cssi/SciSciGPT-SciSciNet huggingface dataset:
Dipolar cycloaddition of rhodium-generated carbonyl ylides with p-quinones.

The dipolar cycloaddition of carbonyl ylides generated by the rhodium-catalyzed decomposition of δ- and e-carbonyl-α-diazoketones with p-quinones leads to both CO and CC addition products. The product ratio is solvent- and catalyst-dependent and has been optimized to favor formation of either product. The CC addition products of naphthoquinones are used in the assembly of structures hybridizing the illudin and anthraquinone anticancer agents.
CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing

High-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.
Iron(II)-promoted amidoglycosylation and amidochlorination of an allal C3–azidoformate

Abstract Iron(II) halides promoted intramolecular alkene amidation in a C3–allal azidoformate. In the presence of alcohols, one-pot β-glycosylation followed. Without added alcohol, amidochlorination occurred using FeCl2, providing an anomeric mixture of glycosyl chlorides which could be used in subsequent silver ion-mediated couplings. A 2-amido glycosyl chloride with a nitrogen-bound iron center is proposed as the glycosylating agent for the in situ amidoglycosylations with FeCl2.
Recurrent Loss-of-Function Mutations Reveal Costs to OAS1 Antiviral Activity in Primates

Immune responses counteract infections but also cause collateral damage to hosts. Oligoadenylate synthetase 1 (OAS1) binds double-stranded RNA from invading viruses and produces 2'-5' linked oligoadenylate (2-5A) to activate ribonuclease L (RNase L), which cleaves RNA to inhibit virus replication. OAS1 can also undergo autoactivation by host RNAs, a potential trade-off to antiviral activity. We investigated functional variation in primate OAS1 as a model for how immune pathways evolve to mitigate costs and observed a surprising frequency of loss-of-function variation. In gorillas, we identified a polymorphism that severely decreases catalytic function, mirroring a common variant in humans that impairs 2-5A synthesis through alternative splicing. OAS1 loss-of-function variation is also common in monkeys, including complete loss of 2-5A synthesis in tamarins. The frequency of loss-of-function alleles suggests that costs associated with OAS1 activation can be so detrimental to host fitness that pathogen-protective effects are repeatedly forfeited.
The Future of Organometallic Chemistry

This article outlines the advocacy role that a journal can play for the field of organometallic chemistry in an era where agency and foundation programs and initiatives are increasingly directed at “grand challenges”, as opposed to fundamental research in traditional disciplines. It introduces a series of essays and articles on the future of organometallic chemistry, followed by the author’s own views, and highlights some developments from the year 2010 that illustrate the sustained importance of the field. Possible roles for readers and practitioners are discussed.
WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial Sequences.

Background Metagenome shotgun sequencing presents opportunities to identify organisms that may prevent or promote disease. The analysis of sample diversity is achieved by taxonomic identification of metagenomic reads followed by generating an abundance profile. Numerous tools have been developed based on different design principles. Tools achieving high precision can lack sensitivity in some applications. Conversely, tools with high sensitivity can suffer from low precision and require long computation time. Methods In this paper, we present WEVOTE (WEighted VOting Taxonomic idEntification), a method that classifies metagenome shotgun sequencing DNA reads based on an ensemble of existing methods using k-mer-based, marker-based, and naive-similarity based approaches. Our evaluation on fourteen benchmarking datasets shows that WEVOTE improves the classification precision by reducing false positive annotations while preserving a high level of sensitivity. Conclusions WEVOTE is an efficient and automated tool that combines multiple individual taxonomic identification methods to produce more precise and sensitive microbial profiles. WEVOTE is developed primarily to identify reads generated by MetaGenome Shotgun sequencing. It is expandable and has the potential to incorporate additional tools to produce a more accurate taxonomic profile. WEVOTE was implemented using C++ and shell scripting and is available at www.github.com/aametwally/WEVOTE.
Syntheses, Structures, and Reactions of Binary and Tertiary Thiomolybdate Complexes Containing the (O)Mo(Sx) AND (S)Mo(Sx) Functional Groups (x = 1, 2, 4)

Publisher Summary This chapter discusses syntheses, structures, and reactions of binary and tertiary thiomolybdate complexes containing the (O)Mo(Sx,) and (S)Mo(Sx,) functional Groups. Molybdenum is one of the most abundant elements on the planet (found as molybdenite—MoS 2 and wulfenite—PbMoO), and in the oceans molybdenum is the most abundant of the redox-active transition elements. The importance of molybdenum sulfur compounds in nonbiological catalysis is exemplified by the hydrodesulfurization (HDS) reaction. The chemistry of molybdenum ions with sulfur ligands is unique when compared to other transition metal ions. At times, the ground states of electron-redistribution isomers of isoelectronic complexes are close enough in energy so that minor perturbations (like crystal packing forces, solvent dielectrics, and ion pairing) are sufficient to stabilize preferentially one isomer over another. A multitude of heterometallic complexes are obtained whereby the thiomolybdate anions serve as ligands. The Mo atoms found at the edges of the MoS 2 crystallites may be bound by any of several oxo, thio, or sulfide ligands and these units may be directly or indirectly involved in the catalytic process.
Gold as Hydrogen. An Experimental and Theoretical Study of the Structures and Bonding in Disilicon Gold Clusters Si2Aun- and Si2Aun (n = 2 and 4) and Comparisons to Si2H2 and Si2H4

In a previous communication, we showed that a single Au atom behaves like H in its bonding to Si in a series of Si-Au clusters, SiAu n (n = 2-4) (Kiran et al. Angew. Chem., Int. Ed. 2004, 43, 2125). In this article, we show that the H analogy of Au is more general. We find that the chemical bonding and potential energy surfaces of two disilicon Au clusters, Si 2 Au 2 and Si 2 Au 4 , are analogous to Si 2 H 2 and Si 2 H 4 , respectively. Photoelectron spectroscopy and ab initio calculations are used to investigate the geometrical and electronic structures of Si 2 Au 2 - , Si 2 Au 4 - , and their neutral species. The most stable structures for both Si 2 Au 2 and Si 2 Au 2 - are found to be C 2 v , in which each Au bridges the two Si atoms. For Si 2 Au 4 - , two nearly degenerate dibridged structures in a cis (C 2 h ) and a trans (C 2 v ) configuration are found to be the most stable isomers. However, in the neural potential energy surface of Si 2 Au 4 , a monobridged isomer is the global minimum. The ground-state structures of Si 2 Au 2 - and Si 2 Au 4 - are confirmed by comparing the computed vertical detachment energies with the experimental data. The various stable isomers found for Si 2 Au 2 and Si 2 Au 4 are similar to those known for Si 2 H 2 and Si 2 H 4 , respectively. Geometrical and electronic structure comparisons with the corresponding silicon hydrides are made to further establish the isolobal analogy between a gold atom and a hydrogen atom.
Bidirectional Tandem Pseudoproline Ligations of Proline-Rich Helical Peptides

We have developed a bidirectional ligation strategy for preparing proline-rich peptides that couples three unprotected segments in tandem to form two pseudoproline bonds (thia- or oxaproline) witho...
The evolutionary advantage of heritable phenotypic heterogeneity.

Phenotypic plasticity is an evolutionary driving force in diverse biological processes, including the adaptive immune system, the development of neoplasms, and the persistence of pathogens despite drug pressure. It is essential, therefore, to understand the evolutionary advantage of an allele that confers on cells the ability to express a range of phenotypes. Here, we study the fate of a new mutation that allows the expression of multiple phenotypic states, introduced into a finite population of individuals that can express only a single phenotype. We show that the advantage of such a mutation depends on the degree of phenotypic heritability between generations, called phenotypic memory. We analyze the fixation probability of the phenotypically plastic allele as a function of phenotypic memory, the variance of expressible phenotypes, the rate of environmental changes, and the population size. We find that the fate of a phenotypically plastic allele depends fundamentally on the environmental regime. In constant environments, plastic alleles are advantageous and their fixation probability increases with the degree of phenotypic memory. In periodically fluctuating environments, by contrast, there is an optimum phenotypic memory that maximizes the probability of the plastic allele’s fixation. This same optimum memory also maximizes geometric mean fitness, in steady state. We interpret these results in the context of previous studies in an infinite-population framework. We also discuss the implications of our results for the design of therapies that can overcome persistence and, indirectly, drug resistance.
Electron transfer emission in simple transition metal donor–acceptor systems

Abstract Electron transfer luminescences have been found in a simple class of covalently linked, CrIII CN RuII, transition metal complexes at 77 K in a DMSO/H2O glass. These emissions are broad, structureless and centered at about 850 nm. The emission lifetimes are on the order of 1 μs, and increase 16–26-fold upon perdeuteration of coordinated am(m)ines. The properties of the electron-transfer excited states are consistent with an inverted region, spin forbidden emission process.
An efficient route to chiral, non-racemic 3-alkyl-3-arylpyrrolidines. Improved stereoselectivity in alkylation of bicyclic lactams and the effect of leaving groups

Abstract Chiral 3-alkyl-3-arylpyrrolidines were prepared from the substituted bicyclic lactams. Stereoselectivity in the alkylation of bicyclic lactams was improved by using an alkyl triflate or tosylate as the electrophile.
Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.

Abstract Objective Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers. Materials and Methods All clinical notes available on the 38,575 patients enrolled in the Mount Sinai BioMe cohort were loaded into the NLP system. We compared analysis of structured and unstructured EHR data using NLP, free-text search, and diagnostic codes with validation against expert adjudication. We then used the NLP findings to measure physician impression of progression from early-stage NAFLD to NASH or cirrhosis. Similarly, we used the same NLP findings to identify mentions of NAFLD in radiology reports that did not persist into clinical notes. Results Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP outperformed ICD and text search in both sensitivity (NLP: 0.93, ICD: 0.28, text search: 0.81) and F2 score (NLP: 0.92, ICD: 0.34, text search: 0.81). Of 2281 NAFLD patients, 673 (29.5%) were believed to have progressed to NASH or cirrhosis. Among 176 where NAFLD was noted prior to NASH, the average progression time was 410 days. 619 (27.1%) NAFLD patients had it documented only in radiology notes and not acknowledged in other forms of clinical documentation. Of these, 170 (28.4%) were later identified as having likely developed NASH or cirrhosis after a median 1057.3 days. Discussion NLP-based approaches were more accurate at identifying NAFLD within the EHR than ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. Analysis of information flows demonstrated loss of key information that could have been used to help prevent the progression of early NAFLD (NAFL) to NASH or cirrhosis. Conclusion For identification of NAFLD, NLP performed better than alternative selection modalities. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.
Almost additive entropy

We explore consequences of a hyperbolic metric induced by the composition property of the Harvda–Charvat/Daroczy/Cressie–Read/Tsallis entropy. We address the special case of systems described by small deviations of the non-extensive parameter q ≈ 1 from the "ordinary" additive case which is described by the Boltzmann/Gibbs/Shannon entropy. By applying the Gromov/Ruh theorem for almost flat manifolds, we show that such systems have a power-law rate of expansion of their configuration/phase space volume. We explore the possible physical significance of some geometric and topological results of this approach.
Reducibility and computational lower bounds for problems with planted sparse structure

Recently, research in unsupervised learning has gravitated towards exploring statistical-computational gaps induced by sparsity. A line of work initiated by Berthet and Rigollet has aimed to explain these gaps through reductions to conjecturally hard problems from complexity theory. However, the delicate nature of average-case reductions has limited the development of techniques and often led to weaker hardness results that only apply to algorithms that are robust to different noise distributions or that do not need to know the parameters of the problem. We introduce several new techniques to give a web of average-case reductions showing strong computational lower bounds based on the planted clique conjecture for planted independent set, planted dense subgraph, biclustering, sparse rank-1 submatrix, sparse PCA and the subgraph stochastic block model. Our results demonstrate that, despite the delicate nature of average-case reductions, using natural problems as intermediates can often be beneficial, as is the case in worst-case complexity. Our main technical contribution is to introduce a set of techniques for average-case reductions that: (1) maintain the level of signal in an instance of a problem; (2) alter its planted structure; and (3) map two initial high-dimensional distributions simultaneously to two target distributions approximately under total variation. We also give algorithms matching our lower bounds and identify the information-theoretic limits of the models we consider.
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples. We propose Rank Pruning (RP) to solve noisy PN learning and the open problem of estimating the noise rates, i.e. the fraction of wrong positive and negative labels. Unlike prior solutions, RP is time-efficient and general, requiring O(T) for any unrestricted choice of probabilistic classifier with T fitting time. We prove RP has consistent noise estimation and equivalent expected risk as learning with uncorrupted labels in ideal conditions, and derive closed-form solutions when conditions are non-ideal. RP achieves state-of-the-art noise estimation and F1, error, and AUC-PR for both MNIST and CIFAR datasets, regardless of the amount of noise and performs similarly impressively when a large portion of training examples are noise drawn from a third distribution. To highlight, RP with a CNN classifier can predict if an MNIST digit is a "one"or "not" with only 0.25% error, and 0.46 error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.
Elasticity Detection: A Building Block for Delay-Sensitive Congestion Control

This paper develops a technique to detect whether the cross traffic competing with a flow is elastic or not, and shows how to use the elasticity detector to improve congestion control. If the cross traffic is elastic, i.e., made up of buffer-filling flows like Cubic or Reno, then one should use a scheme that competes well with such traffic. Such a scheme will not be able to control delays because the cross traffic will not cooperate. If, however, cross traffic is inelastic, then one can use a suitable delay-sensitive congestion control algorithm, which can control delays, but which would have obtained dismal throughput when run concurrently with a buffer-filling algorithm. We use the elasticity detector to demonstrate a congestion control framework that always achieves high utilization, but which can also achieve low delays when cross traffic permits it. The technique uses an asymmetric sinusoidal pulse patternand estimates elasticity by computing the frequency response(FFT) of the cross traffic estimate; we have measured its accuracy to be over 90%. We have developed Nimbus, a protocol that explicitly switches between TCP-competitive and delay-sensitive modes using the elasticity detector. Our results on emulated and real-world paths show that Nimbus achieves throughput comparable to or better than Cubic always, but with delays that are much lower when cross traffic is inelastic. Unlike BBR, Nimbus is fair to Cubic, and has significantly lower delay in all cases; for example, on real-world paths, Nimbus has 11% lower throughput but at 40-50 ms lower packet delay.
Affine Cartesian codes with complementary duals

Abstract A linear code C with the property that C ∩ C ⊥ = { 0 } is said to be a linear complementary dual, or LCD, code. In this paper, we consider generalized affine Cartesian codes which are LCD. Generalized affine Cartesian codes arise naturally as the duals of affine Cartesian codes in the same way that generalized Reed–Solomon codes arise as duals of Reed–Solomon codes. Generalized affine Cartesian codes are evaluation codes constructed by evaluating multivariate polynomials of bounded degree at points in an m-dimensional Cartesian set over a finite field K and scaling the coordinates. The LCD property depends on the scalars used. Because Reed–Solomon codes are a special case, we obtain a characterization of those generalized Reed–Solomon codes which are LCD along with the more general result for generalized affine Cartesian codes. These results are independent of the characteristic of the underlying field.
Sequence classification of the limit order book using recurrent neural networks

Abstract Recurrent neural networks (RNNs) are types of artificial neural networks (ANNs) that are well suited to forecasting and sequence classification. They have been applied extensively to forecasting univariate financial time series, however their application to high frequency trading has not been previously considered. This paper solves a sequence classification problem in which a short sequence of observations of limit order book depths and market orders is used to predict a next event price-flip. The capability to adjust quotes according to this prediction reduces the likelihood of adverse price selection. Our results demonstrate the ability of the RNN to capture the non-linear relationship between the near-term price-flips and a spatio-temporal representation of the limit order book. The RNN compares favorably with other classifiers, including a linear Kalman filter, using S&P500 E-mini futures level II data over the month of August 2016. Further results assess the effect of retraining the RNN daily and the sensitivity of the performance to trade latency.
Einstein metrics and Mostow rigidity

Using the new diffeomorphism invariants of Seiberg and Witten, a uniqueness theorem is proved for Einstein metrics on compact quotients of irreducible 4-dimensional symmetric spaces of non-compact type. The proof also yields a Riemannian version of the Miyaoka-Yau inequality.
A model of the entrance pupil of the human eye

The pupil aperture of the iris is subject to refraction by the cornea, and thus an outside observer sees a virtual image: the "entrance pupil" of the eye. When viewed off-axis, the entrance pupil has an elliptical form. The precise appearance of the entrance pupil is the consequence of the anatomical and optical properties of the eye, and the relative positions of the eye and the observer. This paper presents a ray-traced, exact model eye that provides the parameters of the entrance pupil ellipse for an observer at an arbitrary location. The model is able to reproduce empirical measurements of the shape of the entrance pupil with good accuracy. I demonstrate that accurate specification of the entrance pupil of a stationary eye requires modeling of corneal refraction, the misalignment of the visual and optical axes, and the non-circularity of the pupil aperture. The model, including a three-dimensional ray-tracing function through quadric surfaces, is implemented in open-source MATLAB code.
Efficiently searching for frustrated cycles in MAP inference

Dual decomposition provides a tractable framework for designing algorithms for finding the most probable (MAP) configuration in graphical models. However, for many real-world inference problems, the typical decomposition has a large integrality gap, due to frustrated cycles. One way to tighten the relaxation is to introduce additional constraints that explicitly enforce cycle consistency. Earlier work showed that cluster-pursuit algorithms, which iteratively introduce cycle and other higher-order consistency constraints, allows one to exactly solve many hard inference problems. However, these algorithms explicitly enumerate a candidate set of clusters, limiting them to triplets or other short cycles. We solve the search problem for cycle constraints, giving a nearly linear time algorithm for finding the most frustrated cycle of arbitrary length. We show how to use this search algorithm together with the dual decomposition framework and cluster-pursuit. The new algorithm exactly solves MAP inference problems arising from relational classification and stereo vision.
How regularization affects the critical points in linear networks

This paper is concerned with the problem of representing and learning a linear transformation using a linear neural network. In recent years, there is a growing interest in the study of such networks, in part due to the successes of deep learning. The main question of this body of research (and also of our paper) is related to the existence and optimality properties of the critical points of the mean-squared loss function. An additional primary concern of our paper pertains to the robustness of these critical points in the face of (a small amount of) regularization. An optimal control model is introduced for this purpose and a learning algorithm (backprop with weight decay) derived for the same using the Hamilton's formulation of optimal control. The formulation is used to provide a complete characterization of the critical points in terms of the solutions of a nonlinear matrix-valued equation, referred to as the characteristic equation. Analytical and numerical tools from bifurcation theory are used to compute the critical points via the solutions of the characteristic equation.
How many Adjunctions give Rise to the same Monad

Given an adjoint pair of functors F, G, the composite GF naturally gets the structure of a monad. The same monad may arise from many such adjoint pairs of functors, however. Can one describe all of the adjunctions giving rise to a given monad? In this paper we single out a class of adjunctions with especially good properties, and we develop methods for computing all such adjunctions, up to natural equivalence, which give rise to a given monad. To demonstrate these methods, we explicitly compute the finitary homological presentations of the free A-module monad on the category of sets, for A a Dedekind domain. We also prove a criterion, reminiscent of Beck’s monadicity theorem, for when there is essentially (in a precise sense) only a single adjunction that gives rise to a given monad.
Multi-processor approximate message passing using lossy compression

In this paper, a communication-efficient multi-processor compressed sensing framework based on the approximate message passing algorithm is proposed. We perform lossy compression on the data being communicated between processors, resulting in a reduction in communication costs with a minor degradation in recovery quality. In the proposed framework, a new state evolution formulation takes the quantization error into account, and analytically determines the coding rate required in each iteration. Two approaches for allocating the coding rate, an online back-tracking heuristic and an optimal allocation scheme based on dynamic programming, provide significant reductions in communication costs.
Average behavior of minimal free resolutions of monomial ideals

We describe the typical homological properties of monomial ideals defined by random generating sets. We show that, under mild assumptions, random monomial ideals (RMI's) will almost always have resolutions of maximal length; that is, the projective dimension will almost always be $n$, where $n$ is the number of variables in the polynomial ring. We give a rigorous proof that Cohen-Macaulayness is a "rare" property. We characterize when an RMI is generic/strongly generic, and when it "is Scarf"---in other words, when the algebraic Scarf complex of $M\subset S=k[x_1,\ldots,x_n]$ gives a minimal free resolution of $S/M$. As a result we see that, outside of a very specific ratio of model parameters, RMI's are Scarf only when they are generic. We end with a discussion of the average magnitude of Betti numbers.
An alternating matrix and a vector, with application to Aluffi algebras

Abstract Let X be a generic alternating matrix, t be a generic row vector, and J be the ideal Pf 4 ( X ) + I 1 ( tX ) . We prove that J is a perfect Gorenstein ideal of grade equal to the grade of Pf 4 ( X ) plus two. This result is used by Ramos and Simis in their calculation of the Aluffi algebra of the module of derivations of the homogeneous coordinate ring of a smooth projective hypersurface. We also prove that J defines a domain, or a normal ring, or a unique factorization domain if and only if the base ring has the same property. The main object of study in the present paper is the module N which is equal to the column space of X, calculated mod Pf 4 ( X ) . The module N is a self-dual maximal Cohen–Macaulay module of rank two; furthermore, J is a Bourbaki ideal for N . The ideals which define the homogeneous coordinate rings of the Plucker embeddings of the Schubert subvarieties of the Grassmannian of planes are used in the study of the module N .
Improving Gibbs Sampler Scan Quality with DoGS

The pairwise influence matrix of Dobrushin has long been used as an analytical tool to bound the rate of convergence of Gibbs sampling. In this work, we use Dobrushin influence as the basis of a practical tool to certify and efficiently improve the quality of a discrete Gibbs sampler. Our Dobrushin-optimized Gibbs samplers (DoGS) offer customized variable selection orders for a given sampling budget and variable subset of interest, explicit bounds on total variation distance to stationarity, and certifiable improvements over the standard systematic and uniform random scan Gibbs samplers. In our experiments with joint image segmentation and object recognition, Markov chain Monte Carlo maximum likelihood estimation, and Ising model inference, DoGS consistently deliver higher-quality inferences with significantly smaller sampling budgets than standard Gibbs samplers.
A variational approach to the consistency of spectral clustering

Abstract This paper establishes the consistency of spectral approaches to data clustering. We consider clustering of point clouds obtained as samples of a ground-truth measure. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. We investigate the spectral convergence of both unnormalized and normalized graph Laplacians towards the appropriate operators in the continuum domain. We obtain sharp conditions on how the connectivity radius can be scaled with respect to the number of sample points for the spectral convergence to hold. We also show that the discrete clusters obtained via spectral clustering converge towards a continuum partition of the ground truth measure. Such continuum partition minimizes a functional describing the continuum analogue of the graph-based spectral partitioning. Our approach, based on variational convergence, is general and flexible.
Real rank two geometry

Abstract The real rank two locus of an algebraic variety is the closure of the union of all secant lines spanned by real points. We seek a semi-algebraic description of this set. Its algebraic boundary consists of the tangential variety and the edge variety. Our study of Segre and Veronese varieties yields a characterization of tensors of real rank two.
The Hochschild-Serre property for some p-adic analytic group actions

Let \(H \subseteq G\) be an inclusion of p-adic Lie groups. When H is normal or even subnormal in G, the Hochschild-Serre spectral sequence implies that any topological G-module whose H-cohomology vanishes in all degrees also has vanishing G-cohomology. With an eye towards applications in p-adic Hodge theory, we extend this to some cases where H is not subnormal, assuming that the G-action is analytic in the sense of Lazard.
Unsupervised Representation Learning of Speech for Dialect Identification

In this paper, we explore the use of a factorized hierarchical variational autoencoder (FHVAE) model to learn an unsupervised latent representation for dialect identification (DID). An FHVAE can learn a latent space that separates the more static attributes within an utterance from the more dynamic attributes by encoding them into two different sets of latent variables. Useful factors for dialect identification, such as phonetic or linguistic content, are encoded by a segmental latent variable, while irrelevant factors that are relatively constant within a sequence, such as a channel or a speaker information, are encoded by a sequential latent variable. The disentanglement property makes the segmental latent variable less susceptible to channel and speaker variation, and thus reduces degradation from channel domain mismatch. We demonstrate that on fully-supervised DID tasks, an end-to-end model trained on the features extracted from the FHVAE model achieves the best performance, compared to the same model trained on conventional acoustic features and an i-vector based system. Moreover, we also show that the proposed approach can leverage a large amount of unlabeled data for FHVAE training to learn domain-invariant features for DID, and significantly improve the performance in a low-resource condition, where the labels for the in-domain data are not available.
Eliminating network protocol vulnerabilities through abstraction and systems language design

Incorrect implementations of network protocol message specifications affect the stability, security, and cost of network system development. Most implementation defects fall into one of three categories of well defined message constraints. However, the general process of constructing network protocol stacks and systems does not capture these categorical constraints. We introduce a systems programming language with new abstractions that capture these constraints. Safe and efficient implementations of standard message handling operations are synthesized by our compiler, and whole-program analysis is used to ensure constraints are never violated. We present language examples using the OpenFlow protocol.
Stochastic Bouncy Particle Sampler

We introduce a novel stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear. The algorithm is based on simulating first arrival times in a doubly stochastic Poisson process using the thinning method, and allows efficient sampling of Bayesian posteriors in big datasets. We prove that in the BPS no bias is introduced by noisy evaluations of the log-likelihood gradient. On the other hand, we argue that efficiency considerations favor a small, controllable bias in the construction of the thinning proposals, in exchange for faster mixing. We introduce a simple regression-based proposal intensity for the thinning method that controls this trade-off. We illustrate the algorithm in several examples in which it outperforms both unbiased, but slowly mixing stochastic versions of BPS, as well as biased stochastic gradient-based samplers.
Variants of the Entropy Power Inequality

An extension of the entropy power inequality to the form $N_{r}^\alpha (X+Y) \geq N_{r}^\alpha (X) + N_{r}^\alpha (Y)$ with arbitrary independent summands $X$ and $Y$ in $ {\mathbb {R}}^{n}$ is obtained for the Renyi entropy and powers $\alpha \geq ~(r+1)/2$ .
A tractable approach to finding closest truncated-commute-time neighbors in large graphs

Recently there has been much interest in graph-based learning, with applications in collaborative filtering for recommender networks, link prediction for social networks and fraud detection. These networks can consist of millions of entities, and so it is very important to develop highly efficient techniques. We are especially interested in accelerating random walk approaches to compute some very interesting proximity measures of these kinds of graphs. These measures have been shown to do well empirically (Liben-Nowell & Kleinberg, 2003; Brand, 2005). We introduce a truncated variation on a well-known measure, namely commute times arising from random walks on graphs. We present a very novel algorithm to compute all interesting pairs of approximate nearest neighbors in truncated commute times, without computing it between all pairs. We show results on both simulated and real graphs of size up to 100,000 entities, which indicate near-linear scaling in computation time.
When do links admit homeomorphic C-complexes?

Any two knots admit orientation preserving homeomorphic Seifert surfaces, as can be seen by stabilizing. There is a generalization of a Seifert surface to the setting of links called a C-complex. In this paper, we ask when two links will admit orientation preserving homeomorphic C-complexes. In the case of 2-component links, we find that the pairwise linking number provides a complete obstruction. In the case of links with 3 or more components and zero pairwise linking number, Milnor’s triple linking number provides a complete obstruction.
On the $C^1$ regularity of solutions to divergence form elliptic systems with Dini-continuous coefficients

The author proves C 1 regularity of solutions to divergence form elliptic systems with Dini-continuous coefficients.
Age-optimal information updates in multihop networks

The problem of reducing the age-of-information has been extensively studied in single-hop networks. In this paper, we minimize the age-of-information in general multihop networks. If the packet transmission times over the network links are exponentially distributed, we prove that a preemptive Last Generated First Served (LGFS) policy results in smaller age processes at all nodes of the network (in a stochastic ordering sense) than any other causal policy. In addition, for arbitrary distributions of packet transmission times, the non-preemptive LGFS policy is shown to minimize the age processes at all nodes among all non-preemptive work-conserving policies (again in a stochastic ordering sense). It is surprising that such simple policies can achieve optimality of the joint distribution of the age processes at all nodes even under arbitrary network topologies, as well as arbitrary packet generation and arrival times. These optimality results not only hold for the age processes, but also for any non-decreasing functional of the age processes.
The sensual Apollonian circle packing

Abstract The curvatures of the circles in integral Apollonian circle packings, named for Apollonius of Perga (262–190 BC), form an infinite collection of integers whose Diophantine properties have recently seen a surge in interest. Here, we give a new description of Apollonian circle packings built upon the study of the collection of bases of Z [ i ] 2 , inspired by, and intimately related to, the ‘sensual quadratic form’ of Conway.
W∗-superrigidity for coinduced actions

We prove W∗-superrigidity for a large class of coinduced actions. We prove that if Σ is an amenable almost-malnormal subgroup of an infinite conjugagy class (icc) property (T) countable group Γ, the coinduced action Γ ↷ X from an arbitrary probability measure preserving action Σ ↷ X0 is W∗-superrigid. We also prove a similar statement if Γ is an icc non-amenable group which is measure equivalent to a product of two infinite groups. In particular, we obtain that any Bernoulli action of such a group Γ is W∗-superrigid.
Vapor inhalation of cannabidiol (CBD) in rats.

Abstract Rationale Cannabidiol (CBD), a compound found in many strains of the Cannabis genus, is increasingly available in e-cigarette liquids as well as other products. CBD use has been promoted for numerous purported benefits which have not been rigorously assessed in preclinical studies. Objective To further validate an inhalation model to assess CBD effects in the rat. The primary goal was to determine plasma CBD levels after vapor inhalation and compare that with the levels observed after injection. Secondary goals were to determine if hypothermia is produced in male Sprague-Dawley rats and if CBD affects nociception measured by the warm water tail-withdrawal assay. Methods Blood samples were collected from rats exposed for 30 min to vapor generated by an e-cigarette device using CBD (100, 400 mg/mL in the propylene glycol vehicle). Separate experiments assessed the body temperature response to CBD in combination with nicotine (30 mg/mL) and the anti-nociceptive response to CBD. Results Vapor inhalation of CBD produced concentration-related plasma CBD levels in male and female Wistar rats that were within the range of levels produced by 10 or 30 mg/kg, CBD, i.p. Dose-related hypothermia was produced by CBD in male Sprague-Dawley rats, and nicotine (30 mg/mL) inhalation enhanced this effect. CBD inhalation had no effect on anti-nociception alone or in combination with Δ9-tetrahydrocannabinol inhalation. Conclusions The vapor-inhalation approach is a suitable pre-clinical model for the investigation of the effects of inhaled CBD. This route of administration produces hypothermia in rats, while i.p. injection does not, at comparable plasma CBD levels.
Time-Shared Execution of Realtime Computer Vision Pipelines by Dynamic Partial Reconfiguration

This paper presents an FPGA runtime framework that demonstrates the feasibility of using dynamic partial reconfiguration (DPR) for time-sharing an FPGA by multiple realtime computer vision pipelines. The presented time-sharing runtime framework manages an FPGA fabric that can be round-robin time-shared by different pipelines at the time scale of individual frames. In this new use-case, the challenge is to achieve useful performance despite high reconfiguration time. The paper describes the basic runtime support as well as four optimizations necessary to achieve realtime performance given the limitations of DPR on today's FPGAs. The paper provides a characterization of a working runtime framework prototype on a Xilinx ZC706 development board. The paper also reports the performance of streaming vision pipelines when time-shared.
Liquid-Argon Time Projection Chambers in the U.S

Liquid Argon Time Projection Chamber (LAr TPC) detectors are ideally suited for studying neutrino interactions and probing the parameters that characterize neutrino oscillations. The ability to drift ionization particles over long distances in purified argon and to trigger on abundant scintillation light allows for excellent particle identification and triggering capability. Recent U.S. based work in the development of LAr TPC technology for massive kiloton size detectors will be discussed in this talk, including details of the ArgoNeuT (Argon Neutrino Test) test-beam project, which is a 175 liter LAr TPC exposed to Fermilab's NuMI neutrino beamline.
Maxima of the Q-index: degenerate graphs

Let G be a k-degenerate graph of order n. It is well-known that G has no more edges than Sn,k, the join of a complete graph of order k and an independent set of order n−k. In this note, it is shown that Sn,k is extremal for some spectral parameters of G as well. More precisely, letting µ (H) and q (H) denote the largest eigenvalues of the adjacency matrix and the signless Laplacian of a graph H, the inequalities µ (G) < µ (Sn,k) and q (G) < q (Sn,k) hold, unless G = Sn,k. The latter inequality is deduced from the following general bound, which improves some previous bounds on q (G): If G is a graph of order n, with m edges, with maximum degree ∆ and minimum degree δ, then q (G) ≤ min { 2∆, 1/2 ( ∆ + 2δ − 1 +√(∆ + 2δ − 1)2 + 16m − 8 (n − 1 + ∆) δ)} . Equality holds if and only if G is regular or G has a component of order ∆ + 1 in which every vertex is of degree δ or ∆, and all other components are δ-regular
Probabilistic robustness analysis of stochastic jump linear systems

In this paper, we propose a new method to measure the probabilistic robustness of stochastic jump linear system with respect to both the initial state uncertainties and the randomness in switching. Wasserstein distance which defines a metric on the manifold of probability density functions is used as tool for the performance and the stability measures. Starting with Gaussian distribution to represent the initial state uncertainties, the probability density function of the system state evolves into mixture of Gaussian, where the number of Gaussian components grows exponentially. To cope with computational complexity caused by mixture of Gaussian, we prove that there exists an alternative probability density function that preserves exact information in the Wasserstein level. The usefulness and the efficiency of the proposed methods are demonstrated by example.
On sequential analytic groups

We answer a question of S.~Todor\v{c}evi\'c and C.~Uzc\'ategui from \cite{TU1} by showing that the only possible sequential orders of sequential analytic groups are $1$ and $\omega_1$. Other results on the structure of sequential analytic spaces and their relation to other classes of spaces are given as well. In particular, we provide a full topological classification of sequential analytic groups by showing that all such groups are either metrizable or $k_\omega$-spaces, which, together with a result by Zelenyuk, implies that there are exactly $\omega_1$ non homeomorphic analytic sequential group topologies.
A review of dynamic network models with latent variables

We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the observed features and the unobserved structure of networks. We begin with an overview of the static models, and then we introduce the dynamic extensions. For each dynamic model, we also discuss its applications that have been studied in the literature, with the data source listed in Appendix. Based on the review, we summarize a list of open problems and challenges in dynamic network modeling with latent variables.
Positivity of intersection multiplicity over a two-dimensional base

Abstract We prove the positivity of Serre's Intersection Multiplicity for regular local rings that are essentially smooth over a two-dimensional, regular base. Afterward, we apply this result to prove a transversality theorem for unramified regular local rings via a local analysis on the blowup.
Harmonic vectors and matrix tree theorems

The paper describes an explicit combinatorial formula for a harmonic vector for the Laplacian of a directed graph with arbitrary edge weights. This result was motivated by questions from mathematical economics, and the formula plays a crucial role in forthcoming joint work of the author on the emergence of prices and money in an exchange economy. It turns out that the formula is closely related to well-studied problems in graph theory, in particular to the so-called matrix tree theorem due to W. Tutte and independently to R. Bott and J. Mayberry. As a further application of our considerations, we obtain a short new proof of both the matrix tree theorem as well as its generalization due to S. Chaiken.
Existence and finiteness conditions for risk-sensitive planning: results and conjectures

Decision-theoretic planning with risk-sensitive planning objectives is important for building autonomous agents or decision-support systems for real-world applications. However, this line of research has been largely ignored in the artificial intelligence and operations research communities since planning with risk-sensitive planning objectives is more complicated than planning with risk-neutral planning objectives. To remedy this situation, we derive conditions that guarantee that the optimal expected utilities of the total plan-execution reward exist and are finite for fully observable Markov decision process models with non-linear utility functions. In case of Markov decision process models with both positive and negative rewards, most of our results hold for stationary policies only, but we conjecture that they can be generalized to non-stationary policies.
Heterogeneous multireference alignment: A single pass approach

Multireference alignment (MRA) is the problem of estimating a signal from many noisy and cyclically shifted copies of itself. In this paper, we consider an extension called heterogeneous MRA, where K signals must be estimated, and each observation comes from one of those signals, unknown to us. This is a simplified model for the heterogeneity problem notably arising in cryo-electron microscopy. We propose an algorithm which estimates the K signals without estimating either the shifts or the classes of the observations. It requires only one pass over the data and is based on low-order moments that are invariant under cyclic shifts. Given sufficiently many measurements, one can estimate these invariant features averaged over the K signals. We then design a smooth, non-convex optimization problem to compute a set of signals which are consistent with the estimated averaged features. We find that, in many cases, the proposed approach estimates the set of signals accurately despite non-convexity, and conjecture the number of signals K that can be resolved as a function of the signal length L is on the order of √L.