cssi/SciSciGPT-SciSciNet huggingface dataset:
Increasing flash memory lifetime by dynamic voltage allocation for constant mutual information

The read channel in Flash memory systems degrades over time because the Fowler-Nordheim tunneling used to apply charge to the floating gate eventually compromises the integrity of the cell because of tunnel oxide degradation. While degradation is commonly measured in the number of program/erase cycles experienced by a cell, the degradation is proportional to the number of electrons forced into the floating gate and later released by the erasing process. By managing the amount of charge written to the floating gate to maintain a constant read-channel mutual information, Flash lifetime can be extended. This paper proposes an overall system approach based on information theory to extend the lifetime of a flash memory device. Using the instantaneous storage capacity of a noisy flash memory channel, our approach allocates the read voltage of flash cell dynamically as it wears out gradually over time. A practical estimation of the instantaneous capacity is also proposed based on soft information via multiple reads of the memory cells.
Scale‐free collaboration networks: An author name disambiguation perspective

Several studies have found that collaboration networks are scale-free, proposing that such networks can be modeled by specific network evolution mechanisms like preferential attachment. This study argues that collaboration networks can look more or less scale-free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4M ~ 9.6M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename-initial-based name matching, tend to produce degree distributions better fitted to power-law slopes with the typical scaling parameter (2 < {\alpha} < 3) than networks disambiguated by more accurate algorithm-based methods. Such tendency is observed across collaboration networks generated under various conditions such as cumulative years, 5- & 1-year sliding windows, and random sampling, and through simulation, found to arise due mainly to artefactual entities created by inaccurate disambiguation. This cautionary study calls for special attention from scholars analyzing network data in which entities such as people, organization, and gene can be merged or split by improper disambiguation.
Nef Cones of Nested Hilbert Schemes of Points on Surfaces

Let $X$ be the projective plane, a Hirzebruch surface, or a general $K3$ surface. In this paper, we study the birational geometry of various nested Hilbert schemes of points parameterizing pairs of zero-dimensional subschemes on $X$. We calculate the nef cone for two types of nested Hilbert schemes: $X^{[n+1,n]}$ and universal families. As an application, we recover a theorem of Butler on syzygies on Hirzebruch surfaces.
A Correction for IUE UV Flux Distributions from Comparisons with CALSPEC

A collection of spectral energy distributions (SEDs) is available in the Hubble Space Telescope (HST) CALSPEC database that is based on calculated model atmospheres for pure hydrogen white dwarfs (WDs). A much larger set (~100,000) of UV SEDs covering the range (1150-3350A) with somewhat lower quality are available in the IUE database. IUE low-dispersion flux distributions are compared with CALSPEC to provide a correction that places IUE fluxes on the CALSPEC scale. While IUE observations are repeatable to only 4-10% in regions of good sensitivity, the average flux corrections have a precision of 2-3%. Our re-calibration places the IUE flux scale on the current UV reference standard and is relevant for any project based on IUE archival data, including our planned comparison of GALEX to the corrected IUE fluxes. IUE SEDs may be used to plan observations and cross-calibrate data from future missions, so the IUE flux calibration must be consistent with HST instrumental calibrations to the best possible precision.
Derived Hecke Algebra for Weight One Forms

ABSTRACTWe study the action of the derived Hecke algebra on the space of weight one forms. By analogy with the topological case, we formulate a conjecture relating this to a certain Stark unit. We ...
Scaling limit for escapes from unstable equilibria in the vanishing noise limit: Nontrivial Jordan block case

We consider white noise perturbations of a nonlinear dynamical system in the neighborhood of an unstable critical point with linearization given by a Jordan block of full dimension. For the associa...
Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap

To identify the estimand in missing data problems and observational studies, it is common to base the statistical estimation on the "missing at random" and "no unmeasured confounder" assumptions. However, these assumptions are unverifiable using empirical data and pose serious threats to the validity of the qualitative conclusions of the statistical inference. A sensitivity analysis asks how the conclusions may change if the unverifiable assumptions are violated to a certain degree. In this paper we consider a marginal sensitivity model which is a natural extension of Rosenbaum's sensitivity model that is widely used for matched observational studies. We aim to construct confidence intervals based on inverse probability weighting estimators, such that asymptotically the intervals have at least nominal coverage of the estimand whenever the data generating distribution is in the collection of marginal sensitivity models. We use a percentile bootstrap and a generalized minimax/maximin inequality to transform this intractable problem to a linear fractional programming problem, which can be solved very efficiently. We illustrate our method using a real dataset to estimate the causal effect of fish consumption on blood mercury level.
Ghostbusting and property A

Abstract We show that the Roe algebra of a bounded geometry metric space contains non-compact ghosts if and only if the space does not have property A.
Euclidean quadratic forms and ADC forms: I

Introduction 2 1. Norms and Ideal Norms 4 1.1. Normed rings 4 1.2. Ideal norms 5 1.3. Divisorial norms 5 1.4. Abstract number rings 6 1.5. Euclidean norms 8 2. Euclidean quadratic forms and ADC forms 8 2.1. Euclidean quadratic forms 8 2.2. Euclideanity 9 2.3. ADC-forms 10 2.4. The Main Theorem 10 2.5. The Generalized Cassels-Pfister Theorem 11 2.6. Maximal Lattices 12 3. Localization 12 3.1. Localization and Euclideanity 13 3.2. Localization and Completion of ADC-forms 14 4. Imprimitive forms 15 4.1. Unary forms 17 5. Euclidean Algebras, Ideals and Modules 18 5.1. Normed division algebras over a field 18 5.2. Orders in a normed division algebra 19 5.3. Euclidean ideal classes 20 5.4. Binary forms associated to Euclidean quadratic orders and ideals 22 5.5. Quaternary forms attached to Euclidean quaternion orders and ideals 24 5.6. A Euclidean octonion order 26 6. Maximal Lattices, Hasse Domains and Complete DVRs 26 6.1. CDVRs and Hasse Domains 26 6.2. Classification of Euclidean forms over CDVRs 27 6.3. ADC forms over Hasse domains 28 6.4. Definite quadratic forms over Z 29 6.5. Some preliminary results over Z 31 6.6. Some preliminary results over Fp[t] 32 7. Conjectures and Open Problems 33 7.1. Conjectures On Euclidean Forms 33
Complex multiplication cycles and Kudla-Rapoport divisors

We study the intersections of special cycles on a unitary Shimura variety of signature (n-1,1) and show that the intersection multiplicities of these cycles agree with Fourier coefficients of Eisenstein series. The results are new cases of conjectures of Kudla and suggest a Gross-Zagier theorem for unitary Shimura varieties.
The action on cohomology by compositions of rational maps

We use intuitive results from algebraic topology and intersection theory to clarify the pullback action on cohomology by compositions of rational maps. We use these techniques to prove a simple sufficient criterion for functoriality of a composition of two rational maps on all degrees of cohomology and we then reprove the criteria of Diller-Favre, Bedford-Kim, and Dinh-Sibony. We conclude with a cautionary example.
On nonlinear cross-diffusion systems : an optimal transport approach.

We study a nonlinear, degenerate cross-diffusion model which involves two densities with two different drift velocities. A general framework is introduced based on its gradient flow structure in Wasserstein space to derive a notion of discrete-time solutions. Its continuum limit, due to the possible mixing of the densities, only solves a weaker version of the original system. In one space dimension, we find a stable initial configuration which allows the densities to be segregated. This leads to the evolution of a stable interface between the two densities, and to a stronger convergence result to the continuum limit. In particular derivation of a standard weak solution to the system is available. We also study the incompressible limit of the system, which addresses transport under a height constraint on the total density. In one space dimension we show that the problem leads to a two-phase Hele-Shaw type flow.
Internally $4$-connected binary matroids with every element in three triangles

Let M be an internally 4-connected binary matroid with every element in exactly three triangles. Then M has at least four elements e such that si(M/e) is internally 4-connected. This technical result is a crucial ingredient in Abdi and Guenin’s theorem determining the minimally non-ideal binary clutters that have a triangle.
Massively Parallel Coincidence Counting of High-Dimensional Entangled States

Entangled states of light are essential for quantum technologies and fundamental tests of physics. Current systems rely on entanglement in 2D degrees of freedom, e.g., polarization states. Increasing the dimensionality provides exponential speed-up of quantum computation, enhances the channel capacity and security of quantum communication protocols, and enables quantum imaging; unfortunately, characterizing high-dimensional entanglement of even bipartite quantum states remains prohibitively time-consuming. Here, we develop and experimentally demonstrate a new theory of camera detection that leverages the massive parallelization inherent in an array of pixels. We show that a megapixel array, for example, can measure a joint Hilbert space of 1012 dimensions, with a speed-up of nearly four orders-of-magnitude over traditional methods. The technique uses standard geometry with existing technology, thus removing barriers of entry to quantum imaging experiments, generalizes readily to arbitrary numbers of entangled photons, and opens previously inaccessible regimes of high-dimensional quantum optics.
Algorithms for approximate minimization of the difference between submodular functions, with applications

We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between sub-modular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at every step. We empirically and theoretically show that the per-iteration cost of our algorithms is much less than [30], and our algorithms can be used to efficiently minimize a difference between submodular functions under various combinatorial constraints, a problem not previously addressed. We provide computational bounds and a hardness result on the multiplicative inapproximability of minimizing the difference between submodular functions. We show, however, that it is possible to give worst-case additive bounds by providing a polynomial time computable lower-bound on the minima. Finally we show how a number of machine learning problems can be modeled as minimizing the difference between submodular functions. We experimentally show the validity of our algorithms by testing them on the problem of feature selection with submodular cost features.
Holographic imaging of unlabelled sperm cells for semen analysis: a review.

Quantitative phase maps of living swimming spermatozoa. Male reproductive health in both humans and animals is an important research field in biological study. In order to characterize the morphology, the motility and the concentration of the sperm cells, which are the most important parameters to feature them, digital holography demonstrated to be an attractive technique. Indeed, it is a label-free, non-invasive and high-resolution method that enables the characterization of live specimen. The review is intended both for summarizing the state-of-art on the semen analysis and recent achievement obtained by means of digital holography and for exploring new possible applications of digital holography in this field.
Weyl group q-Kreweras numbers and cyclic sieving

Catalan numbers are known to count noncrossing set partitions, while Narayana and Kreweras numbers refine this count according to the number of blocks in the set partition, and by its collection of block sizes. Motivated by reflection group generalizations of Catalan numbers and their q-analogues, this paper concerns a definition of q-Kreweras numbers for finite Weyl groups W, refining the q-Catalan numbers for W, and arising from work of the second author. We give explicit formulas in all types for the q-Kreweras numbers. In the classical types A, B, C, we also record formulas for the q-Narayana numbers and in the process show that the formulas depend only on the Weyl group (that is, they coincide in types B and C). In addition, we verify that in the classical types A, B, C, D the q-Kreweras numbers obey the expected cyclic sieving phenomena when evaluated at appropriate roots of unity.
An accelerated randomized Kaczmarz algorithm

The randomized Kaczmarz ($\RK$) algorithm is a simple but powerful approach for solving consistent linear systems $Ax=b$. This paper proposes an accelerated randomized Kaczmarz ($\ARK$) algorithm with better convergence than the standard $\RK$ algorithm on ill conditioned problems. The per-iteration cost of $\RK$ and $\ARK$ are similar if $A$ is dense, but $\RK$ is much more able to exploit sparsity in $A$ than is $\ARK$. To deal with the sparse case, an efficient implementation for $\ARK$, called $\SARK$, is proposed. A comparison of convergence rates and average per-iteration complexities among $\RK$, $\ARK$, and $\SARK$ is given, taking into account different levels of sparseness and conditioning. Comparisons with the leading deterministic algorithm --- conjugate gradient applied to the normal equations --- are also given. Finally, the analysis is validated via computational testing.
A greedy algorithm for finding a large 2‐matching on a random cubic graph

A 2-matching of a graph G is a spanning subgraph with maximum degree two. The size of a 2-matching U is the number of edges in U and this is at least n − κ(U) where n is the number of vertices of G and κ denotes the number of components. In this paper, we analyze the performance of a greedy algorithm 2greedy for finding a large 2-matching on a random 3-regular graph. We prove that with high probability, the algorithm outputs a 2-matching U with κ(U) = ˜ � n 1/5 � .
The intertwined roles of particle shape and surface roughness in controlling the shear strength of a granular material

Both the shape of individual particles and their surface properties contribute to the strength of a granular material under shear. Here we show the degree to which these two aspects can be intertwined. In experiments on assemblies of 3D printed, convex lens-shaped particles, we measure the stress–strain response under repeated compressive loading and find that the aggregate’s shear strength falls rapidly when compared to other particle shapes. We probe the granular material at mm-scales with X-ray computed tomography and $$\upmu $$ m-scales with high-resolution surface metrology to look for the cause of the degradation. We find that wear due to accumulated deformation smooths out the lens surfaces in a controlled and systematic manner that correlates with a significant loss of shear strength observed for the assembly as a whole. The sensitivity of lenses to changes in surface properties contrasts with results for assemblies of 3D printed tetrahedra and spheres, which under the same load cycling are found to exhibit only minor degradation in strength. This case study provides insight into the relationship between particle shape, surface wear, and the overall material response, and suggests new strategies when designing a granular material with desired evolution of properties under repeated deformation.
Existence of the solution to electromagnetic wave scattering problem for an impedance body of an arbitrary shape

Abstract A new proof is given for the existence of the solution to electromagnetic (EM) wave scattering problem for an impedance body of an arbitrary shape. The proof is based on the elliptic systems theory and elliptic estimates for the solutions of such systems.
Ramsey Numbers for Partially-Ordered Sets

We present a refinement of Ramsey numbers by considering graphs with a partial ordering on their vertices. This is a natural extension of the ordered Ramsey numbers. We formalize situations in which we can use arbitrary families of partially-ordered sets to form host graphs for Ramsey problems. We explore connections to well studied Turan-type problems in partially-ordered sets, particularly those in the Boolean lattice. We find a strong difference between Ramsey numbers on the Boolean lattice and ordered Ramsey numbers when the partial ordering on the graphs have large antichains.
A fast exact simulation method for a class of Markov jump processes

A new method of the stochastic simulation algorithm (SSA), named the Hashing-Leaping method (HLM), for exact simulations of a class of Markov jump processes, is presented in this paper. The HLM has a conditional constant computational cost per event, which is independent of the number of exponential clocks in the Markov process. The main idea of the HLM is to repeatedly implement a hash-table-like bucket sort algorithm for all times of occurrence covered by a time step with length τ. This paper serves as an introduction to this new SSA method. We introduce the method, demonstrate its implementation, analyze its properties, and compare its performance with three other commonly used SSA methods in four examples. Our performance tests and CPU operation statistics show certain advantages of the HLM for large scale problems.
Modeling and emulation of nonstationary Gaussian fields

Abstract Geophysical and other natural processes often exhibit nonstationary covariances and this feature is important for statistical models that attempt to emulate the physical process. A convolution-based model is used to represent nonstationary Gaussian processes that allows for variation in the correlation range and the variance of the process across space. We apply this model in two steps: windowed estimates of the covariance function under the assumption of local stationarity and encoded local estimates into a single spatial process model that allows for efficient simulation. We show that nonstationary covariance functions based on the Matern family can be reproduced by the LatticeKrig (LK) model, a flexible, multi-resolution representation of Gaussian processes. Stationary models based on the Matern covariance are fit in local windows and these estimates are assembled into a single, global LK model. The LK model is efficient for simulating nonstationary fields even at 1 0 5 locations. This work is motivated by the interest in emulating spatial fields derived from numerical model simulations such as Earth system models. We successfully apply these ideas to emulate fields that describe the uncertainty in the pattern scaling of mean summer (JJA) surface temperature from a series of climate model experiments. The spatial covariance structure developed in this paper is not limited to emulation, and could also be used for spatial prediction and conditional simulation for observational data and leverages embarrassingly parallel strategies for computational efficiency.
On the Effect of Shadowing Correlation on Wireless Network Performance

We propose and analyze a new shadowing field model meant to capture spatial correlations. The interference field associated with this new model is compared to that of the widely used independent shadowing model. Independent shadowing over links is adopted because of the resulting closed forms for performance metrics, and in spite of the well-known fact that the shadowing fields of networks are spatially correlated. The main purpose of this paper is to challenge this independent shadowing approximation. For this, we analyze the interference measured at the origin in networks where 1) nodes which are in the same cell of some random shadowing tessellation share the same shadow, or 2) nodes which share a common mother point in some cluster process share the same shadow. By leveraging stochastic comparison techniques, we give the order relation of the three main user performance metrics, namely coverage probability, Shannon throughput and local delay, under both the correlated and the independent shadowing assumptions. We show that the evaluation of the considered metrics under the independent approximation is systematically pessimistic compared to the correlated shadowing model. The improvement in each metric when adopting the correlated shadow model is quantified and shown to be quite significant.
The menu complexity of "one-and-a-half-dimensional" mechanism design

We study the menu complexity of optimal and approximately-optimal auctions in the context of the "FedEx" problem, a so-called "one-and-a-half-dimensional" setting where a single bidder has both a value and a deadline for receiving an item [FGKK16]. The menu complexity of an auction is equal to the number of distinct (allocation, price) pairs that a bidder might receive [HN13]. We show the following when the bidder has n possible deadlines: • Exponential menu complexity is necessary to be exactly optimal: There exist instances where the optimal mechanism has menu complexity ≥ 2n − 1. This matches exactly the upper bound provided by Fiat et al.'s algorithm, and resolves one of their open questions [FGKK16]. • Fully polynomial menu complexity is necessary and sufficient for approximation: For all instances, there exists a mechanism guaranteeing a multiplicative (1 − ∈)-approximation to the optimal revenue with menu complexity [EQUATION], where vmax denotes the largest value in the support of integral distributions. • There exist instances where any mechanism guaranteeing a multiplicative (1 − O(1/n2))-approximation to the optimal revenue requires menu complexity Ω(n2). Our main technique is the polygon approximation of concave functions [Rot92], and our results here should be of independent interest. We further show how our techniques can be used to resolve an open question of [DW17] on the menu complexity of optimal auctions for a budget-constrained buyer.
On holomorphic domination, I

Abstract Let X be a separable Banach space and u : X → R locally upper bounded. We show that there are a Banach space Z and a holomorphic function h : X → Z with u ( x ) ‖ h ( x ) ‖ for x ∈ X . As a consequence we find that the sheaf cohomology group H q ( X , O ) vanishes if X has the bounded approximation property (i.e., X is a direct summand of a Banach space with a Schauder basis), O is the sheaf of germs of holomorphic functions on X, and q ⩾ 1 . As another consequence we prove that if f is a C 1 -smooth ∂ ¯ -closed ( 0 , 1 ) -form on the space X = L 1 [ 0 , 1 ] of summable functions, then there is a C 1 -smooth function u on X with ∂ ¯ u = f on X.
Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms

This paper introduces partitioning an inference task of a deep neural network between an edge and a host platform in the IoT environment. We present a DNN as an encoding pipeline, and propose to transmit the output feature space of an intermediate layer to the host. Encoding of the feature space is proposed to enhance the maximum input rate supported by the edge platform and/or reduce the energy of the edge platform. Simulation results show that partitioning a DNN coupled with feature space encoding enables significant improvement in the energy-efficiency and throughput over the baseline configurations that perform the entire inference at the edge or at the host.
Path-dependent Hamilton-Jacobi equations in infinite dimensions

Abstract We propose notions of minimax and viscosity solutions for a class of fully nonlinear path-dependent PDEs with nonlinear, monotone, and coercive operators on Hilbert space. Our main result is well-posedness (existence, uniqueness, and stability) for minimax solutions. A particular novelty is a suitable combination of minimax and viscosity solution techniques in the proof of the comparison principle. One of the main difficulties, the lack of compactness in infinite-dimensional Hilbert spaces, is circumvented by working with suitable compact subsets of our path space. As an application, our theory makes it possible to employ the dynamic programming approach to study optimal control problems for a fairly general class of (delay) evolution equations in the variational framework. Furthermore, differential games associated to such evolution equations can be investigated following the Krasovskiĭ–Subbotin approach similarly as in finite dimensions.
Information flow for security in control systems

This paper considers the development of information flow analyses to support resilient design and active detection of adversaries in cyber physical systems (CPS). CPS security, though well studied, suffers from fragmentation. In this paper, we consider control systems as an abstraction of CPS. Here, we use information flow analysis, a well established set of methods developed in software security, to obtain a unified framework that captures and extends results in control system security. Specifically, we propose the Kullback Liebler (KL) divergence as a causal measure of information flow, which quantifies the effect of adversarial inputs on sensor outputs. We show that the proposed measure characterizes the resilience of control systems to specific attack strategies by relating the KL divergence to optimal detection. We then relate information flows to stealthy attack scenarios where an adversary can bypass detection. Finally, this article examines active detection mechanisms where a defender intelligently manipulates control inputs or the system itself to elicit information flows from an attacker's malicious behavior. In all previous cases, we demonstrate an ability to investigate and extend existing results through the proposed information flow analyses.
Wild theories with o-minimal open core

Abstract Let T be a consistent o-minimal theory extending the theory of densely ordered groups and let T ′ be a consistent theory. Then there is a complete theory T ⁎ extending T such that T is an open core of T ⁎ , but every model of T ⁎ interprets a model of T ′ . If T ′ is NIP, T ⁎ can be chosen to be NIP as well. From this we deduce the existence of an NIP expansion of the real field that has no distal expansion.
Localization near the edge for the Anderson Bernoulli model on the two dimensional lattice

We consider a Hamiltonian given by the Laplacian plus a Bernoulli potential on the two dimensional lattice. We prove that, for energies sufficiently close to the edge of the spectrum, the resolvent on a large square is likely to decay exponentially. This implies almost sure Anderson localization for energies sufficiently close to the edge of the spectrum. Our proof follows the program of Bourgain–Kenig, using a new unique continuation result inspired by a Liouville theorem of Buhovsky–Logunov–Malinnikova–Sodin.
The Witt vectors for Green functors

Abstract We define twisted Hochschild homology for Green functors. This construction is the algebraic analogue of the relative topological Hochschild homology THH C n ( − ) , and under flatness conditions it describes the E 2 term of the Kunneth spectral sequence for relative THH. Applied to ordinary rings, we obtain new algebraic invariants. Extending Hesselholt's construction of the Witt vectors of noncommutative rings, we interpret our construction as providing Witt vectors for Green functors.
Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped

This paper presents Stanford Doggo, a quasi-direct-drive quadruped capable of dynamic locomotion. This robot matches or exceeds common performance metrics of state-of-the-art legged robots. In terms of vertical jumping agility, a measure of average vertical speed, Stanford Doggo matches the best performing animal and surpasses the previous best robot by 22%. An overall design architecture is presented with focus on our quasi-direct-drive design methodology. The hardware and software to replicate this robot is open-source, requires only hand tools for manufacturing and assembly, and costs less than $3000.
Learning nonlinear level sets for dimensionality reduction in function approximation

We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variety of design tasks in real-world engineering applications, where practitioners would replace their computationally intensive physical models (e.g., high-resolution fluid simulators) with fast-to-evaluate predictive machine learning models, so as to accelerate the engineering design processes. There are two major challenges in constructing such predictive models: (a) high-dimensional inputs (e.g., many independent design parameters) and (b) small training data, generated by running extremely time-consuming simulations. Thus, reducing the input dimension is critical to alleviate the over-fitting issue caused by data insufficiency. Existing methods, including sliced inverse regression and active subspace approaches, reduce the input dimension by learning a linear coordinate transformation; our main contribution is to extend the transformation approach to a nonlinear regime. Specifically, we exploit reversible networks (RevNets) to learn nonlinear level sets of a high-dimensional function and parameterize its level sets in low-dimensional spaces. A new loss function was designed to utilize samples of the target functions' gradient to encourage the transformed function to be sensitive to only a few transformed coordinates. The NLL approach is demonstrated by applying it to three 2D functions and two 20D functions for showing the improved approximation accuracy with the use of nonlinear transformation, as well as to an 8D composite material design problem for optimizing the buckling-resistance performance of composite shells of rocket inter-stages.
Chest pain while gardening: a Stanford type A dissection involving the aortic root extending into the iliac arteries—an uncommon and potentially catastrophic disease process

An aortic dissection is an uncommon and potentially catastrophic disease process that carries with it a high morbidity and mortality. The inciting event is a tear in the intimal lining of the aorta. This allows passage of blood through the tear and into the aortic media, resulting in the creation of a false lumen. We describe the case of a 71-year-old male with a history of hypertension that suffered a Stanford type A dissection with an intimal flap beginning at the level of the aortic root and extending into the bilateral iliac arteries. His clinical presentation was further complicated by shock, cardiac tamponade, severe coagulopathy, an ischemic right lower extremity, infarction of his thoracic spinal cord, and subacute infarcts secondary to malperfusion and embolic disease. Despite maximal intervention, the patient continued to clinically decline and ultimately died on day 5. The clinical presentation of an acute aortic dissection is often atypical and mimics other common disease processes. The signs and symptoms largely depend on the extent of the aortic dissection and the presence or absence of malperfusion. With a mortality increasing by 1–2% for every hour until definitive treatment, early recognition and prompt operative intervention are crucial for patient survival.
Characterization of a Reactive Rh2 Nitrenoid by Crystalline Matrix Isolation

The fleeting lifetimes of reactive intermediates in C-H functionalization chemistry often prevent their direct characterization. For example, the critical nitrenoid intermediates that mediate Rh2-catalyzed C-H amination have eluded characterization for more than 40 years. In the absence of structural characterization of these species, methodological development is often computationally guided. Here we report the first X-ray crystal structure of a reactive Rh2 nitrenoid, enabled by N2 elimination from an organic azide ligand within a single-crystal matrix. The resulting high-resolution structure displays metrical parameters consistent with a triplet nitrene complex of Rh2. The demonstration of facile access to reactive metal nitrenoids within a crystalline matrix provides a platform for structural characterization of the transient species at the heart of C-H functionalization.
Matrix algebra of sets and variants of decomposition complexity

We introduce matrix algebra of subsets in metric spaces and we apply it to improve results of Yamauchi and Davila regarding Asymptotic Property C. Here is a representative result: Suppose X is an $$\infty $$-pseudo-metric space and $$n\ge 0$$ is an integer. The asymptotic dimension$$\mathrm {asdim}(X)$$ of X is at most n if and only if for any real number $$r > 0$$ and any integer $$m\ge 1$$ there is an augmented $$m\,\times \,(n+1)$$-matrix $${\mathcal {M}}=[{\mathcal {B}} |{\mathcal {A}}]$$ (that means $${\mathcal {B}}$$ is a column-matrix and $${\mathcal {A}}$$ is an $$m\,\times \,n$$-matrix) of subspaces of X of scale-r-dimension 0 such that $${\mathcal {M}}\cdot _\cap {\mathcal {M}}^T$$ is bigger than or equal to the identity matrix and $$B({\mathcal {A}},r)\cdot _\cap B({\mathcal {A}},r)^T$$ is a diagonal matrix.
Simple current auto-equivalences of modular tensor categories

In this short note we investigate the process of constructing auto-equivalences of modular tensor categories using invertible objects. We derive conditions on the invertible object for the resulting auto-equivalence to be either monoidal, braided, or pivotal. We also discuss the composition of these auto-equivalences constructed from invertible objects. To demonstrate the practicality of this construction, we construct auto-equivalences of several real-world examples of modular tensor categories.
Resonant Kuiper belt objects: a review

Our understanding of the history of the solar system has undergone a revolution in recent years, owing to new theoretical insights into the origin of Pluto and the discovery of the Kuiper belt and its rich dynamical structure. The emerging picture of dramatic orbital migration of the planets driven by interaction with the primordial Kuiper belt is thought to have produced the final solar system architecture that we live in today. This paper gives a brief summary of this new view of our solar system’s history and reviews the astronomical evidence in the resonant populations of the Kuiper belt.
Practical concurrent unrolled linked lists using lazy synchronization

Abstract Linked lists and other list-based sets are some of the most ubiquitous data structures in computer science. They are useful in their own right and are frequently used as building blocks in other data structures. A linked list can be “unrolled” to combine multiple keys in each node; this improves storage density and overall performance. This organization also allows an operation to skip over nodes which cannot contain a key of interest. This work introduces a new high-performance concurrent unrolled linked list with a lazy synchronization strategy. Most write operations under this strategy can complete by locking a single node. Experiments show up to 300% improvement over other concurrent list-based sets.
On the tightness of Gaussian concentration for convex functions

The concentration of measure phenomenon in Gauss’ space states that every L-Lipschitz map f on ℝn satisfies $${\gamma _n}\left({\left\{{x:| {f(x) - {M_f}|\,\geqslant t} } \right\}} \right)\,\leqslant 2{e^{- {{{t^2}} \over {2{L^2}}},}}\quad t > 0,$$ where γn is the standard Gaussian measure on ℝn and Mf is a median of f. In this work, we provide necessary and sufficient conditions for when this inequality can be reversed, up to universal constants, in the case when f is additionally assumed to be convex. In particular, we show that if the variance Var(f) (with respect to γn) satisfies $$\alpha L\leqslant \sqrt {{\rm{Var(}}f{\rm{)}}} $$ for some 0 0,$$ where c, C > 0 are constants depending only on α.
SU(8) family unification with boson-fermion balance

We formulate an SU(8) family unification model motivated by requiring that the theory should incorporate the graviton, gravitinos, and the fermions and gauge fields of the standard model, with boson–fermion balance. Gauge field SU(8) anomalies cancel between the gravitinos and spin ½ fermions. The 56 of scalars breaks SU(8) to SU(3)family × SU(5) × U(1)/Z5, with the fermion representation content needed for "flipped" SU(5) with three families, and with residual scalars in the 10 and representations that break flipped SU(5) to the standard model. Dynamical symmetry breaking can account for the generation of 5 representation scalars needed to break the electroweak group. Yukawa couplings of the 56 scalars to the fermions are forbidden by chiral and gauge symmetries, so in the first stage of SU(8) breaking fermions remain massless. In the limit of vanishing gauge coupling, there are N = 1 and N = 8 supersymmetries relating the scalars to the fermions, which restrict the form of scalar self-couplings and should improve the convergence of perturbation theory, if not making the theory finite and "calculable." In an Appendix we give an analysis of symmetry breaking by a Higgs component, such as the (1, 1)(-15) of the SU(8) 56 under SU(8) ⊃ SU(3) × SU(5) × U(1), which has nonzero U(1) generator.
DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data

The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation.
International Student Spouses and the English Language: Co-Creating a Low-Stakes Language Learning Community.

In the context of the internationalization of U.S. higher education, millions of international students have come to study at U.S. institutions. Some students, particularly those pursuing advanced degree, bring their spouses. As part of a larger study, we set out to examine the experiences of international student spouses at a Midwestern university. This article examines the experiences from the perspective of grappling with the English language, a theme cross-cutting through spouses’ experiences. Experiences with English, the focus of this article, are both a discrete element of spouses’ lived experiences and a useful way to understand challenges and opportunities not directly related to language. Utilizing qualitative case study techniques, we found that English language is experienced through anxieties as well as ambitions by the participants in our study.
6mer Seed Toxicity in Viral microRNAs.

Summary MicroRNAs (miRNAs) are short double-stranded noncoding RNAs (19-23 nucleotides) that regulate gene expression by suppressing mRNAs through RNA interference. Targeting is determined by the seed sequence (position 2-7/8) of the mature miRNA. A minimal G-rich seed of just six nucleotides is highly toxic to cells by targeting genes essential for cell survival. A screen of 215 miRNAs encoded by 17 human pathogenic viruses (v-miRNAs) now suggests that a number of v-miRNAs can kill cells through a G-rich 6mer sequence embedded in their seed. Specifically, we demonstrate that miR-K12-6-5p, an oncoviral mimic of the tumor suppressive miR-15/16 family encoded by human Kaposi sarcoma-associated herpes virus, harbors a noncanonical toxic 6mer seed (position 3-8) and that v-miRNAs are more likely than cellular miRNAs to utilize a noncanonical 6mer seed. Our data suggest that during evolution viruses evolved to use 6mer seed toxicity to kill cells.
The Zeckendorf Game

Zeckendorf proved that every positive integer n can be written uniquely as the sum of non-adjacent Fibonacci numbers. We use this to create a two-player game. Given a fixed integer n and an initial decomposition of \(n = n F_1\), the two players alternate by using moves related to the recurrence relation \(F_{n+1} = F_n + F_{n-1}\), and whoever moves last wins. The game always terminates in the Zeckendorf decomposition, though depending on the choice of moves the length of the game and the winner can vary. We find upper and lower bounds on the number of moves possible. The upper bound is on the order of \(n\log n\), and the lower bound is sharp at \(n-Z(n)\) moves, where Z(n) is the number of terms in the Zeckendorf decomposition of n. Notably, Player 2 has the winning strategy for all \(n > 2\); interestingly, however, the proof is non-constructive.
Max Weber: Family History, Economic Policy, Exchange Reform

Max Weber descended from Anglo-German merchant families with world-wide connections. This helped make him a “cosmopolitan nationalist.” Fittingly Weber at first tried to pursue a career in international commercial law. Family connections informed his understanding of South American wheat exports and North American railroad capitalism and of the tariff issues resulting therefrom. This in turn influenced his critique of the Prussian agrarian capitalists, especially of their opposition to trading in grain futures. Weber championed a strong capital market and efficient exchanges. His economic policies remain of exemplary interest insofar as they link the presumptive, if contested, national interest with an unsentimental acceptance of the world economy.
The tree of quadratic transforms of a regular local ring of dimension two

Abstract Let D be a 2-dimensional regular local ring and let Q ( D ) denote the quadratic tree of 2-dimensional regular local overrings of D. We explore the topology of the tree Q ( D ) and the family R ( D ) of rings obtained as intersections of rings in Q ( D ) . If A is a finite intersection of rings in Q ( D ) , then A is Noetherian and the structure of A is well understood. However, other rings in R ( D ) need not be Noetherian. The two main goals of this paper are to examine topological properties of the quadratic tree Q ( D ) , and to examine the structure of rings in the set R ( D ) .
Liquidity and volatility in the U.S. Treasury market

Abstract We model the joint dynamics of intraday liquidity, volume, and volatility in the U.S. Treasury market, especially through the 2007–09 financial crisis and around important economic announcements. Using various specifications based on Bauwens and Giot (2000)’s Log-ACD(1,1) model, we find that liquidity, volume, and volatility are highly persistent, with volatility having a lower short-term persistence than the other two. Market liquidity and volume are important to explaining volatility dynamics but not vice versa. In addition, market dynamics change during the financial crisis, with all variables exhibiting increased responsiveness to their most recent realizations. Our models also reveal different market dynamics around announcements. Finally, we introduce new measures of liquidity risk that are useful for continually monitoring liquidity conditions and the risk of liquidity stress in the market.
Non-parametric learning of lifted Restricted Boltzmann Machines

Abstract We consider the problem of discriminatively learning Restricted Boltzmann Machines in the presence of relational data. Unlike previous approaches that employ a rule learner (for structure learning) and a weight learner (for parameter learning) sequentially, we develop a gradient-boosted approach that performs both simultaneously. Our approach learns a set of weak relational regression trees, whose paths from root to leaf are conjunctive clauses and represent the structure, and whose leaf values represent the parameters. When the learned relational regression trees are transformed into a lifted RBM, its hidden nodes are precisely the conjunctive clauses derived from the relational regression trees. This leads to a more interpretable and explainable model. Our empirical evaluations clearly demonstrate this aspect, while displaying no loss in effectiveness of the learned models.
Pairwise link prediction

Link prediction is a common problem in network science that transects many disciplines. The goal is to forecast the appearance of new links or to find links missing in the network. Typical methods for link prediction use the topology of the network to predict the most likely future or missing connections between a pair of nodes. However, network evolution is often mediated by higher-order structures involving more than pairs of nodes; for example, cliques on three nodes (also called triangles) are key to the structure of social networks, but the standard link prediction framework does not directly predict these structures. To address this gap, we propose a new link prediction task called "pairwise link prediction" that directly targets the prediction of new triangles, where one is tasked with finding which nodes are most likely to form a triangle with a given edge. We develop two PageRank-based methods for our pairwise link prediction problem and make natural extensions to existing link prediction methods. Our experiments on a variety of networks show that diffusion based methods are less sensitive to the type of graphs used and more consistent in their results. We also show how our pairwise link prediction framework can be used to get better predictions within the context of standard link prediction evaluation.