cssi/SciSciGPT-SciSciNet huggingface dataset:
Prospective study of polygenic risk, protective factors, and incident depression following combat deployment in US Army soldiers.

Background Whereas genetic susceptibility increases the risk for major depressive disorder (MDD), non-genetic protective factors may mitigate this risk. In a large-scale prospective study of US Army soldiers, we examined whether trait resilience and/or unit cohesion could protect against the onset of MDD following combat deployment, even in soldiers at high polygenic risk. Methods Data were analyzed from 3079 soldiers of European ancestry assessed before and after their deployment to Afghanistan. Incident MDD was defined as no MDD episode at pre-deployment, followed by a MDD episode following deployment. Polygenic risk scores were constructed from a large-scale genome-wide association study of major depression. We first examined the main effects of the MDD PRS and each protective factor on incident MDD. We then tested the effects of each protective factor on incident MDD across strata of polygenic risk. Results Polygenic risk showed a dose–response relationship to depression, such that soldiers at high polygenic risk had greatest odds for incident MDD. Both unit cohesion and trait resilience were prospectively associated with reduced risk for incident MDD. Notably, the protective effect of unit cohesion persisted even in soldiers at highest polygenic risk. Conclusions Polygenic risk was associated with new-onset MDD in deployed soldiers. However, unit cohesion – an index of perceived support and morale – was protective against incident MDD even among those at highest genetic risk, and may represent a potent target for promoting resilience in vulnerable soldiers. Findings illustrate the value of combining genomic and environmental data in a prospective design to identify robust protective factors for mental health.
Synthesis of β-hydroxy nitriles and 1,3-amino alcohols from epoxides using acetone cyanohydrin as a LiCN precursor

Abstract The reaction of acetone cyanohydrin with MeLi affords a LiCN·acetone complex that can be made to react with epoxides in THF, either in one-pot or using isolated samples of the cyanide complex, to cleanly afford β-hydroxy nitriles upon aqueous workup; in situ hydride reduction of nitriles affords 1,3-amino alcohols.
(E,E)-1-METHOXY-4-TRIMETHYLSILYL-1,3-BUTADIENE : A DIENE FOR THE DIELS-ALDER SYNTHESIS OF METHOXY-SUBSTITUTED ALLYL SILANES

Abstract (E,E)-1-Methoxy-4-trimethylsilyl-1,3-butadiene (1) was prepared from the R,S and S,R adducts of methoxymethyldiphenylphosphine oxide with trans-trimethylsilylpropenal. The (E,E)-diene (1) underwent facile Diels-Alder reaction with naphthoquinone and other dienophiles under mild conditions leading to cis-3-methoxy-6-trimethylsilylcyclohexene derivatives.
Indium-mediated Reductive Cyclizations in Aqueous Ethanol: Highly Efficient Synthesis of Heterocyclic Compounds of Biological Interests

Indium/ammonium chloride in the presence of aqueous ethanol was found to be very effective in reductive cyclization reactions leading to the development of several heterocyclic compounds of biological significance.
Gene2vec: distributed representation of genes based on co-expression

Existing functional description of genes are categorical, discrete, and mostly through manual process. In this work, we explore the idea of gene embedding, distributed representation of genes, in the spirit of word embedding. From a pure data-driven fashion, we trained a 200-dimension vector representation of all human genes, using gene co-expression patterns in 984 data sets from the GEO databases. These vectors capture functional relatedness of genes in terms of recovering known pathways - the average inner product (similarity) of genes within a pathway is 1.52X greater than that of random genes. Using t-SNE, we produced a gene co-expression map that shows local concentrations of tissue specific genes. We also illustrated the usefulness of the embedded gene vectors, laden with rich information on gene co-expression patterns, in tasks such as gene-gene interaction prediction. We proposed a machine learning method that utilizes transcriptome-wide gene co-expression to generate a distributed representation of genes. We further demonstrated the utility of our distribution by predicting gene-gene interaction based solely on gene names. The distributed representation of genes could be useful for more bioinformatics applications.
The structure of protonated diamines and polyamines

The reduced mobilities in air, at 200‡C, of six isomeric C7H18N2 protonated diamines, two triamines (caldine and spermidine), and two tetramines (thermine and spermine) were measured by ion mobility spectrometric (IMS) techniques. The results indicated that all these polyamines undergo proton-induced cyclization, with the proton forming a bridge between two amino groups. It appears as if the favored configuration of the protonated polyamines involves a six- or seven-membered ring rather than a bridge between the terminal amino groups. It is believed that in the tetramines the cyclic structure is formed between the two central, more basic, secondary amine sites.
Muscarinic receptors regulate auditory and prefrontal cortical communication during auditory processing.

Abstract Much of our understanding about how acetylcholine modulates prefrontal cortical (PFC) networks comes from behavioral experiments that examine cortical dynamics during highly attentive states. However, much less is known about how PFC is recruited during passive sensory processing and how acetylcholine may regulate connectivity between cortical areas outside of task performance. To investigate the involvement of PFC and cholinergic neuromodulation in passive auditory processing, we performed simultaneous recordings in the auditory cortex (AC) and PFC in awake head fixed mice presented with a white noise auditory stimulus in the presence or absence of local cholinergic antagonists in AC. We found that a subset of PFC neurons were strongly driven by auditory stimuli even when the stimulus had no associative meaning, suggesting PFC monitors stimuli under passive conditions. We also found that cholinergic signaling in AC shapes the strength of auditory driven responses in PFC, by modulating the intra-cortical sensory response through muscarinic interactions in AC. Taken together, these findings provide novel evidence that cholinergic mechanisms have a continuous role in cortical gating through muscarinic receptors during passive processing and expand traditional views of prefrontal cortical function and the contributions of cholinergic modulation in cortical communication.
Cohesin occupancy and composition at enhancers and promoters are linked to DNA replication origin proximity in Drosophila

Cohesin consists of the SMC1-SMC3-Rad21 tripartite ring and the SA protein that interacts with Rad21. The Nipped-B protein loads cohesin topologically around chromosomes to mediate sister chromatid cohesion and facilitate long-range control of gene transcription. It is largely unknown how Nipped-B and cohesin associate specifically with gene promoters and transcriptional enhancers, or how sister chromatid cohesion is established. Here, we use genome-wide chromatin immunoprecipitation in Drosophila cells to show that SA and the Fs(1)h (BRD4) BET domain protein help recruit Nipped-B and cohesin to enhancers and DNA replication origins, whereas the MED30 subunit of the Mediator complex directs Nipped-B and Vtd in Drosophila (also known as Rad21) to promoters. All enhancers and their neighboring promoters are close to DNA replication origins and bind SA with proportional levels of cohesin subunits. Most promoters are far from origins and lack SA but bind Nipped-B and Rad21 with subproportional amounts of SMC1, indicating that they bind cohesin rings only part of the time. Genetic data show that Nipped-B and Rad21 function together with Fs(1)h to facilitate Drosophila development. These findings show that Nipped-B and cohesin are differentially targeted to enhancers and promoters, and suggest models for how SA and DNA replication help establish sister chromatid cohesion and facilitate enhancer-promoter communication. They indicate that SA is not an obligatory cohesin subunit but a factor that controls cohesin location on chromosomes.
Importance of solution equilibria in the directed assembly of metal chalcogenide mesostructures.

We describe the new nanostructured Pt/Ge/Se materials prepared from the molecular units [Ge2Se6]4− and [GeSe4]4− and linking Pt2+ ions in the presence of surfactant micelles. X-ray diffraction coupled with transmission electron microscopy images reveals hexagonal pore symmetry. The solvent dependence and solution speciation of these building blocks were investigated by means of multinuclear NMR spectroscopy and by fast atom bombardment (FAB) mass spectroscopy and it is shown that rapid exchange equilibrium is reached between species like [Ge4Se10]4−, [Ge2Se6]4−, and [GeSe4]4− in both water and formamide. This results in multiple Ge/Se anions being incorporated in the mesostructured materials which is supported by Raman and IR spectroscopic data. It is likely that the presence of multiple building units both in water and formamide solutions favors the assembly of mesostructured metal chalcogenides with good pore order. Systematic variation of both surfactant headgroup and chain length modulates the optoele...
Escalation of Memory Length in Finite Populations

The escalation of complexity is a commonly cited benefit of coevolutionary systems, but computational simulations generally fail to demonstrate this capacity to a satisfactory degree. We draw on a macroevolutionary theory of escalation to develop a set of criteria for coevolutionary systems to exhibit escalation of strategic complexity. By expanding on a previously developed model of the evolution of memory length for cooperative strategies by Kristian Lindgren, we resolve previously observed limitations on the escalation of memory length by extending operators of evolutionary variation. We present long-term coevolutionary simulations showing that larger population sizes tend to support greater escalation of complexity than smaller ones do. Additionally, we investigate the sensitivity of escalation during transitions of complexity. The Lindgren model has often been used to argue that the escalation of competitive coevolution has intrinsic limitations. Our simulations show that coevolutionary arms races can continue to escalate in computational simulations given sufficient population sizes.
Low-Temperature Solvothermal Synthesis of Phosphorus-Rich Transition-Metal Phosphides

Solvothermal reactions between a metal chloride (CoCl2, NiCl2, and CuCl2) and dissolved yellow phosphorus in superheated toluene (275 °C) yield nano- or submicrometer sized particles of amorphous metal phosphides that, upon modest annealing (350–500 °C), crystallize to phosphorus-rich metal phosphides CoP3, NiP2, and CuP2.
Compressive strengths of PEG gels with glycerol and bioglass particles

Poly(ethylene glycol) (PEG)-based materials can potentially be used as biomechanical matrices in regenerative medicine and tissue engineering implants including the replacement of intervertebral (IV) disks. Glycerol and other generally recognized as safe (GRAS) plasticizers (low-MW PEG, propylene glycol, and sorbitol) were added to the bulk PEG matrix and gelled using chemical and photochemical methods at different temperatures (21, 37, 59, and 80 °C) and pressures (0 and 90 MPa gauge) settings, and their compression testing properties were acquired and analyzed. Surface incorporation of custom-made bioactive glass particles shortened the blood clotting time (78% compared to no glass particles), while alginate and laponite additives improved the gel’s mechanical properties to 645 kPa compressive modulus, 12% yield strain, and 79 kPa yield strength. This IV disk-modeled hydrogel system endured the cyclic loading and unloading tests at 4% compressive strain indicative of an elastic response, but required improvement to its biomechanical tolerance for downstream bioengineering applications.
Direct introduction of CH2OH by intermolecular trapping of CO

Abstract Alkyl iodides are converted to the corresponding hydroxymethyl compounds in good yield by treatment with catalytic triphenylgermane-NaBH3CN-AIBN in benzene-THF under an atmosphhere of CO at high pressure. This methodology is used for the synthesis of an analogue of the B ring of the calicheamicin oligoccharide in which the hydroxylamine at C4 is replaced by a hydroxymethyl group.
A Paper about the Slope of the Equilibrium Line

Abstract There is no simple definition of the slope for multicomponent systems because the vapour liquid equilibrium is represented by a surface (which lies in hyperspace for more than 3 compounds). Thus, for multicomponent systems any pseudo-binary method for estimating tray efficiencies and HETP must rely, in part, on an approximate method of estimating the slope. The objective of this paper is to present a new method of approximating the slope in multicomponent systems based on differentiation of the phase equilibrium surface with respect to the mole fraction of the design component, defined as that compound we seek to eliminate from the mixture in a particular section of the column. There is only one design compound per column section. The approach presented here can be easily applied to absorber and stripper columns, where the concept of key components is not applicable. Two analytical approximate methods of estimating the design slope are shown to be in good agreement with the rigorous calculation even in cases where the relative volatility is not constant. These simple approximations, which need nothing beyond what is readily available from any column simulation program are adequate for design and can be programmed easily in any spreadsheet.
SLNR Based Precoding for One-Bit Quantized Massive MIMO in mmWave Communications

Massive multiple-input multiple-output (MIMO) is a key technology for 5G wireless communications with a promise of significant capacity increase. The use of low-resolution data converters is crucial for massive MIMO to make the overall transmission as cost- and energy-efficient as possible. In this work, we consider a downlink millimeter-wave (mmWave) transmission scenario, where multiple users are served simultaneously by massive MIMO with one-bit digital-to-analog (D&#x002F;A) converters. In particular, we propose a novel precoder design based on signal-to-leakage-plus-noise ratio (SLNR), which minimizes energy leakage into undesired users while taking into account impairments due to nonlinear one-bit quantization. We show that well-known regularized zero-forcing (RZF) precoder is a particular version of the proposed SLNR-based precoder, which is obtained when quantization impairments are totally ignored. Numerical results underscore significant performance improvements along with the proposed SLNR-based precoder as compared to either RZF or zero-forcing (ZF) precoders.
Creativity-enhancing technological change in the production of scientific knowledge

We view scientific publications as a measure of technical knowledge. Using the Solow method of functional decomposition and scientific publication data from the National Institute of Standards and ...
Bounded rationality, monetary policy, and macroeconomic stability

Abstract This paper estimates a Behavioral New Keynesian model to revisit the evidence that passive US monetary policy in the pre-1979 sample led to indeterminate equilibria and sunspot-driven fluctuations, while active policy after 1982, by satisfying the Taylor principle, was instrumental in restoring macroeconomic stability. The model assumes “cognitive discounting”, i.e., consumers and firms pay less attention to variables further into the future. We estimate the model allowing for both determinacy and indeterminacy. The empirical results show that determinacy is preferred both before and after 1979. Even if monetary policy is found to react only mildly to inflation pre-Volcker, the substantial degrees of bounded rationality that we estimate prevent the economy from falling into indeterminacy.
Boosted Generative Models

We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models. Further, our approach allows the ensemble to include discriminative models trained to distinguish real data from model-generated data. We show theoretical conditions under which incorporating a new model in the ensemble will improve the fit and empirically demonstrate the effectiveness of our black-box boosting algorithms on density estimation, classification, and sample generation on benchmark datasets for a wide range of generative models.
Iitaka dimension for cycles

We define the Iitaka dimension of a numerical cycle class and develop its theory. We conjecture that the Iitaka dimension is integer-valued, and give some evidence in this direction. We focus on two cases of geometric interest: Schubert cycles on Grassmannians and cycles contracted by morphisms.
The Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo

From its inception in the 1950s to the modern frontiers of applied statistics, Markov chain Monte Carlo has been one of the most ubiquitous and successful methods in statistical computing. In that time its development has been fueled by increasingly difficult problems and novel techniques from physics. In this article I will review the history of Markov chain Monte Carlo from its inception with the Metropolis method to today's state-of-the-art in Hamiltonian Monte Carlo. Along the way I will focus on the evolving interplay between the statistical and physical perspectives of the method.
Modulation codes for flash memory based on load-balancing theory

In this paper, we consider modulation codes for practical multilevel flash memory storage systems with q cell levels. Instead of maximizing the lifetime of the device [7], [1], [2], [4], we maximize the average amount of information stored per cell-level, which is defined as storage efficiency. Using this framework, we show that the worst-case criterion [7], [1], [2] and the average-case criterion [4] are two extreme cases of our objective function. A self-randomized modulation code is proposed which is asymptotically optimal, as q → ∞, for an arbitrary input alphabet and i.i.d. input distribution. In practical flash memory systems, the number of cell-levels q is only moderately large. So the asymptotic performance as q → ∞ may not tell the whole story. Using the tools from load-balancing theory, we analyze the storage efficiency of the self-randomized modulation code. The result shows that only a fraction of the cells are utilized when the number of cell-levels q is only moderately large. We also propose a load-balancing modulation code, based on a phenomenon known as “the power of two random choices” [10], to improve the storage efficiency of practical systems. Theoretical analysis and simulation results show that our load-balancing modulation codes can provide significant gain to practical flash memory storage systems. Though pseudo-random, our approach achieves the same load-balancing performance, for i.i.d. inputs, as a purely random approach based on the power of two random choices.
Semiautomorphic inverse property loops

ABSTRACTWe define a variety of loops called semiautomorphic, inverse property loops that generalize Moufang and Steiner loops. We first show an equivalence between a previously studied variety of loops. Next we extend several known results for Moufang and Steiner loops. That is, the commutant is a subloop and if a is in the commutant, then a2 is a Moufang element, a3 is a c-element and a6 is in the center. Finally, we give two constructions for semiautomorphic inverse property loops based on Chein’s and de Barros and Juriaans’ doubling constructions.
On the structures of Grassmannian frames

A common criterion in the design of finite Hilbert space frames is minimal coherence, as this leads to error reduction in various signal processing applications. Frames that achieve minimal coherence relative to all unit-norm frames are called Grassmannian frames, a class which includes the well-known equiangular tight frames. However, the notion of “coherence minimization” varies according to the constraints of the ambient optimization problem, so there are other types of “minimally coherent” frames one can speak of. In addition to Grassmannian frames, we consider the class of frames which minimize coherence over the space of frames which are both unit-norm and tight, which we call 1-Grassmannian frames. We observe that these two types of frames coincide in many settings, but not all; accordingly, we investigate some of the differences between the resulting theories. For example, one noteworthy advantage of 1-Grassmannian frames is that their optimality properties are preserved under the Naimark complement.
Simplifying Reward Design through Divide-and-Conquer

Designing a good reward function is essential to robot planning and reinforcement learning, but it can also be challenging and frustrating. The reward needs to work across multiple different environments, and that often requires many iterations of tuning. We introduce a novel divide-and-conquer approach that enables the designer to specify a reward separately for each environment. By treating these separate reward functions as observations about the underlying true reward, we derive an approach to infer a common reward across all environments. We conduct user studies in an abstract grid world domain and in a motion planning domain for a 7-DOF manipulator that measure user effort and solution quality. We show that our method is faster, easier to use, and produces a higher quality solution than the typical method of designing a reward jointly across all environments. We additionally conduct a series of experiments that measure the sensitivity of these results to different properties of the reward design task, such as the number of environments, the number of feasible solutions per environment, and the fraction of the total features that vary within each environment. We find that independent reward design outperforms the standard, joint, reward design process but works best when the design problem can be divided into simpler subproblems.
Threshold selection for multivariate heavy-tailed data

Regular variation is often used as the starting point for modeling multivariate heavy-tailed data. A random vector is regularly varying if and only if its radial part R is regularly varying and is asymptotically independent of the angular part Θ as R goes to infinity. The conditional limiting distribution of Θ given R is large characterizes the tail dependence of the random vector and hence its estimation is the primary goal of applications. A typical strategy is to look at the angular components of the data for which the radial parts exceed some threshold. While a large class of methods has been proposed to model the angular distribution from these exceedances, the choice of threshold has been scarcely discussed in the literature. In this paper, we describe a procedure for choosing the threshold by formally testing the independence of R and Θ using a measure of dependence called distance covariance. We generalize the limit theorem for distance covariance to our unique setting and propose an algorithm which selects the threshold for R. This algorithm incorporates a subsampling scheme that is also applicable to weakly dependent data. Moreover, it avoids the heavy computation in the calculation of the distance covariance, a typical limitation for this measure. The performance of our method is illustrated on both simulated and real data.
Electrochemical kinetics and dimensional considerations, at the nanoscale

It is shown that the consideration of the density of states variation in nanoscale electrochemical systems yields modulations in the rate constant and concomitant electrical currents. The proposed models extend the utility of Marcus-Hush-Chidsey (MHC) kinetics to a larger class of materials and could be used as a test of dimensional character. The implications of the study are of much significance to an understanding and modulation of charge transfer nanostructured electrodes.
Learning Overcomplete HMMs

We study the basic problem of learning overcomplete HMMs---those that have many hidden states but a small output alphabet. Despite having significant practical importance, such HMMs are poorly understood with no known positive or negative results for efficient learning. In this paper, we present several new results---both positive and negative---which help define the boundaries between the tractable-learning setting and the intractable setting. We show positive results for a large subclass of HMMs whose transition matrices are sparse, well-conditioned and have small probability mass on short cycles. We also show that learning is impossible given only a polynomial number of samples for HMMs with a small output alphabet and whose transition matrices are random regular graphs with large degree. We also discuss these results in the context of learning HMMs which can capture long-term dependencies.
Advancing Connectionist Temporal Classification with Attention Modeling

In this study, we propose advancing all-neural speech recognition by directly incorporating attention modeling within the Connectionist Temporal Classification (CTC) framework. In particular, we derive new context vectors using time convolution features to model attention as part of the CTC network. To further improve attention modeling, we utilize content information extracted from a network representing an implicit language model. Finally, we introduce vector based attention weights that are applied on context vectors across both time and their individual components. We evaluate our system on a 3400 hours Microsoft Cortana voice assistant task and demonstrate that our proposed model consistently outperforms the baseline model achieving about 20% relative reduction in word error rates.
Improving Lexical Choice in Neural Machine Translation.

We explore two solutions to the problem of mistranslating rare words in neural machine translation. First, we argue that the standard output layer, which computes the inner product of a vector representing the context with all possible output word embeddings, rewards frequent words disproportionately, and we propose to fix the norms of both vectors to a constant value. Second, we integrate a simple lexical module which is jointly trained with the rest of the model. We evaluate our approaches on eight language pairs with data sizes ranging from 100k to 8M words, and achieve improvements of up to +4.3 BLEU, surpassing phrase-based translation in nearly all settings.
Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System

In the last decade, special purpose computing systems, such as Neuromorphic computing, have become very popular in the field ofcomputer vision and machine learning for classification tasks. In 2015, IBM’s released the TrueNorth Neuromorphic system, kick-starting a new era of Neuromorphic computing. Alternatively, Deep Learning approaches such as Deep Convolutional Neural Networks (DCNN) show almost human-Ievel accuracies for detection and classification tasks. IBM’s 2016 release of a deep learning framework for DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn). Eedn shows promise for delivering high accuracies across a number of different benchmarks, while consuming very low power, using IBM’s TrueNorth chip. However, there are many things that remained undiscovered using the Eedn framework for classification tasks on a Neuromorphic system. In this paper, we have empirically evaluated the performance of different DCNN architectures implemented within the Eedn framework. The goal of this work was discover the most efficient way to implement DCNN models for object classification tasks using the TrueNorth system. We performed our experiments using benchmark data sets such as MNIST, COIL-20, and COIL-100. The experimental results show very promising classification accuracies with very low power consumption on IBM’s NSle Neurosynaptic system. The results show that for datasets with large numbers of classes, wider networks perform better when compared to deep networks comprised of nearly the same core complexity on IBM’s TrueNorth system.
Finding Proofs in Tarskian Geometry

We report on a project to use a theorem prover to find proofs of the theorems in Tarskian geometry. These theorems start with fundamental properties of betweenness, proceed through the derivations of several famous theorems due to Gupta and end with the derivation from Tarski's axioms of Hilbert's 1899 axioms for geometry. They include the four challenge problems left unsolved by Quaife, who two decades ago found some OTTER proofs in Tarskian geometry (solving challenges issued in Wos's 1998 book). There are 212 theorems in this collection. We were able to find OTTER proofs of all these theorems. We developed a methodology for the automated preparation and checking of the input files for those theorems, to ensure that no human error has corrupted the formal development of an entire theory as embodied in two hundred input files and proofs. We distinguish between proofs that were found completely mechanically (without reference to the steps of a book proof) and proofs that were constructed by some technique that involved a human knowing the steps of a book proof. Proofs of length 40---100, roughly speaking, are difficult exercises for a human, and proofs of 100---250 steps belong in a Ph.D. thesis or publication. 29 of the proofs in our collection are longer than 40 steps, and ten are longer than 90 steps. We were able to derive completely mechanically all but 26 of the 183 theorems that have "short" proofs (40 or fewer deduction steps). We found proofs of the rest, as well as the 29 "hard" theorems, using a method that requires consulting the book proof at the outset. Our "subformula strategy" enabled us to prove four of the 29 hard theorems completely mechanically. These are Ph.D. level proofs, of length up to 108.
Gevrey regularity for the supercritical quasi-geostrophic equation☆

Abstract In this paper, following the techniques of Foias and Temam, we establish suitable Gevrey class regularity of solutions to the supercritical quasi-geostrophic equations in the whole space, with initial data in “critical” Sobolev spaces. Moreover, the Gevrey class that we obtain is “near optimal” and as a corollary, we obtain temporal decay rates of higher order Sobolev norms of the solutions. Unlike the Navier–Stokes or the subcritical quasi-geostrophic equations, the low dissipation poses a difficulty in establishing Gevrey regularity. A new commutator estimate in Gevrey classes, involving the dyadic Littlewood–Paley operators, is established that allow us to exploit the cancellation properties of the equation and circumvent this difficulty.
Exponential convergence bounds using integral quadratic constraints

The theory of integral quadratic constraints (IQCs) allows verification of stability and gain-bound properties of systems containing nonlinear or uncertain elements. Gain bounds often imply exponential stability, but it can be challenging to compute useful numerical bounds on the exponential decay rate. In this work, we present a modification of the classical IQC results of Megretski and Rantzer [13] that leads to a tractable computational procedure for finding exponential rate certificates. We demonstrate the effectiveness of our method via a numerical example.
Using optimization to break the epsilon barrier: a faster and simpler width-independent algorithm for solving positive linear programs in parallel

We study the design of nearly-linear-time algorithms for approximately solving positive linear programs. Both the parallel and the sequential deterministic versions of these algorithms require O(∈−4) iterations, a dependence that has not been improved since the introduction of these methods in 1993 by Luby and Nisan. Moreover, previous algorithms and their analyses rely on update steps and convergence arguments that are combinatorial in nature, and do not seem to arise naturally from an optimization viewpoint. In this paper, we leverage insights from optimization theory to construct a novel algorithm that breaks the longstanding O(∈−4) barrier. Our algorithm has a simple analysis and a clear motivation. Our work introduces a number of novel techniques, such as the combined application of gradient descent and mirror descent, and a truncated, smoothed version of the standard multiplicative weight update, which may be of independent interest.
Spectrum of the Kohn Laplacian on the Rossi sphere

We study the spectrum of the Kohn Laplacian □ b t on the Rossi example ( S 3 , ℒ t ) . In particular we show that 0 is in the essential spectrum of □ b t , which yields another proof of the global nonembeddability of the Rossi example.
Uniform nonextendability from nets

Abstract It is shown that there exist Banach spaces X , Y , a 1-net N of X and a Lipschitz function f : N → Y such that every F : X → Y that extends f is not uniformly continuous.
Semicontinuity of 4d N=2 spectrum under renormalization group flow

We study renormalization group flow of four dimensional $$ \mathcal{N}=2 $$ SCFTs defined by isolated hypersurface three-fold singularities. We define the spectrum of $$ \mathcal{N}=2 $$ theory as the set of scaling dimensions of the parameters on the Coulomb branch, which include Coulomb branch moduli, mass parameters and coupling constants. We prove that the spectrum of those theories is semicontinous under the RG flow on the Coulomb branch using the mathematical result about the singularity spectra under deformation. The semi-continuity behavior of $$ \mathcal{N}=2 $$ spectrum implies a theorem under relevant and Coulomb branch moduli deformation, the absence of dangerous irrelevant deformations and can be taken as the necessary condition for the ending point of a RG flow. This behavior is also true for (c, c) ring deformation of two dimensional Landau-Ginzburg model with (2, 2) supersymmetry.
Theorems of Barth–Lefschetz type in Sasakian geometry

Abstract In this paper, we obtain theorems of Barth–Lefschetz type in Sasakian geometry. As a corollary, this gives a new proof of a classical theorem due to J. Milnor. It also implies connectedness principle and Frankel's type theorem.
SPH Methods in the Modelling of Compact Objects

We review the current status of compact object simulations that are based on the smooth particle hydrodynamics (SPH) method. The first main part of this review is dedicated to SPH as a numerical method. We begin by discussing relevant kernel approximation techniques and discuss the performance of different kernel functions. Subsequently, we review a number of different SPH formulations of Newtonian, special- and general relativistic ideal fluid dynamics. We particularly point out recent developments that increase the accuracy of SPH with respect to commonly used techniques. The second main part of the review is dedicated to the application of SPH in compact object simulations. We discuss encounters between two white dwarfs, between two neutron stars and between a neutron star and a stellar-mass black hole. For each type of system, the main focus is on the more common, gravitational wave-driven binary mergers, but we also discuss dynamical collisions as they occur in dense stellar systems such as cores of globular clusters.
Energy-reliability limits in nanoscale circuits

In the nanoscale regime, the behavior of both extant and emerging semiconductor devices are often unreliable. Reliability of such devices often trades-off with their energy consumption, speed, and/or chip area. We study the reliability-energy limits for circuits designed using such devices; examples are drawn from spin electronics. Using the mutual information propagation in logic circuits technique developed by Pippenger, together with optimization, we obtain lower bounds on the energy consumption for computing n-input Boolean functions. We observe that the minimum energy requirement is strictly higher in an order sense (as n scales) than that in the case of reliable gates.
Sieve-based Coreference Resolution in the Biomedical Domain

We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and event coreference resolution. Domain-general coreference resolution algorithms perform poorly on biomedical documents, because the cues they rely on such as gender are largely absent in this domain, and because they do not encode domain-specific knowledge such as the number and type of participants required in chemical reactions. Moreover, it is difficult to directly encode this knowledge into most coreference resolution algorithms because they are not rule-based. Our rule-based architecture uses sequentially applied hand-designed “sieves”, with the output of each sieve informing and constraining subsequent sieves. This architecture provides a 3.2% increase in throughput to our Reach event extraction system with precision parallel to that of the stricter system that relies solely on syntactic patterns for extraction.
An explicit determination of the Springer morphism

ABSTRACTLet G be a simply connected semisimple algebraic groups over ℂ and let ρ:G→GL(Vλ) be an irreducible representation of G of highest weight λ. Suppose that ρ has finite kernel. Springer defined an adjoint-invariant regular map with Zariski dense image from the group to the Lie algebra, &#x1d703;λ:G→&#x1d524;, which depends on λ. This map, &#x1d703;λ, takes the maximal torus T of G to its Lie algebra &#x1d531;. Thus, for a given simple group G and an irreducible representation Vλ, one may write &#x1d703;λ(t)=∑i=1nci(t)αǐ, where we take the simple coroots {αǐ} as a basis for &#x1d531;. We give a complete determination for these coeﬃcients ci(t) for any simple group G as a sum over the weights of the torus action on Vλ.
Some Higher Order Isoperimetric Inequalities via the Method of Optimal Transport

In this paper, we establish some sharp inequalities between the volume and the integral of the $k$-th mean curvature for $k+1$-convex domains in the Euclidean space. The results generalize the classical Alexandrov-Fenchel inequalities for convex domains. Our proof utilizes the method of optimal transportation.
Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full \(512\,\times \,512\) images at \(\approx \)9K images per minute. It ranks third in the Neurofinder competition (\(F_1=0.57\)) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model’s simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.
Exploring Mindset's Applicability to Students' Experiences with Challenge in Transformed College Physics Courses.

Development of an expanded set of mindset categories that can lead to a context-dependent understanding of students' dialogues and actions in college physics.
CLASSIFY PARTICIPANTS IN ONLINE COMMUNITIES

As online communities become increasingly popular, researchers have tried to examine participating activities in online communities as well as how to sustain online communities. However, relatively few studies have tried to understand what kinds of participants constitute online communities. In this study, we try to contribute online community research by developing “common language” to classify different participants in online communities. Specifically, we argue that the previous way to classify participants is not sufficient and accurate, and we propose a continuum to classify participants based on participants’ overall trend of posting activities. In order to further online community research, we also propose potential directions for future studies.
Concave penalized estimation of sparse Gaussian Bayesian networks

We develop a penalized likelihood estimation framework to learn the structure of Gaussian Bayesian networks from observational data. In contrast to recent methods which accelerate the learning problem by restricting the search space, our main contribution is a fast algorithm for score-based structure learning which does not restrict the search space in any way and works on high-dimensional data sets with thousands of variables. Our use of concave regularization, as opposed to the more popular l0 (e.g. BIC) penalty, is new. Moreover, we provide theoretical guarantees which generalize existing asymptotic results when the underlying distribution is Gaussian. Most notably, our framework does not require the existence of a so-called faithful DAG representation, and as a result, the theory must handle the inherent nonidentifiability of the estimation problem in a novel way. Finally, as a matter of independent interest, we provide a comprehensive comparison of our approach to several standard structure learning methods using open-source packages developed for the R language. Based on these experiments, we show that our algorithm obtains higher sensitivity with comparable false discovery rates for high-dimensional data and scales efficiently as the number of nodes increases. In particular, the total runtime for our method to generate a solution path of 20 estimates for DAGs with 8000 nodes is around one hour.
Discrete-time models resulting from dynamic continuous-time perturbations in phase-amplitude modulation-demodulation schemes

We consider discrete-time (DT) systems S in which a DT input is first transformed to a continuous-time (CT) format by phase-amplitude modulation, then modified by a non-linear CT dynamical transformation F, and finally converted back to DT output using an ideal de-modulation scheme. Assuming that F belongs to a special class of CT Volterra series models with fixed degree and memory depth, we provide a complete characterization of S as a series connection of a DT Volterra series model of fixed degree and memory depth, and an LTI system with special properties. The result suggests a new, non-obvious, analytically motivated structure of digital compensation of analog nonlinear distortions (for example, those caused by power amplifiers) in digital communication systems. Results from a MATLAB simulation are used to demonstrate effectiveness of the new compensation scheme, as compared to the standard Volterra series approach.
The emergence of magnetic flux and its role on the onset of solar dynamic events.

A plethora of solar dynamic events, such as the formation of active regions, the emission of jets and the occurrence of eruptions is often associated with the emergence of magnetic flux from the in...
The likelihood encoder for lossy source compression

In this work, a likelihood encoder is studied in the context of lossy source compression. The analysis of the likelihood encoder is based on a soft-covering lemma. It is demonstrated that the use of a likelihood encoder together with the soft-covering lemma gives alternative achievability proofs for classical source coding problems. The case of the rate-distortion function with side information at the decoder (i.e. the Wyner-Ziv problem) is carefully examined and an application of the likelihood encoder to the multi-terminal source coding inner bound (i.e. the Berger-Tung region) is outlined.
Topological Metrizations of Trees, and New Quartet Methods of Tree Inference

Topological phylogenetic trees can be assigned edge weights in several natural ways, highlighting different aspects of the tree. Here, the rooted triple and quartet metrizations are introduced, and applied to formulate novel methods of inferring large trees from rooted triple and quartet data. These methods lead to new statistically consistent procedures for inference of a species tree from gene trees under the multispecies coalescent model.
On some properties of quantum doubles of finite groups

Abstract We prove two results about quantum doubles of finite groups over the complex field. The first result is the integrality theorem for higher Frobenius–Schur indicators for wreath product groups S N ⋉ A N , where A is a finite abelian group. A proof of this result for A = 1 appears in a paper by Iovanov, Montgomery, and Mason. The second result is a lower bound for the largest possible number of irreducible representations of the quantum double of a finite group with at most n conjugacy classes. This answers a question asked by Eric Rowell.
Formal foundations of serverless computing

Serverless computing (also known as functions as a service) is a new cloud computing abstraction that makes it easier to write robust, large-scale web services. In serverless computing, programmers write what are called serverless functions, which are programs that respond to external events. When demand for the serverless function spikes, the platform automatically allocates additional hardware and manages load-balancing; when demand falls, the platform silently deallocates idle resources; and when the platform detects a failure, it transparently retries affected requests. In 2014, Amazon Web Services introduced the first serverless platform, AWS Lambda, and similar abstractions are now available on all major cloud computing platforms. Unfortunately, the serverless computing abstraction exposes several low-level operational details that make it hard for programmers to write and reason about their code. This paper sheds light on this problem by presenting λλ, an operational semantics of the essence of serverless computing. Despite being a small (half a page) core calculus, λλ models all the low-level details that serverless functions can observe. To show that λλ is useful, we present three applications. First, to ease reasoning about code, we present a simplified naive semantics of serverless execution and precisely characterize when the naive semantics and λλ coincide. Second, we augment λλ with a key-value store to allow reasoning about stateful serverless functions. Third, since a handful of serverless platforms support serverless function composition, we show how to extend λλ with a composition language and show that our implementation can outperform prior work.
A PDE for the multi-time joint probability of the Airy process

Abstract This paper gives a PDE for multi-time joint probability of the Airy process, which generalizes Adler and van Moerbeke’s result on the 2-time case. As an intermediate step, the PDE for the multi-time joint probability of the Dyson Brownian motion is also given.
Improved Cauchy radius for scalar and matrix polynomials

We improve the Cauchy radius of both scalar and matrix polynomials, which is an upper bound on the moduli of the zeros and eigenvalues, respectively, by using appropriate polynomial multipliers.
Computing the partition function for graph homomorphisms

We introduce the partition function of edge-colored graph homomorphisms, of which the usual partition function of graph homomorphisms is a specialization, and present an efficient algorithm to approximate it in a certain domain. Corollaries include effcient algorithms for computing weighted sums approximating the number of k-colorings and the number of independent sets in a graph, as well as an effcient procedure to distinguish pairs of edge-colored graphs with many color-preserving homomorphisms G → H from pairs of graphs that need to be substantially modified to acquire a color-preserving homomorphism G → H.