cssi/SciSciGPT-SciSciNet huggingface dataset:
Quotients of the multiplihedron as categorified associahedra

We describe a new sequence of polytopes which characterize A1-maps from a topological monoid to an A1-space. Therefore each of these polytopes is a quotient of the corresponding multiplihedron. Our sequence of polytopes is demonstrated not to be combinatorially equivalent to the associahedra, as was previously assumed in both topological and categorical literature. They are given the new collective name composihedra. We point out how these polytopes are used to parametrize compositions in the formulation of the theories of enriched bicategories and pseudomonoids in a monoidal bicategory. We also present a simple algorithm for determining the extremal points in Euclidean space whose convex hull is the n th polytope in the sequence of composihedra, that is, the n th composihedron CK(n).
Matroidal root structure of skew polynomials over finite fields

A skew polynomial ring R = K [x; σ, δ ] is a ring of polynomials with non-commutative multiplication. This creates a difference between left and right divisibility, evaluations, and roots. A polyno...
Integral Monsky-Washnitzer cohomology and the overconvergent de Rham-Witt complex

In their paper which introduced Monsky-Washnitzer cohomology, Monsky and Washnitzer described conditions under which the definition can be adapted to give integral cohomology groups. It seems to be well-known among experts that their construction always gives well-defined integral cohomology groups, but this fact also does not appear to be explicitly written down anywhere. In this paper, we prove that the integral Monsky-Washnitzer cohomology groups are well-defined, for any nonsingular affine variety over a perfect field of characteristic p. We then compare these cohomology groups with overconvergent de RhamWitt cohomology. It was shown earlier that if the affine variety has small dimension relative to the characteristic of the ground field, then the cohomology groups are isomorphic. We extend this result to show that for any nonsingular affine variety, regardless of dimension, we have an isomorphism between integral Monsky-Washnitzer cohomology and overconvergent de Rham-Witt cohomology in degrees which are small relative to the characteristic.
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal

We analyze the Kozachenko–Leonenko (KL) fixed k-nearest neighbor estimator for the differential entropy. We obtain the first uniform upper bound on its performance for any fixed k over H\"{o}lder balls on a torus without assuming any conditions on how close the density could be from zero. Accompanying a recent minimax lower bound over the H\"{o}lder ball, we show that the KL estimator for any fixed k is achieving the minimax rates up to logarithmic factors without cognizance of the smoothness parameter s of the H\"{o}lder ball for $s \in (0,2]$ and arbitrary dimension d, rendering it the first estimator that provably satisfies this property.
Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in long-duration operations ( $>$ 1 h), where neither frequent surfacing nor consistent bottom tracking is available. We instantiate this concept through mid-depth underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller scale turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved small-scale flow perturbations are present. For a 6-h navigation with an automotive-grade inertial navigation system, the current-aided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent double-gyre flow field, and under 7.3% UDT in a turbulent meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-h mission, positioning result with under 25% UDT for a 24-h navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.
Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients

We give a rigorous analysis of the statistical behavior of gradients in a randomly initialized fully connected network N with ReLU activations. Our results show that the empirical variance of the squares of the entries in the input-output Jacobian of N is exponential in the sum of the reciprocals of the hidden layer widths. Our approach complements the mean field theory analysis of random neural nets. From this point of view, we rigorously compute the finite width corrections to gradients at the edge of chaos.
Unit-Free and Robust Detection of Differential Expression from RNA-Seq Data

Ultra high-throughput sequencing of transcriptomes (RNA-Seq) is a widely used method for quantifying gene expression levels due to its low cost, high accuracy, and wide dynamic range for detection. However, the nature of RNA-Seq makes it nearly impossible to provide absolute measurements of transcript abundances. Several units or data summarization methods for transcript quantification have been proposed in the past to account for differences in transcript lengths and sequencing depths across different genes and different samples. Nevertheless, further between-sample normalization is still needed for reliable detection of differentially expressed genes. In this paper, we propose a unified statistical model for joint detection of differential gene expression and between-sample normalization. Our method is independent of the unit in which gene expression levels are summarized. We also introduce an efficient algorithm for model fitting. Due to the L0-penalized likelihood used in our model, it is able to reliably normalize the data and detect differential gene expression in some cases when more than 50% of the genes are differentially expressed in an asymmetric manner. We compare our method with existing methods using simulated and real data sets.
Representation of dissipative solutions to a nonlinear variational wave equation

The paper introduces a new way to construct dissipative solutions to a second order variational wave equation. By a variable transformation, from the nonlinear PDE one obtains a semilinear hyperbolic system with sources. In contrast with the conservative case, here the source terms are discontinuous and the discontinuities are not always crossed transversally. Solutions to the semilinear system are obtained by an approximation argument, relying on Kolmogorov’s compactness theorem. Reverting to the original variables, one recovers a solution to the nonlinear wave equation where the total energy is a monotone decreasing function of time.
To understand deep learning we need to understand kernel learning

Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, typically over-parametrized, tend to fit the training data exactly. Despite this "overfitting", they perform well on test data, a phenomenon not yet fully understood. The first point of our paper is that strong performance of overfitted classifiers is not a unique feature of deep learning. Using six real-world and two synthetic datasets, we establish experimentally that kernel machines trained to have zero classification or near zero regression error perform very well on test data, even when the labels are corrupted with a high level of noise. We proceed to give a lower bound on the norm of zero loss solutions for smooth kernels, showing that they increase nearly exponentially with data size. We point out that this is difficult to reconcile with the existing generalization bounds. Moreover, none of the bounds produce non-trivial results for interpolating solutions. Second, we show experimentally that (non-smooth) Laplacian kernels easily fit random labels, a finding that parallels results for ReLU neural networks. In contrast, fitting noisy data requires many more epochs for smooth Gaussian kernels. Similar performance of overfitted Laplacian and Gaussian classifiers on test, suggests that generalization is tied to the properties of the kernel function rather than the optimization process. Certain key phenomena of deep learning are manifested similarly in kernel methods in the modern "overfitted" regime. The combination of the experimental and theoretical results presented in this paper indicates a need for new theoretical ideas for understanding properties of classical kernel methods. We argue that progress on understanding deep learning will be difficult until more tractable "shallow" kernel methods are better understood.
Differentiability of a two-parameter family of self-affine functions

Abstract This paper highlights an unexpected connection between expansions of real numbers to noninteger bases (so-called β-expansions) and the infinite derivatives of a class of self-affine functions. Precisely, we extend Okamoto's function (itself a generalization of the well-known functions of Perkins and Katsuura) to a two-parameter family { F N , a : N ∈ N , 1 / ( N + 1 ) a 1 } . We first show that for each x, F N , a ′ ( x ) is either 0, ±∞, or undefined. We then extend Okamoto's theorem by proving that for each N, depending on the value of a relative to a pair of thresholds, the set { x : F N , a ′ ( x ) = 0 } is either empty, uncountable but Lebesgue null, or of full Lebesgue measure. We compute its Hausdorff dimension in the second case. The second result is a characterization of the set D ∞ ( a ) : = { x : F N , a ′ ( x ) = ± ∞ } , which enables us to closely relate this set to the set of points which have a unique expansion in the (typically noninteger) base β = 1 / a . Recent advances in the theory of β-expansions are then used to determine the cardinality and Hausdorff dimension of D ∞ ( a ) , which depend qualitatively on the value of a relative to a second pair of thresholds.
Conducting sparse feature selection on arbitrarily long phrases in text corpora with a focus on interpretability

We propose a general framework for topic-specific summarization of large text corpora, and illustrate how it can be used for analysis in two quite different contexts: an Occupational Safety and Health Administration (OSHA) database of fatality and catastrophe reports (to facilitate surveillance for patterns in circumstances leading to injury or death), and legal decisions on workers' compensation claims (to explore relevant case law). Our summarization framework, built on sparse classification methods, is a compromise between simple word frequency-based methods currently in wide use, and more heavyweight, model-intensive methods such as latent Dirichlet allocation (LDA). For a particular topic of interest (e.g., mental health disability, or carbon monoxide exposure), we regress a labeling of documents onto the high-dimensional counts of all the other words and phrases in the documents. The resulting small set of phrases found as predictive are then harvested as the summary. Using a branch-and-bound approach, this method can incorporate phrases of arbitrary length, which allows for potentially rich summarization. We discuss how focus on the purpose of the summaries can inform choices of tuning parameters and model constraints. We evaluate this tool by comparing the computational time and summary statistics of the resulting word lists to three other methods in the literature. We also present a new R package, textreg. Overall, we argue that sparse methods have much to offer in text analysis and is a branch of research that should be considered further in this context. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016
PBW deformations of braided products

Abstract We present new examples of deformations of smash product algebras that arise from Hopf algebra actions on pairs of module algebras. These examples involve module algebras that are Koszul, in which case a PBW theorem we established previously applies. Our construction generalizes several ‘double’ constructions appearing in the literature, including Weyl algebras and some types of Cherednik algebras, and it complements the braided double construction of Bazlov and Berenstein. Many suggestions of further directions are provided at the end of the work.
Exact simulation of multidimensional reflected Brownian motion

We present the first exact simulation method for multidimensional reflected Brownian motion (RBM). Exact simulation in this setting is challenging because of the presence of correlated local-time-like terms in the definition of RBM. We apply recently developed so-called e-strong simulation techniques (also known as tolerance-enforced simulation) which allow us to provide a piecewise linear approximation to RBM with e (deterministic) error in uniform norm. A novel conditional acceptance–rejection step is then used to eliminate the error. In particular, we condition on a suitably designed information structure so that a feasible proposal distribution can be applied.
Toric matrix Schubert varieties and their polytopes

Given a matrix Schubert variety $\overline{X_\pi}$, it can be written as $\overline{X_\pi}=Y_\pi\times \mathbb{C}^q$ (where $q$ is maximal possible). We characterize when $Y_{\pi}$ is toric (with respect to a $(\mathbb{C}^*)^{2n-1}$-action) and study the associated polytope $\Phi(\mathbb{P}(Y_\pi))$ of its projectivization. We construct regular triangulations of $\Phi(\mathbb{P}(Y_\pi))$ which we show are geometric realizations of a family of subword complexes. Subword complexes were introduced by Knutson and Miller in 2004, who also showed that they are homeomorphic to balls or spheres and raised the question of their polytopal realizations.
The n-level densities of low-lying zeros of quadratic Dirichlet L-functions

ABSTRACT. Previous work by Rubinstein [Rub] and Gao [Gao] computed the n-level densities for families of quadratic Dirichlet L-functions for test functions φ1, . . . , φn supported in ∑ n i=1 |ui| < 2, and showed agreement with random matrix theory predictions in this range for n ≤ 3 but only in a restricted range for larger n. We extend these results and show agreement for n ≤ 7, and reduce higher n to a Fourier transform identity. The proof involves adopting a new combinatorial perspective to convert all terms to a canonical form, which facilitates the comparison of the two sides.
The ring of differential Fourier expansions

Abstract For a fixed prime we prove structure theorems for the kernel and the image of the map that attaches to any differential modular function its differential Fourier expansion. The image of this map, which is the ring of differential Fourier expansions, plays the role of ring of functions on a “differential Igusa curve”. Our constructions are then used to perform an analytic continuation between isogeny covariant differential modular forms on the differential Igusa curves belonging to different primes.
Sensitivity-Driven Adaptive Construction of Reduced-space Surrogates

Surrogate modeling has become a critical component of scientific computing in situations involving expensive model evaluations. However, training a surrogate model can be remarkably challenging and even computationally prohibitive in the case of intensive simulations and large-dimensional systems. We develop a systematic approach for surrogate model construction in reduced input parameter spaces. A sparse set of model evaluations in the original input space is used to approximate derivative based global sensitivity measures (DGSMs) for individual uncertain inputs of the model. An iterative screening procedure is developed that exploits DGSM estimates in order to identify the unimportant inputs. The screening procedure forms an integral part of an overall framework for adaptive construction of a surrogate in the reduced space. The framework is tested for computational efficiency through an initial implementation in simple test cases such as the classic Borehole function, and a semilinear elliptic PDE with a random source function. The framework is then deployed for a realistic application from chemical kinetics, where we study the ignition delay in an $$\hbox {H}_2{/}\hbox {O}_2$$ reaction mechanism with 19 and 33 uncertain rate-controlling parameters. It is observed that significant computational gains can be attained by constructing accurate low-dimensional surrogates using the proposed framework.
Euler–Mahonian statistics via polyhedral geometry

Abstract A variety of descent and major-index statistics have been defined for symmetric groups, hyperoctahedral groups, and their generalizations. Typically associated to a pair of such statistics is an Euler–Mahonian distribution , a bivariate polynomial encoding the statistics; such distributions often appear in rational bivariate generating-function identities. We use techniques from polyhedral geometry to establish new multivariate identities generalizing those giving rise to many of the known Euler–Mahonian distributions. The original bivariate identities are then specializations of these multivariate identities. As a consequence of these new techniques we obtain bijective proofs of the equivalence of the bivariate distributions for various pairs of statistics.
Complexity of Word Collocation Networks: A Preliminary Structural Analysis

In this paper, we explore complex network properties of word collocation networks (Ferret, 2002) from four different genres. Each document of a particular genre was converted into a network of words with word collocations as edges. We analyzed graphically and statistically how the global properties of these networks varied across different genres, and among different network types within the same genre. Our results indicate that the distributions of network properties are visually similar but statistically apart across different genres, and interesting variations emerge when we consider different network types within a single genre. We further investigate how the global properties change as we add more and more collocation edges to the graph of one particular genre, and observe that except for the number of vertices and the size of the largest connected component, network properties change in phases, via jumps and drops.
VERIFAS: a practical verifier for artifact systems

Data-driven workflows, of which IBM's Business Artifacts are a prime exponent, have been successfully deployed in practice, adopted in industrial standards, and have spawned a rich body of research in academia, focused primarily on static analysis. The present research bridges the gap between the theory and practice of artifact verification with VERIFAS, the first implementation of practical significance of an artifact verifier with full support for unbounded data. VERIFAS verifies within seconds linear-time temporal properties over real-world and synthetic workflows of complexity in the range recommended by software engineering practice. Compared to our previous implementation based on the widely-used Spin model checker, VERIFAS not only supports a model with richer data manipulations but also outperforms it by over an order of magnitude. VERIFAS' good performance is due to a novel symbolic representation approach and a family of specialized optimizations.
Closed Walk Sampler: An Efficient Method for Estimating Eigenvalues of Large Graphs

Eigenvalues of a graph are of high interest in graph analytics for Big Data due to their relevance to many important properties of the graph including network resilience, community detection and the speed of viral propagation. Accurate computation of eigenvalues of extremely large graphs is usually not feasible due to the prohibitive computational and storage costs and also because full access to many social network graphs is often restricted to most researchers. In this paper, we present a series of new sampling algorithms which solve both of the above-mentioned problems and estimate the two largest eigenvalues of a large graph efficiently and with high accuracy. Unlike previous methods which try to extract a subgraph with the most influential nodes, our algorithms sample only a small portion of the large graph via a simple random walk, and arrive at estimates of the two largest eigenvalues by estimating the number of closed walks of a certain length. Our experimental results using real graphs show that our algorithms are substantially faster while also achieving significantly better accuracy on most graphs than the current state-of-the-art algorithms.
Fine-Grained Speculative Topological Transformation Scheme for Local Reconnection Methods

A parallel approach for topological transformations for local reconnection methods is presented. The proposed scheme combines known parallel techniques like data overdecomposition and load balancin...
Intracellular nanoparticle delivery by oncogenic KRAS-mediated macropinocytosis.

Background The RAS family of oncogenes (KRAS, HRAS, NRAS) are the most frequent mutations in cancers and regulate key signaling pathways that drive tumor progression. As a result, drug delivery targeting RAS-driven tumors has been a long-standing challenge in cancer therapy. Mutant RAS activates cancer cells to actively take up nutrients, including glucose, lipids, and albumin, via macropinocytosis to fulfill their energetic requirements to survive and proliferate. Purpose We exploit macropinocytosis pathway to deliver nanoparticles (NPs) in cancer cells harboring activating KRAS mutations. Methods NPs were synthesized by the desolvation method. The physicochemical properties and stability of NPs were characterized by dynamic light scattering and transmission electron microscopy. Uptake of fluorescently labelled NPs in wild-type and mutant KRAS cells were quantitively determined by flow cytometry and qualitatively by fluorescent microscopy. NP uptake by KRAS-driven macropinocytosis was confirmed by pharmacological inhibition and genetic knockdown. Results We have synthesized stable albumin NPs that demonstrate significantly greater uptake in cancer cells with activating mutations of KRA S than monomeric albumin (ie, dissociated form of clinically used nab-paclitaxel). From pharmacological inhibition and semi-quantitative fluorescent microscopy studies, these NPs exhibit significantly increased uptake in mutant KRAS cancer cells than wild-type KRAS cells by macropinocytosis. Conclusions The uptake of albumin nanoparticles is driven by KRAS. This NP-based strategy targeting RAS-driven macropinocytosis is a facile approach toward improved delivery into KRAS-driven cancers.
A simple solution to the problem of independence of irrelevant alternatives in Choo and Siow marriage market model

Abstract This paper proposes a simple solution to the independence of irrelevant alternatives (IIA) problem in Choo and Siow (2006b) model, overcoming what is probably the main limitation of this approach. The solution consists of assuming match-specific rather than choice-specific random preferences. The original marriage matching function gets modified by an adjustment factor that improves its empirical properties.
External Societal Costs of Antimicrobial Resistance in Humans Attributable to Antimicrobial Use in Livestock

Antimicrobial use (AMU) in animal agriculture contributes to antimicrobial resistance (AMR) in humans, which imposes significant health and economic costs on society. Economists call these costs ne...
Optimal k-Deletion Correcting Codes

Levenshtein introduced the problem of constructing k-deletion correcting codes in 1966, proved that the optimal redundancy of those codes is O(k log N), and proposed an optimal redundancy single-deletion correcting code (using the so-called VT construction). However, the problem of constructing optimal redundancy k-deletion correcting codes remained open. Our key contribution is a solution to this longstanding open problem. We present a k-deletion correcting code that has redundancy 8k log n + o(log n) and encoding/decoding algorithms of complexity O(n2k+1) for constant k.
Faster Minimum k-cut of a Simple Graph

We consider the (exact, minimum) k-CUT problem: given a graph and an integer k, delete a minimum-weight set of edges so that the remaining graph has at least k connected components. This problem is a natural generalization of the global minimum cut problem, where the goal is to break the graph into k=2 pieces. Our main result is a (combinatorial) k-CUT algorithm on simple graphs that runs in n^ (1+o(1))k time for any constant k, improving upon the previously best n^ (2 ω /3+o(1))k time algorithm of Gupta et al. [FOCS'18] and the previously best n^ (1.981+o(1))k time combinatorial algorithm of Gupta et al. [STOC'19]. For combinatorial algorithms, this algorithm is optimal up to o(1) factors assuming recent hardness conjectures: we show by a straightforward reduction that k-CUT on even a simple graph is as hard as (k-1) -clique, establishing a lower bound of n^ (1-o(1))k for k-CUT. This settles, up to lower-order factors, the complexity of k-CUT on a simple graph for combinatorial algorithms.
Folk Wisdom About the Effects of Relationship Conflict

Three experiments examined cultural differences and similarities in folk wisdom about the effects of perceived conflict. In Study 1, Americans exhibited an optimistic bias relative to East Asians in their beliefs about perceived relationship conflict, but not conflict in general. Study 2 suggests that these findings cannot be accounted for by cultural differences in perceptions about the distinction, or lack thereof, between relationship and task-focused forms of conflict. Furthermore, the results demonstrated an interaction effect such that both groups prefer to address and resolve perceived task conflict proactively, whereas only European Americans perceive that it is relatively unnecessary to address relationship conflict to achieve task performance. Study 3 suggests that these cultural patterns have behavioral implications, such that Americans were more likely than East Asians to join a talented group likely to experience relationship conflict. Together, these results suggest novel implications for intragroup dynamics in intercultural contexts.
Property rights and domestication

This paper combines the property rights approach of Barzel with models from renewable resource and evolutionary economics to examine the domestication of wild animals. Wild animals are governed by weak property rights to stocks and individuals while domesticated animals are governed by private ownership of stocks and individuals. The complex evolutionary process of domestication can be viewed as a conversion of wild populations into private property, as well as a transition from natural selection to economic selection controlled by owners of populations and individuals. In our framework domestication is not the explicit goal of any economic agent, but it emerges as a long-run outcome of an innovation in hunting strategies in a hunter–gatherer society. Our formal model also suggests that the domestication process moves slowly at first but then proceeds rapidly, and is aligned with the archeological evidence on domestication events.
Resilience of Traffic Networks with Partially Controlled Routing

This paper investigates the use of Infrastructure-To-Vehicle (I2V) communication to generate routing suggestions for drivers in transportation systems, with the goal of optimizing a measure of overall network congestion. We define link-wise levels of trust to tolerate the non-cooperative behavior of part of the driver population, and we propose a realtime optimization mechanism that adapts to the instantaneous network conditions and to sudden changes in the levels of trust. Our framework allows us to quantify the improvement in travel time in relation to the degree at which drivers follow the routing suggestions. We then study the resilience of the system, measured as the smallest change in routing choices that results in roads reaching their maximum capacity. Interestingly, our findings suggest that fluctuations in the extent to which drivers follow the provided routing suggestions can cause failures of certain links. These results imply that the benefits of using Infrastructure-To-Vehicle communication come at the cost of new fragilities, that should be appropriately addressed in order to guarantee the reliable operation of the infrastructure.
PHONEME-BASED CONTEXTUALIZATION FOR CROSS-LINGUAL SPEECH RECOGNITION IN END-TO-END MODELS

A method (500) includes receiving audio data encoding an utterance (106) spoken by a native speaker (110) of a first language, and receiving a biasing term list (105) including one or more terms in a second language different than the first language. The method also includes processing, using a speech recognition model (200), acoustic features (105) derived from the audio data to generate speech recognition scores for both wordpieces and corresponding phoneme sequences in the first language. The method also includes rescoring the speech recognition scores for the phoneme sequences based on the one or more terms in the biasing term list, and executing, using the speech recognition scores for the wordpieces and the rescored speech recognition scores for the phoneme sequences, a decoding graph (400) to generate a transcription (116) for the utterance.
Improved Pseudorandom Generators from Pseudorandom Multi-Switching Lemmas.

We give the best known pseudorandom generators for two touchstone classes in unconditional derandomization: an $\varepsilon$-PRG for the class of size-$M$ depth-$d$ $\mathsf{AC}^0$ circuits with seed length $\log(M)^{d+O(1)}\cdot \log(1/\varepsilon)$, and an $\varepsilon$-PRG for the class of $S$-sparse $\mathbb{F}_2$ polynomials with seed length $2^{O(\sqrt{\log S})}\cdot \log(1/\varepsilon)$. These results bring the state of the art for unconditional derandomization of these classes into sharp alignment with the state of the art for computational hardness for all parameter settings: improving on the seed lengths of either PRG would require breakthrough progress on longstanding and notorious circuit lower bounds. The key enabling ingredient in our approach is a new \emph{pseudorandom multi-switching lemma}. We derandomize recently-developed \emph{multi}-switching lemmas, which are powerful generalizations of H{\aa}stad's switching lemma that deal with \emph{families} of depth-two circuits. Our pseudorandom multi-switching lemma---a randomness-efficient algorithm for sampling restrictions that simultaneously simplify all circuits in a family---achieves the parameters obtained by the (full randomness) multi-switching lemmas of Impagliazzo, Matthews, and Paturi [IMP12] and H{\aa}stad [H{\aa}s14]. This optimality of our derandomization translates into the optimality (given current circuit lower bounds) of our PRGs for $\mathsf{AC}^0$ and sparse $\mathbb{F}_2$ polynomials.
A synthetic peptide encoded by a random DNA sequence inhibits discrete red light responses.

We have identified a synthetic peptide that interrupts discrete aspects of seedling development under red light. Previous reports have demonstrated that plants transformed with random DNA sequences produce synthetic peptides that affect plant biology. In this report, one specific peptide is characterized that inhibits discrete aspects of red light-mediated photomorphogenic development in Arabidopsis thaliana . Seedlings expressing the PEP6-32 peptide presented longer hypocotyls and diminished cotyledon expansion when grown under red light. Other red light-mediated seedling processes such as induction of Lhcb (cab) transcripts or loss of vertical growth remained unaffected. Long-term responses to red light in PEP6-32 expressing plants, such as repression of flowering time, did not show defects in red light signaling or integration. A synthesized peptide applied exogenously induced the long-hypocotyl phenotype under red light in non-transformed seedlings. The results indicate that the PEP6-32 peptide causes discrete cell expansion abnormalities during early seedling development in red light that mimic weak phyB alleles, yet only in some aspects of seedling photomorphogenesis. The findings demonstrate that new chemistries derived from random peptide expression can modulate specific facets of plant growth and development.
Scattered products in fundamental groupoids

Infinitary operations, such as products indexed by countably infinite linear orders, arise naturally in the context of fundamental groups and groupoids. We prove that the well-definedness of products indexed by a scattered linear order in the fundamental groupoid of a first countable space is equivalent to the homotopically Hausdorff property. To prove this characterization, we employ the machinery of closure operators, on the $\pi_1$-subgroup lattice, defined in terms of test maps from one-dimensional domains.
H/sub /spl infin//-design with two-degrees-of-freedom controllers

A new procedure to design two-degrees-of freedom controllers is presented. This procedure can be used to independently attain feedback and tracking requirements without the limitations of having to choose a priori a tracking model or a particular two-degrees-of freedom controller structure. The feedback properties are attained using an H/sub /spl infin// optimization method. The main contribution is the design for tracking properties where an H/sub /spl infin// or H/sub 2/ optimization method is used. The latter case results in a systematic approach for combined H/sub /spl infin///H/sub 2/ optimization.
Accurate electromechanical characterization of soft molecular monolayers using piezo force microscopy

We report a new methodology for the electromechanical characterization of organic monolayers based on the implementation of dual AC resonance tracking piezo force microscopy (DART-PFM) combined with a sweep of an applied DC field under a fixed AC field. This experimental design allows calibration of the electrostatic component of the tip response and enables the use of low spring constant levers in the measurement. Moreover, the technique is shown to determine both positive and negative piezo response. The successful decoupling of the electrostatic component from the mechanical response will enable more quantitative electromechanical characterization of molecular and biomaterials and should generate new design principles for soft bio-compatible piezoactive materials. To highlight the applicability, our new methodology was used to successfully characterize the piezoelectric coefficient (d33) of a variety of piezoactive materials, including self-assembled monolayers made of small molecules (dodecane thiol, mercaptoundecanoic acid) or macromolecules (peptides, peptoids), as well as a variety of inorganic materials, including lead zirconate titanate [PZT], quartz, and periodically poled lithium niobate [PPLN]. Due to high differential capacitance, the soft organic monolayers demonstrated exceedingly large electromechanical response (as high as 250 pm V−1) but smaller d33 piezocoefficients. Finally, we find that the capacitive electrostatic response of the organic monolayers studied are significantly larger than conventional inorganic piezoelectric materials (e.g., PZT, PPLN, quartz), suggesting organic electromechanical materials applications can successfully draw from both piezo and electrostatic responses.
The Orbit of WASP-12b Is Decaying

WASP-12b is a transiting hot Jupiter on a 1.09 day orbit around a late-F star. Since the planet's discovery in 2008, the time interval between transits has been decreasing by 29 ± 2 ms yr⁻¹. This is a possible sign of orbital decay, although the previously available data left open the possibility that the planet's orbit is slightly eccentric and is undergoing apsidal precession. Here, we present new transit and occultation observations that provide more decisive evidence for orbital decay, which is favored over apsidal precession by a ΔBIC of 22.3 or Bayes factor of 70,000. We also present new radial-velocity data that rule out the Romer effect as the cause of the period change. This makes WASP-12 the first planetary system for which we can be confident that the orbit is decaying. The decay timescale for the orbit is P/P˙=3.25±0.23. Interpreting the decay as the result of tidal dissipation, the modified stellar tidal quality factor is Q′⋆=1.8×10⁵.
Effective field theory

I give a brief review of effective field theory, discussing the contribution of Feza Gursey in particular and focussing on the literature I am most familiar with.
Does SEC FRR No. 48 disclosure communicate risk management effectiveness

Abstract We hypothesize that the quality of market risk disclosure mandated by the U.S. Securities and Exchange Commission Financial Reporting Release No. 48 (FRR No. 48) provides useful information for assessing risk management effectiveness. Measuring risk disclosure quality as the degree of modification, we find that higher-than-expected disclosure modification is associated with lower future cash flow volatility. On average, an increase in risk disclosure modification from the lowest to the highest decile is associated with a 5.34 percent decrease in cash flow volatility. Given the significant impact of cash flow volatility on firm value and capital investment, our results highlight the importance of market risk disclosures and should be of interest to investors and analysts.
Multiscatter capture of superheavy dark matter by Pop. III stars

If captured by the gravitational field of stars or other compact objects, dark matter can self-annihilate and produce a potentially detectable particle flux. In the case of superheavy dark matter ( mX 108 GeV), a large number of scattering events with nuclei inside stars are necessary to slow down the dark matter particles below the escape velocity of the stars, at which point the Dark Matter (DM) particle becomes trapped, or captured. Using the recently developed analytical formalism for multiscatter capture, combined with the latest results on the constraints of dark-matter-baryon scattering cross-section, we calculate upper bounds on the capture rates for superheavy dark matter particles by the first (Pop. III) stars. Assuming that a non-zero fraction of the products of captured superheavy dark matter (SHDM) annihilations can be trapped and thermalized inside the star, we find that this additional heat source could influence the evolutionary phase of Pop. III stars. Moreover, requiring that Pop. III stars shine with sub-Eddington luminosity, we find upper bounds on the masses of the Pop. III stars. This implies a DM dependent cutoff on the initial mass function (IMF) of Pop. III stars, thus opening up the intriguing possibility of constraining DM properties using the IMF of extremely metal-poor stars.
Binary Quadratic Forms in Difference Sets

We show that if \(h(x,y)=ax^2+bxy+cy^2\in \mathbb {Z}[x,y]\) satisfies \(\varDelta (h)=b^2-4ac\ne 0,\) then any subset of \(\{1,2,\ldots ,N\}\) lacking nonzero differences in the image of h has size at most a constant depending on h times \(N\exp (-c\sqrt{\log N})\), where \(c=c(h)>0\). We achieve this goal by adapting an \(L^2\) density increment strategy previously used to establish analogous results for sums of one or more single-variable polynomials. Our exposition is thorough and self-contained, in order to serve as an accessible gateway for readers who are unfamiliar with previous implementations of these techniques.
HYPERNYM DETECTION USING STRICT PARTIAL ORDER NETWORKS

This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with is-a pairs. We also present an augmented variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.
Visibly Irreducible Polynomials over Finite Fields

AbstractLenstra, in this Monthly, has pointed out that a cubic over F5=Z/5Z of the form (x−a)(x−b)(x−c)+λ(x−d)(x−e) , where {a,b,c,d,e} is some permutation of {0,1,2,3,4} , is irreducible because e...
Workforce Capacity in Municipal Government

The central aim of this paper is to examine trends in the municipal government workforce in metropolitan (urban) areas. It explores, from a local public economies perspective, how the intergovernmental organization of municipalities influences their workforce capacities. The paper situates the local labor market in state-local systems and examines how local governments respond to fragmentation in a metropolitan area. The main finding is that the employment capacity of municipalities varies widely across metro areas, with local and intergovernmental factors affecting municipal workforces and labor expenditures. Local capacities and the state’s labor framework appear to be influential in the level of government employment. Facing various challenges, municipalities adapt their workforce levels to changing conditions in urban areas. While its main contribution is to research on local government capacity, the paper also draws from intergovernmental literature to identify factors that influence the workforce capacity of municipal governments.
Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties

We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties. The theoretical framework we develop allows incorporation of non-linear statistical models for seismic estimation problems. Uncertainty quantification is performed with approximate Bayesian computation. With the help of a synthetic example of estimation of reservoir net-to-gross and average fluid saturations in sub-resolution thin sand reservoir, several nuances are foregrounded regarding the applicability of unsupervised and supervised learning methods for seismic estimation problems. Finally, we demonstrate the efficacy of our approach by estimating posterior uncertainty of reservoir net-to-gross in sub-resolution thin sand reservoir from an offshore delta dataset using 3D pre-stack seismic data.
Family first or the kindness of strangers? Foster care placements and adult outcomes

Abstract We evaluate the long-run effects of placing foster children with extended family, rather than unrelated caregivers. An instrumental variable identification strategy is used to estimate the effects of kinship care relative to traditional foster care. We find former foster youth placed with kin benefit across a host of important adult outcomes, including increases in employment and education, and reductions in public assistance, homelessness, and incarceration. Estimated effects are statistically significant, and robust to multiple specifications. Findings suggest kinship care has the potential to improve important adult outcomes for a large, at-risk population and create large social cost savings.
Constructing sequences one step at a time

We propose a new method for constructing Turing ideals satisfying principles of reverse mathematics below the Chain–Antichain (CAC) Principle. Using this method, we are able to prove several new se...
On the unordered configuration space $$C(RP^n,2)$$

We prove that, if n is a 2-power, the unordered configuration space $$C(RP^n,2)$$ cannot be immersed in $${\mathbb {R}}^{4n-2}$$ nor embedded as a closed subspace of $${\mathbb {R}}^{4n-1}$$, optimal results, while if n is not a 2-power, $$C(RP^n,2)$$ can be immersed in $${\mathbb {R}}^{4n-3}$$. We also obtain cohomological lower bounds for the topological complexity of $$C(RP^n,2)$$, which are nearly optimal when n is a 2-power. We also give a new description of the mod-2 cohomology algebra of the Grassmann manifold $$G_{{n+1},2}$$.
On the logic of Fisherian sexual selection

In Fisher's model of sexual selection, a female preference for a male trait spreads together with the trait because their genetic bases become correlated. This can be interpreted as a "greenbeard" system: a preference gene, by inducing a female to mate with a trait-bearing male, favors itself because the male is disproportionately likely also to carry the preference gene. Here, we use this logic to argue that Fisherian sexual selection in diploids proceeds via two channels: (i) trait-bearing males are disproportionately the product of matings between preference-bearing mothers and trait-bearing fathers, and thus trait and preference genes are correlated "in trans"; (ii) trait and preference genes come into gametic phase disequilibrium, and thus are correlated "in cis." Gametic phase disequilibrium is generated by three distinct mechanisms that we identify. The trans channel does not operate when sexual selection is restricted to the haploid phase, and therefore represents a fundamental difference between haploid and diploid models of sexual selection. We show that the cis and trans channels contribute equally to the spread of the preference when recombination between the preference and trait loci is free, but that the trans channel is substantially more important when linkage is tight.
Is bitcoin a decentralized payment mechanism

We make a distinction between centralized, decentralized, and distributed payment mechanisms. A centralized payment mechanism processes a transaction using a trusted third party. A decentralized payment mechanism processes a transaction between the parties to the transaction. A distributed payment mechanism relies on the network of users to process a transaction on a shared ledger. We maintain that bitcoin is neither a centralized nor a decentralized payment mechanism. It is, instead, a distributed payment mechanism. We then consider decentralized and centralized aspects of the broader bitcoin payment space.
Supervised Machine Learning Techniques for Trojan Detection with Ring Oscillator Network

With the globalization of the semiconductor manufacturing process, electronic devices are powerless against malicious modification of hardware in the supply chain. The ever-increasing threat of hardware Trojan attacks against integrated circuits has spurred a need for accurate and efficient detection methods. Ring oscillator network (RON) is used to detect the Trojan by capturing the difference in power consumption; the power consumption of a Trojan-free circuit is different from the Trojan-inserted circuit. However, the process variation and measurement noise are the major obstacles to detect hardware Trojan with high accuracy. In this paper, we quantitatively compare four supervised machine learning algorithms and classifier optimization strategies for maximizing accuracy and minimizing the false positive rate (FPR). These supervised learning techniques show an improved false positive rate compared to principal component analysis (PCA) and convex hull classification by nearly 40% while maintaining 90% binary classification accuracy.
Virtual Guide Dog: Next-generation pedestrian signal for the visually impaired:

Accessible pedestrian signal was proposed as a mean to achieve the same level of service that is set forth by the Americans with Disabilities Act for the visually impaired. One of the major issues ...