cssi/SciSciGPT-SciSciNet huggingface dataset:
The role of cation–cation interactions in a neptunyl chloride hydrate and topological aspects of neptunyl structural units

Abstract The compound K4(NpO2)3Cl7(H2O)4 was synthesized by evaporation of a Np5+-bearing solution. The crystal structure was determined by single crystal X-ray diffraction and refined to R1=0.0374. The compound is triclinic, P−1, a=8.882(1) A, b=12.082(2) A, c=12.403(2) A, α=65.855(2)°, β=69.604(2)°, γ=74.432(2)°, V=1126.0(3) A3, and Z=2. The structure contains dimers of edge sharing Np5+ pentagonal bipyramids that are linked into infinite chains through cation–cation interactions with an additional Np5+ pentagonal bipyramid. The structural units are linked through bonds to interstitial K cations and by H bonding. A graphical representation for neptunyl structural units including cation–cation interactions is introduced.
Global Epistasis Makes Adaptation Predictable Despite Sequence-Level Stochasticity

Epistatic interactions between mutations can make evolutionary trajectories contingent on the chance occurrence of initial mutations. We used experimental evolution in Saccharomyces cerevisiae to quantify this contingency, finding differences in adaptability among 64 closely related genotypes. Despite these differences, sequencing of 104 evolved clones showed that initial genotype did not constrain future mutational trajectories. Instead, reconstructed combinations of mutations revealed a pattern of diminishing-returns epistasis: Beneficial mutations have consistently smaller effects in fitter backgrounds. Taken together, these results show that beneficial mutations affecting a variety of biological processes are globally coupled; they interact strongly, but only through their combined effect on fitness. As a consequence, fitness evolution follows a predictable trajectory even though sequence-level adaptation is stochastic.
Model-based detection and analysis of introgressed Neanderthal ancestry in modern humans.

Genetic evidence has revealed that the ancestors of modern human populations outside Africa and their hominin sister groups, notably Neanderthals, exchanged genetic material in the past. The distribution of these introgressed sequence tracts along modern-day human genomes provides insight into the selective forces acting on them and the role of introgression in the evolutionary history of hominins. Studying introgression patterns on the X-chromosome is of particular interest, as sex chromosomes are thought to play a special role in speciation. Recent studies have developed methods to localize introgressed ancestries, reporting long regions that are depleted of Neanderthal introgression and enriched in genes, suggesting negative selection against the Neanderthal variants. On the other hand, enriched Neanderthal ancestry in hair- and skin-related genes suggests that some introgressed variants facilitated adaptation to new environments. Here, we present a model-based introgression detection method called dical-admix. We demonstrate its efficiency and accuracy through extensive simulations and apply it to detect tracts of Neanderthal introgression in modern human individuals from the 1000 Genomes Project. Our findings are largely concordant with previous studies, consistent with weak selection against Neanderthal ancestry. We find evidence that selection against Neanderthal ancestry was due to higher genetic load in Neanderthals resulting from small effective population size, rather than widespread Dobzhansky-Muller incompatibilities (DMIs) that could contribute to reproductive isolation. Moreover, we confirm the previously reported low level of introgression on the X-chromosome, but find little evidence that DMIs contributed to this pattern.
Pantocin B, an Antibiotic from Erwinia herbicola Discovered by Heterologous Expression of Cloned Genes

Pantocin B, an antibiotic from Erwinia herbicola discovered by heterologous expression of cloned genes
Attempted preparation of 5,5-difluoro-1,4-diphenylbicyclo[2.1.0]pentane -- serendipitous synthesis of 1,3-difluoro-2,4-diphenylbenzene

Abstract Addition of difluorocarbene to 1,2-diphenylcyclobutene, using PhHgCF 3 , results in formation of 1,3-difluoro-2,4-diphenylbenzene. A mechanism for this reaction is proposed.
Advancing Tetrazine Bioorthogonal Reactions through the Development of New Synthetic Tools

There has been increasing interest in the use of tetrazines as partners in bioorthogonal inverse Diels–Alder cycloadditions. Although tetrazine cycloadditions possess several characteristics that make them attractive compared to alternative coupling strategies, the coupling partners, 1,2,4,5-tetrazines and strained alkenes and cycloalkynes, are not trivial to synthesize. The use of tetrazines and dienophiles in biological applications has required the development of new synthetic methodologies to create stable yet reactive probes conveniently. This paper addresses the synthetic milestones that have enabled and improved tetrazine cycloadditions as a popular form of bioorthogonal chemistry and highlights recent work from our lab that we believe has advanced the methodology available for the synthesis of tetrazines 1 and smaller strained cyclopropene dienophiles. 2 We also outline some future challenges and open questions that remain to be addressed.
Synthesis of griseolic acid B by π-face-dependent radical cyclization

Radical cyclization of 6 affords the bicyclic vinyl ether 9 with the appropriate stereochemistry for elaboration (seven steps) to griseolic acid B (1).
Ring Size and Substituent Effects in Oxyanion-Promoted Cyclizations of Enyne-allenes: Observation of a Myers−Saito Cycloaromatization at Cryogenic Temperature

A series of acetoxy-substituted enyne-allenes, fused to cyclopentene and cyclohexene ring systems, were synthesized and treated with methyllithium to generate the corresponding enolates. It was found that whereas the cyclohexannulated examples underwent either C2−C7 (Myers−Saito) cycloaromatization or C2−C6 (Schmittel) cyclization depending on their terminal subsituents, the cyclopentannulated examples either failed to cyclize altogether or underwent C2−C7 cyclization. Both of these results lie in contrast to the behavior of their benzannulated analogues, which underwent exclusive C2−C6 cyclization independent of substituents. These findings are rationalized on the basis of both ring strain effects and the steric encumbrance of the terminal alkynyl and allenyl subsituents.
Altered Central Nutrient Sensing in Male Mice Lacking Insulin Receptors in Glut4-Expressing Neurons

Insulin signaling in the central nervous system influences satiety, counterregulation, and peripheral insulin sensitivity. Neurons expressing the Glut4 glucose transporter influence peripheral insulin sensitivity. Here, we analyzed the effects of insulin receptor (IR) signaling in hypothalamic Glut4 neurons on glucose sensing as well as leptin and amino acid signaling. By measuring electrophysiological responses to low glucose conditions, we found that the majority of Glut4 neurons in the ventromedial hypothalamus (VMH) were glucose excitatory neurons. GLUT4-Cre-driven insulin receptor knockout mice with a combined ablation of IR in Glut4-expressing tissues showed increased counterregulatory response to either 2-deoxyglucose-induced neuroglycopenia or systemic insulin-induced hypoglycemia. The latter response was recapitulated in mice with decreased VMH IR expression, suggesting that the effects on the counterregulatory response are likely mediated through the deletion of IRs on Glut4 neurons in the VMH. Using immunohistochemistry in fluorescently labeled hypothalamic Glut4 neurons, we showed that IR signaling promoted hypothalamic cellular signaling responses to the rise of insulin, leptin, and amino acids associated with feeding. We concluded that hypothalamic Glut4 neurons modulated the glucagon counterregulatory response and that IR signaling in Glut4 neurons was required to integrate hormonal and nutritional cues for the regulation of glucose metabolism.
Stereochemistry and Conformational Analysis of Hemirubin

Abstract Intramolecularly hydrogen-bonded analogs of the natural bile pigment, bilirubin, are very scarce. Nuclear Overhauser effect NMR studies of the newest analog, a hemirubin ( 1 ), confirms intramolecular hydrogen bonding and a ridge-tile-shaped conformation. 1 H NMR studies of 1 at sufficiently low temperatures detect conformational enantiomerization, for which an activation barrier of Δ G ‡ ∼16 kcal/mol at 25°C in CDCl 3 has been estimated. Evidence for the presence of dimeric association at low temperatures or high concentrations of 1 is found by the appearance of new sets of resonances, data from which may be used to calculate: Δ G ° 298 K +3.4 kcal/mol, Δ H ° −5.6 kcal/mol and Δ S ° −30.3 cal/deg/mol.
Metallacarboranes of the C2B4-Cage Systems

A comprehensive review of the chemistry of polyhedral cluster complexes in whichs,p,d, andf-block metals are incorporated into C2B4-carborane cages to form the respective metallacarboranes is presented. The main focus of the review is directed toward systematizing the chemistry of metallacarboranes in order to promote their use as possible electronic, ceramic, and/or catalytic materials in addition to providing information on which we can test and expand our knowledge about the fundamental interactions that are at work in determining the structures and properties of these cluster complexes.
Reaction kinetics of plutonium with oxygen, water and humid air: moisture enhancement of the corrosion rate

Abstract Kinetic measurements for the reactions of unalloyed plutonium with water vapor and oxygen in the 35 to 400°C range show that moisture enhancement of the corrosion rate is confined to a temperature regime below 200°C. In combination with kinetic data from literature sources and measurements with air at 25°C, the data define a rate-temperature envelope in which the corrosion reaction is accelerated by increasing water pressure. The lower boundary of the envelope is defined by an Arrhenius relationship based on rates measured in the moisture-independent regime above 200°C and in the moisture-dependent regime at lower temperatures. As defined by experimental data and the equilibrium vapor pressure of water, the upper envelope boundary indicates that moisture dependence is confined to the −25 to 200°C range with a rate maximum near 110°C. Activation energies within the envelope vary systematically with water pressure from 17.9 kcal mol−1 in dry (less than 1 ppm H2O) air to 34.4 kcal mol−1 in water-saturated air. The corrosion rate is independent of oxygen pressure and has a square-root dependence on water pressure. The chemistry of moisture-enhanced corrosion is defined by results showing that plutonium oxide catalyzes the combination of hydrogen and oxygen. Hydrogen formed by the inherently rapid reaction of water with plutonium catalytically recombines with oxygen at the gas-oxide interface of the reacting metal. Kinetic anomalies attributed to reaction of β-phase plutonium in earlier studies are correlated with the presence of trace amounts of moisture in the experimental systems. Accelerated corrosion in moist argon is discussed and effects of alloying and humidity on the corrosion rate are compared.
Enantioselective Addition of Diethylzinc to Aldehydes Catalyzed by a Titanate Complex with a Chiral Tetradentate Ligand.

The addition of dialkylzincs to aldehydes is one of the most widely studied carbon-carbon bond-forming reactions. Many systems reported to date use amino alcohols as ligands and zinc complexes as catalysts.1 The asymmetric version of this alkylation reaction can also be catalyzed by chiral titanate complexes2-4 (e.g., TADDOLs2 and chiral sulfonamides3). We have recently studied a titanate complex with tetradentate helical ligand 1 ((1R,2R)-(+)-1,2-bis(3,5-dichloro-2-hydroxybenzenesulfonamido)cyclohexane) for the asymmetric addition of diethylzinc to aldehydes (Figure 1).5 Herein we report the scope of this enantioselective reaction with various substrates. This alkylation approach provides a useful route for the synthesis of some chiral secondary alcohols, especially allylic alcohols,6 and this work contributes to an understanding of the details of this type of reaction.
Single Nanowire Lasers

Ultraviolet lasing from single zinc oxide nanowires is demonstrated at room temperature. Near-field optical microscopy images quantify the localization and the divergence of the laser beam. The linewidths, wavelengths, and power dependence of the nanowire emission characterize the nanowire as an active optical cavity. These individual nanolasers could serve as miniaturized light sources for microanalysis, information storage, and optical computing.
Genetics of Cocaine and Methamphetamine Consumption and Preference in Drosophila melanogaster

Illicit use of psychostimulants, such as cocaine and methamphetamine, constitutes a significant public health problem. Whereas neural mechanisms that mediate the effects of these drugs are well-characterized, genetic factors that account for individual variation in susceptibility to substance abuse and addiction remain largely unknown. Drosophila melanogaster can serve as a translational model for studies on substance abuse, since flies have a dopamine transporter that can bind cocaine and methamphetamine, and exposure to these compounds elicits effects similar to those observed in people, suggesting conserved evolutionary mechanisms underlying drug responses. Here, we used the D. melanogaster Genetic Reference Panel to investigate the genetic basis for variation in psychostimulant drug consumption, to determine whether similar or distinct genetic networks underlie variation in consumption of cocaine and methamphetamine, and to assess the extent of sexual dimorphism and effect of genetic context on variation in voluntary drug consumption. Quantification of natural genetic variation in voluntary consumption, preference, and change in consumption and preference over time for cocaine and methamphetamine uncovered significant genetic variation for all traits, including sex-, exposure- and drug-specific genetic variation. Genome wide association analyses identified both shared and drug-specific candidate genes, which could be integrated in genetic interaction networks. We assessed the effects of ubiquitous RNA interference (RNAi) on consumption behaviors for 34 candidate genes: all affected at least one behavior. Finally, we utilized RNAi knockdown in the nervous system to implicate dopaminergic neurons and the mushroom bodies as part of the neural circuitry underlying experience-dependent development of drug preference.
Primary cilium loss in mammalian cells occurs predominantly by whole-cilium shedding

The primary cilium is a central signaling hub in cell proliferation and differentiation and is built and disassembled every cell cycle in many animal cells. Disassembly is critically important, as misregulation or delay of cilia loss leads to cell cycle defects. The physical means by which cilia are lost are poorly understood but are thought to involve resorption of ciliary components into the cell body. To investigate cilium loss in mammalian cells, we used live-cell imaging to comprehensively characterize individual events. The predominant mode of cilium loss was rapid deciliation, in which the membrane and axoneme of the cilium was shed from the cell. Gradual resorption was also observed, as well as events in which a period of gradual resorption was followed by rapid deciliation. Deciliation resulted in intact shed cilia that could be recovered from culture medium and contained both membrane and axoneme proteins. We modulated levels of katanin and intracellular calcium, two putative regulators of deciliation, and found that excess katanin promotes cilia loss by deciliation, independently of calcium. Together, these results suggest that mammalian ciliary loss involves a tunable decision between deciliation and resorption.
On a Question of Glasby, Praeger, and Xia

A Jordan partition λ(m, n, p) = (λ1, λ2, … , λ m ) is a partition of mn associated with the expression of a tensor V m ⊗ V n of indecomposable KG-modules into a sum of indecomposables, where K is a field of characteristic p and G a cyclic group of p-power order. It is standard if λ i = m + n − 2i + 1 for all i. We answer a recent question of Glasby, Praeger, and Xia who asked for necessary and sufficient conditions for λ(m, n, p) to be standard.
Uniform in time lower bound for solutions to a quantum Boltzmann equation of bosons

In this paper, we consider a quantum Boltzmann equation, which describes the interaction between excited atoms and a condensate. The collision integrals are taken–over energy manifolds, having the full form of the Bogoliubov dispersion law for particle energy. We prove that nonnegative radially symmetric solutions of the quantum Boltzmann equation are bounded from below by a Gaussian distribution, uniformly in time.
Directed rooted forests in higher dimension

For a graph $G$, the generating function of rooted forests, counted by the number of connected components, can be expressed in terms of the eigenvalues of the graph Laplacian. We generalize this result from graphs to cell complexes of arbitrary dimension. This requires generalizing the notion of rooted forest to higher dimension. We also introduce orientations of higher dimensional rooted trees and forests. These orientations are discrete vector fields which lead to open questions concerning expressing homological quantities combinatorially.
Stable Extrapolation of Analytic Functions

This paper examines the problem of extrapolation of an analytic function for $$x > 1$$ given $$N+1$$ perturbed samples from an equally spaced grid on $$[-1,1]$$ . For a function f on $$[-1,1]$$ that is analytic in a Bernstein ellipse with parameter $$\rho > 1$$ , and for a uniform perturbation level $$\varepsilon $$ on the function samples, we construct an asymptotically best extrapolant e(x) as a least squares polynomial approximant of degree $$M^*$$ determined explicitly. We show that the extrapolant e(x) converges to f(x) pointwise in the interval $$I_\rho \in [1,(\rho +\rho ^{-1})/2)$$ as $$\varepsilon \rightarrow 0$$ , at a rate given by a x-dependent fractional power of $$\varepsilon $$ . More precisely, for each $$x \in I_{\rho }$$ we have $$\begin{aligned} |f(x) - e(x)| = \mathcal {O}\left( \varepsilon ^{-\log r(x) / \log \rho } \right) , \quad r(x) = \frac{x+\sqrt{x^2-1}}{\rho }, \end{aligned}$$ up to log factors, provided that an oversampling conditioning is satisfied, viz. $$\begin{aligned} M^* \le \frac{1}{2} \sqrt{N}, \end{aligned}$$ which is known to be needed from approximation theory. In short, extrapolation enjoys a weak form of stability, up to a fraction of the characteristic smoothness length. The number of function samples does not bear on the size of the extrapolation error provided that it obeys the oversampling condition. We also show that one cannot construct an asymptotically more accurate extrapolant from equally spaced samples than e(x), using any other linear or nonlinear procedure. The proofs involve original statements on the stability of polynomial approximation in the Chebyshev basis from equally spaced samples and these are expected to be of independent interest.
Concavity of the Lagrangian phase operator and applications

We study the Dirichlet problem for the Lagrangian phase operator, in both the real and complex setting. Our main result states that if $$\Omega $$ is a compact domain in $${\mathbb {R}}^{n}$$ or $${\mathbb {C}}^n$$ , then there exists a solution to the Dirichlet problem with right-hand side h(x) satisfying $$|h(x)| > (n-2)\frac{\pi }{2}$$ and boundary data $$\varphi $$ if and only if there exists a subsolution.
Connecting toric manifolds by conical Kähler–Einstein metrics

Abstract We give criterions for the existence of toric conical Kahler–Einstein and Kahler–Ricci soliton metrics on any toric manifold in relation to the greatest Ricci and Bakry–Emery–Ricci lower bound. We also show that any two toric manifolds with the same dimension can be joined by a continuous path of toric manifolds with conical Kahler–Einstein metrics in the Gromov–Hausdorff topology.
Grothendieck groups of complexes with null-homotopies

This paper uses differential-graded methods to give a streamlined proof of a theorem of Foxby-Halvorson. The theorem states that certain relative K-groups made from complexes with bounded (but arbitrarily long) length coincide with similar K-groups in which one sets an absolute bound on the length of the complexes.
GPU LSM: A Dynamic Dictionary Data Structure for the GPU

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.
Stability of square root domains associated with elliptic systems of PDEs on nonsmooth domains

Abstract We discuss stability of square root domains for uniformly elliptic partial differential operators L a , Ω , Γ = − ∇ ⋅ a ∇ in L 2 ( Ω ) , with mixed boundary conditions on ∂Ω, with respect to additive perturbations. We consider open, bounded, and connected sets Ω ∈ R n , n ∈ N \ { 1 } , that satisfy the interior corkscrew condition and prove stability of square root domains of the operator L a , Ω , Γ with respect to additive potential perturbations V ∈ L p ( Ω ) + L ∞ ( Ω ) , p > n / 2 . Special emphasis is put on the case of uniformly elliptic systems with mixed boundary conditions.
Weak Concentration for First Passage Percolation Times on Graphs and General Increasing Set-valued Processes

Author(s): Aldous, DJ | Abstract: A simple lemma bounds s.d.(T )/ET for hitting times T in Markov chains with a certain strong monotonicity property. We show how this lemma may be applied to several increasing set-valued processes. Our main result concerns a model of first passage percolation on a finite graph, where the traversal times of edges are independent Exponentials with arbitrary rates. Consider the percolation time X between two arbitrary vertices. We prove that s.d.(X)/EX is small if and only if Ξ/EX is small, where Ξ is the maximal edge-traversal time in the percolation path attaining X.
On stringy Euler characteristics of Clifford non-commutative varieties

Abstract It was shown by Kuznetsov that complete intersections of n generic quadrics in P 2 n − 1 are related by Homological Projective Duality to certain non-commutative (Clifford) varieties which are in some sense birational to double covers of P n − 1 ramified over symmetric determinantal hypersurfaces. Mirror symmetry predicts that the Hodge numbers of the complete intersections of quadrics must coincide with the appropriately defined Hodge numbers of these double covers. We observe that these numbers must be different from the well-known Batyrev's stringy Hodge numbers, else the equality fails already at the level of Euler characteristics. We define a natural modification of stringy Hodge numbers for the particular class of Clifford varieties, and prove the corresponding equality of Euler characteristics in arbitrary dimension.
Clique Minors in Double-critical Graphs

A connected $t$-chromatic graph $G$ is \dfn{double-critical} if $G \backslash\{u, v\}$ is $(t-2)$-colorable for each edge $uv\in E(G)$. A long standing conjecture of Erdős and Lovasz that the complete graphs are the only double-critical $t$-chromatic graphs remains open for all $t\ge6$. Given the difficulty in settling Erdős and Lovasz's conjecture and motivated by the well-known Hadwiger's conjecture, Kawarabayashi, Pedersen and Toft proposed a weaker conjecture that every double-critical $t$-chromatic graph contains a $K_t$ minor and verified their conjecture for $t\le7$. Albar and Goncalves recently proved that every double-critical $8$-chromatic graph contains a $K_8$ minor, and their proof is computer-assisted. In this paper we prove that every double-critical $t$-chromatic graph contains a $K_t$ minor for all $t\le9$. Our proof for $t\le8$ is shorter and computer-free.
AffinityNet: Semi-Supervised Few-Shot Learning for Disease Type Prediction

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the “big p, small N” problem (i.e., a relatively small number of samples with highdimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data.
Quotients in monadic programming: projective algebras are equivalent to coalgebras

In monadic programming, datatypes are presented as free algebras, generated by data values, and by the algebraic operations and equations capturing some computational effects. These algebras are free in the sense that they satisfy just the equations imposed by their algebraic theory, and remain free of any additional equations. The consequence is that they do not admit quotient types. This is, of course, often inconvenient. Whenever a computation involves data with multiple representatives, and they need to be identified according to some equations that are not satisfied by all data, the monadic programmer has to leave the universe of free algebras, and resort to explicit destructors. We characterize the situation when these destructors are preserved under all operations, and the resulting quotients of free algebras are also their subalgebras. Such quotients are called projective. Although popular in universal algebra, projective algebras did not attract much attention in the monadic setting, where they turn out to have a surprising avatar: for any given monad, a suitable category of projective algebras is equivalent with the category of coalgebras for the comonad induced by any monad resolution. For a monadic programmer, this equivalence provides a convenient way to implement polymorphic quotients as coalgebras. The dual correspondence of injective coalgebras and all algebras leads to a different family of quotient types, which seems to have a different family of applications. Both equivalences also entail several general corollaries concerning monadicity and comonadicity.
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition

Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion.
Edge Cloud Offloading Algorithms: Issues, Methods, and Perspectives

Mobile devices supporting the “Internet of Things” often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality, augmented reality, multimedia delivery, and artificial intelligence, which could require broad bandwidth, low response latency, and large computational power. Edge cloud or edge computing is an emerging topic and a technology that can tackle the deficiencies of the currently centralized-only cloud computing model and move the computation and storage resources closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and “offloading” algorithms are needed to allow mobile devices and the edge cloud to work together smoothly. In this survey article, we investigate the key issues, methods, and various state-of-the-art efforts related to the offloading problem. We adopt a new characterizing model to study the whole process of offloading from mobile devices to the edge cloud. Through comprehensive discussions, we aim to draw an overall “big picture” on the existing efforts and research directions. Our study also indicates that the offloading algorithms in the edge cloud have demonstrated profound potentials for future technology and application development.
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning

Encoder-decoder models for unsupervised sentence representation learning using the distributional hypothesis effectively constrain the learnt representation of a sentence to only that needed to reproduce the next sentence. While the decoder is important to constrain the representation, these models tend to discard the decoder after training since only the encoder is needed to map the input sentence into a vector representation. However, parameters learnt in the decoder also contain useful information about the language. In order to utilise the decoder after learning, we present two types of decoding functions whose inverse can be easily derived without expensive inverse calculation. Therefore, the inverse of the decoding function serves as another encoder that produces sentence representations. We show that, with careful design of the decoding functions, the model learns good sentence representations, and the ensemble of the representations produced from the encoder and the inverse of the decoder demonstrate even better generalisation ability and solid transferability.
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning

Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high-dimensional settings. In this paper, we propose to tackle these challenges by (1) assuming a latent additive structure in the function and inferring it properly for more efficient and effective BO, and (2) performing multiple evaluations in parallel to reduce the number of iterations required by the method. Our novel approach learns the latent structure with Gibbs sampling and constructs batched queries using determinantal point processes. Experimental validations on both synthetic and real-world functions demonstrate that the proposed method outperforms the existing state-of-the-art approaches.
Frank-Wolfe optimization for symmetric-NMF under simplicial constraint

Symmetric nonnegative matrix factorization has found abundant applications in various domains by providing a symmetric low-rank decomposition of nonnegative matrices. In this paper we propose a Frank-Wolfe (FW) solver to optimize the symmetric nonnegative matrix factorization problem under a simplicial constraint, which has recently been proposed for probabilistic clustering. Compared with existing solutions, this algorithm is simple to implement, and has no hyperparameters to be tuned. Building on the recent advances of FW algorithms in nonconvex optimization, we prove an $O(1/\varepsilon^2)$ convergence rate to $\varepsilon$-approximate KKT points, via a tight bound $\Theta(n^2)$ on the curvature constant, which matches the best known result in unconstrained nonconvex setting using gradient methods. Numerical results demonstrate the effectiveness of our algorithm. As a side contribution, we construct a simple nonsmooth convex problem where the FW algorithm fails to converge to the optimum. This result raises an interesting question about necessary conditions of the success of the FW algorithm on convex problems.
Design of a time delay reservoir using stochastic logic: A feasibility study

This paper presents a stochastic logic time delay reservoir design. The reservoir is analyzed using a number of metrics, such as kernel quality, generalization rank, performance on simple benchmarks, and is also compared to a deterministic design. A novel re-seeding method is introduced to reduce the adverse effects of stochastic noise, which may also be implemented in other stochastic logic reservoir computing designs, such as echo state networks. Benchmark results indicate that the proposed design performs well on noise-tolerant classification problems, but more work needs to be done to improve the stochastic logic time delay reservoir's robustness for regression problems.
TRIPLE CROSSING NUMBER OF KNOTS AND LINKS

A triple crossing is a crossing in a projection of a knot or link that has three strands of the knot passing straight through it. A triple crossing projection is a projection such that all of the crossings are triple crossings. We prove that every knot and link has a triple crossing projection and then investigate c3(K), which is the minimum number of triple crossings in a projection of K. We obtain upper and lower bounds on c3(K) in terms of the traditional crossing number and show that both are realized. We also relate triple crossing number to the span of the bracket polynomial and use this to determine c3(K) for a variety of knots and links. We then use c3(K) to obtain bounds on the volume of a hyperbolic knot or link. We also consider extensions to cn(K).
Dynamical Gelfand–Zetlin algebra and equivariant cohomology of grassmannians

We consider the rational dynamical quantum group Ey(&#x1d524;&#x1d529;2) and introduce an Ey(&#x1d524;&#x1d529;2)-module structure on ⊕k=0nH GLn×ℂ×∗(T∗Gr k(ℂn))′, where HGL n×ℂ×∗(T∗Gr k(ℂn))′ is the equivariant cohomology algebra HGL n×ℂ×∗(T∗Gr k(ℂn)) of the cotangent bundle of the Grassmannian Grk(ℂn) with coefficients extended by a suitable ring of rational functions in an additional variable λ. We consider the dynamical Gelfand–Zetlin algebra which is a commutative algebra of difference operators in λ. We show that the action of the Gelfand–Zetlin algebra on HGL n×ℂ×∗(T∗Gr k(ℂn))′ is the natural action of the algebra HGL n×ℂ×∗(T∗Gr k(ℂn)) ⊗ ℂ[δ±1] on HGL n×ℂ×∗(T∗Gr k(ℂn))′, where δ : ζ(λ) → ζ(λ + y) is the shift operator. The Ey(&#x1d524;&#x1d529;2)-module structure on ⊕k=0nH GLn×ℂ×∗(T∗Gr k(ℂn))′ is introduced with the help of dynamical stable envelope maps which are dynamical analogs of the stable envelope maps introduced by Maulik and Okounkov [Quantum Groups and quantum cohomology, preprint (2012) 1–276, arXiv:1211.1287]. The dynamical stable envelope maps are defined in terms of the rational dynamical weight functions introduced in [G. Felder, V. Tarasov and A. Varchenko, Solutions of the elliptic QKZB equations and Bethe ansatz I, in Topics in Singularity Theory V. I. Arnold’s 60th Anniversary Collection, Advances in the Mathematical Sciences, AMS Translations, Series 2, Vol. 180 (AMS, 1997), pp. 45–76.] to construct q-hypergeometric solutions of rational qKZB equations. The cohomology classes in HGL n×ℂ×∗(T∗Gr k(ℂn))′ induced by the weight functions are dynamical variants of Chern–Schwartz–MacPherson classes of Schubert cells.
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time

This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in realtime by learning from both human demonstrations and interventions. We implement two components of the Cycle-of Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.
Aggregative Coarsening for Multilevel Hypergraph Partitioning

Algorithms for many hypergraph problems, including partitioning, utilize multilevel frameworks to achieve a good trade-off between the performance and the quality of results. In this paper we introduce two novel aggregative coarsening schemes and incorporate them within state-of-the-art hypergraph partitioner Zoltan. Our coarsening schemes are inspired by the algebraic multigrid and stable matching approaches. We demonstrate the effectiveness of the developed schemes as a part of multilevel hypergraph partitioning framework on a wide range of problems.
Weak adaptive submodularity and group-based active diagnosis with applications to state estimation with persistent sensor faults

In this paper, we consider adaptive decision-making problems for stochastic state estimation with partial observations. First, we introduce the concept of weak adaptive submodularity, a generalization of adaptive submodularity, which has found great success in solving challenging adaptive state estimation problems. Then, for the problem of active diagnosis, i.e., discrete state estimation via active sensing, we show that an adaptive greedy policy has a near-optimal performance guarantee when the reward function possesses this property. We further show that the reward function for group-based active diagnosis, which arises in applications such as medical diagnosis and state estimation with persistent sensor faults, is also weakly adaptive submodular. Finally, in experiments of state estimation for an aircraft electrical system with persistent sensor faults, we observe that an adaptive greedy policy performs equally well as an exhaustive search.
Communication lower bounds for statistical estimation problems via a distributed data processing inequality

We study the tradeoff between the statistical error and communication cost of distributed statistical estimation problems in high dimensions. In the distributed sparse Gaussian mean estimation problem, each of the m machines receives n data points from a d-dimensional Gaussian distribution with unknown mean θ which is promised to be k-sparse. The machines communicate by message passing and aim to estimate the mean θ. We provide a tight (up to logarithmic factors) tradeoff between the estimation error and the number of bits communicated between the machines. This directly leads to a lower bound for the distributed sparse linear regression problem: to achieve the statistical minimax error, the total communication is at least Ω(min{n,d}m), where n is the number of observations that each machine receives and d is the ambient dimension. These lower results improve upon Shamir (NIPS'14) and Steinhardt-Duchi (COLT'15) by allowing multi-round iterative communication model. We also give the first optimal simultaneous protocol in the dense case for mean estimation. As our main technique, we prove a distributed data processing inequality, as a generalization of usual data processing inequalities, which might be of independent interest and useful for other problems.
Translation-like actions of nilpotent groups

We give a new obstruction to translation-like actions on nilpotent groups. Suppose we are given two finitely generated torsion-free nilpotent groups with the same degree of polynomial growth, but non-isomorphic Carnot completions (asymptotic cones). We show that there exists no injective Lipschitz function from one group to the other. It follows that neither group can act translation-like on the other. As Lipschitz injections need not be bi-Lipschitz embeddings, this is a strengthening of a classical result of Pansu in the context of groups of the same homogeneous dimension.
Improved densification of one permutation hashing

The existing work on densification of one permutation hashing [24] reduces the query processing cost of the (K, L)-parameterized Locality Sensitive Hashing (LSH) algorithm with minwise hashing, from O(dKL) to merely O(d + KL), where d is the number of nonzeros of the data vector, K is the number of hashes in each hash table, and L is the number of hash tables. While that is a substantial improvement, our analysis reveals that the existing densification scheme in [24] is sub-optimal. In particular, there is no enough randomness in that procedure, which affects its accuracy on very sparse datasets. In this paper, we provide a new densification procedure which is provably better than the existing scheme [24]. This improvement is more significant for very sparse datasets which are common over the web. The improved technique has the same cost of O(d + KL) for query processing, thereby making it strictly preferable over the existing procedure. Experimental evaluations on public datasets, in the task of hashing based near neighbor search, support our theoretical findings.
Large Nonlinear Kerr Angle in non-Centrosymmetric Fe/AlGaAs (001) Heterostructure

A large nonlinear magneto-optical effect is observed in a non-centrosymmetric Fe/AlGaAs (001) heterostructure. This effect is a direct consequence of interference between second-harmonic optical waves of magnetic and crystallographic origin, generated at ferromagnetic Fe interface and bulk AlGaAs, respectively. The longitudinal nonlinear Kerr rotation is measured to be 1.6° along the [1-10] hard axis, about two orders of magnitude stronger than the linear equivalent. The rotational second-harmonic signal shows large magnetic contrast along all the in-plane directions, demonstrating a high sensitivity to the magnetization of an anisotropic interface in the longitudinal geometry.
Triangle Generative Adversarial Networks

A Triangle Generative Adversarial Network ($\Delta$-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples. $\Delta$-GAN consists of four neural networks, two generators and two discriminators. The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs. The generators and discriminators are trained together using adversarial learning. Under mild assumptions, in theory the joint distributions characterized by the two generators concentrate to the data distribution. In experiments, three different kinds of domain pairs are considered, image-label, image-image and image-attribute pairs. Experiments on semi-supervised image classification, image-to-image translation and attribute-based image generation demonstrate the superiority of the proposed approach.
Critical Set of the Master Function and Characteristic Variety of the Associated Gauss–Manin Differential Equations

We consider a weighted family of n parallelly transported hyperplanes in a k-dimensional affine space and describe the characteristic variety of the Gauss–Manin differential equations for associated hypergeometric integrals. The characteristic variety is given as the zero set of Laurent polynomials, whose coefficients are determined by weights and the associated point in the Grassmannian Gr(k, n). The Laurent polynomials are in involution. These statements generalize (Varchenko, Mathematics 2:218–231, 2014), where such a description was obtained for a weighted generic family of parallelly transported hyperplanes. An intermediate object between the differential equations and the characteristic variety is the algebra of functions on the critical set of the associated master function. We construct a linear isomorphism between the vector space of the Gauss–Manin differential equations and the algebra of functions. The isomorphism allows us to describe the characteristic variety. It also allowed us to define an integral structure on the vector space of the algebra and the associated (combinatorial) connection on the family of such algebras.
SLADS-Net: Supervised learning approach for dynamic sampling using deep neural networks

In scanning microscopy based imaging techniques, there is a need to develop novel data acquisition schemes that can reduce the time for data acquisition and minimize sample exposure to the probing radiation. Sparse sampling schemes are ideally suited for such applications where the images can be reconstructed from a sparse set of measurements. In particular, dynamic sparse sampling based on supervised learning has shown promising results for practical applications. However, a particular drawback of such methods is that it requires training image sets with similar information content which may not always be available. In this paper, we introduce a Supervised Learning Approach for Dynamic Sampling (SLADS) algorithm that uses a deep neural network based training approach. We call this algorithm SLADS- Net. We have performed simulated experiments for dynamic sampling using SLADS-Net in which the training images either have similar information content or completely different information content, when compared to the testing images. We compare the performance across various methods for training such as least- squares, support vector regression and deep neural networks. From these results we observe that deep neural network based training results in superior performance when the training and testing images are not similar. We also discuss the development of a pre-trained SLADS-Net that uses generic images for training. Here, the neural network parameters are pre-trained so that users can directly apply SLADS-Net for imaging experiments.
Lower semicontinuity of mass under $C^0$ convergence and Huisken's isoperimetric mass

Given a sequence of asymptotically flat 3-manifolds of nonnegative scalar curvature with outermost minimal boundary, converging in the pointed $C^0$ Cheeger--Gromov sense to an asymptotically flat limit space, we show that the total mass of the limit is bounded above by the liminf of the total masses of the sequence. In other words, total mass is lower semicontinuous under such convergence. In order to prove this, we use Huisken's isoperimetric mass concept, together with a modified weak mean curvature flow argument. We include a brief discussion of Huisken's work before explaining our extension of that work. The results are all specific to three dimensions.