cssi/SciSciGPT-SciSciNet huggingface dataset:
A Linearly-Convergent Stochastic L-BFGS Algorithm

We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions. Our algorithm draws heavily from a recent stochastic variant of L-BFGS proposed in Byrd et al. (2014) as well as a recent approach to variance reduction for stochastic gradient descent from Johnson and Zhang (2013). We demonstrate experimentally that our algorithm performs well on large-scale convex and non-convex optimization problems, exhibiting linear convergence and rapidly solving the optimization problems to high levels of precision. Furthermore, we show that our algorithm performs well for a wide-range of step sizes, often differing by several orders of magnitude.
ON IHARA'S LEMMA FOR DEGREE ONE AND TWO COHOMOLOGY OVER IMAGINARY QUADRATIC FIELDS

We prove a version of Ihara's Lemma for degree q = 1, 2 cuspidal cohomology of the symmetric space attached to automorphic forms of arbitrary weight (k ≥ 2) over an imaginary quadratic field with torsion (prime power) coefficients. This extends an earlier result of the author [Ihara's lemma for imaginary quadratic fields, J. Number Theory128(8) (2008) 2251–2262] which concerned the case k = 2, q = 1. Our method is different from [Ihara's lemma for imaginary quadratic fields, J. Number Theory128(8) (2008) 2251–2262] and uses results of Diamond [Congruence primes for cusp forms of weight k ≥ 2, Asterisque196–197 (1991) 205–213] and Blasius–Franke–Grunewald [Cohomology of S-arithmetic subgroups in the number field case, Invent. Math.116(1–3) (1994) 75–93]. We discuss the relationship of our main theorem to the problem of the existence of level-raising congruences.
Optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids

The problem of monotonicity testing over the hypergrid and its special case, the hypercube, is a classic question in property testing. We are given query access to f:[k]n -> R (for some ordered range R). The hypergrid/cube has a natural partial order given by coordinate-wise ordering, denoted by prec. A function is monotone if for all pairs x prec y, f(x) ≤ f(y). The distance to monotonicity, ef, is the minimum fraction of values of f that need to be changed to make f monotone. For k=2 (the boolean hypercube), the usual tester is the edge tester, which checks monotonicity on adjacent pairs of domain points. It is known that the edge tester using O(e-1n log|R|) samples can distinguish a monotone function from one where ef > e. On the other hand, the best lower bound for monotonicity testing over general R is Ω(n). We resolve this long standing open problem and prove that O(n/e) samples suffice for the edge tester. For hypergrids, known testers require O(e-1n log k log |R|) samples, while the best known (non-adaptive) lower bound is Ω(e-1 n log k). We give a (non-adaptive) monotonicity tester for hypergrids running in O(e{-1} n log k) time.Our techniques lead to optimal property testers (with the same running time) for the natural Lipschitz property on hypercubes and hypergrids. (A c-Lipschitz function is one where |f(x) - f(y)| ≤ c||x-y||1.) In fact, we give a general unified proof for O(e-1nlog k)-query testers for a class of "bounded-derivative" properties, a class containing both monotonicity and Lipschitz.
Projected Commutator DIIS Method for Accelerating Hybrid Functional Electronic Structure Calculations.

The commutator direct inversion of the iterative subspace (commutator DIIS or C-DIIS) method developed by Pulay is an efficient and the most widely used scheme in quantum chemistry to accelerate the convergence of self-consistent field (SCF) iterations in Hartree–Fock theory and Kohn–Sham density functional theory. The C-DIIS method requires the explicit storage of the density matrix, the Fock matrix, and the commutator matrix. Hence, the method can only be used for systems with a relatively small basis set, such as the Gaussian basis set. We develop a new method that enables the C-DIIS method to be efficiently employed in electronic structure calculations with a large basis set such as planewaves for the first time. The key ingredient is the projection of both the density matrix and the commutator matrix to an auxiliary matrix called the gauge-fixing matrix. The resulting projected commutator-DIIS method (PC-DIIS) only operates on matrices of the same dimension as that consists of Kohn–Sham orbitals. The...
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network

We present a new algorithm to train a robust neural network against adversarial attacks. Our algorithm is motivated by the following two ideas. First, although recent work has demonstrated that fusing randomness can improve the robustness of neural networks (Liu 2017), we noticed that adding noise blindly to all the layers is not the optimal way to incorporate randomness. Instead, we model randomness under the framework of Bayesian Neural Network (BNN) to formally learn the posterior distribution of models in a scalable way. Second, we formulate the mini-max problem in BNN to learn the best model distribution under adversarial attacks, leading to an adversarial-trained Bayesian neural net. Experiment results demonstrate that the proposed algorithm achieves state-of-the-art performance under strong attacks. On CIFAR-10 with VGG network, our model leads to 14\% accuracy improvement compared with adversarial training (Madry 2017) and random self-ensemble (Liu 2017) under PGD attack with $0.035$ distortion, and the gap becomes even larger on a subset of ImageNet.
An integral formula in Kähler geometry with applications

We establish an integral formula on a smooth, precompact domain in a Kahler manifold. We apply this formula to study holomorphic extension of CR functions. Using this formula, we also prove some geometric inequalities when the boundary has positive Hermitian mean curvature.
A DFT+nonhomogeneous DMFT approach for finite systems.

For reliable and efficient inclusion of electron–electron correlation effects in nanosystems we formulate a combined density functional theory/nonhomogeneous dynamical mean-field theory (DFT+DMFT) approach which employs an approximate iterated perturbation theory impurity solver. We further apply the method to examine the size-dependent magnetic properties of iron nanoparticles containing 11–100 atoms. We show that for the majority of clusters the DFT+DMFT solution is in very good agreement with experimental data, much better compared to the DFT and DFT+U results. In particular, it reproduces the oscillations in magnetic moment with size as observed experimentally. We thus demonstrate that the DFT+DMFT approach can be used for accurate and realistic description of nanosystems containing about hundred atoms.
BRAVO—Biased Locking for Reader-Writer Locks

Designers of modern reader-writer locks confront a difficult trade-off related to reader scalability. Locks that have a compact memory representation for active readers will typically suffer under high intensity read-dominated workloads when the "reader indicator"' state is updated frequently by a diverse set of threads, causing cache invalidation and coherence traffic. Other designs, such as cohort reader-writer locks, use distributed reader indicators, one per NUMA node. This improves reader-reader scalability, but also increases the size of each lock instance. We propose a simple transformation BRAVO, that augments any existing reader-writer lock, adding just two integer fields to the lock instance. Readers make their presence known to writers by hashing their thread's identity with the lock address, forming an index into a visible readers table. Readers attempt to install the lock address into that element in the table, making their existence known to potential writers. All locks and threads in an address space can share the visible readers table. Updates by readers tend to be diffused over the table, resulting in a NUMA-friendly design. Crucially, readers of the same lock tend to write to different locations in the array, reducing coherence traffic. Specifically, BRAVO allows a simple compact lock to be augmented so as to provide scalable concurrent reading but with only a modest increase in footprint.
Symmetric states: local unitary equivalence via stabilizers

We classify local unitary equivalence classes of symmetric states via a classification oftheir local unitary stabilizer subgroups. For states whose local unitary stabilizer groupshave a positive number of continuous degrees of freedom, the classification is exhaustive.We show that local unitary stabilizer groups with no continuous degrees of freedom areisomorphic to finite subgroups of the rotation group SO(3), and give examples of stateswith discrete stabilizers.
Angle-Multiplexed Metasurfaces: Encoding Independent Wavefronts in a Single Metasurface under Different Illumination Angles

The angular response of thin diffractive optical elements is highly correlated. For example, the angles of incidence and diffraction of a grating are locked through the grating momentum determined by the grating period. Other diffractive devices, including conventional metasurfaces, have a similar angular behavior due to the fixed locations of the Fresnel zone boundaries and the weak angular sensitivity of the meta-atoms. To alter this fundamental property, we introduce angle-multiplexed metasurfaces, composed of reflective high-contrast dielectric U-shaped meta-atoms, whose response under illumination from different angles can be controlled independently. This enables flat optical devices that impose different and independent optical transformations when illuminated from different directions, a capability not previously available in diffractive optics.
Planar algebras and the decategorification of bordered Khovanov homology

We give a simple, combinatorial construction of a unital, spherical, non-degenerate ∗-planar algebra over the ring ℤ[q1/2,q−1/2]. This planar algebra is similar in spirit to the Temperley–Lieb planar algebra, but computations show that they are different. The construction comes from the combinatorics of the decategorifications of the type A and type D structures in the author’s previous work on bordered Khovanov homology. In particular, the construction illustrates how gluing of tangles occurs in the bordered Khovanov homology and its difference from that in Khovanov’s tangle homology without being encumbered by any extra homological algebra. It also provides a simple framework for showing that these theories are not related through a simple process, thereby confirming recent work of Manion. Furthermore, using Khovanov’s conventions and a state sum approach to the Jones polynomial, we obtain new invariant for tangles in Σ × [−1, 1] where Σ is a compact, planar surface with boundary, and the tangle intersects each boundary cylinder in an even number of points. This construction naturally generalizes Khovanov’s approach to the Jones polynomial.
Innovative Digital Manufacturing Curriculum for Industry 4.0

Abstract Manufacturing companies across all major industries are facing serious challenges trying to competitively design and manage modern products, which are becoming increasingly complex multi-domain systems or “systems of systems”. Model-based systems driven product development (or SDPD, for Systems Driven Product Development) has been proposed as a solution based on driving the product lifecycle from the systems requirements and tracing back performance to stakeholders’ needs through a RFLP (Requirement, Functional, Logical, Physical) traceability process. The SDPD framework integrates system behavioral modeling with downstream product design and manufacturing process practices to support the verification/validation of the systems behavior as products progress through all phases of the lifecycle, as well as the optimization of trade-offs decisions by maintaining the cross-product digital twin and thread for global decision optimization in an efficient and effective way. We have developed an innovative digital manufacturing curriculum (designed around the SDPD paradigm) that is based on the digitalization of the SE (Systems Engineering) process through the integration of modelling and simulation continuum, in the form of Model-based Systems Engineering (MBSE), with Product lifecycle management (PLM). At the core of this curriculum is a shift of focus from theory to implementation and practice, through an applied synthesis of engineering fundamentals and systems engineering, that is driven by a state-of-the-art digital innovation platform for product (or system) development consisting of integrated software (digital) tools spanning the complete lifecycle. The curriculum consists of three key components, namely, modelling and simulation continuum, traceability, and digital thread. The curriculum provides a foundation for implementing the digital twin and supports the training of the next generation of engineers for Industry 4.0. The digital manufacturing (or SDPD) framework is applied in the design and optimization of an electric skateboard. The implementation demonstrates: 1) The benefits of digitalization/model-based engineering when developing complex multi-domain products or systems; 2) The ability of students to effectively complete a real-life modern product development within the time line of one semester; 3) The provision of MBSE curriculum for Engineering Education 4.0, characterized by key, integrated skills for the digital enterprise and Industry 4.0.
Hybrid Cell Assignment and Sizing for Power, Area, Delay-Product Optimization of SRAM Arrays

Memory accounts for a considerable portion of the total power budget and area of digital systems. Furthermore, it is typically the performance bottleneck of the processing units. Therefore, it is critical to optimize the memory with respect to the product of power, area, and delay (PAD). We propose a hybrid cell assignment method based on multi-sized and dual- ${V} _{\mathbf {th}}$ SRAM cells which improves the PAD cost function by 34% compared to the conventional cell assignment. We also utilize the sizing of SRAM cells for minimizing the data retention voltage, and voltages for the read and write operations in the SRAM array. Experimental results in a 32-nm technology show that combining the proposed hybrid cell assignment and the cell sizing methods can lower PAD by up to 41% when compared to the conventional cell design and assignment.
Specialization Models of Network Growth

One of the most important features observed in real networks is that, as a network's topology evolves so does the network's ability to perform various complex tasks. To explain this, it has also been observed that as a network grows certain subnetworks begin to specialize the function(s) they perform. Here, we introduce a class of models of network growth based on this notion of specialization and show that as a network is specialized using this method its topology becomes increasingly sparse, modular, and hierarchical, each of which are important properties observed in real networks. This procedure is also highly flexible in that a network can be specialized over any subset of its elements. This flexibility allows those studying specific networks the ability to search for mechanisms that describe the growth of these particular networks. As an example, we find that by randomly selecting these elements a network's topology acquires some of the most well-known properties of real networks including the small-world property, disassortativity, power-law like degree distributions, and power-law like clustering coefficients. As far as the authors know, this is the first such class of models that creates an increasingly modular and hierarchical network topology with these properties.
Local energy decay for Lipschitz wavespeeds

We prove a logarithmic local energy decay rate for the wave equation with a wavespeed that is a compactly supported Lipschitz perturbation of unity. The key is to establish suitable resolvent estim...
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes

Markov jump processes (MJPs) are used to model a wide range of phenomena from disease progression to RNA path folding. However, maximum likelihood estimation of parametric models leads to degenerate trajectories and inferential performance is poor in nonparametric models. We take a small-variance asymptotics (SVA) approach to overcome these limitations. We derive the small-variance asymptotics for parametric and nonparametric MJPs for both directly observed and hidden state models. In the parametric case we obtain a novel objective function which leads to non-degenerate trajectories. To derive the nonparametric version we introduce the gamma-gamma process, a novel extension to the gamma-exponential process. We propose algorithms for each of these formulations, which we call JUMP-means. Our experiments demonstrate that JUMP-means is competitive with or outperforms widely used MJP inference approaches in terms of both speed and reconstruction accuracy.
Underwater object classification using scattering transform of sonar signals

In this paper, we apply the scattering transform (ST)—a nonlinear map based off of a convolutional neural network (CNN)—to classification of underwater objects using sonar signals. The ST formalizes the observation that the filters learned by a CNN have wavelet-like structure. We achieve effective binary classification both on a real dataset of Unexploded Ordinance (UXOs), as well as synthetically generated examples. We also explore the effects on the waveforms with respect to changes in the object domain (e.g., translation, rotation, and acoustic impedance, etc.), and examine the consequences coming from theoretical results for the scattering transform. We show that the scattering transform is capable of excellent classification on both the synthetic and real problems, thanks to having more quasi-invariance properties that are well-suited to translation and rotation of the object.
Incremental model-based learners with formal learning-time guarantees

Model-based learning algorithms have been shown to use experience efficiently when learning to solve Markov Decision Processes (MDPs) with finite state and action spaces. However, their high computational cost due to repeatedly solving an internal model inhibits their use in large-scale problems. We propose a method based on real-time dynamic programming (RTDP) to speed up two model-based algorithms, RMAX and MBIE (model-based interval estimation), resulting in computationally much faster algorithms with little loss compared to existing bounds. Specifically, our two new learning algorithms, RTDP-RMAX and RTDP-IE, have considerably smaller computational demands than RMAX and MBIE. We develop a general theoretical framework that allows us to prove that both are efficient learners in a PAC (probably approximately correct) sense. We also present an experimental evaluation of these new algorithms that helps quantify the tradeoff between computational and experience demands.
Interdependent defense games: modeling interdependent security under deliberate attacks

We propose interdependent defense (IDD) games, a computational game-theoretic framework to study aspects of the interdependence of risk and security in multi-agent systems under deliberate external attacks. Our model builds upon interdependent security (IDS) games, a model due to Heal and Kunreuther that considers the source of the risk to be the result of a fixed randomized-strategy. We adapt IDS games to model the attacker's deliberate behavior. We define the attacker's pure-strategy space and utility function and derive appropriate cost functions for the defenders. We provide a complete characterization of mixed-strategy Nash equilibria (MSNE), and design a simple polynomial-time algorithm for computing all of them, for an important subclass of IDD games. In addition, we propose a random-instance generator of (general) IDD games based on a version of the real-world Internet-derived Autonomous Systems (AS) graph (with around 27K nodes and 100K edges), and present promising empirical results using a simple learning heuristics to compute (approximate) MSNE in such games.
Higgs boson physics and broken flavor symmetry – LHC phenomenology

The LHC implications are presented of a simplified model of broken flavor symmetry in which a new scalar (a flavon) emerges with mass in the TeV range. We summarize the influence of the model on Higgs boson physics, notably on the production cross section and decay branching fractions. Limits are obtained on the flavon ϕ from heavy Higgs boson searches at the LHC at 7 and 8 TeV. The branching fractions of the flavon are computed as a function of the flavon mass and the Higgs-flavon mixing angle. We explore possible discovery of the flavon at 14 TeV, particularly via the ϕ→Z0Z0 decay channel in the 2l2l′ final state, and through standard model Higgs boson pair production ϕ→hh in the bb‾γγ final state. The flavon mass range up to 500 GeV could probed down to quite small values of the Higgs-flavon mixing angle with 100 fb−1 of integrated luminosity at 14 TeV.
The Influence of Social Ties on Performance in Team-based Online Games

Social ties are the invisible glue that keeps together human ecosystems. Despite the massive amount of research studying the role of social ties in communities (groups, teams, etc.) and society at large, little attention has been devoted to studying their interplay with other human behavioral dynamics. Of particular interest is the influence that social ties have on human performance in collaborative team-based settings. Our research aims to elucidate the influence of social ties on individual and team performance dynamics. We will focus on a popular Multiplayer Online Battle Arena (MOBA) collaborative team-based game, Defense of the Ancients 2 (Dota 2), a rich dataset with millions of players and matches. Our research reveals that, when playing with their friends, individuals are systematically more active in the game as opposed to taking part in a team of strangers. However, we find that increased activity does not homogeneously lead to an improvement in players' performance. Despite being beneficial to low skill players, playing with friends negatively affects the performance of high skill players. Our findings shed light on the mixed influence of social ties on performance and can inform new perspectives on virtual team management and behavioral incentives.
Scattering matrix in conformal geometry

This paper describes the connection between scattering matrices on conformally compact asymptotically Einstein manifolds and conformally invariant objects on their boundaries at infinity. The conformally invariant powers of the Laplacian arise as residues of the scattering matrix and Branson's Q-curvature in even dimensions as a limiting value. The integrated Q-curvature is shown to equal a multiple of the coefficient of the logarithmic term in the renormalized volume expansion.
Targeted matrix completion.

Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix completion assume that the input partially-observed matrix is low rank. The success of these methods depends on the number of observed entries and the rank of the matrix; the larger the rank, the more entries need to be observed in order to accurately complete the matrix. In this paper, we deal with matrices that are not necessarily low rank themselves, but rather they contain low-rank submatrices. We propose Targeted, which is a general framework for completing such matrices. In this framework, we first extract the low-rank submatrices and then apply a matrix-completion algorithm to these low-rank submatrices as well as the remainder matrix separately. Although for the completion itself we use state-of-the-art completion methods, our results demonstrate that Targeted achieves significantly smaller reconstruction errors than other classical matrix-completion methods. One of the key technical contributions of the paper lies in the identification of the low-rank submatrices from the input partially-observed matrices.
Photometric Bundle Adjustment for Vision-Based SLAM

We propose a novel algorithm for the joint refinement of structure and motion parameters from image data directly without relying on fixed and known correspondences. In contrast to traditional bundle adjustment (BA) where the optimal parameters are determined by minimizing the reprojection error using tracked features, the proposed algorithm relies on maximizing the photometric consistency and estimates the correspondences implicitly. Since the proposed algorithm does not require correspondences, its application is not limited to corner-like structure; any pixel with nonvanishing gradient could be used in the estimation process. Furthermore, we demonstrate the feasibility of refining the motion and structure parameters simultaneously using the photometric error in unconstrained scenes and without requiring restrictive assumptions such as planarity. The proposed algorithm is evaluated on range of challenging outdoor datasets, and it is shown to improve upon the accuracy of the state-of-the-art VSLAM methods obtained using the minimization of the reprojection error using traditional BA as well as loop closure.
Matrix variate regressions and envelope models

Summary Modern technology often generates data with complex structures in which both response and explanatory variables are matrix valued. Existing methods in the literature can tackle matrix-valued predictors but are rather limited for matrix-valued responses. We study matrix variate regressions for such data, where the response Y on each experimental unit is a random matrix and the predictor X can be either a scalar, a vector or a matrix, treated as non-stochastic in terms of the conditional distribution Y|X. We propose models for matrix variate regressions and then develop envelope extensions of these models. Under the envelope framework, redundant variation can be eliminated in estimation and the number of parameters can be notably reduced when the matrix variate dimension is large, possibly resulting in significant gains in efficiency. The methods proposed are applicable to high dimensional settings.
Adaptive Sampled Softmax with Kernel Based Sampling

Softmax is the most commonly used output function for multiclass problems and is widely used in areas such as vision, natural language processing, and recommendation. A softmax model has linear costs in the number of classes which makes it too expensive for many real-world problems. A common approach to speed up training involves sampling only some of the classes at each training step. It is known that this method is biased and that the bias increases the more the sampling distribution deviates from the output distribution. Nevertheless, almost any recent work uses simple sampling distributions that require a large sample size to mitigate the bias. In this work, we propose a new class of kernel based sampling methods and develop an efficient sampling algorithm. Kernel based sampling adapts to the model as it is trained, thus resulting in low bias. Kernel based sampling can be easily applied to many models because it relies only on the model's last hidden layer. We empirically study the trade-off of bias, sampling distribution and sample size and show that kernel based sampling results in low bias with few samples.
A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads.

Asking effective questions is a powerful social skill. In this paper we seek to build computational models that learn to discriminate effective questions from ineffective ones. Armed with such a capability, future advanced systems can evaluate the quality of questions and provide suggestions for effective question wording. We create a large-scale, real-world dataset that contains over 400,000 questions collected from Reddit "Ask Me Anything" threads. Each thread resembles an online press conference where questions compete with each other for attention from the host. This dataset enables the development of a class of computational models for predicting whether a question will be answered. We develop a new convolutional neural network architecture with variable-length context and demonstrate the efficacy of the model by comparing it with state-of-the-art baselines and human judges.
Stochastic exits from dormancy give rise to heavy-tailed distributions of descendants in bacterial populations

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for five strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy-tailed, with a few cells effectively 'winning the jackpot' to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early-germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy-tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations and explaining variability in infection outcomes across patients.
On the Computational Power of Online Gradient Descent

We prove that the evolution of weight vectors in online gradient descent can encode arbitrary polynomial-space computations, even in very simple learning settings. Our results imply that, under weak complexity-theoretic assumptions, it is impossible to reason efficiently about the fine-grained behavior of online gradient descent.
Adaptive View Planning for Aerial 3D Reconstruction

With the proliferation of small aerial vehicles, acquiring close up imagery for high quality reconstruction is gaining importance. We present an adaptive view planning method to collect such images in an automated fashion. We first start by sampling a small set of views to build a coarse proxy to the scene. We then present (i) a method that builds a set of adaptive viewing planes for efficient view selection and (ii) an algorithm to plan a trajectory that guarantees high reconstruction quality which does not deviate too much from the optimal one. The vehicle then follows the trajectory to cover the scene, and the procedure is repeated until reconstruction quality converges or a desired level of quality is achieved. The set of viewing planes provides an effective compromise between using the entire 3D free space and using a single view hemisphere to select the views. We compare our algorithm to existing methods in three challenging scenes. Our algorithm generates views which produce the least reconstruction error comparing to three different baseline approaches.
Storage Capacity as an Information-Theoretic Vertex Cover and the Index Coding Rate

Motivated by applications in distributed storage, the storage capacity of a graph was recently defined to be the maximum amount of information that can be stored across the vertices of a graph such that the information at any vertex can be recovered from the information stored at the neighboring vertices. Computing the storage capacity is a fundamental problem in network coding and is related, or equivalent, to some well-studied problems such as index coding with side information and generalized guessing games. In this paper, we consider storage capacity as a natural information-theoretic analogue of the minimum vertex cover of a graph. Indeed, while it was known that storage capacity is upper bounded by minimum vertex cover, we show that by treating it as such we can get a 3/2 approximation for planar graphs, and a 4/3 approximation for triangle-free planar graphs. Since the storage capacity is intimately related to the index coding rate, we get a 2 approximation of index coding rate for planar graphs and 3/2 approximation for triangle-free planar graphs. Previously, only a trivial 4 approximation of the index coding rate was known for planar graphs. We also show a polynomial time approximation scheme for the index coding rate when the alphabet size is constant. We then develop a general method of “gadget covering” to upper bound the storage capacity in terms of the average of a set of vertex covers. This method is intuitive and leads to the exact characterization of storage capacity for various families of graphs. As an illustrative example, we use this approach to derive the exact storage capacity of cycles-with-chords, a family of graphs related to outerplanar graphs. Finally, we generalize the storage capacity notion to include recovery from partial node failures in distributed storage. We show tight upper and lower bounds on this partial recovery capacity that scales nicely with the fraction of failures in a vertex.
Robot Communication Via Motion: Closing the Underwater Human-Robot Interaction Loop

In this paper, we propose a novel method for underwater robot-to-human communication using the motion of the robot as “body language”. To evaluate this system, we develop simulated examples of the system’s body language gestures, called kinemes, and compare them to a baseline system using flashing colored lights through a user study. Our work shows evidence that motion can be used as a successful communication vector which is accurate, easy to learn, and quick enough to be used, all without requiring any additional hardware to be added to our platform. We thus contribute to “closing the loop” for human-robot interaction underwater by proposing and testing this system, suggesting a library of possible body language gestures for underwater robots, and offering insight on the design of nonverbal robot-to-human communication methods.
Relationship between Dynamic Instability of Individual Microtubules and Flux of Subunits into and out of Polymer

Behaviors of dynamic polymers such as microtubules and actin are frequently assessed at one or both of the following scales: (a) net assembly or disassembly of bulk polymer, (b) growth and shortening of individual filaments. Previous work has derived various forms of an equation to relate the rate of change in bulk polymer mass (i.e., flux of subunits into and out of polymer, often abbreviated as "J") to individual filament behaviors. However, these versions of the "J equation" differ in the variables used to quantify individual filament behavior, which correspond to different experimental approaches. For example, some variants of the J equation use dynamic instability parameters, obtained by following particular individual filaments for long periods of time. Another form of the equation uses measurements from many individuals followed over short time steps. We use a combination of derivations and computer simulations that mimic experiments to (a) relate the various forms of the J equation to each other, (b) determine conditions under which these J equation forms are and are not equivalent, and (c) identify aspects of the measurements that can affect the accuracy of each form of the J equation. Improved understanding of the J equation and its connections to experimentally measurable quantities will contribute to efforts to build a multiscale understanding of steady-state polymer behavior.
On the Gaussianity of Kolmogorov Complexity of Mixing Sequences

It has been proved that for all stationary and ergodic processes the average Kolmogorov complexity of the first $n$ observations converges almost surely to its Shannon’s conditional entropy. This paper studies the convergence rate of this asymptotic result. In particular, we show that if the process satisfies certain mixing conditions, then a central limit theorem will be respected. Furthermore, we show that under slightly stronger mixing conditions one may obtain non-asymptotic concentration bounds for the Kolmogorov complexity.
On Falconer's distance set problem in the plane

If $$E \subset \mathbb {R}^2$$ is a compact set of Hausdorff dimension greater than 5 / 4, we prove that there is a point $$x \in E$$ so that the set of distances $$\{ |x-y| \}_{y \in E}$$ has positive Lebesgue measure.
Should We Defend the Administrative State

Troubled by actions of the Trump Administration, some academics have defended the administrative state. This may be a mistake. The scholarly definition of the administrative state has shifted over decades, and today scholarly usage of the term diverges substantially from popular usage. When academics invoke the concept, they may unwittingly trigger negative associations in the minds of non-academics and defeat their own cause. This mistake is easily avoided, because academics often do not need to talk about the administrative state at all. Scholarly inquiry would be improved by using different terms to describe three distinct ideas: the state, the administrative component of the state, and the administrative state, a type of state that emerged at a specific moment in American history. If academics want to defend the public service in the realm of politics, it would be better to do so in those terms, rather than using a phrase that triggers fears about big and irresponsible government.
Extended Hopanoid Loss Reduces Bacterial Motility and Surface Attachment and Leads to Heterogeneity in Root Nodule Growth Kinetics in a Bradyrhizobium-Aeschynomene Symbiosis

Hopanoids are steroid-like bacterial lipids that enhance membrane rigidity and promote bacterial growth under diverse stresses. Hopanoid biosynthesis genes are conserved in nitrogen-fixing plant symbionts, and we previously found that the extended (C35) class of hopanoids in Bradyrhizobium diazoefficiens are required for efficient symbiotic nitrogen fixation in the tropical legume host Aeschynomene afraspera. Here, we demonstrate that the nitrogen-fixation defect conferred by extended hopanoid loss can be fully explained by a reduction in root nodule sizes rather than per-bacteroid nitrogen-fixation levels. Using a single-nodule tracking approach to quantify A. afraspera nodule development, we provide a quantitative model of root nodule development in this host, uncovering both the baseline growth parameters for wild-type nodules and a surprising heterogeneity of extended hopanoid mutant developmental phenotypes. These phenotypes include a delay in root nodule initiation and the presence of a subpopulation of nodules with slow growth rates and low final volumes, which are correlated with reduced motility and surface attachment in vitro and lower bacteroid densities in planta, respectively. This work provides a quantitative reference point for understanding the phenotypic diversity of ineffective symbionts in A. afraspera and identifies specific developmental stages affected by extended hopanoid loss for future mechanistic work.
On the Determining Wavenumber for the Nonautonomous Subcritical SQG Equation

A time-dependent determining wavenumber was introduced in Cheskidov and Dai (Phys D Nonlinear Phenom 376–377:204–215, 2018) to estimate the number of determining modes for the surface quasi-geostrophic equation. In this paper we continue this investigation focusing on the subcritical case and study trajectories inside an absorbing set bounded in $$L^\infty $$ . Utilizing this bound we find a time-independent determining wavenumber that improves the estimate obtained in Cheskidov and Dai (Phys D Nonlinear Phenom 376–377:204–215, 2018). This classical approach is more direct, but it is contingent on the existence of the $$L^\infty $$ absorbing set.
Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation

Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of efficient traffic management strategies that result in minimizing traffic delay and maximizing effective utilization of transport system. High fidelity route choice models are required to predict traffic levels with higher accuracy. Existing route choice models do not take into account dynamic contextual conditions such as the occurrence of an accident, the socio-cultural and economic background of drivers, other human behaviors, the dynamic personal risk level, etc. As a result, they can only make predictions at an aggregate level and for a fixed set of contextual factors. For higher fidelity, it is highly desirable to use a model that captures significance of subjective or contextual factors in route choice. This paper presents a novel approach for developing high-fidelity route choice models with increased predictive power by augmenting existing aggregate level baseline models with information on drivers’ responses to contextual factors obtained from Stated Choice Experiments carried out in an Immersive Virtual Environment through the use of knowledge distillation.
Simple Theory of Stress Strain Properties of Filled Polymers

By the use of simple models of filled plastics, approximate equations are derived for the elongation to break in the case of perfect adhesion between the phases and for the tensile strength in the case of no adhesion between the polymer and filler phases. By combining these equations with equations for the modulus (assuming Hookean behavior) all the stress–strain properties can be derived, including rough estimates of the impact strength, as a function of filler concentration. Among other things, the theory predicts a very rapid decrease in elongation to break as filler concentration increases, especially for the case of good adhesion. It is also predicted for the case of good adhesion that the tensile strength of a filled polymer can be greater than that of an unfilled polymer.
P2-422: PRESERVED FUNCTIONAL CONNECTIVITY IN THE DEFAULT MODE AND SALIENCE NETWORKS IS ASSOCIATED WITH YOUTHFUL MEMORY IN SUPERAGING

9Superagers9 are older adults who, despite their advanced age, maintain youthful memory. Previous morphometry studies revealed multiple default mode network (DMN) and salience network (SN) regions whose cortical thickness is preserved in superagers and correlates with memory performance. In this study, we examined the intrinsic functional connectivity within DMN and SN in 41 young (24.5 ± 3.6 years old) and 40 elderly adults (66.9 ± 5.5 years old). As in prior studies, superaging was defined as youthful performance on a memory recall task, the California Verbal Learning Test (CVLT). Participants underwent a resting state fMRI scan and performed a separate visual-verbal recognition memory task. As predicted, within both DMN and SN, superagers had stronger connectivity compared to typical older adults and similar connectivity compared to young adults. Superagers also performed similarly to young adults and better than typical older adults on the recognition task, demonstrating youthful episodic memory that generalized across memory tasks. Stronger connectivity within each network independently predicted better performance on both the CVLT and recognition task in older adults. Variation in intrinsic connectivity explained unique variance in memory performance, above and beyond preserved neuroanatomy. A post-hoc analysis revealed that DMN and SN nodes were more strongly inversely correlated in superagers than in typical older adults but were similarly correlated in superagers and young adults. Stronger between-network inverse correlations also predicted better memory performance in the entire sample of older adults. These results extend our understanding of the neural basis of superaging as a model of successful aging.
Helping or Harming? The Effect of Trigger Warnings on Individuals With Trauma Histories:

Trigger warnings alert trauma survivors about potentially disturbing forthcoming content. However, empirical studies on trigger warnings suggest that they are functionally inert or cause small adve...
Design principles of selective transport through biopolymer barriers

In biological systems, polymeric materials block the movement of some macromolecules while allowing the selective passage of others. In some cases, binding enables selective transport, while in others the most inert particles appear to transit most rapidly. To study the general principles of filtering, we develop a model motivated by features of the nuclear pore complex (NPC) which are highly conserved and could potentially be applied to other biological systems. The NPC allows selective transport of proteins called transport factors, which transiently bind to disordered flexible proteins called phenylalanine-glycine-nucleoporins. While the NPC is tuned for transport factors and their cargo, we show that a single feature is sufficient for selective transport: the bound-state motion resulting from transient binding to flexible filaments. Interchain transfer without unbinding can further improve selectivity, especially for cross-linked chains. We generalize this observation to model nanoparticle transport through mucus and show that bound-state motion accelerates transport of transient nanoparticle application, even with clearance by mucus flow. Our model provides a framework to control binding-induced selective transport in biopolymeric materials.
Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls, by reanalyzing pre-existing, publicly available microarray expression datasets. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we used generalized least squares models with disease state, age, sex, smoking status and study as effects that also included binary interactions, followed by likelihood ratio tests (LRT). We found 3,315 statistically significant (Storey-adjusted q-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from LRT q-value <0.05 and model estimates’ 10% two-tailed quantiles of mean differences between COPD and control), to identify 679 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the common array genes as features, which enabled prediction of disease status with 81.7% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.