cssi/SciSciGPT-SciSciNet huggingface dataset:
Cryo-electron tomography reveals that dynactin recruits a team of dyneins for processive motility

Cytoplasmic dynein is a protein complex that transports molecular cargo along microtubules (MTs), playing a key role in the intracellular trafficking network. Vertebrate dynein’s movement becomes strikingly enhanced upon interacting with dynactin and a cargo adaptor such as BicaudalD2. However, the mechanisms responsible for increased transport activity are not well understood, largely owing to limited structural information. We used cryo-electron tomography (cryo-ET) to visualize the 3D structure of the MT-bound dynein–dynactin complex from Mus musculus and show that the dynactin–cargo adaptor complex binds two dimeric dyneins. This configuration imposes spatial and conformational constraints on both dynein dimers, positioning the four motor domains in proximity to one another and oriented toward the MT minus end. We propose that grouping multiple dyneins onto a single dynactin scaffold promotes collective force production, increased processivity, and unidirectional movement, suggesting mechanistic parallels to axonemal dynein. These findings provide structural insights into a previously unknown mechanism for dynein regulation.
IMPROVED SYNTHESIS OF 2H-1,3(3H)-OXAZINE-2,6-DIONE (OXAURACIL)

(1995). IMPROVED SYNTHESIS OF 2H-1,3(3H)-OXAZINE-2,6-DIONE (OXAURACIL) Organic Preparations and Procedures International: Vol. 27, No. 6, pp. 651-652.
Product Identification and Kinetics of Reactions of HCl with HNO3/H2SO4/H2O Solutions

The gas-phase products of the reaction between HNO3/H2SO4/H2O acid solutions and HCl were identified using IR and UV−Vis spectroscopy and chemical ionization mass spectrometry. The major products of the reaction were Cl2 and ClNO, with quantifiable amounts of ClNO2 sometimes present. On the basis of quantitative product yield determinations, the overall proposed reaction is 3HCl + HNO3 → Cl2 + ClNO + 2H2O. Kinetic studies were also performed and the reaction was found to follow a second-order rate law: Rate = k[HCl][HNO3]. On the basis of the products and intermediates observed and the rate law, a mechanism is proposed which involves the NO2+ ion as the key reactive species. The rate of the reaction is shown to depend strongly on the total acid concentration of the solution and temperature-dependent studies were also performed. The potential significance of this reaction in connection to stratospheric ozone depletion is evaluated by using the composition and temperature dependence of the rate to estimate...
Efficient Synthesis of Functionalized Aziridinium Salts

Various aziridinium salts were efficiently prepared from bromination of a series of backbone substituted N,N-bisubstituted beta-amino alcohols and isolated via flash column chromatography. The effect of C-substitution, N-substitution, solvent, leaving group, and counteranions on formation of the isolable aziridinium salts was investigated.
Heritable variation in bleaching responses and its functional genomic basis in reef-building corals (Orbicella faveolata).

Reef-building corals are highly sensitive to rising ocean temperatures, and substantial adaptation will be required for corals and the ecosystems they support to persist in changing ocean conditions. Genetic variation that might support adaptive responses has been measured in larval stages of some corals, but these estimates remain unavailable for adult corals and the functional basis of this variation remains unclear. In this study, we focused on the potential for adaptation in Orbicella faveolata, a dominant reef-builder in the Caribbean. We conducted thermal stress experiments using corals collected from natural populations in Bocas del Toro, Panama, and used multilocus SNP genotypes to estimate genetic relatedness among samples. This allowed us to estimate narrow-sense heritability of variation in bleaching responses, revealing that variation in these responses was highly heritable (h2 = 0.58). This suggests substantial potential for adaptive responses to warming by natural populations of O. faveolata in this region. We further investigated the functional basis for this variation using genomic and transcriptomic approaches. We used a publicly available genetic linkage map and genome assembly to map markers associated with bleaching responses, identifying twelve markers associated with variation in bleaching responses. We also profiled gene expression in corals with contrasting bleaching phenotypes, uncovering substantial differences in transcriptional stress responses between heat-tolerant and heat-susceptible corals. Together, our findings contribute to the growing body of evidence that natural populations of corals possess genetic variation in thermal stress responses that may potentially support adaptive responses to rising ocean temperatures.
Synthesis of N-Cycloalkenylazoles

N-(1-Cycloalkenyl)pyrroles 3a,b, -pyrazoles 6a,b, and -imidazoles 9a,b were synthesized via elimination of benzotriazole or 5-phenyltetrazole from the corresponding 1-[1-(heterocycyl)cycloalkyl]benzotriazoles 2, 5, and 8 or 1-[1-(heterocycyl)cyclohexyl]-5-phenyltetrazole (12 and 14). Intermediates 2, 5, 8, 12 and 14 were obtained by cyclizations of dihaloalkanes with N-(benzotriazol-1-ylmethyl)heterocycles, 1-imidazol-1-ylmethyl-5-phenyltetrazole (11), or 1-pyrazol-1-ylmethyl-5-phenyltetrazole (13) in the presence of n-BuLi.
Resilient Infrastructure for Thriving Firms : A Review of The Evidence

This review examines the literature on the role of infrastructure in determining the productivity and competitiveness of firms. It shows that the existing evidence base is clear in concluding that reliable and high-quality infrastructure is a crucial foundation for enabling businesses to thrive. It demonstrates that the provision of electricity, transport, water, and telecommunications systems increases firm-level productivity. It also shows that providing infrastructure per se is not enough to boost productivity, unless it offers reliable service. Disruptions and irregular service have substantial adverse effects on firms, not least due to disrupted supply chains, underutilization of production capacity, and costly adaptation measures.
Three polymorphs (α, β, and δ) of d-mannitol at 100 K

In the monoclinic delta polymorph of D-mannitol, C(6)H(14)O(6), both the molecule and the packing have approximate twofold rotational symmetry. The P2(1) structure thus approximates space group C222(1), and the alpha' polymorph, previously reported in that space group, is almost certainly identical to the delta polymorph. However, torsion angles along the main backbone of the molecule deviate from twofold symmetry by as much as 7.4 (3) degrees and the hydrogen-bonding pattern does not conform to the higher symmetry. The alpha polymorph reported here is identical to the previously reported kappa polymorph, and the low-temperature structure of the beta polymorph agrees well with previously reported room-temperature determinations. The range of C-O bond lengths over the three polymorphs is 1.428 (2)-1.437 (4) A, and the range of C-C distances is 1.515 (4)-1.5406 (19) A. The delta polymorph has the highest density of the three, both at room temperature and at 100 K.
Formation and ring-opening of 8-oxabicyclo[3.2.1]oct-6-en-3-ones from (4+3)-cycloaddition of furan and chlorocyclopentanone derivatives

Abstract During the course of studying an ene reaction of an oxyallylic cation derived from a 2-chlorocyclopentanone system, the reactive species was found to undergo quite facile (4+3)-cycloadditions with furans. Moreover, the (4+3)-cycloadducts derived from furans were prone to ring-opening, which resulted in the formation of 2-furanyl cyclopentanones in excellent yields. An acid-catalyzed mechanism was proposed for the ring-opening process. Several examples of both reactions were identified.
A new enantiospecific route to the pseudopterosins

Abstract An efficient and highly stereoselective process is described for the preparation of ketones 3 and 4 , key compounds for the total synthesis of pseudopterosins such as 1 and 2 .
DNA Repair Gene Expression Adjusted by the PCNA Metagene Predicts Survival in Multiple Cancers

Removal of the proliferation component of gene expression by proliferating cell nuclear antigen (PCNA) adjustment via statistical methods has been addressed in numerous survival prediction studies for breast cancer and all cancers in the Cancer Genome Atlas (TCGA). These studies indicate that the removal of proliferation in gene expression by PCNA adjustment removes the statistical significance for predicting overall survival (OS) when gene selection is performed on a genome-wide basis. Since cancers become addicted to DNA repair as a result of forced cellular replication, increased oxidation, and repair deficiencies from oncogenic loss or genetic polymorphisms, we hypothesized that PCNA adjustment of DNA repair gene expression does not remove statistical significance for OS prediction. The rationale and importance of this translational hypothesis is that new lists of repair genes which are predictive of OS can be identified to establish new targets for inhibition therapy. A candidate gene approach was employed using TCGA RNA-Seq data for 121 DNA repair genes in 8 molecular pathways to predict OS for 18 cancers. Statistical randomization test results indicate that after PCNA adjustment, OS could be predicted significantly by sets of DNA repair genes for 61% (11/18) of the cancers. These findings suggest that removal of the proliferation signal in expression by PCNA adjustment does not remove statistical significance for predicting OS. In conclusion, it is likely that previous studies on PCNA adjustment and survival were biased because genes identified through a genome-wide approach are strongly co-regulated by proliferation.
Temporal Trends of Sex Differences in Transient Ischemic Attack Incidence Within a Population.

Abstract Objective: Previously we reported that ischemic stroke incidence is declining over time for men but not women. We sought to describe temporal trends of sex differences in incidence of transient ischemic attack (TIA) within the same large, biracial population. Methods: Among the population of 1.3 million in the Greater Cincinnati Northern Kentucky Stroke Study (GCNKSS) region, TIAs among area residents (≥20 years old) were identified at all local hospitals. Out of hospital cases were ascertained using a sampling scheme. First-ever cases and first within each study period for a patient was included in incidence rates. All cases were physician-adjudicated. Incidence rates (during July 93-June 94 and calendar years 1999, 2005, and 2010) were calculated using the age-, race-, and sex-specific number of TIAs divided by the GCNKSS population in that group; rates were standardized to the 2010 U.S. population. t Tests with Bonferroni correction were used to compare rates over time. Results: There were a total of 4746 TIA events; 53% were female, and 12% were black. In males, incidence decreased from 153 (95% confidence interval [CI] 139-167) per 100,000 in 1993/4 to 117 (95% CI 107-128) in 2010 (P .05). Conclusions: Within the GCNKSS population, TIA incidence decreased significantly over time in males but not females, data which parallels trends in ischemic stroke in the GCNKSS over the same time period. Future research is needed to determine if these sex differences in incidence over time continue past 2010.
Corporate Sector Vulnerabilities in Ireland

The paper uses both macro- and micro-level data to assess how has the financial health of the Irish non-financial corporate (NFC) sector changed in the post financial crisis period. The analysis suggests that vulnerabilities have generally declined in recent years, but the NFC sector and especially smaller domestic firms remain vulnerable. A sensitivity analysis indicates that a non-extreme shock, which comprises a decline in profitability and an increase in interest rates, is likely to push many firms into a vulnerable state and that the share of firms with interest cover ratio of lower than one would triple to nearly fifty percent, largely reflecting the deterioration in the financial health of small firms. In such a scenario, the share of risky debt would increase to the level observed during the financial crisis, resulting in a significant increase in new corporate defaults.
Validation of a low-cost, carbon dioxide-based cryoablation system for percutaneous tumor ablation

Breast cancer rates are rising in low- and middle-income countries (LMICs), yet there is a lack of accessible and cost-effective treatment. As a result, the cancer burden and death rates are highest in LMICs. In an effort to meet this need, our work presents the design and feasibility of a low-cost cryoablation system using widely-available carbon dioxide as the only consumable. This system uses an 8-gauge outer-diameter needle and Joule-Thomson expansion to percutaneously necrose tissue with cryoablation. Bench top experiments characterized temperature dynamics in ultrasound gel demonstrated that isotherms greater than 2 cm were formed. Further, this system was applied to mammary tumors in an in vivo rat model and necrosis was verified by histopathology. Finally, freezing capacity under a large heat load was assessed with an in vivo porcine study, where volumes of necrosis greater than 1.5 cm in diameter confirmed by histopathology were induced in a highly perfused liver after two 7-minute freeze cycles. These results demonstrate the feasibility of a carbon-dioxide based cryoablation system for improving solid tumor treatment options in resource-constrained environments.
Unwanted and wanted sound

Active noise control is becoming a practical way to quieten our environment, with, for example, interior trim panels in aircraft cabins that can react to and cancel out noise. Other automated acoustic systems can act as sound mirrors in the ocean, to probe currents and allow underwater communication; imitate the directional quality of different instruments; and even tell the difference between an oboe and a saxophone.
Dense packings with nonparallel cylinders

A \emph{cylinder packing} is a family of congruent infinite circular cylinders with mutually disjoint interiors in $3$-dimensional Euclidean space. The \emph{local density} of a cylinder packing is the ratio between the volume occupied by the cylinders within a given sphere and the volume of the entire sphere. The \emph{global density} of the cylinder packing is obtained by letting the radius of the sphere approach infinity. It is known that the greatest global density is obtained when all cylinders are parallel to each other and each cylinder is surrounded by exactly six others. In this case, the global density of the cylinder packing equals $\pi/\sqrt{12}= 0.90689\ldots$. The question is how large a density can a cylinder packing have if one imposes the restriction that \emph{no two cylinders are parallel}. In this paper we prove two results. First, we show that there exist cylinder packings with no two cylinders parallel to each other, whose local density is arbitrarily close to the local density of a packing with parallel cylinders. Second, we construct a cylinder packing with no two cylinders parallel to each other whose global density is $1/2$. This improves the results of K. Kuperberg, C. Graf and P. Paukowisch.
Snorkel: rapid training data creation with weak supervision

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8X faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8X speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets.
A 2-category of chronological cobordisms and odd Khovanov homology

We create a framework for odd Khovanov homology in the spirit of Bar-Natan's construction for the ordinary Khovanov homology. Namely, we express the cube of resolutions of a link diagram as a diagram in a certain 2-category of chronological cobordisms and show that it is 2-commutative: the composition of 2-morphisms along any 3-dimensional subcube is trivial. This allows us to create a chain complex, whose homotopy type modulo certain relations is a link invariant. Both the original and the odd Khovanov homology can be recovered from this construction by applying certain strict 2-functors. We describe other possible choices of functors, including the one that covers both homology theories and another generalizing dotted cobordisms to the odd setting. Our construction works as well for tangles and is conjectured to be functorial up to sign with respect to tangle cobordisms.
Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks

Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens. SSNet was designed based on the framework of image-to-image conditional generative adversarial networks (cGAN). Specifically, the Global Convolutional Network (GCN) was used as the generator to reduce false negatives, while the Markovian discriminator (PatchGAN) was used to alleviate false positives. A cohort of clinically acquired 3D MRI scans (both T1 weighted and T2 weighted) from patients with splenomegaly were used to train and test the networks. The experimental results demonstrated that a mean Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet on independently tested MRI volumes of patients with splenomegaly.
The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes

We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres. Evaluating the Hessian, H, of the loss function on these hypersurfaces, we observe 1) an unusual excess of the number of positive eigenvalues of H, and 2) a large value of Tr(H)/||H|| at a well defined range of configuration space radii, corresponding to a thick, hollow, spherical shell we refer to as the Goldilocks zone. We observe this effect for fully-connected neural networks over a range of network widths and depths on MNIST and CIFAR-10 datasets with the ReLU and tanh non-linearities, and a similar effect for convolutional networks. Using our observations, we demonstrate a close connection between the Goldilocks zone, measures of local convexity/prevalence of positive curvature, and the suitability of a network initialization. We show that the high and stable accuracy reached when optimizing on random, low-dimensional hypersurfaces is directly related to the overlap between the hypersurface and the Goldilocks zone, and as a corollary demonstrate that the notion of intrinsic dimension is initialization-dependent. We note that common initialization techniques initialize neural networks in this particular region of unusually high convexity/prevalence of positive curvature, and offer a geometric intuition for their success. Furthermore, we demonstrate that initializing a neural network at a number of points and selecting for high measures of local convexity such as Tr(H)/||H||, number of positive eigenvalues of H, or low initial loss, leads to statistically significantly faster training on MNIST. Based on our observations, we hypothesize that the Goldilocks zone contains an unusually high density of suitable initialization configurations.
A note on applications of the d-invariant and Donaldson's theorem

This paper contains two remarks about the application of the d-invariant in Heegaard Floer homology and Donaldson's diagonalization theorem to knot theory. The first is the equivalence of two obstructions they give to a 2-bridge knot being smoothly slice. The second carries out a suggestion by Stefan Friedl to replace the use of Heegaard Floer homology by Donaldson's theorem in the proof of the main result of [J. E. Greene, Lattices, graphs, and Conway mutation, Invent. Math. 192(3) (2013) 717–750] concerning Conway mutation of alternating links.
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models

Dialog response selection is an important step towards natural response generation in conversational agents. Existing work on neural conversational models mainly focuses on offline supervised learning using a large set of context-response pairs. In this paper, we focus on online learning of response selection in retrieval-based dialog systems. We propose a contextual multi-armed bandit model with a nonlinear reward function that uses distributed representation of text for online response selection. A bidirectional LSTM is used to produce the distributed representations of dialog context and responses, which serve as the input to a contextual bandit. In learning the bandit, we propose a customized Thompson sampling method that is applied to a polynomial feature space in approximating the reward. Experimental results on the Ubuntu Dialogue Corpus demonstrate significant performance gains of the proposed method over conventional linear contextual bandits. Moreover, we report encouraging response selection performance of the proposed neural bandit model using the Recall@k metric for a small set of online training samples.
The Splitting of Reductions of an Abelian Variety

Consider an absolutely simple abelian variety A defined over a number field K. For most places v of K, we study how the reduction A_v of A modulo v splits up to isogeny. Assuming the Mumford-Tate conjecture for A and possibly increasing K, we will show that A_v is isogenous to the m-th power of an absolutely simple abelian variety for all places v of K away from a set of density 0, where m is an integer depending only on the endomorphism ring End(A_Kbar). This proves many cases, and supplies justification, for a conjecture of Murty and Patankar. Under the same assumptions, we will also describe the Galois extension of Q generated by the Weil numbers of A_v for most v.
Critical Points of Random Polynomials and Characteristic Polynomials of Random Matrices

Let $p_n$ be the characteristic polynomial of an $n \times n$ random matrix drawn from one of the compact classical matrix groups. We show that the critical points of $p_n$ converge to the uniform distribution on the unit circle as $n$ tends to infinity. More generally, we show the same limit for a class of random polynomials whose roots lie on the unit circle. Our results extend the work of Pemantle-Rivin and Kabluchko to the setting where the roots are neither independent nor identically distributed.
Noncommutative hyperbolic geometry on the unit ball of B (H)n

In this paper we introduce a hyperbolic (Poincare–Bergman type) distance δ on the noncommutative open ball [B(H)n]1:={(X1,…,Xn)∈B(H)n:‖X1X1∗+⋯+XnXn∗‖1/2<1}, where B(H) is the algebra of all bounded linear operators on a Hilbert space H. It is proved that δ is invariant under the action of the free holomorphic automorphism group of [B(H)n]1, i.e., δ(Ψ(X),Ψ(Y))=δ(X,Y),X,Y∈[B(H)n]1, for all Ψ∈Aut([B(H)n]1). Moreover, we show that the δ-topology and the usual operator norm topology coincide on [B(H)n]1. While the open ball [B(H)n]1 is not a complete metric space with respect to the operator norm topology, we prove that [B(H)n]1 is a complete metric space with respect to the hyperbolic metric δ. We obtain an explicit formula for δ in terms of the reconstruction operator RX:=X1∗⊗R1+⋯+Xn∗⊗Rn,X:=(X1,…,Xn)∈[B(H)n]1, associated with the right creation operators R1,…,Rn on the full Fock space with n generators. In the particular case when H=C, we show that the hyperbolic distance δ coincides with the Poincare–Bergman distance on the open unit ball Bn:={z=(z1,…,zn)∈Cn:‖z‖2<1}. We obtain a Schwarz–Pick lemma for free holomorphic functions on [B(H)n]1 with respect to the hyperbolic metric, i.e., if F:=(F1,…,Fm) is a contractive (‖F‖∞⩽1) free holomorphic function, then δ(F(X),F(Y))⩽δ(X,Y),X,Y∈[B(H)n]1. As consequences, we show that the Caratheodory and the Kobayashi distances, with respect to δ, coincide with δ on [B(H)n]1. The results of this paper are presented in the more general context of Harnack parts of the closed ball [B(H)n]1−, which are noncommutative analogues of the Gleason parts of the Gelfand spectrum of a function algebra.
Controlling Mn depth profiles in GaMnAs during high-temperature molecular beam epitaxial growth

Abstract Mn-doped GaAs thin films were grown at a high substrate temperature of 580 °C. During the growth process the Mn cell temperature was ramped at different rates, resulting in a variety of different Mn concentration depth profile slopes, as measured by dynamic secondary ion mass spectrometry (SIMS). Results show that controlling the Mn deposition rate via temperature during molecular beam epitaxy (MBE) growth can mitigate the effect of Mn atoms diffusing toward the surface. Most importantly, the slope of the Mn concentration as a function of depth inside the sample can be tuned from negative to positive.
Coarse quotients by group actions and the maximal Roe algebra

For a finitely generated group G acting on a metric space X, Roe defined the warped space XG, which one can view as a kind of large scale quotient of X by the action of G. In this paper, we general...
Nearness to Local Subspace Algorithm for Subspace and Motion Segmentation

This letter presents a clustering algorithm for high dimensional data that comes from a union of lower dimensional subspaces of equal and known dimensions. The algorithm estimates a local subspace for each data point, and computes the distances between the local subspaces and the points to convert the problem to a one-dimensional data clustering problem. The algorithm is reliable in the presence of noise, and applied to the Hopkins 155 Dataset, it generates the best results to date for motion segmentation. The two motion, three motion, and overall segmentation rates for the video sequences are 99.43%, 98.69%, and 99.24%, respectively.
Max Consensus in Sensor Networks: Non-Linear Bounded Transmission and Additive Noise

A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence of communication noise, however, the maximum estimate will incorrectly drift and the estimate at each sensor will diverge. As a result, a soft-max approximation together with a non-linear consensus algorithm is introduced herein. A design parameter controls the tradeoff between the soft-max error and convergence speed. An analysis of this tradeoff gives a guideline toward how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded transmit function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems

We study the problem of adaptive control of a high dimensional linear quadratic (LQ) system. Previous work established the asymptotic convergence to an optimal controller for various adaptive control schemes. More recently, for the average cost LQ problem, a regret bound of O(√T) was shown, apart form logarithmic factors. However, this bound scales exponentially with p, the dimension of the state space. In this work we consider the case where the matrices describing the dynamic of the LQ system are sparse and their dimensions are large. We present an adaptive control scheme that achieves a regret bound of O(p√T), apart from logarithmic factors. In particular, our algorithm has an average cost of (1 + e) times the optimum cost after T = polylog(p)O(1/e2). This is in comparison to previous work on the dense dynamics where the algorithm requires time that scales exponentially with dimension in order to achieve regret of e times the optimal cost. We believe that our result has prominent applications in the emerging area of computational advertising, in particular targeted online advertising and advertising in social networks.
A note on positive-definite, symplectic four-manifolds

We prove that a positive definite smooth four-manifold with $b_2^+ \geq 2$ and having either no 1-handles or no 3-handles cannot admit a symplectic structure.
Tensor network non-zero testing

Tensor networks are an important tool in condensed matter physics. In this paper, we study the task of tensor network non-zero testing (TNZ): Given a tensor network T, does T represent a non-zero vector? We show that TNZ is not in the Polynomial-Time Hierarchy unless the hierarchy collapses. We next show (among other results) that the special cases of TNZ on non-negative and injective tensor networks are in NP. Using this, we make a simple observation: The commuting variant of the MA-complete stoquastic k-SAT problem on D-dimensional qudits is in NP for k ∈ O(log n) and D ∈ O(1). This reveals the first class of quantum Hamiltonians whose commuting variant is known to be in NP for all (1) logarithmic k, (2) constant D, and (3) for arbitrary interaction graphs.
Lifting involutions in a Weyl group to the torus normalizer

Let N be the normalizer of a maximal torus T in a split reductive group over F_q and let w be an involution in the Weyl group N/T. We construct explicitly a lifting n of w in N such that the image of n under the Frobenius map is equal to the inverse of n.
A transport-based multifidelity preconditioner for Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) sampling of posterior distributions arising in Bayesian inverse problems is challenging when evaluations of the forward model are computationally expensive. Replacing the forward model with a low-cost, low-fidelity model often significantly reduces computational cost; however, employing a low-fidelity model alone means that the stationary distribution of the MCMC chain is the posterior distribution corresponding to the low-fidelity model, rather than the original posterior distribution corresponding to the high-fidelity model. We propose a multifidelity approach that combines, rather than replaces, the high-fidelity model with a low-fidelity model. First, the low-fidelity model is used to construct a transport map that deterministically couples a reference Gaussian distribution with an approximation of the low-fidelity posterior. Then, the high-fidelity posterior distribution is explored using a non-Gaussian proposal distribution derived from the transport map. This multifidelity “preconditioned” MCMC approach seeks efficient sampling via a proposal that is explicitly tailored to the posterior at hand and that is constructed efficiently with the low-fidelity model. By relying on the low-fidelity model only to construct the proposal distribution, our approach guarantees that the stationary distribution of the MCMC chain is the high-fidelity posterior. In our numerical examples, our multifidelity approach achieves significant speedups compared with single-fidelity MCMC sampling methods.
On non-commutative rank and tensor rank

We study the relationship between the commutative and the non-commutative rank of a linear matrix. We give examples that show that the ratio of the two ranks comes arbitrarily close to 2. Such exam...
Geometrostatic Manifolds of Small ADM Mass

We bound the locations of outermost minimal surfaces in geometrostatic manifolds whose ADM mass is small relative to the separation between the black holes and prove the Intrinsic Flat Stability of the Positive Mass Theorem in this setting.
Universal low-rank matrix recovery from Pauli measurements

We study the problem of reconstructing an unknown matrix M of rank r and dimension d using O(rd poly log d) Pauli measurements. This has applications in quantum state tomography, and is a non-commutative analogue of a well-known problem in compressed sensing: recovering a sparse vector from a few of its Fourier coefficients. We show that almost all sets of O(rd log6 d) Pauli measurements satisfy the rank-r restricted isometry property (RIP). This implies that M can be recovered from a fixed ("universal") set of Pauli measurements, using nuclear-norm minimization (e.g., the matrix Lasso), with nearly-optimal bounds on the error. A similar result holds for any class of measurements that use an orthonormal operator basis whose elements have small operator norm. Our proof uses Dudley's inequality for Gaussian processes, together with bounds on covering numbers obtained via entropy duality.
Sequential document representations and simplicial curves

The popular bag of words assumption represents a document as a histogram of word occurrences. While computationally efficient, such a representation is unable to maintain any sequential information. We present a continuous and differentiable sequential document representation that goes beyond the bag of words assumption, and yet is efficient and effective. This representation employs smooth curves in the multinomial simplex to account for sequential information. In contrast to n-grams the new representation is able to robustly model long rage sequential trends in the document. We discuss the representation and its geometric properties and demonstrate its applicability for the task of text classification.
Scalar-on-image regression via the soft-thresholded Gaussian process

This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational aigorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially-varying regression coefficient functions. In addition, under some mild regularity conditions the soft-thresholded Gaussian proess prior leads to the posterior consistency for parameter estimation and variable selection for scalar-on-image regression, even when the number of predictors is larger than the sample size. The proposed method is compared to alternatives via simulation and applied to an electroen-cephalography study of alcoholism.
Sparse sampling of water density fluctuations near liquid-vapor coexistence

The free energetics of water density fluctuations in bulk water, at interfaces, and in hydrophobic confinement inform the hydration of hydrophobic solutes as well as their interactions and assembly...
Irreducibility of Random Polynomials

ABSTRACTWe study the probability that a random polynomial with integer coefficients is reducible when factored over the rational numbers. Using computer-generated data, we investigate a number of different models, including both monic and non-monic polynomials. Our data support conjectures made by Odlyzko and Poonen and by Konyagin, and we formulate a universality heuristic and new conjectures that connect their work with Hilbert’s Irreducibility Theorem and work of van der Waerden. The data indicate that the probability that a random polynomial is reducible divided by the probability that there is a linear factor appears to approach a constant and, in the large-degree limit, this constant appears to approach 1. In cases where the model makes it impossible for the random polynomial to have a linear factor, the probability of reducibility appears to be close to the probability of having a nonlinear, low-degree factor. We also study characteristic polynomials of random matrices with + 1 and − 1 entries.
Riesz Wavelets, Tiling and Spectral Sets in LCA Groups

Let G be a locally compact abelian (LCA) group equipped with a Haar measure. A collection of measurable subsets $$\{\Omega _i\}_{i=1}^m$$ in $$\hat{G}$$ is called a Riesz wavelet collection if there are countable subsets $$A\subset \mathcal {A}ut(G)$$ and $$\Lambda _i\subset G$$ such that for $$\hat{\psi }_i:= 1_{\Omega _i}$$ , the family $$\begin{aligned} \mathcal {W}:=\cup _{i=1}^m\{\Delta (a)^{1/2} \psi _i(a(x)-\lambda ): \ \lambda \in \Lambda _i, a\in A\} \end{aligned}$$ is a Riesz basis for $$L^2(G)$$ . In this paper we show that if $$\Omega _i$$ ’s are Riesz spectral sets and $$\Omega =\cup _i \Omega _i$$ is a multiplicative tiling set for $$\hat{G}$$ , then $$\mathcal {W}$$ is a Riesz basis for $$L^2(G)$$ . The converse also holds if the unit element $$e\in G$$ belongs to all $$\Lambda _i$$ . As a result, if $$\Omega _i$$ ’s multi-tile $$\hat{G}$$ additively by lattice and $$\Omega $$ is a multiplicative tiling, then $$\mathcal {W}$$ is a Riesz basis for $$L^2(G)$$ . When $$m=1$$ , we show that the multiplicative tiling property of $$\Omega $$ is equivalent to the Riesz spectral property of $$\hat{\alpha }(\Omega )$$ , $$\alpha \in A$$ , provided that $$\mathcal {W}$$ is a Riesz basis.
Structure Learning of ${H}$-colorings.

We study the structure learning problem for $H$-colorings, an important class of Markov random fields that capture key combinatorial structures on graphs, including proper colorings and independent sets, as well as spin systems from statistical physics. The learning problem is as follows: for a fixed (and known) constraint graph $H$ with $q$ colors and an unknown graph $G=(V,E)$ with $n$ vertices, given uniformly random $H$-colorings of $G$, how many samples are required to learn the edges of the unknown graph $G$? We give a characterization of $H$ for which the problem is identifiable for every $G$, i.e., we can learn $G$ with an infinite number of samples. We also show that there are identifiable constraint graphs for which one cannot hope to learn every graph $G$ efficiently. We focus particular attention on the case of proper vertex $q$-colorings of graphs of maximum degree $d$ where intriguing connections to statistical physics phase transitions appear. We prove that in the tree uniqueness region (when $q>d$) the problem is identifiable and we can learn $G$ in ${\rm poly}(d,q) \times O(n^2\log{n})$ time. In contrast for soft-constraint systems, such as the Ising model, the best possible running time is exponential in $d$. In the tree non-uniqueness region (when $q\leq d$) we prove that the problem is not identifiable and thus $G$ cannot be learned. Moreover, when $q<d-\sqrt{d} + \Theta(1)$ we prove that even learning an equivalent graph (any graph with the same set of $H$-colorings) is computationally hard---sample complexity is exponential in $n$ in the worst case. We further explore the connection between the efficiency/hardness of the structure learning problem and the uniqueness/non-uniqueness phase transition for general $H$-colorings and prove that under the well-known Dobrushin uniqueness condition, we can learn $G$ in ${\rm poly}(d,q)\times O(n^2\log{n})$ time.
Generic Behavior of a Measure Preserving Transformation

Del Junco–Lemanczyk [Generic spectral properties of measure-preserving maps and applications. Proc. Amer. Math. Soc., 115 (3) (1992)] showed that a generic measure-preserving transformation satisfies certain orthogonality conditions. More precisely, there is a dense . As a corollary, we show that the same orthogonality conditions as in the result by Del Junco–Lemanczyk hold for a generic unitary operator on a separable infinite-dimensional Hilbert space.
On a Conjecture of Braverman and Kazhdan

In this paper a proof of Conjecture 9.12 of Braverman and Kazhdan in their article "gamma-functions of representations and lifting" on the acyclicity of their l-adic gamma-sheaves over certain affine spaces is given for GL(n).
Local and global analysis of nodal sets

This is a survey for the JDG 50th Anniversary conference of recent results on nodal sets of eigenfunctions of the Laplacian on a compact Riemannian manifold. In part the techniques are `local', i.e. only assuming eigenfunctions are defined on small balls, and in part the techniques are `global', i.e. exploiting dynamics of the geodesic flow. The local part begins with a review of doubling indices and freqeuency functions as local measures of fast or slow growth of eigenfunctions. The pattern of boxes with maximal doubling indices plays a central role in the results of Logunov-Malinnokova, giving upper and lower bounds for hypersurface measures of nodal sets in the setting of general $C^{\infty}$ metrics. The proofs of both their polynomial upper bound and sharp lower bound are sketched. The survey continues with a global proof of the sharp upper bound for real analytic metrics (originally proved by Donnelly-Fefferman with local arguments), using analytic continuation to Grauert tubes. Then it reviews results of Toth-Zelditch giving sharp upper bounds on Hausdorff measures of intersections of nodal sets with real analytic submanifolds in the real analytic setting. Last, it goes over lower bounds of Jung-Zelditch on numbers of nodal domains in the case of $C^{\infty}$ surfaces of non-positive curvature and concave boundary or on negatively curved `real' Riemann surfaces, which are based on ergodic properties of the geodesic flow and eigenfunctions, and on estimates of restrictions of eigenfunctions to hypersurfaces. The last section details recent results on restrictions.
A Distributional Perspective on Reinforcement Learning

In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established body of literature studying the value distribution, thus far it has always been used for a specific purpose such as implementing risk-aware behaviour. We begin with theoretical results in both the policy evaluation and control settings, exposing a significant distributional instability in the latter. We then use the distributional perspective to design a new algorithm which applies Bellman's equation to the learning of approximate value distributions. We evaluate our algorithm using the suite of games from the Arcade Learning Environment. We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning. Finally, we combine theoretical and empirical evidence to highlight the ways in which the value distribution impacts learning in the approximate setting.
Combinatorics of the tropical Torelli map

This paper is a combinatorial and computational study of the moduli space of tropical curves of genus g, the moduli space of principally polarized tropical abelian varieties, and the tropical Torelli map. These objects were introduced recently by Brannetti, Melo, and Viviani. Here, we give a new definition of the category of stacky fans, of which the aforementioned moduli spaces are objects and the Torelli map is a morphism. We compute the poset of cells of tropical M_g and of the tropical Schottky locus for genus at most 5. We show that tropical A_g is Hausdorff, and we also construct a finite-index cover for A_3 which satisfies a tropical-type balancing condition. Many different combinatorial objects, including regular matroids, positive semidefinite forms, and metric graphs, play a role.
On the Spanning Trees of the Hypercube and other Products of Graphs

We give two combinatorial proofs of a product formula for the number of spanning trees of the $n$-dimensional hypercube. The first proof is based on the assertion that if one chooses a uniformly random rooted spanning tree of the hypercube and orient each edge from parent to child, then the parallel edges of the hypercube get orientations which are independent of one another. This independence property actually holds in a more general context and has intriguing consequences. The second proof uses some "killing involutions'' in order to identify the factors in the product formula. It leads to an enumerative formula for the spanning trees of the $n$-dimensional hypercube augmented with diagonals edges, counted according to the number of edges of each type. We also discuss more general formulas, obtained using a matrix-tree approach, for the number of spanning trees of the Cartesian product of complete graphs.
Nuclear physics in the cosmos

We observe photons and neutrinos from stars. Based on these observations, complemented by measurements of cosmic rays energies and composition, we have been able to constrain several models for the Big Bang and for stellar evolution. But that is not enough. We also need to help this effort with laboratory experiments. We are still far from being able to reproduce stellar environments in a terrestrial laboratory. But in many cases we can obtain accurate nuclear reaction rates needed for modeling primordial nucleosynthesis and hydrostatic burning in stars. The relevant reactions are difficult to measure directly in the laboratory at the small astrophysical energies. In recent years indirect reaction methods have been developed and applied to extract low-energy astrophysical S-factors. These methods require a combination of new experimental techniques and theoretical efforts, which are the subject of this review.
Categoricity properties for computable algebraic fields

We examine categoricity issues for computable algebraic fields. Such fields behave nicely for computable dimension: we show that they cannot have finite computable dimension greater than 1. However, they behave less nicely with regard to relative computable categoricity: we give a structural criterion for relative computable categoricity of these fields, and use it to construct a field that is computably categorical, but not relatively computably categorical. Finally, we show that computable categoricity for this class of fields is � 0 -complete.
Jointly Embedding Entities and Text with Distant Supervision.

Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.