cssi/SciSciGPT-SciSciNet huggingface dataset:
Low-Temperature Oxidation of Nitrided Iron Surfaces

The low-temperature (<150 K) oxidation of nitrided iron surfaces exposed to oxygen and water was probed using X-ray photoelectron spectroscopy (XPS), reflection absorption infrared spectroscopy (RAIRS), and mass spectrometry (MS). During exposure of nitrided iron surfaces to oxygen, iron oxynitride (FexNyOz), nitrosonium ions (NO+), as well as nitrite/nitrito- and nitrate-type species were observed. The production of nitrite/nitrito and nitrate species is taken as evidence for the existence of oxygen insertion chemistry into the iron nitride lattice under these low-temperature oxidation conditions. No molecular nitrogen was produced during reactions with oxygen or water in contrast to oxidation studies on other transition metal nitrides. Upon annealing the oxidized overlayer, nitrogen desorbs exclusively as nitric oxide (NO) between 250 and 400 K. In contrast to oxygen, the reactivity of nitrided iron surfaces toward water was limited to the production of adsorbed N−O species.
Silencing of ANKRD12 circRNA induces molecular and functional changes associated with invasive phenotypes

Circular RNAs (circRNAs) that form through non-canonical backsplicing events of pre-mRNA transcripts are evolutionarily conserved and abundantly expressed across species. However, the functional relevance of circRNAs remains a topic of debate. We identified one of the highly expressed circRNA (circANKRD12) in cancer cell lines and characterized it validated it by Sanger sequencing, Real-Time PCR. siRNA mediated silencing of the circular junction of circANKRD12 was followed by RNA Seq analysis of circANKRD12 silenced cells and control cells to identify the differentially regulated genes. A series of cell biology and molecular biology techniques (MTS assay, Migration analysis, 3D organotypic models, Real-Time PCR, Cell cycle analysis, Western blot analysis, and Seahorse Oxygen Consumption Rate analysis) were performed to elucidate the function, and underlying mechanisms involved in circANKRD12 silenced breast and ovarian cancer cells. In this study, we identified and characterized a circular RNA derived from Exon 2 and Exon 8 of the ANKRD12 gene, termed here as circANKRD12. We show that this circRNA is abundantly expressed in breast and ovarian cancers. The circANKRD12 is RNase R resistant and predominantly localized in the cytoplasm in contrast to its source mRNA. We confirmed the expression of this circRNA across a variety of cancer cell lines and provided evidence for its functional relevance through downstream regulation of several tumor invasion genes. Silencing of circANKRD12 induces a strong phenotypic change by significantly regulating cell cycle, increasing invasion and migration and altering the metabolism in cancer cells. These results reveal the functional significance of circANKRD12 and provide evidence of a regulatory role for this circRNA in cancer progression. Our study demonstrates the functional relevance of circANKRD12 in various cancer cell types and, based on its expression pattern, has the potential to become a new clinical biomarker.
Two Loci Contribute Epistastically to Heterospecific Pollen Rejection, a Postmating Isolating Barrier Between Species

Recognition and rejection of heterospecific male gametes occurs in a broad range of taxa, although the complexity of mechanisms underlying these components of postmating cryptic female choice is poorly understood. In plants, the arena for postmating interactions is the female reproductive tract (pistil), within which heterospecific pollen tube growth can be arrested via active molecular recognition and rejection. Unilateral incompatibility (UI) is one such postmating barrier in which pollen arrest occurs in only one direction of an interspecific cross. We investigated the genetic basis of pistil-side UI between Solanum species, with the specific goal of understanding the role and magnitude of epistasis between UI QTL. Using heterospecific introgression lines (ILs) between Solanum pennellii and S. lycopersicum , we assessed the individual and pairwise effects of three chromosomal regions ( ui1.1 , ui3.1 , and ui12.1 ) previously associated with interspecific UI among Solanum species. Specifically, we generated double introgression (‘pyramided’) genotypes that combined ui12.1 with each of ui1.1 and ui3.1 , and assessed the strength of UI pollen rejection in the pyramided lines, compared to single introgression genotypes. We found that none of the three QTL individually showed UI rejection phenotypes, but lines combining ui3.1 and ui12.1 showed significant pistil-side pollen rejection. Furthermore, double ILs (DILs) that combined different chromosomal regions overlapping ui3.1 differed significantly in their rate of UI, consistent with at least two genetic factors on chromosome three contributing quantitatively to interspecific pollen rejection. Together, our data indicate that loci on both chromosomes 3 and 12 are jointly required for the expression of UI between S. pennellii and S. lycopersicum , suggesting that coordinated molecular interactions among a relatively few loci underlie the expression of this postmating prezygotic barrier. In addition, in conjunction with previous data, at least one of these loci appears to also contribute to conspecific self-incompatibility (SI), consistent with a partially shared genetic basis between inter- and intraspecific mechanisms of postmating prezygotic female choice.
Asymmetric synthesis of (S)-carbinoxamine. New aspects of oxazaborolidine-catalyzed enantioselective carbonyl reduction

Abstract A new process has been developed for the highly enantioselective catalytic reduction of 2-aroylpyridines and successfully applied to the synthesis of (S)-carbinoxamine ( 1 ), a therapeutically important histamine H 1 antagonist. Related enantioselective reductions of 4-aroylpyridines and ortho-substituted benzophenones are also described.
TRuML: A Translator for Rule-Based Modeling Languages

Rule-based modeling languages, such as the Kappa and BioNetGen languages (BNGL), are powerful frameworks for modeling the dynamics of complex biochemical reaction networks. Each language is distributed with a distinct software suite and modelers may wish to take advantage of both toolsets. This paper introduces a practical application called TRuML that translates models written in either Kappa or BNGL into the other language. While similar in many respects, key differences between the two languages makes translation sufficiently complex that automation becomes a useful tool. TRuML accommodates the languages' complexities and produces a semantically equivalent model in the alternate language of the input model when possible and an approximate model in certain other cases. Here, we discuss a number of these complexities and provide examples of equivalent models in both Kappa and BNGL.
Laterality of the frontal aslant tract (FAT) explains externalizing behaviors through its association with executive function

We investigated the development of a recently identified white matter pathway, the frontal aslant tract (FAT) and its association with executive function and externalizing behaviors in a sample of 129 neurotypical male and female human children ranging in age from 7 months to 19 years. We found that the FAT could be tracked in 92% of those children, and that the pathway showed age-related differences into adulthood. The change in white matter microstructure was very rapid until about 6 years, and then plateaued, only to show age-related increases again after the age of 11 years. In a subset of those children (5-18 years; n = 70), left laterality of the microstructural properties of the FAT was associated with greater attention problems as measured by the Child Behavior Checklist (CBCL). However, this relationship was fully mediated by higher executive dysfunction as measured by the Behavior Rating Inventory of Executive Function (BRIEF). This relationship was specific to the FAT-we found no relationship between laterality of a control pathway, or of the white matter of the brain in general, and attention and executive function. These findings suggest that the degree to which the developing brain favors a right lateralized structural dominance of the FAT is directly associated with executive function and attention. This novel finding provides a new potential structural biomarker to assess attention deficit hyperactivity disorder (ADHD) and associated executive dysfunction during development.
The first enantioselective total synthesis of (-)-arisugacin A

Abstract The first enantioselective synthesis of (−)-arisugacin A in 17 steps is described here, featuring a CBS asymmetric ketone reduction and a highly stereoselective formal [3+3] cycloaddition approach. This concise synthesis of the enantiomer unambiguously confirms the original assignment of the absolute configuration.
Thermal and microwave hydrolysis of organotrifluoroborates mediated by alumina

Hydrolysis of organotrifluoroborates to the corresponding organoboronic acids is readily achieved under either thermal or microwave conditions in the presence of alumina. The organoboronic acid products are obtained in good to excellent yields with essentially no loss of boronated reagent due to protoboronation.
Substituted benzyl ethers as radical stable protecting groups

Abstract Sixteen substituted benzyl ethers were studied for their potential to serve as hydroxyl protecting groups with the ability to withstand radical brominating conditions using N-bromosuccinimide during the conversion of benzylidene 2 into bromobenzoate 3 .
Assessment of DNA Methylation Patterns in the Bone and Cartilage of a Nonhuman Primate Model of Osteoarthritis

ObjectiveOsteoarthritis (OA) affects humans and several other animals. Thus, the mechanisms underlying this disorder, such as specific skeletal tissue DNA methylation patterns, may be evolutionary ...
Genetic differences in the behavioral organization of binge eating, conditioned food reward, and compulsive-like eating in C57BL/6J and DBA/2J strains

Abstract Binge eating (BE) is a heritable symptom of eating disorders associated with anxiety, depression, malnutrition, and obesity. Genetic analysis of BE could facilitate therapeutic discovery. We used an intermittent, limited access BE paradigm involving sweetened palatable food (PF) to examine genetic differences in BE, conditioned food reward, and compulsive-like eating between C57BL/6J (B6J) and DBA/2J (D2J) inbred mouse strains. D2J mice showed a robust escalation in intake and conditioned place preference for the PF-paired side. D2J mice also showed a unique style of compulsive-like eating in the light/dark conflict test where they rapidly hoarded and consumed PF in the preferred unlit environment. BE and compulsive-like eating exhibited narrow-sense heritability estimates between 56 and 73%. To gain insight into the genetic basis, we phenotyped and genotyped a small cohort of 133 B6J × D2J-F2 mice at the peak location of three quantitative trait loci (QTL) previously identified in F2 mice for sweet taste (chromosome 4: 156 Mb), bitter taste (chromosome 6: 133 Mb) and behavioral sensitivity to drugs of abuse (chromosome 11: 50 Mb). The D2J allele on chromosome 6 was associated with greater PF intake on training days and greater compulsive-like PF intake, but only in males, suggesting that decreased bitter taste may increase BE in males. The D2J allele on chromosome 11 was associated with an increase in final PF intake and slope of escalation across days. Future studies employing larger crosses and genetic reference panels comprising B6J and D2J alleles will identify causal genes and neurobiological mechanisms.
Synthesis of cyclic sulfates by halocyclization.

We report the synthesis of cyclic sulfates by halocyclization. The resulting cyclic sulfate products can be opened selectively with sodium azide to transform them into highly functionalized compounds that contain azide, alcohol, and halide groups.
Convex domains, Hankel operators, and maximal estimates

Let $1\leq q\leq (n-1)$. We first show that a necessary condition for a Hankel operator on $(0,q-1)$-forms on a convex domain to be compact is that its symbol is holomorphic along $q$-dimensional analytic varieties in the boundary. Because maximal estimates (equivalently, a comparable eigenvalues condition on the Levi form of the boundary) turn out to be favorable for compactness of Hankel operators, this result then implies that on a convex domain, maximal estimates exclude analytic varieties from the boundary, except ones of top dimension $(n-1)$ (and their subvarieties). Some of our techniques apply to general pseudoconvex domains to show that if the Levi form has comparable eigenvalues, or equivalently, if the domain admits maximal estimates, then compactness and subellipticity hold for forms at some level $q$ if and only if they hold at all levels.
To Infinity and Beyond: Scaling Economic Theories via Logical Compactness

Many economic-theoretic models incorporate finiteness assumptions that, while introduced for simplicity, play a real role in the analysis. Such assumptions introduce a conceptual problem, as results that rely on finiteness are often implicitly nonrobust; for example, they may depend upon edge effects or artificial boundary conditions. Here, we present a unified method that enables us to remove finiteness assumptions, such as those on datasets, market sizes, and time horizons. We then apply our approach to a variety of revealed preference, matching, and exchange economy settings. The key to our approach is Logical Compactness, a core result from Propositional Logic. Building on Logical Compactness, in a revealed-preference setting, we reprove Reny's [2015] infinite-data version of Afriat's [1967] theorem and (newly) prove an infinite-data version of McFadden and Richter's [1971, 1990] characterization of rationalizable stochastic datasets. In a matching setting, we reprove large-market existence results implied by Fleiner's [2003] analysis, and prove both the strategy-proofness of the man-optimal stable mechanism in infinite markets, and an infinite-market version of Nguyen and Vohra's [2018] existence result for near-feasible stable matchings with couples. In a trading-network setting, we prove that the Hatfield et al. [2013] result on existence of Walrasian equilibria extends to infinite markets. Finally, we prove that Pereyra's [2013] existence result for dynamic two-sided matching markets extends to a doubly-infinite time horizon.
Random Function Priors for Correlation Modeling.

The likelihood model of high dimensional data $X_n$ can often be expressed as $p(X_n|Z_n,\theta)$, where $\theta\mathrel{\mathop:}=(\theta_k)_{k\in[K]}$ is a collection of hidden features shared across objects, indexed by $n$, and $Z_n$ is a non-negative factor loading vector with $K$ entries where $Z_{nk}$ indicates the strength of $\theta_k$ used to express $X_n$. In this paper, we introduce random function priors for $Z_n$ for modeling correlations among its $K$ dimensions $Z_{n1}$ through $Z_{nK}$, which we call \textit{population random measure embedding} (PRME). Our model can be viewed as a generalized paintbox model~\cite{Broderick13} using random functions, and can be learned efficiently with neural networks via amortized variational inference. We derive our Bayesian nonparametric method by applying a representation theorem on separately exchangeable discrete random measures.
Effects of Disorder on Thermoelectric Properties of Semiconducting Polymers

Organic materials have attracted recent interest as thermoelectric (TE) converters due to their low cost and ease of fabrication. We examine the effects of disorder on the TE properties of semiconducting polymers based on the Gaussian disorder model (GDM) for site energies while employing Pauli’s master equation approach to model hopping between localized sites. Our model is in good agreement with experimental results and a useful tool to study hopping transport. We show that stronger overlap between sites can improve the electrical conductivity without adversely affecting the Seebeck coefficient. We find that positional disorder aids the formation of new conduction paths with an increased probability of carriers in high energy sites, leading to an increase in electrical conductivity while leaving the Seebeck unchanged. On the other hand, energetic disorder leads to increased energy gaps between sites, hindering transport. This adversely affects conductivity while only slightly increasing Seebeck and results in lower TE power factors. Furthermore, positional correlation primarily affects conductivity, while correlation in site energies has no effect on TE properties of polymers. Our results also show that the Lorenz number increases with Seebeck coefficient, largely deviating from the Sommerfeld value, in agreement with experiments and in contrast to band conductors. We conclude that reducing energetic disorder and positional correlation, while increasing positional disorder can lead to higher TE power factors.
Symmetric Enhancements of Involutory Virtual Birack Counting Invariants

We consider involutory virtual biracks with good involutions, also known as symmetric involutory virtual biracks. Any good involution on an involutory virtual birack denes an enhancement of the counting invariant. We provide examples demonstrating that the enhancement is stronger than the unenhanced counting invariant.
A note on the independent domination number versus the domination number in bipartite graphs

Let γ(G) and i(G) be the domination number and the independent domination number of G, respectively. Rad and Volkmann posted a conjecture that i(G)/γ(G) ≤ Δ(G)/2 for any graph G, where Δ(G) is its maximum degree (see N. J. Rad, L. Volkmann (2013)). In this work, we verify the conjecture for bipartite graphs. Several graph classes attaining the extremal bound and graphs containing odd cycles with the ratio larger than Δ(G)/2 are provided as well.
Calibration-Free B0 Correction of EPI Data Using Structured Low Rank Matrix Recovery

We introduce a structured low rank algorithm for the calibration-free compensation of field inhomogeneity artifacts in echo planar imaging (EPI) MRI data. We acquire the data using two EPI readouts that differ in echo-time. Using time segmentation, we reformulate the field inhomogeneity compensation problem as the recovery of an image time series from highly undersampled Fourier measurements. The temporal profile at each pixel is modeled as a single exponential, which is exploited to fill in the missing entries. We show that the exponential behavior at each pixel, along with the spatial smoothness of the exponential parameters, can be exploited to derive a 3-D annihilation relation in the Fourier domain. This relation translates to a low rank property on a structured multi-fold Toeplitz matrix, whose entries correspond to the measured k-space samples. We introduce a fast two-step algorithm for the completion of the Toeplitz matrix from the available samples. In the first step, we estimate the null space vectors of the Toeplitz matrix using only its fully sampled rows. The null space is then used to estimate the signal subspace, which facilitates the efficient recovery of the time series of images. We finally demonstrate the proposed approach on spherical MR phantom data and human data and show that the artifacts are significantly reduced.
Universal properties of Wilson loop operators in large N QCD

Eigenvalues of a Wilson loop operator are gauge invariant and their distribution undergoes a transition at infinite N as the size of the loop is changed. We study this transition using the average characteristic polynomial associated with the Wilson loop operator. We derive the scaling function in a certain double scaling limit for two dimensional QCD and hypothesize that the transition in three and four dimensional QCD are in the same universality class. Numerical evidence for this hypothesis is provided in three dimensions.
Dehn fillings and elementary splittings

We consider conditions on relatively hyperbolic groups about the non-existence of certain kinds of splittings, and show these properties persist in long Dehn fillings. We deduce that certain connectivity properties of the Bowditch boundary persist un-
Traveling in randomly embedded random graphs

We consider the problem of traveling among random points in Euclidean space, when only a random fraction of the pairs are joined by traversable connections. In particular, we show a threshold for a pair of points to be connected by a geodesic of length arbitrarily close to their Euclidean distance, and analyze the minimum length Traveling Salesperson Tour, extending the Beardwood-Halton-Hammersley theorem to this setting.
A Liouville Type Theorem for Special Lagrangian Equations with Constraints

We derive a Liouville type result for special Lagrangian equations with certain “convexity” and restricted linear growth assumptions on the solutions.
Riesz outer product Hilbert space frames: Quantitative bounds, topological properties, and full geometric characterization

Abstract Outer product frames are important objects in Hilbert space frame theory. But very little is known about them. In this paper, we make the first detailed study of the family of outer product frames induced directly by vector sequences. We are interested in both the quantitative attributes of these outer product sequences (in particular, their Riesz and frame bounds) and their independence and spanning properties. We show that Riesz sequences of vectors yield Riesz sequences of outer products with the same (or better) Riesz bounds. Equiangular tight frames are shown to produce Riesz sequences with optimal Riesz bounds for outer products. We provide constructions of frames which produce Riesz outer product bases with “good” Riesz bounds. We show that the family of unit norm frames which yield independent outer product sequences is open and dense (in a Euclidean-analytic sense) within the topological space ⊗ i = 1 M S N − 1 where M is less than or equal to the dimension of the space of symmetric operators on H N ; that is to say, almost every frame with such a bound on its cardinality will induce a set of independent outer products. Thus, this would mean that finding the necessary and sufficient conditions such that the induced outer products are dependent is a more interesting question. For the coup de grâce, we give a full analytic and geometric classification of such sequences which produce dependent outer products.
Relative topological complexity of a pair

Abstract For a pair of spaces X and Y such that Y ⊆ X , we define the relative topological complexity of the pair ( X , Y ) as a new variant of relative topological complexity. Intuitively, this corresponds to counting the smallest number of motion planning rules needed for a continuous motion planner from X to Y. We give basic estimates on the invariant, and we connect it to both Lusternik–Schnirelmann category and topological complexity. In the process, we compute this invariant for several example spaces including wedges of spheres, topological groups, and spatial polygon spaces. In addition, we connect the invariant to the existence of certain types of axial maps.
Trinomials defining quintic number fields

Given a quintic number field K/ℚ, we study the set of irreducible trinomials, polynomials of the form x5 + ax + b, that have a root in K. We show that there is a genus 4 curve CK whose rational points are in bijection with such trinomials. This curve CK maps to an elliptic curve defined over a number field, and using this map, we are able (in some cases) to determine all the rational points on CK using elliptic curve Chabauty.
Existence of frequency modes coupling seismic waves and vibrating tall buildings

Abstract We prove in this paper an existence result for frequency modes coupling seismic waves and vibrating tall buildings. The derivation from physical principles of a set of equations modeling this phenomenon was done in previous studies. In this model all vibrations are assumed to be anti-plane and time harmonic so the two dimensional Helmholtz equation can be used. A coupling frequency mode is obtained once we can determine a wavenumber such that the solution of the corresponding Helmholtz equation in the lower half plane with relevant Neumann and Dirichlet conditions at the interface satisfies a specific integral equation at the base of an idealized tall building. Although numerical simulations suggest that such wavenumbers should exist, as far as we know, to date, there is no theoretical proof of their existence. This is what this present study offers to provide.
The Largest Pure Partial Planes of Order 6 Have Size 25

In this paper, we prove that the largest pure partial plane of order 6 has size 25. At the same time, we classify all pure partial planes of order 6 and size 25 up to isomorphism. Our major approach is computer search. The search space is very large so we use combinatorial arguments to rule out some of the cases. For the remaining cases, we subdivide each search by phases and use multiple checks to reduce search space via symmetry.
Certifying Global Optimality of Graph Cuts via Semidefinite Relaxation: A Performance Guarantee for Spectral Clustering

Spectral clustering has become one of the most widely used clustering techniques when the structure of the individual clusters is non-convex or highly anisotropic. Yet, despite its immense popularity, there exists fairly little theory about performance guarantees for spectral clustering. This issue is partly due to the fact that spectral clustering typically involves two steps which complicated its theoretical analysis: First, the eigenvectors of the associated graph Laplacian are used to embed the dataset, and second, k-means clustering algorithm is applied to the embedded dataset to get the labels. This paper is devoted to the theoretical foundations of spectral clustering and graph cuts. We consider a convex relaxation of graph cuts, namely ratio cuts and normalized cuts, that makes the usual two-step approach of spectral clustering obsolete and at the same time gives rise to a rigorous theoretical analysis of graph cuts and spectral clustering. We derive deterministic bounds for successful spectral clustering via a spectral proximity condition that naturally depends on the algebraic connectivity of each cluster and the inter-cluster connectivity. Moreover, we demonstrate by means of some popular examples that our bounds can achieve near optimality. Our findings are also fundamental to the theoretical understanding of kernel k-means. Numerical simulations confirm and complement our analysis.
A Recoverable Mutex Algorithm with Sub-logarithmic RMR on Both CC and DSM

In light of recent advances in non-volatile main memory technology, Golab and Ramaraju reformulated the traditional mutex problem into the novel Recoverable Mutual Exclusion (RME) problem. In the best known solution for RME, due to Golab and Hendler from PODC 2017, a process incurs at most O(√ log n log log n) remote memory references (RMRs) per passage on a system with n processes, where a passage is an interval from when a process enters the Try section to when it subsequently returns to Remainder. Their algorithm, however, guarantees this bound only for cache-coherent (CC) multiprocessors, leaving open the question of whether a similar bound is possible for distributed shared memory (DSM) multiprocessors.We answer this question affirmatively by designing an algorithm for a system with n processes, such that, it satisfies the same complexity bound as Golab and Hendler's for both CC and DSM multiprocessors. Our algorithm has some additional advantages over Golab and Hendler's: (i) its Exit section is wait-free, (ii) it uses only the Fetch-and-Store instruction, and (iii) on a CC machine our algorithm needs each process to have a cache of only O(1) words, while their algorithm needs a cache of size that is a function of n.
Szlenk and w⁎-dentability indices of C(K)

Abstract Given any compact, Hausdorff space K and 1 p ∞ , we compute the Szlenk and w ⁎ -dentability indices of the spaces C ( K ) and L p ( C ( K ) ) . We show that if K is compact, Hausdorff, scattered, C B ( K ) is the Cantor–Bendixson index of K, and ξ is the minimum ordinal such that C B ( K ) ⩽ ω ξ , then S z ( C ( K ) ) = ω ξ and D z ( C ( K ) ) = S z ( L p ( C ( K ) ) ) = ω 1 + ξ .
Boundary-degenerate elliptic operators and Hölder continuity for solutions to variational equations and inequalities

Abstract We prove local supremum bounds, a Harnack inequality, Holder continuity up to the boundary, and a strong maximum principle for solutions to a variational equation defined by an elliptic operator which becomes degenerate along a portion of the domain boundary and where no boundary condition is prescribed, regardless of the sign of the Fichera function. In addition, we prove Holder continuity up to the boundary for solutions to variational inequalities defined by this boundary-degenerate elliptic operator.
Learning in Auctions: Regret is Hard, Envy is Easy

An extensive body of recent work studies the welfare guarantees of simple and prevalent combinatorial auction formats, such as selling m items via simultaneous second price auctions (SiSPAs) [1], [2], [3]. These guarantees hold even when the auctions are repeatedly executed and the players use no-regret learning algorithms to choose their actions. Unfortunately, off-the-shelf no-regret learning algorithms for these auctions are computationally inefficient as the number of actions available to the players becomes exponential. We show that this obstacle is inevitable: there are no polynomial-time no-regret learning algorithms for SiSPAs, unless RP ⊆ NP, even when the bidders are unit-demand. Our lower bound raises the question of how good outcomes polynomially-bounded bidders may discover in such auctions. To answer this question, we propose a novel concept of learning in auctions, termed "no-envy learning." This notion is founded upon Walrasian equilibrium, and we show that it is both efficiently implementable and results in approximately optimal welfare, even when the bidders have valuations from the broad class of fractionally subadditive (XOS) valuations (assuming demand oracle access to the valuations) or coverage valuations (even without demand oracles). No-envy learning outcomes are a relaxation of no-regret learning outcomes, which maintain their approximate welfare optimality while endowing them with computational tractability. Our positive and negative results extend to several auction formats that have been studied in the literature via the smoothness paradigm. Our positive results for XOS valuations are enabled by a novel Follow-The-Perturbed-Leader algorithm for settings where the number of experts and states of nature are both infinite, and the payoff function of the learner is non-linear. We show that this algorithm has applications outside of auction settings, establishing significant gains in a recent application of no-regret learning in security games. Our efficient learning result for coverage valuations is based on a novel use of convex rounding schemes and a reduction to online convex optimization.
Black Box Variational Inference

Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally requires signicant model-specic analysis. These eorts can hinder and deter us from quickly developing and exploring a variety of models for a problem at hand. In this paper, we present a \black box" variational inference algorithm, one that can be quickly applied to many models with little additional derivation. Our method is based on a stochastic optimization of the variational objective where the noisy gradient is computed from Monte Carlo samples from the variational distribution. We develop a number of methods to reduce the variance of the gradient, always maintaining the criterion that we want to avoid dicult model-based derivations. We evaluate our method against the corresponding black box sampling based methods. We nd that our method reaches better predictive likelihoods much faster than sampling methods. Finally, we demonstrate that Black Box Variational Inference lets us easily explore a wide space of models by quickly constructing and evaluating several models of longitudinal healthcare data.
Random walks on simplicial complexes and harmonics

In this paper, we introduce a class of random walks with absorbing states on simplicial complexes. Given a simplicial complex of dimension d, a random walk with an absorbing state is defined which relates to the spectrum of the k-dimensional Laplacian for 1 ≤ k ≤ d. We study an example of random walks on simplicial complexes in the context of a semi-supervised learning problem. Specifically, we consider a label propagation algorithm on oriented edges, which applies to a generalization of the partially labelled classification problem on graphs. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 49, 379-405, 2016.
Data-dependent Initializations of Convolutional Neural Networks

Abstract: Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent initialization procedure, that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while being roughly three orders of magnitude faster. When combined with pre-training methods, our initialization significantly outperforms prior work, narrowing the gap between supervised and unsupervised pre-training.
A simple convergence analysis of Bregman proximal gradient algorithm

In this paper, we provide a simple convergence analysis of proximal gradient algorithm with Bregman distance, which provides a tighter bound than existing result. In particular, for the problem of minimizing a class of convex objective functions, we show that proximal gradient algorithm with Bregman distance can be viewed as proximal point algorithm that incorporates another Bregman distance. Consequently, the convergence result of the proximal gradient algorithm with Bregman distance follows directly from that of the proximal point algorithm with Bregman distance, and this leads to a simpler convergence analysis with a tighter convergence bound than existing ones. We further propose and analyze the backtracking line-search variant of the proximal gradient algorithm with Bregman distance.
Toward depth estimation using mask-based lensless cameras

Recently, coded masks have been used to demonstrate a thin form-factor lensless camera, FlatCam, in which a mask is placed immediately on top of a bare image sensor. In this paper, we present an imaging model and algorithm to jointly estimate depth and intensity information in the scene from a single or multiple FlatCams. We use a light field representation to model the mapping of 3D scene onto the sensor in which light rays from different depths yield different modulation patterns. We present a greedy depth pursuit algorithm to search the 3D volume and estimate the depth and intensity of each pixel within the camera field-of-view. We present simulation results to analyze the performance of our proposed model and algorithm with different FlatCam settings.
An almost sudden jump in quantum complexity

The Quantum Satisfiability problem (QSAT) is the generalization of the canonical NP-complete problem - Boolean Satisfiability. (k, s)-QSAT is the following variant of the problem: given a set of projectors of rank 1, acting non-trivially on k qubits out of n qubits, such that each qubit appears in at most s projectors, decide whether there exists a quantum state in the null space of all the projectors. Let f* (k) be the maximal integer s such that every (k, s)-QSAT instance is satisfiable. Deciding (k, f*(k))-QSAT is computationally easy: by definition the answer is "satisfiable". But, by relaxing the conditions slightly, we show that (k, f* (k) + 2)-QSAT is QMA1-hard, for k ≥ 15. This is a quantum analogue of a classical result by Kratochvil et al. [1]. We use the term "an almost sudden jump" to stress that the complexity of (k, f* (k) + 1)-QSAT is open, where the jump in the classical complexity is known to be sudden. We present an implication of this finding to the quantum PCP conjecture, arguably one of the most important open problems in the field of Hamiltonian complexity. Our implication imposes some constraints on one possible way to refute the quantum PCP.
An enumeration process for racks

Given a presentation for a rack $\mathcal R$, we define a process which systematically enumerates the elements of $\mathcal R$. The process is modeled on the systematic enumeration of cosets first given by Todd and Coxeter. This generalizes and improves the diagramming method for $n$-quandles introduced by Winker. We provide pseudocode that is similar to that given by Holt for the Todd-Coxeter process. We prove that the process terminates if and only if $\mathcal R$ is finite, in which case, the procedure outputs an operation table for the finite rack. We conclude with an application to knot theory.
Redundancy of Unbounded Memory Markov Classes with Continuity Conditions

We study the redundancy of universally compressing strings $X_{1}, \ldots,X_{n}$ generated by a binary Markov source $p$ without any bound on the memory. To better understand the connection between compression and estimation in the Markov regime, we consider a class of Markov sources restricted by a continuity condition. In the absence of an upper bound on memory, the continuity condition implies that $p({{X}}_{0}\vert X_{-m}^{-1})$ gets closer to the true probability $p(X_{0}\vert X_{-\infty}^{-1})$ as $m$ increases, rather than vary around arbitrarily. For such sources, we prove asymptotically matching upper and lower bounds on the redundancy. In the process, we identify what sources in the class matter the most from a redundancy perspective.
Optimizing curbside parking resources subject to congestion constraints

To gain theoretical insight into the relationship between parking scarcity and congestion, we describe block-faces of curbside parking as a network of queues. Due to the nature of this network, canonical queueing network results are not available to us. We present a new kind of queueing network subject to customer rejection due to the lack of available servers. We provide conditions for such networks to be stable, a tractable “single node” view of such a network, and show that maximizing the occupancy through price control of such queues, and subject to constraints on the allowable congestion between queues searching for an available server, is a convex optimization problem. We demonstrate an application of this method in the Mission District of San Francisco; our results suggest congestion due to drivers searching for parking stems from an inefficient spatial utilization of parking resources.
Torus link homology and the nabla operator

Abstract In recent work, Elias and Hogancamp develop a recurrence for the Poincare series of the triply graded Khovanov–Rozansky homology of certain links, one of which is the ( n , n ) torus link. In this case, Elias and Hogancamp give a combinatorial formula for this homology that is reminiscent of the combinatorics of the modified Macdonald polynomial eigenoperator ∇. We give a combinatorial formula for the homologies of all complexes considered by Elias and Hogancamp. Our first formula is not easily computable, so we show how to transform it into a computable version. Finally, we conjecture a direct relationship between the ( n , n ) torus link case of our formula and the symmetric function ∇ p 1 n .
Phase Transitions in Spectral Community Detection

Consider a network consisting of two subnetworks (communities) connected by some external edges. Given the network topology, the community detection problem can be cast as a graph partitioning problem that aims to identify the external edges as the graph cut that separates these two subnetworks. In this paper, we consider a general model where two arbitrarily connected subnetworks are connected by random external edges. Using random matrix theory and concentration inequalities, we show that when one performs community detection via spectral clustering there exists an abrupt phase transition as a function of the random external edge connection probability. Specifically, the community detection performance transitions from almost perfect detectability to low detectability near some critical value of the random external edge connection probability. We derive upper and lower bounds on the critical value and show that the bounds are equal to each other when two subnetwork sizes are identical. Using simulated and experimental data we show how these bounds can be empirically estimated to validate the detection reliability of any discovered communities.
Inferring pore connectivity from sorption hysteresis in multiscale porous media

Abstract Hypothesis Vapor adsorption experiments are widely used to assess pore size distributions, but the large hysteresis sometimes observed between sorption and desorption isotherms remains difficult to interpret. Such hysteresis is influenced pore network connectivity, which has previously been modeled by percolation on infinite lattices. Our hypothesis is that percolation occurs instead through finite networks of micropores connecting accessible macropores, always exposed to the outside environment. Theory We derive a general formula for sorption/desorption isotherms that introduces a simple measure of hierarchical pore connectivity - the fraction of always exposed pores. The model thus accounts for “small world” connections in finite-size percolation, while also incorporating other hysteresis mechanisms, in single-pore filling, liquid insertion into the solid matrix, and cavitation. Findings Our formula is able to fit and interpret both primary and scanning sorption/desorption isotherms for a variety of adsorbates (noble gases, water, and organics) and porous materials (cement pastes, dental enamels, porous glasses, carbon black and nanotubes), including cases with broad pore-size distributions and large hysteresis. It allows quantification of the prevalence of percolating macropores in the material, even though these pores are never filled during the sorption experiments. A distinct bump in sorption isotherms is explained as a lowering of the barrier to nucleation of the vapor phase with a universal temperature scaling.
An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets

Various automobile and mobility companies, for instance Ford, Uber and Waymo, are currently testing their pre-produced autonomous vehicle (AV) fleets on the public roads. However, due to rareness of the safety-critical cases and, effectively, unlimited number of possible traffic scenarios, these on-road testing efforts have been acknowledged as tedious, costly, and risky. In this study, we propose Accelerated Deployment framework to safely and efficiently estimate the AVs performance on public streets. We showed that by appropriately addressing the gradual accuracy improvement and adaptively selecting meaningful and safe environment under which the AV is deployed, the proposed framework yield to highly accurate estimation with much faster evaluation time, and more importantly, lower deployment risk. Our findings provide an answer to the currently heated and active discussions on how to properly test AV performance on public roads so as to achieve safe, efficient, and statistically-reliable testing framework for AV technologies.
A General Characterization of the Statistical Query Complexity

Statistical query (SQ) algorithms are algorithms that have access to an {\em SQ oracle} for the input distribution $D$ instead of i.i.d.~ samples from $D$. Given a query function $\phi:X \rightarrow [-1,1]$, the oracle returns an estimate of ${\bf E}_{ x\sim D}[\phi(x)]$ within some tolerance $\tau_\phi$ that roughly corresponds to the number of samples. In this work we demonstrate that the complexity of solving general problems over distributions using SQ algorithms can be captured by a relatively simple notion of statistical dimension that we introduce. SQ algorithms capture a broad spectrum of algorithmic approaches used in theory and practice, most notably, convex optimization techniques. Hence our statistical dimension allows to investigate the power of a variety of algorithmic approaches by analyzing a single linear-algebraic parameter. Such characterizations were investigated over the past 20 years in learning theory but prior characterizations are restricted to the much simpler setting of classification problems relative to a fixed distribution on the domain (Blum et al., 1994; Bshouty and Feldman, 2002; Yang, 2001; Simon, 2007; Feldman, 2012; Szorenyi, 2009). Our characterization is also the first to precisely characterize the necessary tolerance of queries. We give applications of our techniques to two open problems in learning theory and to algorithms that are subject to memory and communication constraints.
Extremal Properties of Product Sets

We find the nearly optimal size of a set A ⊂ [N]:= {1,...,N} so that the product set AA satisfies either (i) |AA| ~ |A|2/2 or (ii) |AA| ~ |[N][N]|. This settles problems recently posed in a paper of J. Cilleruelo, D. S. Ramana and O. Ramare.
Conditional image generation with PixelCNN decoders

This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet, with greatly reduced computational cost.
An efficient framework for learning sentence representations

In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classification problem. Given a sentence and its context, a classifier distinguishes context sentences from other contrastive sentences based on their vector representations. This allows us to efficiently learn different types of encoding functions, and we show that the model learns high-quality sentence representations. We demonstrate that our sentence representations outperform state-of-the-art unsupervised and supervised representation learning methods on several downstream NLP tasks that involve understanding sentence semantics while achieving an order of magnitude speedup in training time.