cssi/SciSciGPT-SciSciNet huggingface dataset:
Formal Analysis of Robotic Cell Injection Systems Using Theorem Proving

Cell injection is an approach used for the delivery of small sample substances into a biological cell and is widely used in drug development, gene injection, intracytoplasmic sperm injection (ICSI) and in-virto fertilization (IVF). Robotic cell injection systems provide the automation of the process as opposed to the manual and semi-automated cell injection systems, which require expert operators and involve time consuming processes and also have lower success rates. The automation of the cell injection process is achieved by controlling the injection force and planning the motion of the injection pipette. Traditionally, these systems are analyzed using paper-and-pencil proof and computer simulation methods. However, the former is human-error prone and the later is based on the numerical algorithms, where the approximation of the mathematical expressions introduces inaccuracies in the analysis. Formal methods can overcome these limitations and thus provide an accurate analysis of the cell injection systems. Model checking, i.e., a state-based formal method, has been recently proposed for the analysis of these systems. However, it involves the discretization of the differential equations that are used for modeling the dynamics of the system and thus compromises on the completeness of the analysis of these safety-critical systems. In this paper, we propose to use higher-order-logic theorem proving, a deductive-reasoning based formal method, for the modeling and analysis of the dynamical behaviour of the robotic cell injection systems. The proposed analysis, based on the HOL Light theorem prover, enabled us to identify some discrepancies in the simulation and model checking based analysis of the same robotic cell injection system.
More Adaptive Algorithms for Adversarial Bandits

We develop a novel and generic algorithm for the adversarial multi-armed bandit problem (or more generally the combinatorial semi-bandit problem). When instantiated differently, our algorithm achieves various new data-dependent regret bounds improving previous work. Examples include: 1) a regret bound depending on the variance of only the best arm; 2) a regret bound depending on the first-order path-length of only the best arm; 3) a regret bound depending on the sum of first-order path-lengths of all arms as well as an important negative term, which together lead to faster convergence rates for some normal form games with partial feedback; 4) a regret bound that simultaneously implies small regret when the best arm has small loss and logarithmic regret when there exists an arm whose expected loss is always smaller than those of others by a fixed gap (e.g. the classic i.i.d. setting). In some cases, such as the last two results, our algorithm is completely parameter-free. The main idea of our algorithm is to apply the optimism and adaptivity techniques to the well-known Online Mirror Descent framework with a special log-barrier regularizer. The challenges are to come up with appropriate optimistic predictions and correction terms in this framework. Some of our results also crucially rely on using a sophisticated increasing learning rate schedule.
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction

We submitted two systems to the SemEval2016 Task 12: Clinical TempEval challenge, participating in Phase 1, where we identified text spans of time and event expressions in clinical notes and Phase 2, where we predicted a relation between an event and its parent document creation time. For temporal entity extraction, we find that a joint inference-based approach using structured prediction outperforms a vanilla recurrent neural network that incorporates word embeddings trained on a variety of large clinical document sets. For document creation time relations, we find that a combination of date canonicalization and distant supervision rules for predicting relations on both events and time expressions improves classification, though gains are limited, likely due to the small scale of training data.
Kernel task-driven dictionary learning for hyperspectral image classification

Dictionary learning algorithms have been successfully used in both reconstructive and discriminative tasks, where the input signal is represented by a linear combination of a few dictionary atoms. While these methods are usually developed under l 1 sparsity constrain (prior) in the input domain, recent studies have demonstrated the advantages of sparse representation using structured sparsity priors in the kernel domain. In this paper, we propose a supervised dictionary learning algorithm in the kernel domain for hyperspectral image classification. In the proposed formulation, the dictionary and classifier are obtained jointly for optimal classification performance. The supervised formulation is task-driven and provides learned features from the hyperspectral data that are well suited for the classification task. Moreover, the proposed algorithm uses a joint (l 12 ) sparsity prior to enforce collaboration among the neighboring pixels. The simulation results illustrate the efficiency of the proposed dictionary learning algorithm.
Classification of inductive limits of 1-dimensional NCCW complexes

Abstract A classification result is obtained for the C ∗ -algebras that are (stably isomorphic to) inductive limits of 1-dimensional noncommutative CW complexes with trivial K 1 -group. The classifying functor Cu ∼ is defined in terms of the Cuntz semigroup of the unitization of the algebra. For the simple C ∗ -algebras covered by the classification, Cu ∼ reduces to the ordered K 0 -group, the cone of traces, and the pairing between them. As an application of the classification, it is shown that the crossed products by a quasi-free action O 2 ⋊ λ R are all isomorphic for a dense set of positive irrational numbers λ .
On Zaremba's conjecture

Zaremba’s 1971 conjecture predicts that every integer appears as the denominator of a nite continued fraction whose partial quotients are bounded by an absolute constant. We conrm this conjecture for a set of density one.
Minimum complexity pursuit

The fast growing field of compressed sensing is founded on the fact that if a signal is simple and has some ‘structure’, then it can be reconstructed accurately with far fewer samples than its ambient dimension. Many different plausible structures have been explored in this field, ranging from sparsity to low-rankness and to finite rate of innovation. However, there are important abstract questions that are yet to be answered. For instance, what are the general abstract meanings of structure and simplicity? Does there exist universal algorithms for recovering such simple structured objects from fewer samples than their ambient dimension? In this paper, we aim to address these two questions. Using algorithmic information theory tools such as Kolmogorov complexity, we provide a unified method of describing simplicity and structure. We then explore the performance of an algorithm motivated by Ocams Razor (called MCP for minimum complexity pursuit) and show that it requires O(k logn) number of samples to recover a signal, where k and n represent its complexity and ambient dimension, respectively. Finally, we discuss more general classes of signals and provide guarantees on the performance of MCP.
Generalized high-dimensional trace regression via nuclear norm regularization

Abstract We study the generalized trace regression with a near low-rank regression coefficient matrix, which extends notion of sparsity for regression coefficient vectors. Specifically, given a matrix covariate X , the probability density function of the response Y is f ( Y | X ) = c ( Y ) exp ( ϕ − 1 − Y η ∗ + b ( η ∗ ) ) , where η ∗ = tr ( Θ ∗ T X ) . This model accommodates various types of responses and embraces many important problem setups such as reduced-rank regression, matrix regression that accommodates a panel of regressors, matrix completion, among others. We estimate Θ ∗ through minimizing empirical negative log-likelihood plus nuclear norm penalty. We first establish a general theory and then for each specific problem, we derive explicitly the statistical rate of the proposed estimator. They all match the minimax rates in the linear trace regression up to logarithmic factors. Numerical studies confirm the rates we established and demonstrate the advantage of generalized trace regression over linear trace regression when the response is dichotomous. We also show the benefit of incorporating nuclear norm regularization in dynamic stock return prediction and in image classification.
On the chain-level intersection pairing for $PL$ pseudomanifolds

For a compact oriented PL manifold, M , the intersection pairing on chain complexes, which induces the intersection pairing algebra on H∗(M), dates back to Lefschetz [17]. However, Lefschetz’s pairing does not provide an algebra structure on C∗(M), itself, as two chains may only be intersected if they are in general position. This difficulty does not descend to the homology groups since any pair of cycles are homologous to cycles in general position, and the resulting intersection product turns out to be independent of the choices made while putting chains into general position. This approach to pairings and duality was supplanted
Integrable systems and modular forms of level 2

A set of nonlinear differential equations associated with the Eisenstein series of the congruent subgroup Γ0(2) of the modular group is constructed. These nonlinear equations are analogues of the well-known Ramanujan equations, as well as the Chazy and Darboux–Halphen equations associated with the modular group. The general solutions of these equations can be realized in terms of the Schwarz triangle function S(0, 0, 1/2; z).
Ideal forms of Coppersmith's theorem and Guruswami-Sudan list decoding

We develop a framework for solving polynomial equations with size constraints on solutions. We obtain our results by showing how to apply a technique of Coppersmith for finding small solutions of polynomial equations modulo integers to analogous problems over polynomial rings, number fields, and function fields. This gives us a unified view of several problems arising naturally in cryptography, coding theory, and the study of lattices. We give (1) a polynomial-time algorithm for finding small solutions of polynomial equations modulo ideals over algebraic number fields, (2) a faster variant of the Guruswami-Sudan algorithm for list decoding of Reed-Solomon codes, and (3) an algorithm for list decoding of algebraic-geometric codes that handles both single-point and multi-point codes. Coppersmith's algorithm uses lattice basis reduction to find a short vector in a carefully constructed lattice; powerful analogies from algebraic number theory allow us to identify the appropriate analogue of a lattice in each application and provide efficient algorithms to find a suitably short vector, thus allowing us to give completely parallel proofs of the above theorems.
The surface Laplacian technique in EEG: Theory and methods

This paper reviews the method of surface Laplacian differentiation to study EEG. We focus on topics that are helpful for a clear understanding of the underlying concepts and its efficient implementation, which is especially important for EEG researchers unfamiliar with the technique. The popular methods of finite difference and splines are reviewed in detail. The former has the advantage of simplicity and low computational cost, but its estimates are prone to a variety of errors due to discretization. The latter eliminates all issues related to discretization and incorporates a regularization mechanism to reduce spatial noise, but at the cost of increasing mathematical and computational complexity. These and several other issues deserving further development are highlighted, some of which we address to the extent possible. Here we develop a set of discrete approximations for Laplacian estimates at peripheral electrodes. We also provide the mathematical details of finite difference approximations that are missing in the literature, and discuss the problem of computational performance, which is particularly important in the context of EEG splines where data sets can be very large. Along this line, the matrix representation of the surface Laplacian operator is carefully discussed and some figures are given illustrating the advantages of this approach. In the final remarks, we briefly sketch a possible way to incorporate finite-size electrodes into Laplacian estimates that could guide further developments.
Statistical Watermarking for Networked Control Systems

Watermarking can detect sensor attacks in control systems by injecting a private signal into the control, whereby attacks are identified by checking the statistics of the sensor measurements and private signal. However, past approaches assume full state measurements or a centralized controller, which is not found in networked LTI systems with subcontrollers. Since generally the entire system is neither controllable nor observable by a single subcontroller, communication of sensor measurements is required to ensure closed-loop stability. The possibility of attacking the communication channel has not been explicitly considered by previous watermarking schemes, and requires a new design. In this paper, we derive a statistical watermarking test that can detect both sensor and communication attacks. A unique (compared to the non-networked case) aspect of the implementing this test is the state-feedback controller must be designed so that the closed-loop system is controllable by each sub-controller, and we provide two approaches to design such a controller using Heymann's lemma and a multi-input generalization of Heymann's lemma. The usefulness of our approach is demonstrated with a simulation of detecting attacks in a platoon of autonomous vehicles. Our test allows each vehicle to independently detect attacks on both the communication channel between vehicles and on the sensor measurements.
PAC reinforcement learning with rich observations

We propose and study a new model for reinforcement learning with rich observations, generalizing contextual bandits to sequential decision making. These models require an agent to take actions based on observations (features) with the goal of achieving long-term performance competitive with a large set of policies. To avoid barriers to sample-efficient learning associated with large observation spaces and general POMDPs, we focus on problems that can be summarized by a small number of hidden states and have long-term rewards that are predictable by a reactive function class. In this setting, we design and analyze a new reinforcement learning algorithm, Least Squares Value Elimination by Exploration. We prove that the algorithm learns near optimal behavior after a number of episodes that is polynomial in all relevant parameters, logarithmic in the number of policies, and independent of the size of the observation space. Our result provides theoretical justification for reinforcement learning with function approximation.
Bethe Bounds and Approximating the Global Optimum

Inference in general Markov random fields (MRFs) is NP-hard, though identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with submodular cost functions is efficiently solvable using graph cuts. Marginal inference, however, even for this restricted class, is #P-hard. Restricting to binary pairwise models, we prove new formulations of derivatives of the Bethe free energy, provide bounds on the derivatives and bracket the locations of stationary points. Several results apply whether the model is associative or not. Applying these to discretized pseudo-marginals in the associative case, we present a polynomial time approximation scheme for global optimization of the Bethe free energy provided the maximum degree ∆ = O(log n), where n is the number of variables. Runtime is guaranteed O( − 3 2nΣ 3 4 Ω 3 2 ), where Σ = O(∆n ) is the fraction of possible edges present and Ω is a function of MRF parameters. We examine use of the algorithm in practice, demonstrating runtime that is typically much faster, and discuss several extensions.
Lower bounds on the Hausdorff measure of nodal sets

Let $\ncal_{\phi_{\lambda}}$ be the nodal hypersurface of a $\Delta$-eigenfunction $\phi_{\lambda}$ of eigenvalue $\lambda^2$ on a smooth Riemannian manifold. We prove the following lower bound for its surface measure: $\hcal^{n-1}(\ncal_{\phi_{\lambda}}) \geq C \lambda^{\frac74-\frac{3n}4} $. The best prior lower bound appears to be $e^{- C \lambda}$.
Implicit Regularization in Matrix Factorization

We study implicit regularization when optimizing an underdetermined quadratic objective over a matrix $X$ with gradient descent on a factorization of X. We conjecture and provide empirical and theoretical evidence that with small enough step sizes and initialization close enough to the origin, gradient descent on a full dimensional factorization converges to the minimum nuclear norm solution.
Jets and Centrality in p(d)-A Collisions

Abstract The production of jets, and high- p T leading pions from jets, in d-Au collisions at the Relativistic Heavy-Ion Collider (RHIC) and p-Pb collisions at the Large Hadron Collider (LHC) are studied. Using a modified version of the event generator PYTHIA, in conjunction with a nuclear Glauber Monte-Carlo event generator, we demonstrate how events with a hard jet may be simulated, in such a way that the parton distribution function of the projectile nucleon is frozen during its interaction with the extended nucleus. Using our hybrid Monte-Carlo event generator, we demonstrate that the enhancement in R pA seen in peripheral events at RHIC and at LHC, as well as the depletion in central or semi-central events, is mainly due to “mis-binning” of central and semi-central events with a jet, as peripheral events. This occurs due to the reduction of soft particle production caused by a depletion of energy available in a nucleon (of the deuteron in the case of d-Au collisions), after the production of a hard jet. This represents a form of “color transparency” of the projectile nucleon, which has fluctuated to a state with fewer and harder partons, in events which lead to jet production. We conclude with discussions of the form of multi-parton correlations in a nucleon which may be responsible for such a startling effect.
Spectra of uniform hypergraphs

Abstract We present a spectral theory of uniform hypergraphs that closely parallels Spectral Graph Theory. A number of recent developments building upon classical work has led to a rich understanding of “symmetric hyperdeterminants” of hypermatrices, a.k.a. multidimensional arrays. Symmetric hyperdeterminants share many properties with determinants, but the context of multilinear algebra is substantially more complicated than the linear algebra required to address Spectral Graph Theory (i.e., ordinary matrices). Nonetheless, it is possible to define eigenvalues of a hypermatrix via its characteristic polynomial as well as variationally. We apply this notion to the “adjacency hypermatrix” of a uniform hypergraph, and prove a number of natural analogs of basic results in Spectral Graph Theory. Open problems abound, and we present a number of directions for further study.
Doubling Property and Vanishing Order of Steklov Eigenfunctions

The paper is concerned with the doubling estimates and vanishing order of the Steklov eigenfunctions on the boundary of a smooth domain in ℝ n . The eigenfunction is given by a Dirichlet-to-Neumann...
A Tamper-Free Semi-Universal Communication System for Deletion Channels

We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka, jamming) adversarial model. To this goal, we develop a theoretical framework based on probabilistic finite-state automata to define novel encoding and decoding schemes that ensure small error probability in both message decoding as well as tamper detecting. We then experimentally verify the reliability and tamper-detection property of our scheme.
Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing

This paper studies the problem of sequential Gaussian shift-in-mean hypothesis testing in a distributed multi-agent network. A sequential probability ratio test (SPRT) type algorithm in a distributed framework of the consensus+innovations form is proposed, in which the agents update their decision statistics by simultaneously processing latest observations (innovations) sensed sequentially over time and information obtained from neighboring agents (consensus). For each pre-specified set of type I and type II error probabilities, local decision parameters are derived which ensure that the algorithm achieves the desired error performance and terminates in finite time almost surely (a.s.) at each network agent. Large deviation exponents for the tail probabilities of the agent stopping time distributions are obtained and it is shown that asymptotically (in the number of agents or in the high signal-to-noise-ratio regime) these exponents associated with the distributed algorithm approach that of the optimal centralized detector. The expected stopping time for the proposed algorithm at each network agent is evaluated and is benchmarked with respect to the optimal centralized algorithm. The efficiency of the proposed algorithm in the sense of the expected stopping times is characterized in terms of network connectivity. Finally, simulation studies are presented which illustrate and verify the analytical findings.
Autoimmunity in common variable immunodeficiency.

Abstract Objective Common variable immunodeficiency (CVID) is a primary immunodeficiency that is clinically heterogeneous, characterized by both infectious and noninfectious complications. Although the hallmark of disease presentation is commonly a history of recurrent sinopulmonary infections, autoimmunity and noninfectious inflammatory conditions are increasingly associated with CVID. Data Sources A comprehensive literature search using PubMed of basic science and clinical articles was performed. Study Selections Articles discussing the association of autoimmunity with primary immunodeficiency, specifically CVID, were selected. Results The most common autoimmune conditions are cytopenias, including immune thrombocytopenia purpura and hemolytic anemia, but organ-specific autoimmune/inflammatory complications involving the gastrointestinal, skin, joints, connective tissue, and respiratory tract. In most cases, immunoglobulin replacement therapy does not ameliorate or treat these inflammatory complications, and additional immunomodulatory treatments are needed. Conclusion Mechanisms producing these conditions are poorly understood but include cytokine and cellular inflammatory pathways, and loss of tolerance to self-antigens through the multiple signaling molecules and pathways common to tolerance and immune deficiency.
Signature ( n ???2,2) CM types and the unitary Colmez conjecture

Abstract Colmez conjectured a formula relating the Faltings height of CM abelian varieties to a certain linear combination of logarithmic derivatives of L-functions. In this paper, we study the case of unitary CM fields and by studying the class functions that arise, we reduce the conjecture to a special case. Using the Galois action, we prove more cases of the Colmez Conjecture.
Gradient sliding for composite optimization

We consider in this paper a class of composite optimization problems whose objective function is given by the summation of a general smooth and nonsmooth component, together with a relatively simple nonsmooth term. We present a new class of first-order methods, namely the gradient sliding algorithms, which can skip the computation of the gradient for the smooth component from time to time. As a consequence, these algorithms require only $$\mathcal{O}(1/\sqrt{\epsilon })$$O(1/∈) gradient evaluations for the smooth component in order to find an $$\epsilon $$∈-solution for the composite problem, while still maintaining the optimal $$\mathcal{O}(1/\epsilon ^2)$$O(1/∈2) bound on the total number of subgradient evaluations for the nonsmooth component. We then present a stochastic counterpart for these algorithms and establish similar complexity bounds for solving an important class of stochastic composite optimization problems. Moreover, if the smooth component in the composite function is strongly convex, the developed gradient sliding algorithms can significantly reduce the number of graduate and subgradient evaluations for the smooth and nonsmooth component to $$\mathcal{O} (\log (1/\epsilon ))$$O(log(1/∈)) and $$\mathcal{O}(1/\epsilon )$$O(1/∈), respectively. Finally, we generalize these algorithms to the case when the smooth component is replaced by a nonsmooth one possessing a certain bi-linear saddle point structure.
Investigation of Microstructure and Corrosion Resistance of Dissimilar Welded Joint between 304 Stainless Steel and Pure Copper

Nowadays, welding of dissimilar metals has become significant. In this process, a number of parameters including but not limited to type of electrode, amount of current, preheating temperature, and welding rate, that are essential to be taken into account. For welding of dissimilar metals, various methods are exploited including shielded metal arc welding (SMAW) and gas tungsten arc welding (GTAW). The stimulus for studying welding of 304L stainless steel to pure copper originates from difficulties in joining copper parts of water-circulating molds to their steel part. In this study, the welding is performed on plates of steel and copper using SMAW, GTAW and combined SMAW+GTAW welding methods with EL-CuMn 2 , ENiCrMo-6 and ER70S-4 electrodes. In order to investigate the microstructure and corrosion resistance behavior of welds, the samples were characterized using microstructural study and polarization test. It was observed that among all four welding methods, only combined SMAW+GTAW welding process resulted in successful joint between 304L stainless steel and copper. Both obtained joints possess suitable microstructure and corrosion resistance.
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence

Regularization is typically understood as improving generalization by altering the landscape of local extrema to which the model eventually converges. Deep neural networks (DNNs), however, challenge this view: We show that removing regularization after an initial transient period has little effect on generalization, even if the final loss landscape is the same as if there had been no regularization. In some cases, generalization even improves after interrupting regularization. Conversely, if regularization is applied only after the initial transient, it has no effect on the final solution, whose generalization gap is as bad as if regularization never happened. This suggests that what matters for training deep networks is not just whether or how, but when to regularize. The phenomena we observe are manifest in different datasets (CIFAR-10, CIFAR-100, SVHN, ImageNet), different architectures (ResNet-18, All-CNN), different regularization methods (weight decay, data augmentation, mixup), different learning rate schedules (exponential, piece-wise constant). They collectively suggest that there is a "critical period'' for regularizing deep networks that is decisive of the final performance. More analysis should, therefore, focus on the transient rather than asymptotic behavior of learning.
On the shape of resolvent modes in wall-bounded turbulence

This work develops a methodology for approximating the shape of leading resolvent modes for incompressible, quasi-parallel, shear-driven turbulent flows using prescribed analytic functions. We demonstrate that these functions, which arise from the consideration of wavepacket pseudoeigenmodes of simplified linear operators (Trefethen, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 461, 2005, pp. 3099–3122. The Royal Society), give an accurate approximation for the energetically dominant wall-normal vorticity component of a class of nominally wall-detached modes that are centred about the critical layer. We validate our method on a model operator related to the Squire equation, and show for this simplified case how wavepacket pseudomodes relate to truncated asymptotic expansions of Airy functions. Following the framework developed in McKeon & Sharma (J. Fluid Mech., vol. 658, 2010, pp. 336–382), we next apply a sequence of simplifications to the resolvent formulation of the Navier–Stokes equations to arrive at a scalar differential operator that is amenable to such analysis. The first simplification decomposes the resolvent operator into Orr–Sommerfeld and Squire suboperators, following Rosenberg & McKeon (Fluid Dyn. Res., vol. 51, 2019, 011401). The second simplification relates the leading resolvent response modes of the Orr–Sommerfeld suboperator to those of a simplified scalar differential operator – which is the Squire operator equipped with a non-standard inner product. This characterisation provides a mathematical framework for understanding the origin of leading resolvent mode shapes for the incompressible Navier–Stokes resolvent operator, and allows for rapid estimation of dominant resolvent mode characteristics without the need for operator discretisation or large numerical computations. We explore regions of validity for this method, and show that it can predict resolvent response mode shape (though not necessary the corresponding resolvent gain) over a wide range of spatial wavenumbers and temporal frequencies. In particular, we find that our method remains relatively accurate even when the modes have some amount of ‘attachment’ to the wall, and that that the region of validity contains the regions in parameter space where large-scale and very-large-scale motions typically reside. We relate these findings to classical lift-up and Orr amplification mechanisms in shear-driven flows.
A Riemann-Hilbert Approach to Asymptotic Analysis of Toeplitz+Hankel Determinants

In this paper we will formulate $4\times4$ Riemann-Hilbert problems for Toeplitz+Hankel determinants and the associated system of orthogonal polynomials, when the Hankel symbol is supported on the unit circle and also when it is supported on an interval $[a,b]$, $0<a<b<1$. The distinguishing feature of this work is that in the formulation of the Riemann-Hilbert problem no specific relationship is assumed between the Toeplitz and Hankel symbols. We will develop nonlinear steepest descent methods for analysing these problems in the case where the symbols are smooth (i.e., in the absence of Fisher-Hartwig singularities) and admit an analytic continuation in a neighborhood of the unit circle (if the symbol's support is the unit circle). We will finally introduce a model problem and will present its solution requiring certain conditions on the ratio of Hankel and Toeplitz symbols. This in turn will allow us to find the asymptotics of the norms $h_n$ of the corresponding orthogonal polynomials and, in fact, the large $n$ asymptotics of the polynomials themselves. We will explain how this solvable case is related to the recent operator-theoretic approach in [Basor E., Ehrhardt T., in Large Truncated Toeplitz Matrices, Toeplitz Operators, and Related Topics, Oper. Theory Adv. Appl., Vol. 259, Birkhauser/Springer, Cham, 2017, 125-154, arXiv:1603.00506] to Toeplitz+Hankel determinants. At the end we will discuss the prospects of future work and outline several technical, as well as conceptual, issues which we are going to address next within the $4\times 4$ Riemann-Hilbert framework introduced in this paper.
On modules for meromorphic open-string vertex algebras

We study modules of the meromorphic open-string vertex algebra (MOSVAs hereafter), a noncommutative generalization of vertex (operator) algebra defined by Yi-Zhi Huang. We start by recalling the definition of a MOSVA V and left V-modules given in Huang’s paper. Then we define right V-modules and V-bimodules that reflect the noncommutative nature of V. When V satisfies a condition on the order of poles of the correlation function (which we call pole-order condition), we prove that the rationality of products of two vertex operators implies the rationality of products of any numbers of vertex operators. Also, the rationality of iterates of any numbers of vertex operators is established and is used to construct the opposite MOSVA Vop of V. It is proved here that right (respectively, left) V-modules are equivalent to left (respectively, right) Vop-modules. Using this equivalence, we prove that if V and a grading-restricted left V-module W are endowed with a Mobius structure, then the graded dual W′ of W is a right V-module. This is the only proof in this paper that needs the grading-restriction condition. Also, this result is generalized to not-grading-restricted modules under a strong pole-order condition that is satisfied by all existing examples of MOSVAs and modules.We study modules of the meromorphic open-string vertex algebra (MOSVAs hereafter), a noncommutative generalization of vertex (operator) algebra defined by Yi-Zhi Huang. We start by recalling the definition of a MOSVA V and left V-modules given in Huang’s paper. Then we define right V-modules and V-bimodules that reflect the noncommutative nature of V. When V satisfies a condition on the order of poles of the correlation function (which we call pole-order condition), we prove that the rationality of products of two vertex operators implies the rationality of products of any numbers of vertex operators. Also, the rationality of iterates of any numbers of vertex operators is established and is used to construct the opposite MOSVA Vop of V. It is proved here that right (respectively, left) V-modules are equivalent to left (respectively, right) Vop-modules. Using this equivalence, we prove that if V and a grading-restricted left V-module W are endowed with a Mobius structure, then the graded dual W′ of W is a ...
PRC2 activates interferon-stimulated genes indirectly by repressing miRNAs in glioblastoma

Polycomb repressive complex 2 (PRC2) is a chromatin binding complex that represses gene expression by methylating histone H3 at K27 to establish repressed chromatin domains. PRC2 can either regulate genes directly through the methyltransferase activity of its component EZH2 or indirectly by regulating other gene regulators. Gene expression analysis of glioblastoma (GBM) cells lacking EZH2 showed that PRC2 regulates hundreds of interferon-stimulated genes (ISGs). We found that PRC2 directly represses several ISGs and also indirectly activates a distinct set of ISGs. Assessment of EZH2 binding proximal to miRNAs showed that PRC2 directly represses miRNAs encoded in the chromosome 14 imprinted DLK1-DIO3 locus. We found that repression of this locus by PRC2 occurs in immortalized GBM-derived cell lines as well as in primary bulk tumors from GBM and anaplastic astrocytoma patients. Through repression of these miRNAs and several other miRNAs, PRC2 activates a set of ISGs that are targeted by these miRNAs. This PRC2-miRNA-ISG network is likely to be important in regulating gene expression programs in GBM.
A Set-Theoretic Model Reference Adaptive Control Architecture with Dead-Zone Effect

By introducing a system error dependent learning rate, the recently proposed set-theoretic model reference adaptive control architecture provides user-defined worst-case performance guarantees on the system error between an uncertain dynamical system of interest and a given reference model. In this architecture, the adaptation process is always active. However, it is of practical interest to stop the adaptation process when it is not needed (i.e., in the presence of small system errors). Motivated from this standpoint, we present a new set-theoretic model reference adaptive control architecture with dead-zone effect. The key feature of our framework utilizes a modified and continuous generalized restricted potential function in the adaptation update law, where it not only stops the adaptation process inside the dead-zone (i.e., when the system error is small) but also allows the norm of the system error to be less than a-priori, user-defined worst-case performance bound. The efficacy of the proposed architecture is also demonstrated through an illustrative numerical example.
Will Mentoring a Student Teacher Harm My Evaluation Scores? Effects of Serving as a Cooperating Teacher on Evaluation Metrics:

Growing evidence suggests that preservice candidates receive better coaching and are more instructionally effective when they are mentored by more instructionally effective cooperating teachers (CT...
Resolution and dose dependence of radiation damage in biomolecular systems.

The local Fourier-space relation between diffracted intensity I, diffraction wavevector q and dose D, \tilde I(q,D), is key to probing and understanding radiation damage by X-rays and energetic particles in both diffraction and imaging experiments. The models used in protein crystallography for the last 50 years provide good fits to experimental I(q) versus nominal dose data, but have unclear physical significance. More recently, a fit to diffraction and imaging experiments suggested that the maximum tolerable dose varies as q−1 or linearly with resolution. Here, it is shown that crystallographic data have been strongly perturbed by the effects of spatially nonuniform crystal irradiation and diffraction during data collection. Reanalysis shows that these data are consistent with a purely exponential local dose dependence, \tilde I(q,D) = I0(q)exp[−D/De(q)], where De(q) ∝ qα with α ≃ 1.7. A physics-based model for radiation damage, in which damage events occurring at random locations within a sample each cause energy deposition and blurring of the electron density within a small volume, predicts this exponential variation with dose for all q values and a decay exponent α ≃ 2 in two and three dimensions, roughly consistent with both diffraction and imaging experiments over more than two orders of magnitude in resolution. The B-factor model used to account for radiation damage in crystallographic scaling programs is consistent with α = 2, but may not accurately capture the dose dependencies of structure factors under typical nonuniform illumination conditions. The strong q dependence of radiation-induced diffraction decays implies that the previously proposed 20–30 MGy dose limit for protein crystallography should be replaced by a resolution-dependent dose limit that, for atomic resolution data sets, will be much smaller. The results suggest that the physics underlying basic experimental trends in radiation damage at T ≃ 100 K is straightforward and universal. Deviations of the local I(q, D) from strictly exponential behavior may provide mechanistic insights, especially into the radiation-damage processes responsible for the greatly increased radiation sensitivity observed at T ≃ 300 K.
Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data

Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs). Networks are trained with biologically motivated, unsupervised learning rules to obtain a self-organized grid of filters via cooperative and competitive excitatory-inhibitory interactions. Several inhibition strategies are developed and tested, such as (i) incrementally increasing inhibition level over the course of network training, and (ii) switching the inhibition level from low to high (two-level) after an initial training segment. During the labeling phase, the spiking activity generated by data with known labels is used to assign neurons to categories of data, which are then used to evaluate the network’s classification ability on a held-out set of test data. Several biologically plausible evaluation rules are proposed and compared, including a population-level confidence rating, and an n-gram inspired method. The effectiveness of the proposed self-organized learning mechanism is tested using the MNIST benchmark dataset, as well as using images produced by playing the Atari Breakout game.
Look-Ahead SCOPF (LASCOPF) for Tracking Demand Variation via Auxiliary Proximal Message Passing (APMP) Algorithm

Abstract In this paper, we will consider the Look-Ahead Security Constrained Optimal Power Flow (LASCOPF) problem looking forward multiple dispatch intervals, in which the load demand varies over dispatch intervals according to some forecast. We will consider the base-case and several contingency scenarios in the upcoming as well as in the subsequent dispatch intervals. We will formulate and solve the problem in a Model Predictive Control (MPC) paradigm. We will present the Auxiliary Proximal Message Passing (APMP) algorithm to solve this problem, which is a bi-layered decomposition-coordination type distributed algorithm, consisting of an outer Auxiliary Problem Principle (APP) layer and an inner Proximal Message Passing (PMP) layer. The APP part of the algorithm distributes the computation across several dispatch intervals and the PMP part performs the distributed computation within each of the dispatch interval across different devices (i.e. generators, transmission lines, loads) and nodes or nets. We will demonstrate the effectiveness of our method with a series of numerical simulations.
Formalizing computability theory via partial recursive functions

We present an extension to the mathlib library of the Lean theorem prover formalizing the foundations of computability theory. We use primitive recursive functions and partial recursive functions as the main objects of study, and we use a constructive encoding of partial functions such that they are executable when the programs in question provably halt. Main theorems include the construction of a universal partial recursive function and a proof of the undecidability of the halting problem. Type class inference provides a transparent way to supply Godel numberings where needed and encapsulate the encoding details.
III-nitride photovoltaics

III-nitride solar cells comprised of planar and core-shell nanowire arrays are demonstrated. The nanowire architecture enables advantages such as elastic strain relief within the InGaN shell and increased absorption by nanowire light scattering.
Genomic Basis of Circannual Rhythm in the European Corn Borer Moth

Summary Synchronizing the annual timing of physiological, morphological, and behavioral transitions with seasons enables survival in temperate environments [ 1 ]. The capacity to adjust life history timing and track local seasonal cycles can facilitate geographic expansion [ 2 ], adaptation [ 3 ], and tolerance [ 4 , 5 , 6 ] during rapid environmental change. Understanding the proximate causes of variation in seasonal timing improves prediction of future response and persistence [ 7 , 8 ]. However, relatively little is known about the molecular basis generating this diversity [ 9 ], particularly in Lepidoptera, a group with many species in decline [ 10 , 11 ]. In insects, the stress-tolerant physiological state of diapause enables coping with seasonal challenges [ 1 , 12 , 13 , 14 , 15 ]. Seasonal changes in photoperiod and temperature are used to synchronize diapause with winter, and timing of diapause transitions varies widely within and among species [ 9 , 16 ]. Changes in spring diapause termination in the European corn borer moth (Ostrinia nubilalis) have allowed populations to respond to shorter winters and emerge ∼3 weeks earlier in the year [ 17 ]. Multiple whole-genome approaches suggest two circadian clock genes, period (per) and pigment-dispersing factor receptor (Pdfr), underlie this polymorphism. Per and Pdfr are within interacting quantitative trait loci (QTL) and differ in allele frequency among individuals that end diapause early or late, with alleles maintained in high linkage disequilibrium. Our results provide testable hypotheses about the physiological role of circadian clock genes in the circannual timer. We predict these gene candidates will be targets of selection for future adaptation under continued global climate change [ 18 ].
Aiming control: residence probability and ( D, T )-stability

Abstract In this paper, the problem of aiming control is formulated and analyzed in terms of the residence probability measure. Specifically, the notion of residence probability in a domain is introduced and its asymptotic expression is derived for linear systems with small, additive white noise. The associated notion of ( D , T )-stability, which characterizes the performance of stochastic systems with no equilibrium points, is introduced and investigated. Finally, the controllability of residence probability is studied and the necessary and sufficient conditions for ( D , T )-stabilizability are derived. The development is based on the asymptotic large deviations theory.
Are Transport Models Able To Predict Charge Carrier Mobilities in Organic Semiconductors

Organic photovoltaic devices have been steadily becoming more efficient through a combination of reduction in voltage losses, minimization of recombination pathways, and an increase in dimensionali...
CACs and Doorknobs

In response to debt crises, policy makers often feature Collective Action Clauses (CACs) in sovereign bonds among the pillars of international financial architecture. However, the content of official pronouncements about CACs suggests that CACs are more like doorknobs: a process tool with limited impact on the incidence or ultimate outcome of a debt restructuring. We ask whether CACs are welfare improving and, if so, whether they are pillars or doorknobs. The history of CACs in corporate debt suggests that CACs can be good, bad or unimportant depending on their vulnerability to abuse and the available alternatives, including bankruptcy and debt exchanges. The history of CACs in sovereign bond workouts is recent and thin. Without restructuring data, the empirical literature has focused on the ex-ante (pricing) effects of sovereign CACs. To the extent that CACs leave borrowing costs unchanged or even lower them, they are likely to be welfare improving. But the magnitude of the welfare effects cannot be inferred from these studies. Based on the evidence so far, we conjecture that sovereign CACs are like doorknobs: useful, but perhaps not essential. To date, there is no evidence of abuse of the sort observed in U.S. corporate bond restructurings in the 1920 and 1930s. The bulk of pricing studies suggests that any increases in borrowing costs are small. On the other hand, debt exchanges using transactional techniques other than CACs have had a decent track record, suggesting that CACs are not the only way to resolve a debt crisis in the absence of a treaty-based bankruptcy alternative. Future empirical work should focus on how CACs perform in debt restructuring rather than on their ex ante effects.