cssi/SciSciGPT-SciSciNet huggingface dataset:
Classification accuracy improvement for neuromorphic computing systems with one-level precision synapses

Brain inspired neuromorphic computing has demonstrated remarkable advantages over traditional von Neumann architecture for its high energy efficiency and parallel data processing. However, the limited resolution of synaptic weights degrades system accuracy and thus impedes the use of neuromorphic systems. In this work, we propose three orthogonal methods to learn synapses with one-level precision, namely, distribution-aware quantization, quantization regularization and bias tuning, to make image classification accuracy comparable to the state-of-the-art. Experiments on both multi-layer perception and convolutional neural networks show that the accuracy drop can be well controlled within 0.19% (5.53%) for MNIST (CIFAR-10) database, compared to an ideal system without quantization.
Explore Recurrent Neural Network for PUE Attack Detection in Practical CRN Models

The proliferation of the Internet of Things (IoTs) and pervasive use of many different types of mobile computing devices make wireless communication spectrum a precious resource. In order to accommodate the still fast increasing number of devices requesting wireless connection, more efficient, fine-grained spectrum allocation and sharing schemes are badly in need. Cognitive radio networks (CRNs) have been widely recognized as one promising solution, in which the secondary users (SUs) are allowed to share channels with licensed primary users (PUs) as long as bringing no interference to the normal operations of the PUs. However, malicious attackers or selfish SUs may mimic the behavior of PUs to occupy the channels illegally. It is nontrivial to accurately, timely detect such kind of primary user emulation (PUE) attacks. In this paper, an efficient PUE attacked detection method is introduced leveraging the recurrent neural network (RNN). After a fundamental algorithm using basic RNN, an advanced version taking advantage of long-short-term-memory (LSTM) is proposed, which is more efficient on processing time series with long term memory. The experimental study has provided deeper insights about the different performances the RNNs achieved and validated the effectiveness of the proposed detectors.
Monochromatic cycle partitions of graphs with large minimum degree

Abstract Lehel conjectured that in every 2-coloring of the edges of K n , there is a vertex disjoint red and blue cycle which span V ( K n ) . Łuczak, Rodl, and Szemeredi proved Lehel's conjecture for large n , Allen gave a different proof for large n , and finally Bessy and Thomasse gave a proof for all n . Balogh, Barat, Gerbner, Gyarfas, and Sarkozy proposed a significant strengthening of Lehel's conjecture where K n is replaced by any graph G with δ ( G ) > 3 n / 4 ; if true, this minimum degree condition is essentially best possible. We prove that their conjecture holds when δ ( G ) > ( 3 / 4 + o ( 1 ) ) n . Our proof uses Szemeredi's regularity lemma along with the absorbing method of Rodl, Rucinski, and Szemeredi by first showing that the graph can be covered with monochromatic subgraphs having certain robust expansion properties.
Importance of CME Radial Expansion on the Ability of Slow CMEs to Drive Shocks

Coronal mass ejections (CMEs) may disturb the solar wind either by overtaking it, or by expanding into it, or both. CMEs whose front moves faster in the solar wind frame than the fast magnetosonic speed, drive shocks. Such shocks are important contributors to space weather, by triggering substorms, compressing the magnetosphere and accelerating particles. In general, near 1 AU, CMEs with speed greater than about 500 km s$^{-1}$ drive shocks, whereas slower CMEs do not. However, CMEs as slow as 350 km s$^{-1}$ may sometimes, although rarely, drive shocks. Here, we study these slow CMEs with shocks and investigate the importance of CME expansion in contributing to their ability to drive shocks and in enhancing shock strength. Our focus is on CMEs with average speeds under 375 km s$^{-1}$. From Wind measurements from 1996 to 2016, we find 22 cases of such shock-driving slow CMEs, and, for about half of them (11 out of the 22), the existence of the shock appears to be strongly related to CME expansion. We also investigate the proportion of all CMEs with speeds under 500 km s$^{-1}$ with and without shocks in solar cycles 23 and 24, depending on their speed. We find no systematic difference, as might have been expected on the basis of the lower solar wind and Alfven speeds reported for solar cycle 24 vs. 23. The slower expansion speed of CMEs in solar cycle 24 might be an explanation for this lack of increased frequency of shocks, but further studies are required.
The bootstrap in kernel regression for stationary ergodic data when both response and predictor are functions

Abstract We consider the double functional regression model Y = r ( X ) + e , where the response variable Y is Hilbert space-valued and the covariate X takes values in a pseudometric space. The data satisfy an ergodicity criterion which dates back to Laib and Louani (2010) and are arranged in a triangular array. So our model also applies to samples obtained from spatial processes, e.g., stationary random fields. We study a kernel estimator of the Nadaraya–Watson type for the operator r and derive its limiting law which is a Gaussian operator on the Hilbert space. Moreover, we investigate both a naive and a wild bootstrap procedure in the double functional setting and demonstrate their asymptotic validity. This is quite useful as building confidence sets based on an asymptotic Gaussian distribution is often difficult.
Dimensions of irreducible modules for partition algebras and tensor power multiplicities for symmetric and alternating groups

The partition algebra $$\mathsf {P}_k(n)$$ and the symmetric group $$\mathsf {S}_n$$ are in Schur–Weyl duality on the k-fold tensor power $$\mathsf {M}_n^{\otimes k}$$ of the permutation module $$\mathsf {M}_n$$ of $$\mathsf {S}_n$$ , so there is a surjection $$\mathsf {P}_k(n) \rightarrow \mathsf {Z}_k(n) := \mathsf {End}_{\mathsf {S}_n}(\mathsf {M}_n^{\otimes k})$$ , which is an isomorphism when $$n \ge 2k$$ . We prove a dimension formula for the irreducible modules of the centralizer algebra $$\mathsf {Z}_k(n)$$ in terms of Stirling numbers of the second kind. Via Schur–Weyl duality, these dimensions equal the multiplicities of the irreducible $$\mathsf {S}_n$$ -modules in $$\mathsf {M}_n^{\otimes k}$$ . Our dimension expressions hold for any $$n \ge 1$$ and $$k\ge 0$$ . Our methods are based on an analog of Frobenius reciprocity that we show holds for the centralizer algebras of arbitrary finite groups and their subgroups acting on a finite-dimensional module. This enables us to generalize the above result to various analogs of the partition algebra including the centralizer algebra for the alternating group acting on $$\mathsf {M}_n^{\otimes k}$$ and the quasi-partition algebra corresponding to tensor powers of the reflection representation of $$\mathsf {S}_n$$ .
Modules of infinite regularity over commutative graded rings

In this work, we prove that if a graded, commutative algebra $R$ over a field $k$ is not Koszul then, denoting by $\mathfrak{m}$ the maximal homogeneous ideal, the nonzero modules of the form $\mathfrak{m} M$ have infinite Castelnuovo-Mumford regularity. We also prove that over complete intersections which are not Koszul, a nonzero direct summand of a syzygy of $k$ has infinite regularity. Finally, we relate the vanishing of the graded deviations of $R$ to having a nonzero direct summand of a syzygy of $k$ of finite regularity.
ON LOCALLY CONFORMALLY FLAT GRADIENT SHRINKING RICCI SOLITONS

In this paper, we first apply an integral identity on Ricci solitons to prove that closed locally conformally flat gradient Ricci solitons are of constant sectional curvature. We then generalize this integral identity to complete noncompact gradient shrinking Ricci solitons, under the conditions that the Ricci curvature is bounded from below and the Riemannian curvature tensor has at most exponential growth. As a consequence, we classify complete locally conformally flat gradient shrinking Ricci solitons with Ricci curvature bounded from below.
Sample-Efficient Algorithms for Recovering Structured Signals From Magnitude-Only Measurements

We consider the problem of recovering a signal $ \mathbf {x^{*}}\in \mathbb {R}^{n}$ , from magnitude-only measurements, $y_{i} = \left |{ \left \langle{ { \mathbf {a}_{i}}, \mathbf {x^{*}} }\right \rangle }\right | $ for $i=\{1,2,\ldots ,m\}$ . This is a stylized version of the classical phase retrieval problem , and is a fundamental challenge in nano- and bio-imaging systems, astronomical imaging, and speech processing. It is well-known that the above problem is ill-posed, and therefore some additional assumptions on the signal and/or the measurements are necessary. In this paper, we consider the case where the underlying signal $ \mathbf {x^{*}}$ is $s$ -sparse. For this case, we develop a novel recovery algorithm that we call Compressive Phase Retrieval with Alternating Minimization , or CoPRAM . Our algorithm is simple and is obtained via a natural combination of the classical alternating minimization approach for phase retrieval with the CoSaMP algorithm for sparse recovery. Despite its simplicity, we prove that our algorithm achieves a sample complexity of $ \mathcal {O}\left ({{s^{2} \log n}}\right )$ with Gaussian measurements $ { \mathbf {a}_{i}}$ , which matches the best known existing results; moreover, it also demonstrates linear convergence in theory and practice. An appealing feature of our algorithm is that it requires no extra tuning parameters other than the signal sparsity level $s$ . Moreover, we show that our algorithm is robust to noise. The quadratic dependence of sample complexity on the sparsity level is sub-optimal, and we demonstrate how to alleviate this via additional assumptions beyond sparsity. First, we study the (practically) relevant case where the sorted coefficients of the underlying sparse signal exhibit a power law decay. In this scenario, we show that the CoPRAM algorithm achieves a sample complexity of $ \mathcal {O}\left ({{s \log n}}\right )$ , which is close to the information-theoretic limit. We then consider the case where the underlying signal $ \mathbf {x^{*}}$ arises from structured sparsity models. We specifically examine the case of block-sparse signals with uniform block size of $b$ and block sparsity $k=s/b$ . For this problem, we design a recovery algorithm that we call Block CoPRAM that further reduces the sample complexity to $ \mathcal {O}\left ({{ks \log n}}\right )$ . For sufficiently large block lengths of $b=\Theta (s)$ , this bound equates to $ \mathcal {O}\left ({{s \log n}}\right )$ . To our knowledge, our approach constitutes the first family of linearly convergent algorithms for signal recovery from magnitude-only Gaussian measurements that exhibit a sub-quadratic dependence on the signal sparsity level.
A lattice of finite-type invariants of virtual knots

We construct an infinite commutative lattice of groups whose dual spaces give Kauffman finite-type invariants of long virtual knots. The lattice is based "horizontally" upon the Polyak algebra and extended "vertically" using Manturov's functorial map $f$. For each $n$, the $n$-th vertical line in the lattice contains an infinite dimensional subspace of Kauffman finite-type invariants of degree $n$. Moreover, the lattice contains infinitely many inequivalent extensions of the Conway polynomial to long virtual knots, all of which satisfy the same skein relation. Bounds for the rank of each group in the lattice are obtained.
Which Regular Expression Patterns Are Hard to Match

Regular expressions constitute a fundamental notion in formal language theory and are frequently used in computer science to define search patterns. In particular, regular expression matching and membership testing are widely used computational primitives, employed in many programming languages and text processing utilities. A classic algorithm for these problems constructs and simulates a non-deterministic finite automaton corresponding to the expression, resulting in an O(m n) running time (where m is the length of the pattern and n is the length of the text). This running time can be improved slightly (by a polylogarithmic factor), but no significantly faster solutions are known. At the same time, much faster algorithms exist for various special cases of regular expressions, including dictionary matching, wildcard matching, subset matching, word break problem etc. In this paper, we show that the complexity of regular expression matching can be characterized based on its depth (when interpreted as a formula). Our results hold for expressions involving concatenation, OR, Kleene star and Kleene plus. For regular expressions of depth two (involving any combination of the above operators), we show the following dichotomy: matching and membership testing can be solved in near-linear time, except for "concatenations of stars", which cannot be solved in strongly sub-quadratic time assuming the Strong Exponential Time Hypothesis (SETH). For regular expressions of depth three the picture is more complex. Nevertheless, we show that all problems can either be solved in strongly sub-quadratic time, or cannot be solved in strongly sub-quadratic time assuming SETH. An intriguing special case of membership testing involves regular expressions of the form "a star of an OR of concatenations", e.g., [a|ab|bc]*. This corresponds to the so-called word break problem, for which a dynamic programming algorithm with a runtime of (roughly) O(n √m) is known. We show that the latter bound is not tight and improve the runtime to O(n m0.44…).
Quasigeodesic pseudo-Anosov flows in hyperbolic 3-manifolds and connections with large scale geometry

Abstract In this article we obtain a simple topological and dynamical systems condition which is necessary and sufficient for an arbitrary pseudo-Anosov flow in a closed, hyperbolic three manifold to be quasigeodesic. Quasigeodesic means that orbits are efficient in measuring length up to a bounded multiplicative distortion when lifted to the universal cover. We prove that such flows are quasigeodesic if and only if there is an upper bound, depending only on the flow, on the number of orbits which are freely homotopic to an arbitrary closed orbit of the flow. The main ingredient is a proof that, under the boundedness condition, the fundamental group of the manifold acts as a uniform convergence group on a flow ideal boundary of the universal cover. We also construct a flow ideal compactification of the universal cover, and prove that it is equivariantly homeomorphic to the Gromov compactification. This implies the quasigeodesic behavior of the flow. The flow ideal boundary and flow ideal compactification are constructed using only the structure of the flow.
ABELIAN COVERS OF SURFACES AND THE HOMOLOGY OF THE LEVEL L MAPPING CLASS GROUP

We calculate the first homology group of the mapping class group with coefficients in the first rational homology group of the universal abelian ℤ/L-cover of the surface. If the surface has one marked point, then the answer is ℚτ(L), where τ(L) is the number of positive divisors of L. If the surface instead has one boundary component, then the answer is ℚ. We also perform the same calculation for the level L subgroup of the mapping class group. Set HL = H1(Σg; ℤ/L). If the surface has one marked point, then the answer is ℚ[HL], the rational group ring of HL. If the surface instead has one boundary component, then the answer is ℚ.
Geometry of Complex Networks and Topological Centrality

Abstract We explore the geometry of complex networks in terms of an n -dimensional Euclidean embedding represented by the Moore–Penrose pseudo-inverse of the graph Laplacian ( L + ) . The squared distance of a node i to the origin in this n -dimensional space ( l i i + ) , yields a topological centrality index, defined as C ∗ ( i ) = 1 / l i i + . In turn, the sum of reciprocals of individual node centralities, ∑ i 1 / C ∗ ( i ) = ∑ i l i i + , or the trace of L + , yields the well-known Kirchhoff index ( K ) , an overall structural descriptor for the network. To put into context this geometric definition of centrality, we provide alternative interpretations of the proposed indices that connect them to meaningful topological characteristics — first, as forced detour overheads and frequency of recurrences in random walks that has an interesting analogy to voltage distributions in the equivalent electrical network; and then as the average connectedness of i in all the bi-partitions of the graph. These interpretations respectively help establish the topological centrality ( C ∗ ( i ) ) of node i as a measure of its overall position as well as its overall connectedness in the network; thus reflecting the robustness of i to random multiple edge failures. Through empirical evaluations using synthetic and real world networks, we demonstrate how the topological centrality is better able to distinguish nodes in terms of their structural roles in the network and, along with Kirchhoff index, is appropriately sensitive to perturbations/re-wirings in the network.
Small simplicial complexes with prescribed torsion in homology

For $$d \ge 2$$ and G a finite abelian group, define $$T_d(G)$$ to be the minimum number of vertices n so that there exists a simplicial complex X on n vertices which has the torsion part of $$H_{d - 1}(X)$$ isomorphic to G. Here we use the probabilistic method, in particular the Lovasz Local Lemma, to establish an upper bound on $$T_d(G)$$ which matches the known lower bound up to a constant factor. That is, we prove that for every $$d \ge 2$$ there exist constants $$c_d$$ and $$C_d$$ so that for any finite abelian group $$\begin{aligned} c_d(\log |G|)^{1/d} \le T_d(G) \le C_d(\log |G|)^{1/d}. \end{aligned}$$
Self-limiting trajectories of a particle moving deterministically in a random medium

We study the motion of a particle moving on a two-dimensional honeycomb lattice, whose sites are randomly occupied by either right or left rotators. These rotators deterministically scatter the particle to the right or left, additionally changing orientation from left to right or from right to left after scattering the particle. In the model we consider, the scatterers are each initially oriented to the right with probability The initial configuration of scatterers, which forms the medium through which the particle moves, is set up so that the scatterer's orientations are independent and identically distributed. For we show that as the particle moves through the lattice, it creates a number of reflecting structures. These structures ultimately limit the particle's motion, causing it to have a periodic trajectory. As p approaches either 0 or 1, and the medium becomes increasingly homogenous, the particle's dynamics undergoes a discontinuous transition from this self-limiting, periodic motion to a self-avoiding motion, where the particle's trajectory, away from its initial position, is a self-avoiding walk. In a generalization of this model, we also show the same periodic behavior exists if the model's initial configuration of scatterers are independently but not identically distributed. However, if these orientations are not chosen independently, we demonstrate that this can drastically change the particle's motion causing it to have a nonperiodic behavior.
Motion planning for fluid manipulation using simplified dynamics

We present an optimization-based motion planning algorithm to compute a smooth, collision-free trajectory for a manipulator used to transfer a liquid from a source to a target container. We take into account fluid dynamics constraints as part of the trajectory computation. In order to avoid the high complexity of exact fluid simulation, we introduce a simplified dynamics model based on physically inspired approximations and system identification. Our optimization approach can incorporate various other constraints such as collision avoidance with obstacles, kinematic and dynamics constraints of the manipulator, and fluid dynamics characteristics. We demonstrate the performance of our planner on different benchmarks corresponding to various obstacles and container shapes. We also evaluate its accuracy by validating the motion plan using an accurate but computationally costly Navier-Stokes fluid simulation.
Amalgamated free product rigidity for group von Neumann algebras

Abstract We provide a fairly large family of amalgamated free product groups Γ = Γ 1 ⁎ Σ Γ 2 whose amalgam structure can be completely recognized from their von Neumann algebras. Specifically, assume that Γ i is a product of two icc non-amenable bi-exact groups, and Σ is icc amenable with trivial one-sided commensurator in Γ i , for every i = 1 , 2 . Then Γ satisfies the following rigidity property: any group Λ such that L ( Λ ) is isomorphic to L ( Γ ) admits an amalgamated free product decomposition Λ = Λ 1 ⁎ Δ Λ 2 such that the inclusions L ( Δ ) ⊆ L ( Λ i ) and L ( Σ ) ⊆ L ( Γ i ) are isomorphic, for every i = 1 , 2 . This result significantly strengthens some of the previous Bass–Serre rigidity results for von Neumann algebras. As a corollary, we obtain the first examples of amalgamated free product groups which are W⁎-superrigid.
Undesired Parking Spaces and Contractible Pieces of the Noncrossing Partition Link

There are two natural simplicial complexes associated to the noncrossing partition lattice: the order complex of the full lattice and the order complex of the lattice with its bounding elements removed. The latter is a complex that we call the noncrossing partition link because it is the link of an edge in the former. The first author and his coauthors conjectured that various collections of simplices of the noncrossing partition link (determined by the undesired parking spaces in the corresponding parking functions) form contractible subcomplexes. In this article we prove their conjecture by combining the fact that the star of a simplex in a flag complex is contractible with the second author's theory of noncrossing hypertrees.
The All-or-Nothing Phenomenon in Sparse Linear Regression

We study the problem of recovering a hidden binary $k$-sparse $p$-dimensional vector $\beta$ from $n$ noisy linear observations $Y=X\beta+W$ where $X_{ij}$ are i.i.d. $\mathcal{N}(0,1)$ and $W_i$ are i.i.d. $\mathcal{N}(0,\sigma^2)$. A closely related hypothesis testing problem is to distinguish the pair $(X,Y)$ generated from this structured model from a corresponding null model where $(X,Y)$ consist of purely independent Gaussian entries. In the low sparsity $k=o(p)$ and high signal to noise ratio $k/\sigma^2=\Omega\left(1\right)$ regime, we establish an `All-or-Nothing' information-theoretic phase transition at a critical sample size $n^*=2 k\log \left(p/k\right) /\log \left(1+k/\sigma^2\right)$, resolving a conjecture of \cite{gamarnikzadik}. Specifically, we show that if $\liminf_{p\to \infty} n/n^*>1$, then the maximum likelihood estimator almost perfectly recovers the hidden vector with high probability and moreover the true hypothesis can be detected with a vanishing error probability. Conversely, if $\limsup_{p\to \infty} n/n^*<1$, then it becomes information-theoretically impossible even to recover an arbitrarily small but fixed fraction of the hidden vector support, or to test hypotheses strictly better than random guess. Our proof of the impossibility result builds upon two key techniques, which could be of independent interest. First, we use a conditional second moment method to upper bound the Kullback-Leibler (KL) divergence between the structured and the null model. Second, inspired by the celebrated area theorem, we establish a lower bound to the minimum mean squared estimation error of the hidden vector in terms of the KL divergence between the two models.
Legal Environment and Corporate Tax Avoidance: Evidence from State Tax Codes

This study examines the effect of legal environment on corporate state income tax avoidance. We find that the extent of penalties on corporate officers reduces state tax avoidance. However, we find...
The Il9 CNS-25 Regulatory Element Controls Mast Cell and Basophil IL-9 Production

IL-9 is an important mediator of allergic disease that is critical for mast cell-driven diseases. IL-9 is produced by many cell types, including T cells, basophils, and mast cells. Yet, how IL-9 is regulated in mast cells or basophils is not well characterized. In this report, we tested the effects of deficiency of a mouse Il9 gene regulatory element (Il9 CNS-25) in these cells in vivo and in vitro. In mast cells stimulated with IL-3 and IL-33, the Il9 CNS-25 enhancer is a potent regulator of mast cell Il9 gene transcription and epigenetic modification at the Il9 locus. Our data show preferential binding of STAT5 and GATA1 to CNS-25 over the Il9 promoter in mast cells and that T cells and mast cells have differing requirements for the induction of IL-9 production. Il9 CNS-25 is required for IL-9 production from T cells, basophils, and mast cells in a food allergy model, and deficiency in IL-9 expression results in decreased mast cell expansion. In a Nippostrongylus brasiliensis infection model, we observed a similar decrease in mast cell accumulation. Although decreased mast cells correlated with higher parasite egg burden and delayed clearance in vivo, T cell deficiency in IL-9 also likely contributes to the phenotype. Thus, our data demonstrate IL-9 production in mast cells and basophils in vivo requires Il9 CNS-25, and that Il9 CNS-25-dependent IL-9 production is required for mast cell expansion during allergic intestinal inflammation.
Dynamic Walking with Compliance on a Cassie Bipedal Robot

The control of bipedal robotic walking remains a challenging problem in the domains of computation and experiment, due to the multi-body dynamics and various sources of uncertainty. In recent years, there has been a rising trend towards model reduction and the design of intuitive controllers to overcome the gap between assumed model and reality. Despite its viability in practical implementation, this local representation of true dynamics naturally indicate limited scalibility towards more dynamical behaviors. With the goal of moving towards increasingly dynamic behaviors, we leverage the detailed full body dynamics to generate controllers for the robotic system which utilizes compliant elements in the passive dynamics. In this process, we present a feasible computation method that yields walking trajectories for a highly complex robotic system. Direct implementation of these results on physical hardware is also performed with minimal tuning and heuristics. We validate the suggested method by applying a consistent control scheme across simulation, optimization and experiment, the result is that the bipedal robot Cassie walks over a variety of indoor and outdoor terrains reliably.
Characterizing a workload for the comparison of interactive services

Characterizing the workload is a necessary part of any system performance evaluation effort. This necessity is a consequence of the sensitivity of the performance indices of interest, be they productivity or responsiveness indices, to the nature, composition and intensity of the workload processed by the system being evaluated. No matter how we define it, performance cannot be expressed by quantities independent of the system’s workload. 1
Posing Fair Generalization Tasks for Natural Language Inference

Deep learning models for semantics are generally evaluated using naturalistic corpora. Adversarial testing methods, in which models are evaluated on new examples with known semantic properties, have begun to reveal that good performance at these naturalistic tasks can hide serious shortcomings. However, we should insist that these evaluations be fair – that the models are given data sufficient to support the requisite kinds of generalization. In this paper, we define and motivate a formal notion of fairness in this sense. We then apply these ideas to natural language inference by constructing very challenging but provably fair artificial datasets and showing that standard neural models fail to generalize in the required ways; only task-specific models that jointly compose the premise and hypothesis are able to achieve high performance, and even these models do not solve the task perfectly.
Barrier Certificates for Assured Machine Teaching

Machine teaching can be viewed as optimal control for learning. Given a learner's model, machine teaching aims to determine the optimal training data to steer the learner towards a target hypothesis. In this paper, we are interested in providing assurances for machine teaching algorithms using control theory. In particular, we study a well-established learner's model in the machine teaching literature that is captured by the local preference over a version space. We interpret the problem of teaching a preference-based learner as solving a partially observable Markov decision process (POMDP). We then show that the POMDP formulation can be cast as a special hybrid system, i.e., a discrete-time switched system. Subsequently, we use barrier certificates to verify set-theoric properties of this special hybrid system. We show how the computation of the barrier certificate can be decomposed and numerically implemented as the solution to a sum-of-squares (SOS) program. For illustration, we show how the proposed framework based on control theory can be used to verify the teaching performance of two well-known machine teaching methods.
Flood-Risk Analysis on Terrains under the Multiflow-Direction Model

An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this article, we study a number of flood-risk related problems: Given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, a rain distribution R, and a volume of rain ψ, determine which portions of Σ are flooded. We develop efficient algorithms for flood-risk analysis under the multiflow-directions (MFD) model, in which water at a point can flow along multiple downslope edges and which more accurately represent flooding events.We present three main results: First, we present an O(n log n)-time algorithm to answer a terrain-flood query: if it rains a volume ψ according to a rain distribution R, determine what regions of Σ will be flooded. Second, we present a O(n log n + nm)-time algorithm for preprocessing Σ containing m sinks into a data structure of size O(nm) for answering point-flood queries: Given a rain distribution R, a volume of rain ψ falling according to R, and point q ∈ Σ, determine whether q will be flooded. A point-flood query can be answered in O(vRvk+k2) time, where k is the number of maximal depressions in Σ containing the query point q and vRv is the number of vertices in R with positive rainfall. Finally, we present algorithms for answering a flood-time query: given a rain distribution R and a point q ∈ Σ, determine the volume of rain that must fall before q is flooded. Assuming that the product of two k × k matrices can be computed in O(kω) time, we show that a flood-time query can be answered in O(nk + kω) time. We also give an α-approximation algorithm, for α > 1, which runs in O(n log n log α ρ)-time, where ρ is a variable on the terrain that depends on the ratio between depression volumes. We implemented our algorithms for computing terrain and point-flood queries as well as approximate flood-time queries. We tested the efficacy and efficiency of these algorithms on three real terrains of different types (urban, suburban, and mountainous.)
PMU Data Characterization and Application to Stability Monitoring

This paper provides a methodology to characterize the accuracy of PMU data (GPS-synchronized) and the applicability of this data for monitoring system stability. GPS-synchronized equipment (PMUs) is in general higher precision equipment as compared to typical SCADA systems. Conceptually, PMU data are time tagged with precision better than 1 microsecond and magnitude accuracy that is better than 0.1%. This potential performance is not achieved in an actual field installation due to errors from instrumentation channels and system imbalances. Presently, PMU data precision from substation installed devices is practically unknown. On the other hand, specific applications of PMU data require specific accuracy of data. Applications vary from simple system monitoring to wide area protection and control to voltage instability prediction and transient stability monitoring. The paper focuses on the last application, i.e. transient stability monitoring. We propose an approach that is based on the energy functions (Lyapunov indirect method). Specifically, we provide a methodology for determining the required data accuracy for the reliable real time estimation of the energy function. When the data meet these requirements, the estimated energy function can be visualized and animated providing a powerful visual tool for observing the transient stability or instability of the system
AppMine: Behavioral Analytics for Web Application Vulnerability Detection

Web applications in widespread use have always been the target of large-scale attacks, leading to massive disruption of services and financial loss, as in the Equifax data breach. It has become common practice to deploy web applications in containers like Docker for better portability and ease of deployment. We design a system called AppMine for lightweight monitoring of web applications running in Docker containers and detection of unknown web vulnerabilities. AppMine is an unsupervised learning system, trained only on legitimate workloads of web applications, to detect anomalies based on either traditional models (PCA and one-class SVM), or more advanced neural-network architectures (LSTM). In our evaluation, we demonstrate that the neural network model outperforms more traditional methods on a range of web applications and recreated exploits. For instance, AppMine achieves average AUC scores as high as 0.97 for the Apache Struts application (with the CVE-2017-5638 exploit used in the Equifax breach), while the AUC scores for PCA and one-class SVM are 0.81 and 0.83, respectively.
Sparse reconstruction of log-conductivity in current density impedance tomography.

A new nonlinear optimization approach is proposed for the sparse reconstruction of log-conductivities in current density impedance imaging. This framework comprises of minimizing an objective functional involving a least squares fit of the interior electric field data corresponding to two boundary voltage measurements, where the conductivity and the electric potential are related through an elliptic PDE arising in electrical impedance tomography. Further, the objective functional consists of a $$L^1$$ regularization term that promotes sparsity patterns in the conductivity and a Perona–Malik anisotropic diffusion term that enhances the edges to facilitate high contrast and resolution. This framework is motivated by a similar recent approach to solve an inverse problem in acousto-electric tomography. Several numerical experiments and comparison with an existing method demonstrate the effectiveness of the proposed method for superior image reconstructions of a wide variety of log-conductivity patterns.
The Cenozoic magmatism of East Africa: Part II – Rifting of the mobile belt

Abstract Despite the foundational role East Africa has played in the advancement of our understanding of continental rifting, there remains substantial ambiguity as to how magmatism has evolved during progressive rift development. This contribution is a comparative study that explores the temporal development of magmatism in both rifts, and within the interposing Turkana Depression. Notwithstanding the independent evolution of the Main Ethiopian Rift and Kenya Rift prior to their Quaternary linkage, magmatic events within them show remarkable parallelism. Following an initial pulse of basaltic magmatism ca. 20 Ma, more evolved compositions (flood phonolites in Kenya, rhyolites in Ethiopia) dominated the landscape until ca. 12 Ma. From ca. 12 Ma to 9 Ma, a renewed phase of widespread basaltic volcanism (Mid-Miocene Resurgence Phase) impacted the entire region from the Afar margin to Kenya, though activity in the south slightly predates equivalent basaltic events to the north. Following this widespread basaltic event, silicic magmatism again dominated the now nascent rifts until a renewed phase of basaltic activity commenced ca. 4 to 1.6 Ma termed the ‘Stratoid Phase’. Following the termination of the Stratoid Phase, the modern expression of volcanism in both rifts has been dominated by central silicic volcanoes. Magmatic activity within the Turkana Depression broadly parallels that in both rifts – basaltic events are evident at ca. 20 Ma, 12 Ma, and 4 Ma, however the silicic interludes that are evident within the rifts, are generally absent. Moreover, from 4 Ma to present, there is a progressive decrease in magmatic activity from an initial fissural basalt, to a series of large shield volcanoes, to modern belts of cinder cones. The results of this synthesis suggest widespread pulsed basaltic events precede silicic flare-ups throughout the East African Rift System, and the lack of such flare-ups in Turkana may relate to the Mesozoic rifting that modified the continental lithosphere in that region.
Periodic Symplectic Cohomologies

Goodwillie \cite{Goodwillie} introduced a periodic cyclic homology group associated to a mixed complex. In this paper, we apply this construction to the symplectic cochain complex of a Liouville domain $M$ and obtain two periodic symplectic cohomology theories, denoted as $HP^*_{S^1}(M)$ and $HP^*_{S^1, \mathrm{loc}}(M)$. Our main result is that both cohomology theories are invariant under Liouville isomorphisms and there is a natural isomorphism $HP^*_{S^1, \mathrm{loc}}(M, \mathbb{Q}) \cong H^*(M, \mathbb{Q})((u))$, which can be seen as a localization theorem for $HP^*_{S^1, \mathrm{loc}}(M, \mathbb{Q})$.
Accurate numerical solutions of 2-D elastodynamics problems using compact high-order stencils

Abstract A new approach for the increase in the order of accuracy of high-order numerical techniques used for 2-D time-dependent elasticity (structural dynamics and wave propagation) has been suggested on uniform square and rectangular meshes. It is based on the optimization of the coefficients of the corresponding semi-discrete stencil equations with respect to the local truncation error. It is shown that the second order of accuracy of the linear finite elements with 9-point stencils is optimal and cannot be improved. However, we have calculated new 9-point stencils with the second order of accuracy that yield more accurate results than those obtained by the linear finite elements. We have also developed new 25-point stencils (similar to those for the quadratic finite and isogeometric elements) with the optimal sixth order of accuracy for the non-diagonal mass matrix and with the optimal fourth order of accuracy for the diagonal mass matrix. The numerical results are in good agreement with the theoretical findings as well as they show a big increase in accuracy of the new stencils compared with those for the high-order finite elements. At the same number of degrees of freedom, the new approach yields significantly more accurate results than those obtained by the high-order (up to the eighth order) finite elements with the non-diagonal mass matrix. The numerical experiments also show that the new approach with 25-point stencils yields very accurate results for nearly incompressible materials with Poisson’s ratio 0.4999 .
Fused Deposition Modeling 3D Printing Technology in Textile and Fashion Industry: Materials and Innovation

Over the last ten years, researchers, technologists, designers, retailers and manufacturers have worked on embedding 3D printing (3DP) technology in all types of manufacturing industries including textile, apparel and fashion industry. The development of this technology has enabled designers to design more conveniently and manufacturers to reduce production time and cost significantly. Although 3D printing technology is gradually developing in textile and fashion industry, textile and fashion researchers and manufacturers have mostly worked on designing wearable technology, footwear and fashion accessories but rarely on everyday wearing. This review paper aims to describe the Fused Deposition Modeling (FDM) 3D printing technology, as it is mostly used by textile and fashion, designers, manufacturers and researchers for developing various textile patterns and fashion items.
On learning Hamiltonian systems from data

Concise, accurate descriptions of physical systems through their conserved quantities abound in the natural sciences. In data science, however, current research often focuses on regression problems, without routinely incorporating additional assumptions about the system that generated the data. Here, we propose to explore a particular type of underlying structure in the data: Hamiltonian systems, where an "energy" is conserved. Given a collection of observations of such a Hamiltonian system over time, we extract phase space coordinates and a Hamiltonian function of them that acts as the generator of the system dynamics. The approach employs an autoencoder neural network component to estimate the transformation from observations to the phase space of a Hamiltonian system. An additional neural network component is used to approximate the Hamiltonian function on this constructed space, and the two components are trained jointly. As an alternative approach, we also demonstrate the use of Gaussian processes for the estimation of such a Hamiltonian. After two illustrative examples, we extract an underlying phase space as well as the generating Hamiltonian from a collection of movies of a pendulum. The approach is fully data-driven and does not assume a particular form of the Hamiltonian function.
Guiding center dynamics as motion on a formal slow manifold in loop space

Since the late 1950s, the dynamics of a charged particle’s “guiding center” in a strong, inhomogeneous magnetic field have been understood in terms of near-identity coordinate transformations. The basic idea has been to approximately transform away the coupling between the fast gyration around magnetic fields lines and the remaining slow dynamics. This basic understanding now serves as a foundation for describing the kinetic theory of strongly magnetized plasmas. I present a new way to understand guiding center dynamics that does not involve complicated coordinate transformations. Starting from a dynamical system formulation of the motion of parameterized loops in a charged particle’s phase space, I identify a formal slow manifold in loop space. Dynamics on this formal slow manifold are equivalent to guiding center dynamics to all orders in perturbation theory. After demonstrating that loop space dynamics comprises an infinite-dimensional noncanonical Hamiltonian system, I recover the well-known Hamiltonian formulation of guiding center motion by restricting the (pre)symplectic structure on loop space to the finite-dimensional guiding center formal slow manifold.
Colored Trees and Noncommutative Symmetric Functions

Let ${\cal CRF}_S$ denote the category of $S$-colored rooted forests, and H$_{{\cal CRF}_S}$ denote its Ringel-Hall algebra as introduced by Kremnizer and Szczesny. We construct a homomorphism from a $K^+_0({\cal CRF}_S)$–graded version of the Hopf algebra of noncommutative symmetric functions to H$_{{\cal CRF}_S}$. Dualizing, we obtain a homomorphism from the Connes-Kreimer Hopf algebra to a $K^+_0({\cal CRF}_S)$–graded version of the algebra of quasisymmetric functions. This homomorphism is a refinement of one considered by W. Zhao.
Optimal Multi-Period Dispatch of Distributed Energy Resources in Unbalanced Distribution Feeders

This paper presents an efficient algorithm for the multi-period optimal dispatch of deterministic inverter-interfaced energy storage in an unbalanced distribution feeder with significant solar PV penetration. The three-phase, non-convex loss-minimization problem is formulated as a convex second-order cone program (SOCP) for the dispatch of batteries in a receding-horizon fashion in order to counter against the variable, renewable generation. The solution of the SOCP is used to initialize a nonlinear program (NLP) in order to ensure a physically realizable solution. The phenomenon of simultaneous charging and discharging of batteries is rigorously analyzed and conditions are derived that guarantee it is avoided. Simulation scenarios are implemented with GridLab-D for the IEEE-13 and IEEE-123 node test feeders and illustrate not only AC feasibility of the solution, but also near-optimal performance and solve-times within a minute.
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations

Temporal models based on recurrent neural networks have proven to be quite powerful in a wide variety of applications, including language modeling and speech processing. However, training these models often relies on backpropagation through time (BPTT), which entails unfolding the network over many time steps, making the process of conducting credit assignment considerably more challenging. Furthermore, the nature of backpropagation itself does not permit the use of nondifferentiable activation functions and is inherently sequential, making parallelization of the underlying training process difficult. Here, we propose the parallel temporal neural coding network (P-TNCN), a biologically inspired model trained by the learning algorithm we call local representation alignment. It aims to resolve the difficulties and problems that plague recurrent networks trained by BPTT. The architecture requires neither unrolling in time nor the derivatives of its internal activation functions. We compare our model and learning procedure with other BPTT alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization. We show that it outperforms these on-sequence modeling benchmarks such as Bouncing MNIST, a new benchmark we denote as Bouncing NotMNIST, and Penn Treebank. Notably, our approach can, in some instances, outperform full BPTT as well as variants such as sparse attentive backtracking. Significantly, the hidden unit correction phase of P-TNCN allows it to adapt to new data sets even if its synaptic weights are held fixed (zero-shot adaptation) and facilitates retention of prior generative knowledge when faced with a task sequence. We present results that show the P-TNCN’s ability to conduct zero-shot adaptation and online continual sequence modeling.