cssi/SciSciGPT-SciSciNet huggingface dataset:
Synthesis of N-arylindazoles and benzimidazoles from a common intermediate

A variety of N-aryl-1H-indazoles and benzimidazoles were synthesized from common arylamino oximes in good to excellent yields. The product selectivity depends upon the base used in the reaction, as triethylamine promoted the formation of benzimidazoles, whereas 2-aminopyridine promoted the formation of N-arylindazoles. This method is valuable to the synthetic community because both indazoles and benzimidazoles are prevalent in pharmaceuticals.
Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification

The Inductive Logic Programming community has considered proof-complexity and model-complexity, but, until recently, size-complexity has received little attention. Recently a challenge was issued "to the international computing community" to discover low size-complexity Prolog programs for classifying trains. The challenge was based on a problem first proposed by Ryszard Michalski, 20 years ago. We interpreted the challenge as a problem in cost-sensitive classification and we applied a recently developed cost-sensitive classifier to the competition. Our algorithm was relatively successful (we won a prize). This paper presents our algorithm and analyzes the results of the competition.
The Chemistry of Life's Origin: A Carbonaceous Meteorite Perspective

Carbonaceous chondrite meteorites are primitive asteroidal fragments that contain organic carbon and offer a glimpse of the abiotic chemical processes that preceded the onset of life. Their organic material displays structures as diverse as kerogen-like macromolecules and simpler soluble compounds that range from polar amino acids and polyols to nonpolar hydrocarbons. Several of these compounds have identical counterparts in terrestrial biomolecules and some of the amino acids show a unique l-asymmetry, suggesting their possible contribution to terrestrial molecular evolution and the origin of biological homochirality.
Recent studies of persistent carbodications

Publisher Summary This chapter presents various aspects of persistent carbodications. Persistent carbodications, bearing two positive charges in the same molecule, show unusual behavior in their reactivity and spectral characteristics. Carbodications show uniquely high reactivities in electrophilic substitution reactions, and thus superelectrophilic activation of carbomonocations in suitable cases have direct synthetic applications. The prototypical pentacoordinated carbocation, the parent nonclassical carbocation, CH+5, 1, has been extensively studied in the gas phase, as well as by theoretical calculations. The electronic structures and energies of superelectrophilic dications derived from protonation of methyl and dimethylmethyleniminium ions were also calculated at the ab initio level and it was found that the ionization of 1,6-dihalodiamantane (71) in SbF5/SO2 CIF or other superacids under various conditions gave only the monocation monodonor–acceptor complex and the 1,6-dication, 73, was not observed. The formation of the dication involves protonation of one of the sulfur atoms forming the sulfonium ion and subsequent elimination of H2S. A single-crystal X-ray diffraction of the dication showed that the two tropylium rings are twisted with each other by an angle of 44°, and the tropylium rings are slightly bent into boat conformations.
Total Synthesis of (±)-Dactylol and Related Studies

The marine sesquiterpene (±)-dactylol was prepared from 2,5-dimethyl-7-(3-methyl-4-pentenyl)tropone in six steps. Salient features of the synthesis include: (1) a stereo- and regioselective intramolecular tropone-alkene [6π+2π] photocyclization to furnish the dactylol carbocyclic skeleton, (2) a regioselective Baeyer-Villiger oxidation of a bisneopentyl ketone, and (3) a chemoselective 1,4-reduction of a cyclocta-1,3-diene moiety
Short synthesis of 4-aryl-3-pyrrolin-2-ones

Abstract A three step, convergent synthesis of 4-aryl-3-pyrrolin-2-ones from a tetramic acid has been developed. The key transformation utilized a Suzuki–Miyaura cross-coupling reaction between a 4-tosyloxy-3-pyrrolin-2-one and an arylboronic acid. This work also provides access to 4-arylpyrrolidin-2-ones, cyclic analogs of γ-aminobutyric acid (GABA). Hydrogenation of 4-(4′-chlorophenyl)-3-pyrrolin-2-one proceeded smoothly to give baclofen lactam.
SELECTIVE OXYGEN TRANSFERS WITH IRON(III) PORPHYRIN NITRITE

The reaction of octaethylporphyrin iron(III) chloride with potassium crown ether (18-crown-6) nitrite in N-methylpyrrolidone−1% acetic acid under argon generates the iron(III) nitrite salt (PFeNO2). The latter is a unique and selective oxygen atom transfer reagent. The reaction of a broad range of substrates (S) proceeds quantitatively to yield the oxidized substrate and the iron(II) porphyrin−nitrosyl adduct: PFeNO2 + S → PFeNO + SO. Diatomic molecules to which oxygen is directly transferred from PFeNO2 are NO, CO, and O2. The conversion NO to NO2 is shown via 15NO2- labeling experiments to proceed exclusively by the O atom transfer process. The ozone, generated from dioxygen, was trapped with nitrite ion and the two olefins 2-methyl-2 butene and 2,3-dimethyl-2 butene. These substances are inert to PFeNO2 under argon. However, in an oxygen-saturated reaction mixture, nitrite produced nitrate. The olefins, following reduction of the reaction mixture with Zn/HOAc, yielded 1 mol of acetone and acetaldehyde...
DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding

Motivation Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites. Results We describe a sequence + shape kernel that leverages DNA sequence and shape information to better understand protein-DNA binding preference and affinity. This kernel extends an existing class of k-mer based sequence kernels, based on the recently described di-mismatch kernel. Using three in vitro benchmark datasets, derived from universal protein binding microarrays (uPBMs), genomic context PBMs (gcPBMs) and SELEX-seq data, we demonstrate that incorporating DNA shape information improves our ability to predict protein-DNA binding affinity. In particular, we observe that (i) the k-spectrum + shape model performs better than the classical k-spectrum kernel, particularly for small k values; (ii) the di-mismatch kernel performs better than the k-mer kernel, for larger k; and (iii) the di-mismatch + shape kernel performs better than the di-mismatch kernel for intermediate k values. Availability and implementation The software is available at https://bitbucket.org/wenxiu/sequence-shape.git. Contact rohs@usc.edu or william-noble@uw.edu. Supplementary information Supplementary data are available at Bioinformatics online.
Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criterion for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. Encouragingly, the model’s performance is robust to slide preparation artifacts associated with frozen section preparation. The model substantially outperforms a model trained on image patches of isolated glomeruli, in terms of both accuracy and speed. The methodology overcomes the technical challenge of applying a pretrained CNN bottleneck model to whole-slide image classification. The traditional patch-based approach, while exhibiting deceptively good performance classifying isolated patches, does not translate successfully to whole-slide image segmentation in this setting. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.
Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies

We describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centromere and allele segregation patterns. Because the method uses only the genomic sequence, it alleviates the need for tetrad-specific barcodes or other genetic modifications to the strains. Using this method, strains derived from randomly arrayed spores can be efficiently grouped back into tetrads.
Dynamics of Human Gut Microbiota and Short-Chain Fatty Acids in Response to Dietary Interventions with Three Fermentable Fibers

ABSTRACT Production of short-chain fatty acids (SCFAs), especially butyrate, in the gut microbiome is required for optimal health but is frequently limited by the lack of fermentable fiber in the diet. We attempted to increase butyrate production by supplementing the diets of 174 healthy young adults for 2 weeks with resistant starch from potatoes (RPS), resistant starch from maize (RMS), inulin from chicory root, or an accessible corn starch control. RPS resulted in the greatest increase in total SCFAs, including butyrate. Although the majority of microbiomes responded to RPS with increases in the relative abundance of bifidobacteria, those that responded with an increase in Ruminococcus bromii or Clostridium chartatabidum were more likely to yield higher butyrate concentrations, especially when their microbiota were replete with populations of the butyrate-producing species Eubacterium rectale. RMS and inulin induced different changes in fecal communities, but they did not generate significant increases in fecal butyrate levels. IMPORTANCE These results reveal that not all fermentable fibers are equally capable of stimulating SCFA production, and they highlight the importance of the composition of an individual’s microbiota in determining whether or not they respond to a specific dietary supplement. In particular, R. bromii or C. chartatabidum may be required for enhanced butyrate production in response to RS. Bifidobacteria, though proficient at degrading RS and inulin, may not contribute to the butyrogenic effect of those fermentable fibers in the short term.
p62-mediated Selective Autophagy Endows Virus-Transformed Cells With Insusceptibility to DNA Damage Under Oxidative Stress

DNA damage response (DDR) and selective autophagy both can be activated by reactive oxygen/nitrogen species (ROS/RNS), and both are of paramount importance in cancer development. The selective autophagy receptor and ubiquitin (Ub) sensor p62 plays a key role in their crosstalk. ROS production has been well documented in latent infection of oncogenic viruses including Epstein-Barr Virus (EBV). However, p62-mediated selective autophagy and its interplay with DDR have not been investigated in these settings. In this study, we provide evidence that considerable levels of p62-mediated selective autophagy are spontaneously induced, and correlate with ROS-Keap1-NRF2 pathway activity, in virus-transformed cells. Inhibition of autophagy results in p62 accumulation in the nucleus, and promotes ROS-induced DNA damage and cell death, as well as downregulates the DNA repair proteins CHK1 and RAD51. In contrast, MG132-mediated proteasome inhibition, which induces rigorous autophagy, promotes p62 degradation but accumulation of the DNA repair proteins CHK1 and RAD51. However, pretreatment with an autophagy inhibitor offsets the effects of MG132 on CHK1 and RAD51 levels. These findings imply that p62 accumulation in the nucleus in response to autophagy inhibition promotes proteasome-mediated CHK1 and RAD51 protein instability. This claim is further supported by the findings that transient expression of a p62 mutant, which is constitutively localized in the nucleus, in B cell lines with low endogenous p62 levels recaptures the effects of autophagy inhibition on CHK1 and RAD51 protein stability. These results indicate that proteasomal degradation of RAD51 and CHK1 is dependent on p62 accumulation in the nucleus. However, small hairpin RNA (shRNA)-mediated p62 depletion in EBV-transformed lymphoblastic cell lines (LCLs) had no apparent effects on the protein levels of CHK1 and RAD51, likely due to the constitutive localization of p62 in the cytoplasm and incomplete knockdown is insufficient to manifest its nuclear effects on these proteins. Rather, shRNA-mediated p62 depletion in EBV-transformed LCLs results in significant increases of endogenous RNF168-γH2AX damage foci and chromatin ubiquitination, indicative of activation of RNF168-mediated DNA repair mechanisms. Our results have unveiled a pivotal role for p62-mediated selective autophagy that governs DDR in the setting of oncogenic virus latent infection, and provide a novel insight into virus-mediated oncogenesis.
Strong Negative Thermal Expansion along the O−Cu−O Linkage in CuScO2

The structure of 2H CuScO2 has been refined from single-crystal X-ray diffraction data obtained at room temperature and from neutron powder diffraction data obtained from 11 to 1206 K. Strong negative thermal expansion (−4.0 × 10-6/K) has been found for the apparent Cu−O bond length below 300 K. This is the first example of strong negative thermal expansion in a metal oxide based on an O−M−O linkage instead of a M−O−M linkage. It is now the transverse thermal motion of Cu, instead of the transverse thermal motion of oxygen, that causes the negative thermal expansion. As expected for this mechanism for negative thermal expansion, the thermal displacement parameter for Cu is very pronounced perpendicular to the linear O−Cu−O linkage.
Efficient enantioselective synthesis of functionalized tetrahydropyrans by Ru-catalyzed asymmetric ring-opening metathesis/cross-metathesis (AROM/CM).

An efficient method for enantioselective synthesis of highly functionalized pyrans (up to 98% ee) through Ru-catalyzed asymmetric ring-opening metathesis/cross-metathesis is described. Reactions are promoted by a recyclable chiral Ru−chloride or a new chiral Ru−iodide complex; the latter catalyst is less efficient but gives rise to significantly higher levels of enantioselectivity. Catalytic reactions can be performed in undistilled solvent and with a wide range of substrates, including those that contain secondary and tertiary alcohols. Representative regioselective functionalizations that highlight the utility of the catalytic method are also presented.
NEW SIMPLIFIED INHIBITORS OF EPSP SYNTHASE : THE IMPORTANCE OF RING SIZE FOR RECOGNITION AT THE SHIKIMATE 3-PHOSPHATE SITE

Abstract The simple 4-[(α-hydroxy)phosphonomethyl]-1H-pyrrole-2-carboxylate 4 is a surprisingly good S3P-analog inhibitor of EPSP synthase (competitive with S3P, Ki(app) = 14.5 ± 0.7 μM). The affinity of 4 for free enzyme is nearly comparable to that of S3P (Kd = 7 ± 1.2 μM). Compound 4 thus represents the first successful attempt to replace the highly substituted shikimate ring in S3P with a readily accessible heterocyclic scaffold.
Volume Estimates for Tubes Around Submanifolds Using Integral Curvature Bounds

We generalize an inequality of Heintze and Karcher (Ann Sci Ecole Norm Sup 11(4):451–470, 1978) for the volume of tubes around minimal submanifolds to an inequality based on integral bounds for k-Ricci curvature. Even in the case of a pointwise bound, this generalizes the classical inequality by replacing a sectional curvature bound with a k-Ricci bound. This work is motivated by the estimates of Petersen–Shteingold–Wei (Geom Funct Anal 7(6):1011–1030, 1997) for the volume of tubes around a geodesic and generalizes their result. Using similar ideas we also prove a Hessian comparison theorem for k-Ricci curvature which generalizes the usual Hessian and Laplacian comparison for distance functions from a point and give several applications.
The molecular architecture of engulfment during Bacillus subtilis sporulation

The study of bacterial cell biology is limited by difficulties in visualizing cellular structures at high spatial resolution within their native milieu. Here, we visualize Bacillus subtilis sporulation using cryo-electron tomography coupled with cryo-focused ion beam milling, allowing the reconstruction of native-state cellular sections at molecular resolution. During sporulation, an asymmetrically-positioned septum generates a larger mother cell and a smaller forespore. Subsequently, the mother cell engulfs the forespore. We show that the septal peptidoglycan is not completely degraded at the onset of engulfment. Instead, the septum is uniformly and only slightly thinned as it curves towards the mother cell. Then, the mother cell membrane migrates around the forespore in tiny finger-like projections, whose formation requires the mother cell SpoIIDMP protein complex. We propose that a limited number of SpoIIDMP complexes tether to and degrade the peptidoglycan ahead of the engulfing membrane, generating an irregular membrane front.
High-Fidelity Simulations of Afterbody Vortex Flows

A numerical study has been conducted to investigate the unsteady nature of afterbody vortices shed from an upswept region of a simple fuselage configuration. The upsweep angle considered is ϕ=28 d...
Matrix Scaling and Balancing via Box Constrained Newton's Method and Interior Point Methods

In this paper, we study matrix scaling and balancing, which are fundamental problems in scientific computing, with a long line of work on them that dates back to the 1960s. We provide algorithms for both these problems that, ignoring logarithmic factors involving the dimension of the input matrix and the size of its entries, both run in time \widetilde{O}(m\log \kappa \log^2 (1/&#x2265;ilon)) where &#x2265;ilon is the amount of error we are willing to tolerate. Here, \kappa represents the ratio between the largest and the smallest entries of the optimal scalings. This implies that our algorithms run in nearly-linear time whenever \kappa is quasi-polynomial, which includes, in particular, the case of strictly positive matrices. We complement our results by providing a separate algorithm that uses an interior-point method and runs in time \widetilde{O}(m^{3/2} \log (1/&#x2265;ilon)).In order to establish these results, we develop a new second-order optimization framework that enables us to treat both problems in a unified and principled manner. This framework identifies a certain generalization of linear system solving that we can use to efficiently minimize a broad class of functions, which we call second-order robust. We then show that in the context of the specific functions capturing matrix scaling and balancing, we can leverage and generalize the work on Laplacian system solving to make the algorithms obtained via this framework very efficient.
Deep Unsupervised Learning using Nonequilibrium Thermodynamics

A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.
On wave equations of the p-Laplacian type with supercritical nonlinearities

Abstract This article focuses on a quasilinear wave equation of p -Laplacian type: u t t − Δ p u − Δ u t = f ( u ) in a bounded domain Ω ⊂ R 3 with a sufficiently smooth boundary Γ = ∂ Ω subject to a generalized Robin boundary condition featuring boundary damping and a nonlinear source term. The operator Δ p , 2 p 3 , denotes the classical p -Laplacian. The interior and boundary terms f ( u ) , h ( u ) are sources that are allowed to have a supercritical exponent, in the sense that their associated Nemytskii operators are not locally Lipschitz from W 1 , p ( Ω ) into L 2 ( Ω ) or L 2 ( Γ ) . Under suitable assumptions on the parameters we provide a rigorous proof of existence of a local weak solution which can be extended globally in time, provided the damping terms dominate the corresponding sources in an appropriate sense. Moreover, a blow-up result is proved for solutions with negative initial total energy.
Computing dynamics of thin films via large scale GPU-based simulations

Abstract We present the results of large scale simulations of 4th order nonlinear partial differential equations of diffusion type that are typically encountered when modeling dynamics of thin fluid films on substrates. The simulations are based on the alternate direction implicit (ADI) method, with the main part of the computational work carried out in the GPU computing environment. Efficient and accurate computations allow for simulations on large computational domains in three spatial dimensions (3D) and for long computational times. We apply the methods developed to the particular problem of instabilities of thin fluid films of nanoscale thickness. The large scale of the simulations minimizes the effects of boundaries, and also allows for simulating domains of the size encountered in published experiments. As an outcome, we can analyze the development of instabilities with an unprecedented level of detail. A particular focus is on analyzing the manner in which instability develops, in particular regarding differences between spinodal and nucleation types of dewetting for linearly unstable films, as well as instabilities of metastable films. Simulations in 3D allow for consideration of some recent results that were previously obtained in the 2D geometry [28] . Some of the new results include using Fourier transforms as well as topological invariants (Betti numbers) to distinguish the outcomes of spinodal and nucleation types of instabilities, describing in precise terms the complex processes that lead to the formation of satellite drops, as well as distinguishing the shape of the evolving film front in linearly unstable and metastable regimes. We also discuss direct comparison between simulations and available experimental results for nematic liquid crystal and polymer films.
Mass angular momentum inequality for axisymmetric vacuum data with small trace

In this paper, we proved the mass angular momentum inequality[16][12][23] for axisymmetric, asymptotically flat, vacuum constraint data sets with small trace. Given an initial data set with small trace, we construct a boost evolution spacetime of the Einstein vacuum equations as [10]. Then a perturbation method is used to solve the maximal surface equation in the spacetime under certain growing condition at infinity. When the initial data set is axisymmetric, we get an axisymmetric maximal graph with the same ADM mass and angular momentum as the given data. The inequality follows from the known results[16][12][23] about the maximal graph.
When does randomness come from randomness

A result of Shen says that if F : 2 N ź 2 N is an almost-everywhere computable, measure-preserving transformation, and y ź 2 N is Martin-Lof random, then there is a Martin-Lof random x ź 2 N such that F ( x ) = y . Answering a question of Bienvenu and Porter, we show that this property holds for computable randomness, but not Schnorr randomness. These results, combined with other known results, imply that the set of Martin-Lof randoms is the largest subset of 2 N satisfying this property and also satisfying randomness conservation: if F : 2 N ź 2 N is an almost-everywhere computable, measure-preserving map, and if x ź 2 N is random, then F ( x ) is random.
Non-abelian Littlewood–Offord inequalities

Abstract In 1943, Littlewood and Offord proved the first anti-concentration result for sums of independent random variables. Their result has since then been strengthened and generalized by generations of researchers, with applications in several areas of mathematics. In this paper, we present the first non-abelian analogue of the Littlewood–Offord result, a sharp anti-concentration inequality for products of independent random variables.
New advances and theoretical insights into EDML

EDML is a recently proposed algorithm for learning MAP parameters in Bayesian networks. In this paper, we present a number of new advances and insights on the EDML algorithm. First, we provide the multivalued extension of EDML, originally proposed for Bayesian networks over binary variables. Next, we identify a simplified characterization of EDML that further implies a simple fixed-point algorithm for the convex optimization problem that underlies it. This characterization further reveals a connection between EDML and EM: a fixed point of EDML is a fixed point of EM, and vice versa. We thus identify also a new characterization of EM fixed points, but in the semantics of EDML. Finally, we propose a hybrid EDML/EM algorithm that takes advantage of the improved empirical convergence behavior of EDML, while maintaining the mono-tonic improvement property of EM.
Applicability Study of the PRIMAD Model to LIGO Gravitational Wave Search Workflows

The PRIMAD model with its six components (i.e., Platform, Research Objective, Implementation, Methods, Actors, and Data) provides an abstract taxonomy to represent computational experiments and promote reproducibility by design. In this paper, we employ a post-hoc assessment of the model applicability to a set of Laser Interferometer Gravitational-Wave Observatory (LIGO) workflows from literature sources (i.e., published papers). Our work outlines potential advantages and limitations of the model in terms of its levels of abstraction and means of application.
Green-Tao theorem in function fields

We adapt the proof of the Green-Tao theorem on arithmetic progressions in primes to the setting of polynomials over a finite field, to show that for every $k$, the irreducible polynomials in $\mathbf{F}_q[t]$ contain configurations of the form $\{f+ Pg : \d(P)<k \}, g \neq 0$.
ISA mapper: a compute and hardware agnostic deep learning compiler

Domain specific accelerators present new challenges for code generation onto novel instruction sets, communication fabrics, and memory architectures. We introduce a shared intermediate representation to describe both deep learning programs and hardware capabilities, then formulate and apply instruction mapping to determine how a computation can be performed on a hardware system. Our scheduler chooses a specific mapping and determines data movement and computation order. With this system, we demonstrate automated extraction of matrix multiplication kernels from recent deep learning operations. We demonstrate 2--5X better performance on GEMM and GRU execution versus state-of-the-art on new hardware and up to 85% of state-of-the-art performance on existing hardware.
Octopus: A Cooperative Hierarchical Caching Strategy for Cloud Radio Access Networks

Recently, implementing Radio Access Network (RAN) functionality on cloud-based computing platform has become an emerging solution that leverages the many advantages of cloud infrastructure, such as shared computing resources and storage capacity, while lowering the operational cost. In this paper, we propose a novel caching framework aimed at fully exploiting the potential of such systems through cooperative hierarchical caching which minimizes the network costs of content delivery and improves users' Quality of Experience (QoE). In particular, the cloud-cache in the cloud processing unit (CPU) presents a new layer in the RAN cache hierarchy, bridging the capacity-performance gap between the traditional edge-based and core-based caching schemes. A delay cost model is introduced to characterize and formulate the cache placement optimization problem, which is shown to be NP-complete. As such, a low complexity, heuristic cache management strategy is proposed, constituting of a proactive cache distribution algorithm and a reactive cache replacement algorithm. Extensive numerical simulations are carried out using both real-world YouTube video requests and synthetic content requests. It is demonstrated that our proposed Octopus caching strategy significantly outperforms the traditional caching strategies in terms of cache hit ratio, average content access delay and backhaul traffic load.
Non-archimedean analytification of algebraic spaces

It is now a classical result that an algebraic space locally of finite type over $\mathbf{C}$ is analytifiable if and only if it is locally separated. In this paper we study non-archimedean analytifications of algebraic spaces. We construct a quotient for any etale non-archimedean analytic equivalence relation whose diagonal is a closed immersion, and deduce that any separated algebraic space locally of finite type over any non-archimedean field $k$ is analytifiable in both the category of rigid spaces and the category of analytic spaces over $k$. Also, though local separatedness remains a necessary condition for analytifiability in either of these categories, we present many surprising examples of non-analytifiable locally separated smooth algebraic spaces over $k$ that can even be defined over the prime field.
Learning structural changes of Gaussian graphical models in controlled experiments

Graphical models are widely used in scientific and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce significant structural changes in the corresponding graphical models. Therefore, it is of great importance to be able to detect such structural changes from data, so as to gain novel insights into where and how the structural changes take place and help the system adapt to the new environment. Here we report an effective learning strategy to extract structural changes in Gaussian graphical model using l1-regularization based convex optimization. We discuss the properties of the problem formulation and introduce an efficient implementation by the block coordinate descent algorithm. We demonstrate the principle of the approach on a numerical simulation experiment, and we then apply the algorithm to the modeling of gene regulatory networks under different conditions and obtain promising yet biologically plausible results.
Rational periodic points for degree two polynomial morphisms on projective space

This article addresses the existence of $\Q$-rational periodic points for morphisms of projective space. In particular, we construct an infinitely family of morphisms on $\P^N$ where each component is a degree 2 homogeneous form in $N+1$ variables which has a $\Q$-periodic point of primitive period $\frac{(N+1)(N+2)}{2} + \lfloor \frac{N-1}{2}\rfloor$. This result is then used to show that for $N$ large enough there exists morphisms of $\P^N$ with $\Q$-rational periodic points with primitive period larger that $c(k)N^k$ for any $k$ and some constant $c(k)$.
Minimal reachability problems

In this paper, we address a collection of state space reachability problems, for linear time-invariant systems, using a minimal number of actuators. In particular, we design a zero-one diagonal input matrix B, with a minimal number of non-zero entries, so that a specified state vector is reachable from a given initial state. Moreover, we design a B so that a system can be steered either into a given subset, or sufficiently close to a desired state. This work extends the results of [1] and [2], where a zero-one diagonal or column matrix B is constructed so that the involved system is controllable. Specifically, we prove that the first two of our aforementioned problems are NP-hard; these results hold for a zero-one column matrix B as well. Then, we provide efficient algorithms for their general solution, along with their worst case approximation guarantees. Finally, we illustrate their performance over large random networks.
On a notion of toric special linear systems

Abstract In this note, we study linear systems on complete toric varieties X with an invariant point whose orbit under the action of Aut ( X ) contains the dense torus T of X . We give a characterization of such varieties in terms of its defining fan and introduce a new definition of expected dimension of linear systems which consider the contribution given by certain toric subvarieties. Finally, we study degenerations of linear systems on these toric varieties induced by toric degenerations.
Logarithmic upper bounds for weak solutions to a class of parabolic equations

It is well known that a weak solution φ to the initial boundary value problem for the uniformly parabolic equation satisfies the uniform estimate provided that can be improved. To be precise, we establish
Modeling and analysis of water-hammer in coaxial pipes

The fluid-structure interaction is studied for a system composed of two coaxial pipes in an annular geometry, for both homogeneous isotropic metal pipes and fiber-reinforced (anisotropic) pipes. Multiple waves, traveling at different speeds and amplitudes, result when a projectile impacts on the water fillinhe annular space between the pipes. In the case of carbon fiber-reinforced plastic thin pipes we compute the wavespeeds, the fluid pressure and mechanical strains as functions of the fiber winding angle. This generalizes the single-pipe analysis of J. H. You, and K. Inaba, Fluid-structure interaction in water-filled pipes of anisotropic composite materials, J. Fl. Str. 36 (2013). Comparison with a set of experimental measurements seems to validate our models and predictions.
Weak* Solutions I: A New Perspective on Solutions to Systems of Conservation Laws

We introduce a new notion of solution, which we call weak* solutions, for systems of conservation laws. These solutions can be used to handle singular situations that standard weak solutions cannot, such as vacuums in Lagrangian gas dynamics or cavities in elasticity. Our framework allows us to treat the systems as ODEs in Banach space. Starting with the observation that solutions act linearly on test functions $\alpha\in X$, we require solutions to take values in the dual space $X^*$ of $X$. Moreover, we weaken the usual requirement of measurability of solutions. In order to do this, we develop the calculus of the Gelfand integral, which is appropriate for weak* measurable functions. We then use the Gelfand calculus to define weak* solutions, and show that they are stronger than the usual notion of weak solution, although for $BV$ solutions the notions are equivalent. It is expected that these solutions will also shed light on vexing issues of ill-posedness for multi-dimensional systems.
Decidability of Non-interactive Simulation of Joint Distributions

We present decidability results for a sub-class of "non-interactive" simulation problems, a well-studied class of problems in information theory. A non-interactive simulation problem is specified by two distributions P(x, y) and Q(u, v): The goal is to determine if two players, Alice and Bob, that observe sequences Xn and Yn respectively where {(Xi, Yi)}ni = 1 are drawn i.i.d. from P(x, y) can generate pairs U and V respectively (without communicating with each other) with a joint distribution that is arbitrarily close in total variation to Q(u, v). Even when P and Q are extremely simple: e.g., P is uniform on the triples (0, 0), (0,1), (1,0) and Q is a "doubly symmetric binary source", i.e., U and V are uniform ± 1 variables with correlation say 0.49, it is open if P can simulate Q. In this work, we show that whenever P is a distribution on a finite domain and Q is a 2 × 2 distribution, then the non-interactive simulation problem is decidable: specifically, given δ > 0 the algorithm runs in time bounded by some function of P and δ and either gives a non-interactive simulation protocol that is δ-close to Q or asserts that no protocol gets O(δ)-close to Q. The main challenge to such a result is determining explicit (computable) convergence bounds on the number n of samples that need to be drawn from P(x, y) to get δ-close to Q. We invoke contemporary results from the analysis of Boolean functions such as the invariance principle and a regularity lemma to obtain such explicit bounds.
On invariants of link maps in dimension four

We fill a gap in the proof that the proposed link homotopy invariant ω of Li is well defined. It is also shown that if the homotopy invariant τ of Schneiderman–Teichner is to be adapted to a link homotopy invariant of link maps, the result coincides with ω.
Regularized Decomposition of High-Dimensional Multistage Stochastic Programs with Markov Uncertainty

We develop a quadratic regularization approach for the solution of high-dimensional multistage stochastic optimization problems characterized by a potentially large number of time periods/stages (e.g., hundreds), a high-dimensional resource state variable, and a Markov information process. The resulting algorithms are shown to converge to an optimal policy after a finite number of iterations under mild technical assumptions. Computational experiments are conducted using the setting of optimizing energy storage over a large transmission grid, which motivates both the spatial and temporal dimensions of our problem. Our numerical results indicate that the proposed methods exhibit significantly faster convergence than their classical counterparts, with greater gains observed for higher-dimensional problems.
Market making with decreasing utility for information

We study information elicitation in cost-function-based combinatorial prediction markets when the market maker's utility for information decreases over time. In the sudden revelation setting, it is known that some piece of information will be revealed to traders, and the market maker wishes to prevent guaranteed profits for trading on the sure information. In the gradual decrease setting, the market maker's utility for (partial) information decreases continuously over time. We design adaptive cost functions for both settings which: (1) preserve the information previously gathered in the market; (2) eliminate (or diminish) rewards to traders for the publicly revealed information; (3) leave the reward structure unaffected for other information; and (4) maintain the market maker's worst-case loss. Our constructions utilize mixed Bregman divergence, which matches our notion of utility for information.
Descent polynomials for k bubble-sortable permutations of type B

Abstract Motivated by the work of Chung, Claesson, Dukes, and Graham (2010) [5] , we define a natural type B analog of the classic bubble sort, and use it to define a type B analog of the maximum drop statistic. We enumerate (by explicit, recursive, and generating function formulas) signed permutations with r type B descents and type B maximum drop at most k . We also find a connection between these signed permutations and certain 2-colored juggling sequences.
Energy-Efficient Time-Domain Vector-by-Matrix Multiplier for Neurocomputing and Beyond

We propose an extremely energy-efficient mixed-signal ${N} \times {N}$ vector-by-matrix multiplication (VMM) in a time domain. Multi-bit inputs/outputs are represented with time-encoded digital signals, while multi-bit matrix weights are realized with adjustable current sources, e.g., transistors biased in subthreshold regime. The major advantage of the proposed approach over other types of mixed-signal implementations is very compact peripheral circuits, which would be essential for achieving high energy efficiency and speed at the system level. As a case study, we have designed a multilayer perceptron, based on two layers of 10 $ \times $ 10 four-quadrant multipliers, in 55-nm process with embedded NOR flash memory technology, which allows for compact implementation of adjustable current sources. Our analysis, based on memory cell measurements, shows that >6 bit operation can be ensured for larger ( ${N} >$ 50) VMMs. Post-layout estimates for 55-nm 6-bit VMM, which take into account the impact of PVT variations, noise, and overhead of I/O circuitry for converting between conventional digital and time domain representations, show ~7 fJ/Op for ${N} >$ 500. The energy efficiency can be further improved to POp/J regime for more optimal and aggressive designs.
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning.

We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent’s exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants.
Inverse Reinforcement Learning Under Noisy Observations

We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, noisy observations of the trajectory are available. We generalize the previous method of expectation-maximization for inverse reinforcement learning, which allows the trajectory of the expert to be partially hidden from the learner, to incorporate an observation model.
On binary codes from conics in PG(2,q)

Let A be the q(q-1)2xq(q-1)2 incidence matrix of passant lines and internal points with respect to a conic in PG(2,q), where q is an odd prime power. In this article, we study both geometric and algebraic properties of the column F"2-null space L of A. In particular, using methods from both finite geometry and modular presentation theory, we manage to compute the dimension of L, which provides a proof for the conjecture on the dimension of the binary code generated by L.
On the number of outer automorphisms of the automorphism group of a right-angled Artin group

We show that there is no uniform upper bound on |Out(Aut(A))| when A ranges over all right-angled Artin groups. This is in contrast with the cases where A is free or free abelian: for all n, Dyer-Formanek and Bridson-Vogtmann showed that Out(Aut(F_n)) = 1, while Hua-Reiner showed |Out(Aut(Z^n)| = |Out(GL(n,Z))| < 5. We also prove the analogous theorem for Out(Out(A)). We establish our results by giving explicit examples; one useful tool is a new class of graphs called austere graphs.
Total Positivity and Cluster Algebras

This is a brief and informal introduction to cluster algebras. It roughly follows the historical path of their discovery, made jointly with A. Zelevinsky. Total positivity serves as the main motivation.
The floating body in real space forms

We carry out a systematic investigation on floating bodies in real space forms. A new unifying approach not only allows us to treat the important classical case of Euclidean space as well as the recent extension to the Euclidean unit sphere, but also the new extension of floating bodies to hyperbolic space. Our main result establishes a relation between the derivative of the volume of the floating body and a certain surface area measure, which we called the floating area. In the Euclidean setting the floating area coincides with the well known affine surface area, a powerful tool in the affine geometry of convex bodies.
Geometry of compact complex homogeneous spaces with vanishing first Chern class

Abstract We prove that any compact complex homogeneous space with vanishing first Chern class, after an appropriate deformation of the complex structure, admits a homogeneous Calabi–Yau with torsion structure, provided that it also has an invariant volume form. A description of such spaces among the homogeneous C-spaces is given as well as many examples and a classification in the 3-dimensional case. We calculate the cohomology ring of some of the examples and show that in dimension 14 there are infinitely many simply-connected spaces admitting such a structure with the same Hodge numbers and torsional Chern classes. We provide also an example solving the Strominger's equations in heterotic string theory.
Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of new computing models. Researchers have shown the suitability of memristive devices for swarm intelligence algorithms to solve a maze in hardware. In this paper, we utilize swarm intelligence of memristive networks to perform image edge detection. First, we propose a hardware-friendly algorithm for image edge detection based on ant colony optimization. Second, we implement the image edge detection algorithm using memristive networks. Furthermore, we explain the impact of various parameters of the memristors on the efficacy of the implementation. Our results show 28% improvement in the energy compared to a low power CMOS hardware implementation based on stochastic circuits. Furthermore, our design occupies up to $5\boldsymbol \times $ less area.
Atomic representations of Cuntz algebras

Abstract To a representation of O N (the Cuntz algebra with N generators) we associate a projection valued measure and we study the case when this measure has atoms. The main technical tool are the spaces invariant for all the operators S i ⁎ . We classify the purely atomic representations and find when such representations are permutative. Applications include: wavelet representations, representations generated by finitely correlated states, representation associated to Hadamard triples (or fractal spectral measures) and representations associated to generalized Walsh bases.