cssi/SciSciGPT-SciSciNet huggingface dataset:
Irreversible electrocatalytic reduction of V(V) to V(IV) using phosphomolybdic acid

Although VO_22^+(aq) reduction is kinetically slow at glassy carbon and Pt electrodes, phosphomolybdic acid is shown to catalyze the electrochemical reduction of VO_2^+(aq) to VO_2^+(aq) in 1.0 M H_2SO_4(aq). A second-order rate constant of 33 M^(-1) s^(-1) was observed for this process. ^(31)P NMR spectra demonstrated that PMo_(11)VO_(40)^(4-) and PMo_(18)V_2O_(40)^(50-) were the dominant P-containing species under electrocatalytic conditions. The incorporation of V^V into the polyoxoanion led to a shift in potential from E^o(VO^(2+)(aq)/VO^(2+)(aq)) = +0.80 V vs Ag/AgCl for free V^V/V^(IV) to Eo' = +0.55 V vs Ag/AgCl for V^(V)/V^(Iv) bound in the heteropolyoxometalate (PMo_(11)VO_(40_^(4-)). This shift in formal potential corresponded to an equilibrium constant of 1.7 X 10^4 M^(-1) for preferential binding of V^V over V^(IV) by the heteropolyoxoanion. This negative shift in redox potential, combined with the slow electrochemical kinetics of free VO_2^+(aq) reduction and with the facile reaction of bound V^(IV) with free V^V in 1.0 M H_2SO_4(aq), resulted in the irreversible electrocatalytic reduction of VO_2^+(aq) to VO^(2+)(aq).
Modified reaction conditions to achieve high regioselectivity in the two component synthesis of 1,5-diarylpyrazoles ☆

Abstract The factors affecting regioselectivity during the formation of 1,5-diarylpyrazoles from aryl hydrazines and 1,3-diketones are identified and the regioisomers were characterized by 1D NOESY, LC–NMR and X-ray analyses. A simple alteration in the usual reaction conditions is reported, which allows the exclusive formation of 1,5-diarylpyrazoles.
Cu(I)-catalyzed synthesis of N-tosyl-4-iminoquinolizines

Abstract Pyridines were found to be a convenient nucleophile in the copper(I)-catalyzed synthesis of N-tosyl-4-iminoquinolizines from 2-pyridyl compounds. Intramolecular cyclization of an in situ generated ketenimine and a tethered pyridine, followed by a base-promoted oxidation, affords the desired products in moderate to good yields under mild reaction conditions.
SYNTHESIS AND ANTI-HIV ACTIVITY OF DIHYDROISOXAZOLE 6-CHLOROPURINE AND ADENINE

Abstract Dihydroisoxazole 6-chloropurine and adenine were efficiently prepared via 1,3-dipolar cycloaddition reaction of a nitrile oxide with vinyl 6-chloropurine, and found to be moderately effective against HIV-1 in acutely infected primary human lymphocytes.
Facile Palladium‐Catalyzed Synthesis of 3‐Arylpyrazolo[1,5‐a]pyrimidines

An efficient palladium-catalyzed synthesis of 3-arylpyrazolo[1,5-a]pyrimidines has been investigated. The key step in the synthesis is a Suzuki biaryl coupling of 3-bromo-2,5-dimethyl-7-aminopyrazolo[1,5-a]pyrimidines with arylboronic acids to provide 3-arylpyrazolo[1,5-a]pyrimidines in moderate to good yield. The synthetic utility of this methodology has been demonstrated by a concise and convergent synthesis of R121920, a potent CRHR1 antagonist recently undergoing clinical evaluations.
Nitrosation of methyl and phenyl styryl ketoximes under oxygen. Formation of 4-nitro-1-hydroxypyrazole 2-oxides and 3,5-diphenyl-4-nitrato-4,5-dihydroisoxazole

The nitrosation of the oximes of 4-phenyl-3-buten-2-one and 1,3-diphenyl-2-propen-1-one under oxygen has been reinvestigated. In addition to 4-oxo- and 4-oximino-4H-pyrazole 1,2-dioxides previously reported, the reactions give 4-nitro-1-hydroxypyrazole 2-oxides. In the case of 1,3-diphenyl-2-propen-1-one oxime the nitrosation reaction also gives 3,5-diphenyl-4-nitrato-4,5-dihydroisoxazole. Evidence is presented suggesting that the nitrate ester is formed through the rearrangement of a peroxynitrite intermediate.
Glycolate aldol reactions with boron enolates of bis-4-methoxyphenyl dioxanone

Abstract The boron enolate of 5 S ,6 S -bis-5,6-(4-methoxyphenyl)-2-dioxanone reacted with various aldehydes to produce anti glycolate aldol products in high yield with good selectivity. The outcome is consistent with an E -enolate reacting through a closed transition state. The adducts were protected and the auxiliary was conveniently removed with ceric ammonium nitrate to give useful intermediates.
A Carbocyclic 7-Deazapurine-Pyrimidine Hybrid Nucleoside

The design, synthesis and rationale for the first example of a carbocyclic 7-deazapurine- pyrimidine hybrid nucleoside is described. The marriage of key structural features from the purine and pyrimidine nucleobase scaffolds has given rise to novel hybrid nucleobases designed to be recognized by biologically relevant purine- and pyrimidine-metabolizing enzymes. Pairing these hybrid bases with chemotherapeutically beneficial carbocyclic "sugars", in addition to utilizing the highly effective 7-deaza ring system, should result in chemotherapeutically useful nucleosides. The approach to realize the first 7-deazapurine-pyrimidine hybrid nucleoside 3 is presented herein.
Isokite, CaMg(PO4)F0.8(OH)0.2, isomorphous with titanite

This study presents the first structural report of natural isokite (calcium magnesium phosphate fluoride), with the formula CaMg(PO(4))F(0.8)(OH)(0.2) (i.e. some substitution of OH for F), based on single-crystal X-ray diffraction data. Isokite belongs to the C2/c titanite mineral group, in which Mg is on an inversion centre and the Ca, P and F/OH atoms are on twofold axes. The structure is composed of kinked chains of corner-sharing MgO(4)F(2) octahedra that are crosslinked by isolated PO(4) tetrahedra, forming a three-dimensional polyhedral network. The Ca(2+) cations occupy the interstitial sites coordinated by six O atoms and one F anion.
Regioselective dicouplings: application to differentially substituted pyrroles.

In an effort to develop a more concise route to differentially substituted pyrroles (such as that found in the lamellarins), a completely regioselective one-pot double Suzuki coupling has been discovered. The key feature is the use of a ligand-free palladium catalyst under optimized conditions, which results only in coupling of the C5 bromide. At this point, addition of a second boronic acid and a phosphine ligand enables coupling at the remaining C4 bromide.
Golgi Stabilization, Not Its Front-Rear Bias, Is Associated with EMT-Enhanced Fibrillar Migration

Epithelial-to-mesenchymal transition (EMT) and maturation of collagen fibrils in the tumor microenvironment play a significant role in cancer cell invasion and metastasis. Confinement along fiber-like tracks enhances cell migration. To what extent and in what manner EMT further promotes migration in a microenvironment already conducive to migration is poorly understood. Here, we show that TGFβ-mediated EMT significantly enhances migration on fiber-like micropatterned tracks of collagen, doubling migration speed and tripling persistence relative to untreated mammary epithelial cells. Thus, cell-intrinsic EMT and extrinsic fibrillar tracks have nonredundant effects on motility. To better understand EMT-enhanced fibrillar migration, we investigated the regulation of Golgi positioning, which is involved in front-rear polarization and persistent cell migration. Confinement along fiber-like tracks has been reported to favor posterior Golgi positioning, whereas anterior positioning is observed during 2-day wound healing. Although EMT also regulates cell polarity, little is known about its effect on Golgi positioning. Here, we show that EMT induces a 2:1 rearward bias in Golgi positioning; however, positional bias explains less than 2% of single-cell variability in migration speed and persistence. Meanwhile, EMT significantly stabilizes Golgi positioning. Cells that enhance migration in response to TGFβ maintain Golgi position for 2- to 4-fold longer than nonresponsive counterparts irrespective of whether the Golgi is ahead or behind the nucleus. In fact, 28% of TGFβ-responsive cells exhibit a fully committed Golgi phenotype with the organelle either in the anterior or posterior position for over 90% of the time. Furthermore, single-cell differences in Golgi stability capture up to 18% of variations in migration speed. These results suggest a hypothesis that the Golgi may be part of a core physical scaffold that affects how cell-generated forces are distributed during migration. A stable scaffold would be expected to more consistently and therefore more productively distribute forces over time, leading to efficient migration.
Large-scale meta-analysis suggests low regional modularity in lateral frontal cortex

Extensive fMRI study of human lateral frontal cortex (LFC) has yet to yield a consensus mapping between discrete anatomy and psychological states, partly due to the difficulty of inferring mental states from brain activity. Despite this, there have been few large-scale efforts to map the full range of psychological states across the entirety of LFC. Here, we used a data-driven approach to generate a comprehensive functional-anatomical mapping of LFC from 11 406 neuroimaging studies. We identified putatively separable LFC regions on the basis of whole-brain co-activation, revealing 14 clusters organized into 3 whole-brain networks. Next, we generated functional preference profiles by using multivariate classification to identify the psychological states that best predicted activity within each cluster. We observed large functional differences between networks, suggesting brain networks support distinct modes of processing. Within each network, however, we observed relatively low functional specificity, suggesting discrete psychological states are not strongly localized to individual regions; instead, our results are consistent with the view that individual LFC regions work as part of distributed networks to give rise to flexible behavior. Collectively, our results provide a comprehensive synthesis of a diverse neuroimaging literature using relatively unbiased data-driven methods.
Condensin controls mitotic chromosome stiffness and stability without forming a structurally contiguous scaffold

During cell division, chromosomes must be folded into their compact mitotic form to ensure their segregation. This process is thought to be largely controlled by the action of condensin SMC protein complexes on chromatin fibers. However, how condensins organize metaphase chromosomes is not understood. We have combined micromanipulation of single human mitotic chromosomes, sub-nanonewton force measurement, siRNA interference of condensin subunit expression, and fluorescence microscopy, to analyze the role of condensin in large-scale chromosome organization. Condensin depletion leads to a dramatic (~ 10-fold) reduction in chromosome elastic stiffness relative to the native, non-depleted case. We also find that prolonged metaphase stalling of cells leads to overloading of chromosomes with condensin, with abnormally high chromosome stiffness. These results demonstrate that condensin is a main element controlling the stiffness of mitotic chromosomes. Isolated, slightly stretched chromosomes display a discontinuous condensing staining pattern, suggesting that condensins organize mitotic chromosomes by forming isolated compaction centers that do not form a continuous scaffold.
An Efficient Two-Step Synthesis of 3-Amino-1-Benzhydrylazetidine:

A streamlined process for the synthesis of 3-amino-1-benzhydrylazetidine is described. Commercially available 1-benzhydrylazetidin-3-ol was reacted with methanesulfonyl chloride in the presence of triethylamine in acetonitrile, upon quench with water, the mesylate intermediate (3) was isolated by filtration. The wet filter cake was subsequently treated with ammonium hydroxide/isopropanol in a Parr reactor at ∼70°C. The procedure afforded the titled compound as mono acetate salt in 72-84% yield.
Benzotriazole-Assisted Synthesis of N-(α-Cyanoalkyl)sulfonamides

Abstract N-(α-Benzotriazol-1-ylalkyl)sulfonamides, readily available from benzotriazole, an aldehyde and a sulfonamide, are converted into the corresponding N-(α-cyanoalkyl)sulfonamides in good yields by treatment with potassium cyanide.
Synthesis of β-tosylethylhydrazine and its use in preparation of N-protected pyrazoles and 5-aminopyrazoles

Abstract β-Tosylethylhydrazine ( 6 ) can be prepared efficiently in one step from commercially available p-tolyl vinyl sulfone ( 7 ) and hydrazine hydrate. This hydrazine reacts with both 1,3-diketones and conjugated ynones in glacial acetic acid to provide a variety of N-tosylethyl-protected (TSE) pyrazoles in good yields. The TSE group can be removed from the pyrazoles using potassium t-butoxide in THF at −30 °C–rt. In addition, hydrazine 6 condenses with β-ketonitriles and β-aminoacrylonitriles to afford 5-aminopyrazoles, which can be deprotected by brief treatment with NaOEt in EtOH/DMSO at 45 °C.
AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids

We demonstrate progress on the deployment of two sets of technologies to support distribution grid operators integrating high shares of renewable energy sources, based on a market for trading local energy flexibilities. An artificial-intelligence (AI) grid modelling tool, based on probabilistic graphs, predicts congestions and estimates the amount and location of energy flexibility required to avoid such events. A scalable time-series forecasting system delivers large numbers of short-term predictions of distributed energy demand and generation. We discuss the deployment of the technologies at three trial demonstration sites across Europe, in the context of a research project carried out in a consortium with energy utilities, technology providers and research institutions.
Frequent nonallelic gene conversion on the human lineage and its effect on the divergence of gene duplicates

Abstract Gene conversion is the copying of a genetic sequence from a “donor” region to an “acceptor.” In nonallelic gene conversion (NAGC), the donor and the acceptor are at distinct genetic loci. Despite the role NAGC plays in various genetic diseases and the concerted evolution of gene families, the parameters that govern NAGC are not well characterized. Here, we survey duplicate gene families and identify converted tracts in 46% of them. These conversions reflect a large GC bias of NAGC. We develop a sequence evolution model that leverages substantially more information in duplicate sequences than used by previous methods and use it to estimate the parameters that govern NAGC in humans: a mean converted tract length of 250 bp and a probability of 2.5 × 10 − 7 2.5×10−7 per generation for a nucleotide to be converted (an order of magnitude higher than the point mutation rate). Despite this high baseline rate, we show that NAGC slows down as duplicate sequences diverge—until an eventual “escape” of the sequences from its influence. As a result, NAGC has a small average effect on the sequence divergence of duplicates. This work improves our understanding of the NAGC mechanism and the role that it plays in the evolution of gene duplicates.
Indium- and zinc-mediated allylation of difluoroacetyltrialkylsilanes in aqueous media

Metal-mediated allylations of difluoroacetyltriakylsilanes with various allyl bromides in aqueous media form homoallylic alcohols exclusively. The common Brook rearrangement, carbon to oxygen-silyl migration, is totally suppressed with no detectable formation of silyl enol ether. Influence of solvents on reaction is studied. Reactivity differences between indium and zinc are also described.
Synthesis of Bicyclo[4.3.0]Non-3-ene-7,9-diones via Solventless Diels–Alder Methodology

Abstract Four novel bicyclo[4.3.0]non-3-ene-7,9-diones have been synthesized in moderate to good yields through a solventless Diels–Alder cycloaddition method and characterized. Atom economies after purification ranged from 21% to 32%, and E factors ranged from 2.7 to 3.7. All reaction products exhibited endo selectivity. Dienophiles with heteroatoms that contribute electrons to the π system exhibited faster reaction times than the carbocyclic analog. Hydrogen-bond donating ability in the diene had no noticeable effect on reaction times.
Semi-adequate closed braids and volume

Abstract In this paper, we show that the volumes for a family of A-adequate closed braids can be bounded above and below in terms of the twist number, the number of braid strings, and a quantity that can be read from the combinatorics of a given closed braid diagram. We also show that the volumes for many of these closed braids can be bounded in terms of a single stable coefficient of the colored Jones polynomial, thus showing that this collection of closed braids satisfies a Coarse Volume Conjecture. By expanding to a wider family of closed braids, we also obtain volume bounds in terms of the number of positive and negative twist regions in a given closed braid diagram. Furthermore, for a family of A-adequate closed 3-braids, we show that the volumes can be bounded in terms of the parameter s from the Schreier normal form of the 3-braid. Finally we show that, for the same family of A-adequate closed 3-braids, the parameters k and s from the Schreier normal form can actually be read off of the original 3-braid word.
On the Universality of the Logistic Loss Function

A loss function measures the discrepancy between the true values (observations) and their estimated fits, for a given instance of data. A loss function is said to be proper (unbiased, Fisher consistent) if the fits are defined over a unit simplex, and the minimizer of the expected loss is the true underlying probability of the data. Typical examples are the zero-one loss, the quadratic loss and the Bernoulli log-likelihood loss (log-loss). In this work we show that for binary classification problems, the divergence associated with smooth, proper and convex loss functions is bounded from above by the Kullback-Leibler (KL) divergence, up to a multiplicative normalization constant. It implies that by minimizing the log-loss (associated with the KL divergence), we minimize an upper bound to any choice of loss functions from this set. This property justifies the broad use of log-loss in regression, decision trees, deep neural networks and many other applications. In addition, we show that the KL divergence bounds from above any separable Bregman divergence that is convex in its second argument (up to a multiplicative normalization constant). This result introduces a new set of divergence inequalities, similar to the well-known Pinsker inequality.
Pattern avoidance in permutations and their squares

Abstract We study permutations p such that both p and p 2 avoid a given pattern q . We obtain a generating function for the case of q = 312 (equivalently, q = 231 ), we prove that if q is monotone increasing, then above a certain length, there are no such permutations, and we prove an upper bound for q = 321 . We also present some intriguing questions in the case of q = 132 .
Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity

Motivated by the growing importance of demand response in modern power system's operations, we propose an architecture and supporting algorithms for privacy preserving thermal inertial load management as a service provided by the load serving entity (LSE). We focus on an LSE managing a population of its customers’ air conditioners, and propose a contractual model where the LSE guarantees quality of service to each customer in terms of keeping their indoor temperature trajectories within respective bands around the desired individual comfort temperatures. We show how the LSE can price the contracts differentiated by the flexibility embodied by the width of the specified bands. We address architectural questions of (i) how the LSE can strategize its energy procurement based on price and ambient temperature forecasts, (ii) how an LSE can close the real-time control loop at the aggregate level while providing individual comfort guarantees to loads, without ever measuring the states of an air conditioner for privacy reasons. Control algorithms to enable our proposed architecture are given, and their efficacy is demonstrated on real data.
State-Secrecy Codes for Networked Linear Systems

In this paper, we study the problem of remote state estimation, in the presence of a passive eavesdropper. An authorized user estimates the state of an unstable linear plant, based on the packets received from a sensor, while the packets may also be intercepted by the eavesdropper. Our goal is to design a coding scheme at the sensor, which encodes the state information, in order to impair the eavesdropper's estimation performance while enabling the user to successfully decode the sent messages. We introduce a novel class of codes, termed State-Secrecy Codes, which use acknowledgment signals from the user and apply linear time-varying transformations to the current and previously received states. By exploiting the properties of the system's process noise, the channel physical model, and the dynamics, these codes manage to be fast, efficient, and suitable for real-time dynamical systems. We prove that under minimal conditions, State-Secrecy Codes achieve perfect secrecy, namely the eavesdropper's minimum mean square error (mmse) grows unbounded almost surely, whereas the user's estimation performance is optimal. These conditions only require that at least once, the user receives the corresponding packet while the eavesdropper fails to intercept it. Even one occurrence of this event renders the eavesdropper's mmse unbounded with asymptotically optimal rate of increase. State-Secrecy Codes are provided and studied for two cases, first, when direct state measurements are available and second, when we only have output measurements. The theoretical results are illustrated in simulations.
Vector-valued modular forms and the modular orbifold of elliptic curves

This paper presents the theory of holomorphic vector-valued modular forms from a geometric perspective. More precisely, we define certain holomorphic vector bundles on the modular orbifold of generalized elliptic curves whose sections are vector-valued modular forms. This perspective simplifies the theory, and it clarifies the role that exponents of representations of SL2(Z) play in the holomorphic theory of vector-valued modular forms. Further, it allows one to use standard techniques in algebraic geometry to deduce free-module theorems and dimension formulae (deduced previously by other authors using different techniques), by identifying the modular orbifold with the weighted projective line P(4, 6).
Bayesian Q-learning with Assumed Density Filtering.

While off-policy temporal difference methods have been broadly used in reinforcement learning due to their efficiency and simple implementation, their Bayesian counterparts have been relatively understudied. This is mainly because the max operator in the Bellman optimality equation brings non-linearity and inconsistent distributions over value function. In this paper, we introduce a new Bayesian approach to off-policy TD methods using Assumed Density Filtering, called ADFQ, which updates beliefs on action-values (Q) through an online Bayesian inference method. Uncertainty measures in the beliefs not only are used in exploration but they provide a natural regularization in the belief updates. We also present a connection between ADFQ and Q-learning. Our empirical results show the proposed ADFQ algorithms outperform comparing algorithms in several task domains. Moreover, our algorithms improve general drawbacks in BRL such as computational complexity, usage of uncertainty, and nonlinearity.
The off-switch game

It is clear that one of the primary tools we can use to mitigate the potential risk from a misbehaving AI system is the ability to turn the system off. As the capabilities of AI systems improve, it is important to ensure that such systems do not adopt subgoals that prevent a human from switching them off. This is a challenge because many formulations of rational agents create strong incentives for self-preservation. This is not caused by a built-in instinct, but because a rational agent will maximize expected utility and cannot achieve whatever objective it has been given if it is dead. Our goal is to study the incentives an agent has to allow itself to be switched off. We analyze a simple game between a human H and a robot R, where H can press R's off switch but R can disable the off switch. A traditional agent takes its reward function for granted: we show that such agents have an incentive to disable the off switch, except in the special case where H is perfectly rational. Our key insight is that for R to want to preserve its off switch, it needs to be uncertain about the utility associated with the outcome, and to treat H's actions as important observations about that utility. (R also has no incentive to switch itself off in this setting.) We conclude that giving machines an appropriate level of uncertainty about their objectives leads to safer designs, and we argue that this setting is a useful generalization of the classical AI paradigm of rational agents.
Fingerprint Distortion Rectification Using Deep Convolutional Neural Networks

Elastic distortion of fingerprints has a negative effect on the performance of fingerprint recognition systems. This negative effect brings inconvenience to users in authentication applications. However, in the negative recognition scenario where users may intentionally distort their fingerprints, this can be a serious problem since distortion will prevent recognition system from identifying malicious users. Current methods aimed at addressing this problem still have limitations. They are often not accurate because they estimate distortion parameters based on the ridge frequency map and orientation map of input samples, which are not reliable due to distortion. Secondly, they are not efficient and requiring significant computation time to rectify samples. In this paper, we develop a rectification model based on a Deep Convolutional Neural Network (DCNN) to accurately estimate distortion parameters from the input image. Using a comprehensive database of synthetic distorted samples, the DCNN learns to accurately estimate distortion bases ten times faster than the dictionary search methods used in the previous approaches. Evaluating the proposed method on public databases of distorted samples shows that it can significantly improve the matching performance of distorted samples.
Network Structure, Efficiency, and Performance in WikiProjects

The internet has enabled collaborations at a scale never before possible, but the best practices for organizing such large collaborations are still not clear. Wikipedia is a visible and successful example of such a collaboration which might offer insight into what makes large-scale, decentralized collaborations successful. We analyze the relationship between the structural properties of WikiProject coeditor networks and the performance and efficiency of those projects. We confirm the existence of an overall performance-efficiency trade-off, while observing that some projects are higher than others in both performance and efficiency, suggesting the existence factors correlating positively with both. Namely, we find an association between low-degree coeditor networks and both high performance and high efficiency. We also confirm results seen in previous numerical and small-scale lab studies: higher performance with less skewed node distributions, and higher performance with shorter path lengths. We use agent-based models to explore possible mechanisms for degree-dependent performance and efficiency. We present a novel local-majority learning strategy designed to satisfy properties of real-world collaborations. The local-majority strategy as well as a localized conformity-based strategy both show degree-dependent performance and efficiency, but in opposite directions, suggesting that these factors depend on both network structure and learning strategy. Our results suggest possible benefits to decentralized collaborations made of smaller, more tightly-knit teams, and that these benefits may be modulated by the particular learning strategies in use.
Optimal Binary Classifier Aggregation for General Losses

We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-convex losses, extending a recent analysis of the problem for misclassification error. The result is a family of semi-supervised ensemble aggregation algorithms which are as efficient as linear learning by convex optimization, but are minimax optimal without any relaxations. Their decision rules take a form familiar in decision theory -- applying sigmoid functions to a notion of ensemble margin -- without the assumptions typically made in margin-based learning.
On Measuring Social Biases in Sentence Encoders

The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the Word Embedding Association Test to measure bias in sentence encoders. We then test several sentence encoders, including state-of-the-art methods such as ELMo and BERT, for the social biases studied in prior work and two important biases that are difficult or impossible to test at the word level. We observe mixed results including suspicious patterns of sensitivity that suggest the test’s assumptions may not hold in general. We conclude by proposing directions for future work on measuring bias in sentence encoders.
Second-kind integral equations for the Laplace-Beltrami problem on surfaces in three dimensions

The Laplace-Beltrami problem ΔΓψ = f has several applications in mathematical physics, differential geometry, machine learning, and topology. In this work, we present novel second-kind integral equations for its solution which obviate the need for constructing a suitable parametrix to approximate the in-surface Green’s function. The resulting integral equations are well-conditioned and compatible with standard fast multipole methods and iterative linear algebraic solvers, as well as more modern fast direct solvers. Using layer-potential identities known as Calderon projectors, the Laplace-Beltrami operator can be pre-conditioned from the left and/or right to obtain second-kind integral equations. We demonstrate the accuracy and stability of the scheme in several numerical examples along surfaces described by curvilinear triangles.
A mathematical formalism for natural selection with arbitrary spatial and genetic structure

We define a general class of models representing natural selection between two alleles. The population size and spatial structure are arbitrary, but fixed. Genetics can be haploid, diploid, or otherwise; reproduction can be asexual or sexual. Biological events (e.g. births, deaths, mating, dispersal) depend in arbitrary fashion on the current population state. Our formalism is based on the idea of genetic sites. Each genetic site resides at a particular locus and houses a single allele. Each individual contains a number of sites equal to its ploidy (one for haploids, two for diploids, etc.). Selection occurs via replacement events, in which alleles in some sites are replaced by copies of others. Replacement events depend stochastically on the population state, leading to a Markov chain representation of natural selection. Within this formalism, we define reproductive value, fitness, neutral drift, and fixation probability, and prove relationships among them. We identify four criteria for evaluating which allele is selected and show that these become equivalent in the limit of low mutation. We then formalize the method of weak selection. The power of our formalism is illustrated with applications to evolutionary games on graphs and to selection in a haplodiploid population.
On occupation times of the first and third quadrants for planar Brownian motion

In Bingham and Doney (1988) the authors presented the applied probability community with a question which is very simply stated, yet is extremely difficult to solve: what is the distribution of the quadrant occupation time of planar Brownian motion? In this paper we study an alternate formulation of this long-standing open problem: let X(t), Y(t) t≥0, be standard Brownian motions starting at x, y, respectively. Find the distribution of the total time T=Leb{t∈[0,1]: X(t)×Y(t)>0}, when x=y=0, i.e. the occupation time of the union of the first and third quadrants. If two adjacent quadrants are used, the problem becomes much easier and the distribution of T follows the arcsine law.
Scalable Bayesian variable selection for structured high-dimensional data

Variable selection for structured covariates lying on an underlying known graph is a problem motivated by practical applications, and has been a topic of increasing interest. However, most of the existing methods may not be scalable to high-dimensional settings involving tens of thousands of variables lying on known pathways such as the case in genomics studies. We propose an adaptive Bayesian shrinkage approach which incorporates prior network information by smoothing the shrinkage parameters for connected variables in the graph, so that the corresponding coefficients have a similar degree of shrinkage. We fit our model via a computationally efficient expectation maximization algorithm which scalable to high-dimensional settings ( p ∼ 100 , 000 ). Theoretical properties for fixed as well as increasing dimensions are established, even when the number of variables increases faster than the sample size. We demonstrate the advantages of our approach in terms of variable selection, prediction, and computational scalability via a simulation study, and apply the method to a cancer genomics study.
Discrete Stratified Morse Theory: A User's Guide.

Inspired by the works of Forman on discrete Morse theory, which is a combinatorial adaptation to cell complexes of classical Morse theory on manifolds, we introduce a discrete analogue of the stratified Morse theory of Goresky and MacPherson. We describe the basics of this theory and prove fundamental theorems relating the topology of a general simplicial complex with the critical simplices of a discrete stratified Morse function on the complex. We also provide an algorithm that constructs a discrete stratified Morse function out of an arbitrary function defined on a finite simplicial complex; this is different from simply constructing a discrete Morse function on such a complex. We borrow Forman's idea of a "user's guide," where we give simple examples to convey the utility of our theory.
A Markov chain model for the search time for max degree nodes in a graph using a biased random walk

We consider the problem of estimating the expected time to find a maximum degree node on a graph using a (parameterized) biased random walk. For assortative graphs the positive degree correlation serves as a local gradient for which a bias towards selecting higher degree neighbors will on average reduce the search time. Unfortunately, although the expected absorption time on the graph can be written down using the theory of absorbing Markov chains, computing this time is infeasible for large graphs. With this motivation, we construct an absorbing Markov chain with a state for each degree of the graph, and observe computing the expected absorption time is now computationally feasible. Our paper finds preliminary results along the following lines: i) there are graphs for which the proposed Markov model does and graphs for which the model does not capture the absorbtion time, ii) there are graphs where random sampling outperforms biased random walks, and graphs where biased random walks are superior, and iii) the optimal bias parameter for the random walk is graph dependent, and we study the dependence on the graph assortativity.
Privacy Preserving and Collusion Resistant Energy Sharing

Energy has been increasingly generated or collected by different entities on the power grid (e.g., universities, hospitals and households) via solar panels, wind turbines or local generators in the past decade. With local energy, such electricity consumers can be considered as “microgrids” which can simultaneously generate and consume energy. Some microgrids may have excessive energy that can be shared to other power consumers on the grid. To this end, all the entities have to share their local private information (e.g., their local demand, local supply and power quality data) to each other or a third-party to find and implement the optimal energy sharing solution. However, such process is constrained by privacy concerns raised by the microgrids. In this paper, we propose a privacy preserving scheme for all the microgrids which can securely implement their energy sharing against both semi-honest and colluding adversaries. The proposed approach includes two secure communication protocols that can ensure quantified privacy leakage and handle collusions.
Choosing to Grow a Graph: Modeling Network Formation as Discrete Choice

We provide a framework for modeling social network formation through conditional multinomial logit models from discrete choice and random utility theory, in which each new edge is viewed as a “choice” made by a node to connect to another node, based on (generic) features of the other nodes available to make a connection. This perspective on network formation unifies existing models such as preferential attachment, triadic closure, and node fitness, which are all special cases, and thereby provides a flexible means for conceptualizing, estimating, and comparing models. The lens of discrete choice theory also provides several new tools for analyzing social network formation; for example, the significance of node features can be evaluated in a statistically rigorous manner, and mixtures of existing models can be estimated by adapting known expectation-maximization algorithms. We demonstrate the flexibility of our framework through examples that analyze a number of synthetic and real-world datasets. For example, we provide rigorous methods for estimating preferential attachment models and show how to separate the effects of preferential attachment and triadic closure. Non-parametric estimates of the importance of degree show a highly linear trend, and we expose the importance of looking carefully at nodes with degree zero. Examining the formation of a large citation graph, we find evidence for an increased role of degree when accounting for age.
Polyphase equiangular tight frames and abelian generalized quadrangles

An equiangular tight frame (ETF) is a type of optimal packing of lines in a finite-dimensional Hilbert space. ETFs arise in various applications, such as waveform design for wireless communication, compressed sensing, quantum information theory and algebraic coding theory. In a recent paper, signature matrices of ETFs were constructed from abelian distance regular covers of complete graphs. We extend this work, constructing ETF synthesis operators from abelian generalized quadrangles, and vice versa. This produces a new infinite family of complex ETFs as well as a new proof of the existence of certain generalized quadrangles. This work involves designing matrices whose entries are polynomials over a finite abelian group. As such, it is related to the concept of a polyphase matrix of a finite filter bank.
Complete Nevanlinna–Pick Kernels☆

Abstract We give a new treatment of Quiggin's and McCullough's characterization of complete Nevanlinna–Pick kernels. We show that a kernel has the matrix-valued Nevanlinna–Pick property if and only if it has the vector-valued Nevanlinna–Pick property. We give a representation of all complete Nevanlinna–Pick kernels, and show that they are all restrictions of a universal complete Nevanlinna–Pick kernel.
Geometric adaptive control of attitude dynamics on SO(3) with state inequality constraints

This paper presents a new geometric adaptive control system with state inequality constraints for the attitude dynamics of a rigid body. The control system is designed such that the desired attitude is asymptotically stabilized, while the controlled attitude trajectory avoids undesired regions defined by an inequality constraint. In addition, we develop an adaptive update law that enables attitude stabilization in the presence of unknown disturbances. The attitude dynamics and the proposed control systems are developed on the special orthogonal group such that singularities and ambiguities of other attitude parameterizations, such as Euler angles and quaternions are completely avoided. The effectiveness of the proposed control system is demonstrated through numerical simulations and experimental results.
Gradual Release of Sensitive Data under Differential Privacy

We introduce the problem of releasing private data under differential privacy when the privacy level is subject to change over time. Existing work assumes that privacy level is determined by the system designer as a fixed value before private data is released. For certain applications, however, users may wish to relax the privacy level for subsequent releases of the same data after either a re-evaluation of the privacy concerns or the need for better accuracy. Specifically, given a database containing private data, we assume that a response y1 that preserves \( \epsilon _1\)-differential privacy has already been published. Then, the privacy level is relaxed to \( \epsilon _2\), with \( \epsilon _2 > \epsilon _1\), and we wish to publish a more accurate response y2 while the joint response (y1,y2) preserves \( \epsilon _2\)-differential privacy. How much accuracy is lost in the scenario of gradually releasing two responses y1 and y2 compared to the scenario of releasing a single response that is \( \epsilon _2\)-differentially private? Our results consider the more general case with multiple privacy level relaxations and show that there exists a composite mechanism that achieves no loss in accuracy. We consider the case in which the private data lies within Rn with an adjacency relation induced by the \( \ell _1\)-norm, and we initially focus on mechanisms that approximate identity queries. We show that the same accuracy can be achieved in the case of gradual release through a mechanism whose outputs can be described by a lazy Markov stochastic process. This stochastic process has a closed form expression and can be efficiently sampled. Moreover, our results extend beyond identity queries to a more general family of privacy-preserving mechanisms. To this end, we demonstrate the applicability of our tool to multiple scenarios including Google’s project RAPPOR, trading of private data, and controlled transmission of private data in a social network. Finally, we derive similar results for the approximated differential privacy.
Externalization of Packet Processing in Software Defined Networking

Current software defined networking (SDN) controllers aggregate all control plane subsystems into a monolithic program. A controller that follows the aggregated approach defines its own set of programming interfaces and services, making application development dependent on a particular SDN controller, and restricting portability of management applications across controllers. We propose a new architecture that disaggregates controller functionality and externalizes packet processing, a critical first step towards migrating from a centralized, monolithic design to a decentralized microservice control plane architecture in which SDN controller functions are divided into a smaller, interconnected set. We argue that dividing a monolithic controller into smaller pieces has advantages.
Quantum predictive learning and communication complexity with single input

We define a new model of quantum learning that we call Predictive Quantum (PQ). This is a quantum analogue of PAC, where during the testing phase the student is only required to answer a polynomial number of testing queries. We demonstrate a relational concept class that is efficiently learnable in PQ, while in any "reasonable" classical model exponential amount of training data would be required. This is the first unconditional separation between quantum and classical learning. We show that our separation is the best possible in several ways; in particular, there is no analogous result for a functional class, as well as for several weaker versions of quantum learning. In order to demonstrate tightness of our separation we consider a special case of one-way communication that we call single-input mode, where Bob receives no input. Somewhat surprisingly, this setting becomes nontrivial when relational communication tasks are considered. In particular, any problem with two-sided input can be transformed into a single-input relational problem of equal classical one-way cost. We show that the situation is different in the quantum case, where the same transformation can make the communication complexity exponentially larger. This happens if and only if the original problem has exponential gap between quantum and classical one-way communication costs. We believe that these auxiliary results might be of independent interest.
The role of tuning uncertain inference systems

This study examined the effects of "tuning" the parameters of the incremental function of MYCIN, the independent function of PROSPECTOR, a probability model that assumes independence, and a simple additive linear equation. The parameters of each of these models were optimized to provide solutions which most nearly approximated those from a full probability model for a large set of simple networks. Surprisingly, MYCIN, PROSPECTOR, and the linear equation performed equivalently; the independence model was clearly more accurate on the networks studied.
A quantitative metric differentiation theorem

The purpose of this note is to point out a simple consequence of some earlier work of the authors, "Hard Sard: Quantitative implicit function and extension theorems for Lipschitz maps". For $f$, a Lipschitz function from a Euclidean space into a metric space, we give quantitative estimates for how often the pullback of the metric under $f$ is approximately a seminorm. This is a quantitative version of Kirchheim's metric differentiation result from 1994. Our result is in the form of a Carleson-type estimate.
FAQ: Questions Asked Frequently

We define and study the Functional Aggregate Query (FAQ) problem, which encompasses many frequently asked questions in constraint satisfaction, databases, matrix operations, probabilistic graphical models and logic. This is our main conceptual contribution. We then present a simple algorithm called "InsideOut" to solve this general problem. InsideOut is a variation of the traditional dynamic programming approach for constraint programming based on variable elimination. Our variation adds a couple of simple twists to basic variable elimination in order to deal with the generality of FAQ, to take full advantage of Grohe and Marx's fractional edge cover framework, and of the analysis of recent worst-case optimal relational join algorithms.As is the case with constraint programming and graphical model inference, to make InsideOut run efficiently we need to solve an optimization problem to compute an appropriate variable ordering. The main technical contribution of this work is a precise characterization of when a variable ordering is `semantically equivalent' to the variable ordering given by the input FAQ expression. Then, we design an approximation algorithm to find an equivalent variable ordering that has the best `fractional FAQ-width'. Our results imply a host of known and a few new results in graphical model inference, matrix operations, relational joins, and logic.We also briefly explain how recent algorithms on beyond worst-case analysis for joins and those for solving SAT and #SAT can be viewed as variable elimination to solve FAQ over compactly represented input functions.