrence for each character. Such uncertain strings are prevalent in various applications such as biological sequence data, event monitoring and automatic ECG annotations. We explore the problem of indexing uncertain strings to support efficient string searching. In this paper we consider two basic problems of string searching, namely substring searching and string listing. In substring searching, the task is to find the occurrences of a deterministic string in an uncertain string. We formulate the string listing problem for uncertain strings, where the objective is to output all the strings from a collection of strings, that contain probable occurrence of a deterministic query string. Indexing solution for both these problems are significantly more challenging for uncertain strings than for deterministic strings. Given a construction time probability value $\tau$, our indexes can be constructed in linear space and supports queries in near optimal time for arbitrary values of probability threshold parameter greater than $\tau$. To the best of our knowledge, this is the first indexing solution for searching in uncertain strings that achieves strong theoretical bound and supports arbitrary values of probability threshold parameter. We also propose an approximate substring search index that can answer substring search queries with an additive error in optimal time. We conduct experiments to evaluate the performance of our indexes.
Stationary and Transition Probabilities in Slow Mixing, Long Memory Markov Processes

We observe a length-n sample generated by an unknown, stationary ergodic Markov process (model) over a finite alphabet A. Given any string w of symbols from A we want estimates of the conditional probability distribution of symbols following w, as well as the stationary probability of w. Two distinct problems that complicate estimation in this setting are (i) long memory, and (ii) slow mixing which could happen even with only one bit of memory. For our problem, any consistent estimator can only converge pointwise over the class of all ergodic Markov models. Namely, given any estimator and any sample size n, the underlying model could be such that the estimator performs poorly on a sample of size n with high probability. But can we look at a length-n sample and identify if an estimate is likely to be accurate? Since the memory is unknown a-priori, a natural approach is to estimate a potentially coarser model with memory kn = O(log n)1. As n grows, estimates get refined and this approach is consistent with the above scaling of kn also known to be essentially optimal. But while effective asymptotically, the situation is quite different when we want the best answers possible with a length-n sample, rather than just consistency. Combining results in universal compression with Aldous’ coupling arguments, we obtain sufficient conditions on the length-n sample (even for slow mixing models) to identify when naive (i) estimates of the model parameters and (ii) estimates related to the stationary probabilities are accurate; and also bound the deviations of the naive estimates from true values.
Kodaira–Saito vanishing via Higgs bundles in positive characteristic

The goal of this paper is to give a new proof of a special case of the Kodaira-Saito vanishing theorem for a variation of Hodge structure on the complement of a divisor with normal crossings. The proof does not use the theory of mixed Hodge modules, but instead reduces it to a more general vanishing theorem for semistable nilpotent Higgs bundles, which is then proved by using some facts about Higgs bundles in positive characteristic.
Effects of flow collisionality on ELM replication in plasma guns

Abstract Degradation of first wall materials due to plasma disturbances severely limit both the lifetime and longevity of fusion reactors. Among the various kinds of disturbances, type I edge localized modes (ELMs) in particular present significant design challenges due to their expected heat loading and relative frequency in next step fusion reactors. Plasma gun devices have been used extensively to replicate ELM conditions in the laboratory, however feature higher density, lower temperatures, and thus higher flow collisionality than those expected in fusion conditions. This work presents experimental visualizations that indicate strong shocks form in gun devices over spatial and temporal scales that precede ablation dynamics. These measurements are used to validate detailed magnetohydrodynamic simulations that capture the production of plasma jets and the shielding effect collisionality plays in particle transport to material surfaces. Simulations show that self-shielding effects in plasma guns reduce the free streaming heat flux by up to 90% and further reduce the incoming particle kinetic energy impinging on material surfaces. These simulations are performed over a range of operating conditions for gun devices and a discussion is provided regarding how existing experimental measurements can be interpreted when extrapolating to fusion conditions.
Büttiker Probe-Based Modeling of TDDB: Application to Dielectric Breakdown in MTJs and MOS Devices

Dielectric layers are gradually being downscaled in different electronic devices like MOSFETs and magnetic tunnel junctions (MTJ) with shrinking device sizes. As a result, time dependent dielectric breakdown has become a major issue in such devices. In this paper we propose a generalized way of modeling the stress-induced leakage current (SILC) and postbreakdown current (PBC) due to time dependent wear-out of the dielectric layer. We model the traps formed in dielectric layer using Buttiker probe and incorporate the Buttiker probe self-energies in standard self-consistent nonequilibrium Green’s function formalism in order to determine SILC and PBC. In addition, we have shown the impact of breakdown in the dielectric layer on the spin current and spin filtering characteristics of an MTJ. The proposed model is generic in nature. It can be extended from MTJs and conventional CMOS technology to any other devices with any type of single and multiple layers of dielectric material(s).
A Cohen-Macaulay algebra has only finitely many semidualizing modules

We prove the result stated in the title, which answers the equicharacteristic case of a question of Vasconcelos.
Fast, accurate second order methods for network optimization

Dual descent methods are commonly used to solve network flow optimization problems, since their implementation can be distributed over the network. These algorithms, however, often exhibit slow convergence rates. Approximate Newton methods which compute descent directions locally have been proposed as alternatives to accelerate the convergence rates of conventional dual descent. The effectiveness of these methods, is limited by the accuracy of such approximations. In this paper, we propose an efficient and accurate distributed second order method for network flow problems. Our approach utilizes the sparsity pattern of the dual Hessian to approximate the the Newton direction using a novel distributed solver for symmetric diagonally dominant linear equations. We analyze the properties of the proposed algorithm and show that superlinear convergence within a neighborhood of the optimal value. We finally demonstrate the effectiveness of the approach in a set of experiments.
RIPEx: Extracting Malicious IP Addresses from Security Forums Using Cross-Forum Learning

Is it possible to extract malicious IP addresses reported in security forums in an automatic way? This is the question at the heart of our work. We focus on security forums, where security professionals and hackers share knowledge and information, and often report misbehaving IP addresses. So far, there have only been a few efforts to extract information from such security forums. We propose RIPEx, a systematic approach to identify and label IP addresses in security forums by utilizing a cross-forum learning method. In more detail, the challenge is twofold: (a) identifying IP addresses from other numerical entities, such as software version numbers, and (b) classifying the IP address as benign or malicious. We propose an integrated solution that tackles both these problems. A novelty of our approach is that it does not require training data for each new forum. Our approach does knowledge transfer across forums: we use a classifier from our source forums to identify seed information for training a classifier on the target forum. We evaluate our method using data collected from five security forums with a total of 31 K users and 542 K posts. First, RIPEx can distinguish IP address from other numeric expressions with 95% precision and above 93% recall on average. Second, RIPEx identifies malicious IP addresses with an average precision of 88% and over 78% recall, using our cross-forum learning. Our work is a first step towards harnessing the wealth of useful information that can be found in security forums.
Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds.

Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal ...
On the enumeration of interval graphs

We present upper and lower bounds for the number $i_n$ of interval graphs on $n$ vertices. Answering a question posed by Hanlon, we show that the ordinary generating function $I(x) = \sum_{n\ge 0} i_n\,x^n$ for the number $i_n$ of $n$-vertex interval graphs has radius of convergence zero. We also show that the exponential generating function $J(x) = \sum_{n\ge 0} i_n\,x^n/n!$ has radius of convergence at least $1/2$.
Graphs with at most two trees in a forest-building process

Given a graph, we can form a spanning forest by first sorting the edges in a random order, and then only keeping edges incident to a vertex which is not incident to any previous edge. The resulting forest is dependent on the ordering of the edges, and so we can ask, for example, how likely is it for the process to produce a graph with k trees.We look at all graphs which can produce at most two trees in this process and determine the probabilities of having either one or two trees. From this we construct infinite families of graphs which are nonisomorphic but produce the same probabilities.
On joins and intersections of subgroups in free groups

We study graphs of (generalized) joins and intersections of finitely generated subgroups of a free group. We show how to disprove a lemma of Imrich and Muller on these graphs and how to repair this lemma.
Divisor Divisibility Sequences on Tori

We define the $\textit{Divisor Divisibility Sequence}$ associated to a Laurent polynomial $f\in\mathbb{Z}[X_1^{\pm1},\ldots,X_N^{\pm1}]$ to be the sequence $W_n(f)=\prod f(\zeta_1,\ldots,\zeta_N)$, where $\zeta_1,\ldots,\zeta_N$ range over all $n$'th roots of unity with $f(\zeta_1,\ldots,\zeta_N)\ne0$. More generally, we define $W_\Lambda(f)$ analogously for any finite subgroup $\Lambda\subset(\mathbb C^*)^N$. We investigate divisibility, factorization, and growth properties of $W_\Lambda(f)$ as a function of $\Lambda$. In particular, we give the complete factorization of $W_\Lambda(f)$ when $f$ has generic coefficients, and we prove an analytic estimate showing that the rank-of-apparition sets for $W_\Lambda(f)$ are not too large.
Assessing key decisions for transcriptomic data integration in biochemical networks

To gain insights into complex biological processes, genome-scale data (e.g., RNA-Seq) are often overlaid on biochemical networks. However, many networks do not have a one-to-one relationship between genes and network edges, due to the existence of isozymes and protein complexes. Therefore, decisions must be made on how to overlay data onto networks. For example, for metabolic networks, these decisions include (1) how to integrate gene expression levels using gene-protein-reaction rules, (2) the approach used for selection of thresholds on expression data to consider the associated gene as "active", and (3) the order in which these steps are imposed. However, the influence of these decisions has not been systematically tested. We compared 20 decision combinations using a transcriptomic dataset across 32 tissues and showed that definition of which reaction may be considered as active (i.e., reactions of the genome-scale metabolic network with a non-zero expression level after overlaying the data) is mainly influenced by thresholding approach used. To determine the most appropriate decisions, we evaluated how these decisions impact the acquisition of tissue-specific active reaction lists that recapitulate organ-system tissue groups. These results will provide guidelines to improve data analyses with biochemical networks and facilitate the construction of context-specific metabolic models.
Adically Finite Chain Complexes

We investigate the similarities between adic finiteness and homological finiteness for chain complexes over a commutative Noetherian ring. In particular, we extend the isomorphism properties of certain natural morphisms from homologically finite complexes to adically finite complexes. We do the same for characterizations of certain homological dimensions. In addition, we study transfer of adic finiteness along ring homomorphisms, all with a view toward subsequent applications.
Deterministic Positioning of Colloidal Quantum Dots on Silicon Nitride Nanobeam Cavities

We experimentally demonstrated deterministic positioning of solution processed colloidal quantum dots on a silicon nitride nanobeam resonator, with potential applications in nonlinear optics, multi-functional optical devices, and on-chip, solid-state quantum simulators. © 2019 The Author(s)
Energy-efficient hybrid CMOS-NEMS LIF neuron circuit in 28 nm CMOS process

Designing analog sub-threshold neuromorphic circuits in deep sub-micron technologies e.g. 28 nm can be a daunting task due to the problem of excessive leakage current. We propose novel energy-efficient hybrid CMOS-nano electro-mechanical switches (NEMS) Leaky Integrate and Fire (LIF) neuron and synapse circuits and investigate the impact of NEM switches on the leakage power and overall energy consumption. We analyze the performance of biologically-inspired neuron circuit in terms of leakage power consumption and present new energy-efficient neural circuits that operate with biologically plausible firing rates. Our results show the proposed CMOS-NEMS neuron circuit is, on average, 35% more energy-efficient than its CMOS counterpart with same complexity in 28 nm process. Moreover, we discuss how NEM switches can be utilized to further improve the scalability of mixed-signal neuromorphic circuits.
A Globally Asymptotically Stable Polynomial Vector Field with Rational Coefficients and no Local Polynomial Lyapunov Function

Abstract We give an explicit example of a two-dimensional polynomial vector field of degree seven that has rational coefficients, is globally asymptotically stable, but does not admit an analytic Lyapunov function even locally.
Multi-Observation Elicitation.

We study loss functions that measure the accuracy of a prediction based on multiple data points simultaneously. To our knowledge, such loss functions have not been studied before in the area of property elicitation or in machine learning more broadly. As compared to traditional loss functions that take only a single data point, these multi-observation loss functions can in some cases drastically reduce the dimensionality of the hypothesis required. In elicitation, this corresponds to requiring many fewer reports; in empirical risk minimization, it corresponds to algorithms on a hypothesis space of much smaller dimension. We explore some examples of the tradeoff between dimensionality and number of observations, give some geometric characterizations and intuition for relating loss functions and the properties that they elicit, and discuss some implications for both elicitation and machine-learning contexts.