cssi/SciSciGPT-SciSciNet huggingface dataset:
Crystal Growth of Bismuth(V) Oxides from Hydroxide Fluxes

Abstract The first example of a 2 H-perovskite related A3A′BiO6 oxide, (Sr2.59Ba0.41)NaBiO6, with two different divalent cations on the A site, was synthesized in a reactive, mixed strontium-, barium-, sodium-hydroxide flux. (Sr2.59Ba0.41)NaBiO6 crystallizes in the space group R 3 ¯ c , with a = 9.9839(2) A and c = 11.8008(4) A. Crystals of isostructural oxides, Sr3NaBiO6 and Sr3LiBiO6, were also obtained from reactive Sr(OH)2, NaOH and LiOH fluxes. Sr3NaBiO6 and Sr3LiBiO6 crystallize in the space group R 3 ¯ c with a = 9.9193(2) A, c = 11.7473(5) A and a = 9.9293(2) A, c = 11.4188(5) A, respectively. In addition, we report the crystal growth of a strontium bismuth oxyhydroxide, Sr4(BiO6)(OH), isolated from a reactive Sr(OH)2 flux contained in sealed silver tubes. Sr4(BiO6)(OH) crystallizes in the monoclinic space group, P21 with a = 6.9429(8) A, b = 6.1286(7) A, c = 9.2746(10) A, and β = 102.176(2)°. The structure consists of layers of connected SrOx polyhedra, with isolated BiO6 octahedra located in every second Sr–O layer.
Synthesis of Some Biologically Relevant β-C-Glycoconjugates

An esterification−RCM approach to a variety of biologically relevant β-C-glycoconjugates is reported herein. A range of carboxylic acids were coupled with several different olefin alcohols 1 to provide esters 3. The esters were then converted to the final ring-closed product 6 in three steps in 49−60% overall yield. The formed compounds are biologically relevant and serve as stable carbohydrate mimics of the corresponding O-glycosides.
JQ1 reduces Epstein-Barr virus-associated lymphoproliferative disease in mice without sustained oncogene repression.

Small molecule inhibitors of bromodomain and extra-terminal (BET) proteins are seeing increased investigation in clinical trials for treatment of hematological malignancies. These compounds also re...
Internal corporate venture planning autonomy, strategic evolution, and venture performance

Theory and research typically suggest that internal corporate (ICV) venture managers should be granted the freedom needed to manage their new business initiatives as they choose, with little or no interference from senior levels of corporate management. The current research investigates the relationship between venture planning autonomy and venture performance, arguing that this relationship is affected by the types (i.e., goal related or value proposition related) and levels (low-to-high) of strategic evolution occurring in the ICV. Data collected from 145 ICVs operating in 72 corporations indicate that venture planning autonomy is most positively related to venture performance when those ICVs’ goals remain stable over the course of venture operations, but the value propositions of those ICVs are evolving.
One-pot N-alkylation/Heck approach to substituted indoles

Abstract Here, we report the palladium-catalyzed one-pot N-alkylation/Heck cyclization of anilines to substituted indoles employing Pd(OAc)2/XPhos. The scope and limitations of this methodology will be described.
Synthesis of glycosyl chlorides with acid-labile protecting groups

Abstract Glycosyl chlorides with acid-labile protecting groups were prepared from their corresponding alcohols under basic conditions in good yields. The synthesis of a 1,6-C-linked disaccharide was carried out via reaction of a glycosyl chloride with an aldehyde in the presence of samarium(II) diiodide.
Liquid/Liquid Biphasic Recovery/Reuse of Soluble Polymer- Supported Catalysts

Liquid/liquid separations using gravity and two phases of different density are a practical and viable way to recover, separate and reuse soluble polymer-supported catalysts. Such separations are facilitated by using polymers that have phase-selective solubility under biphasic conditions. Such polymers can be designed to be soluble either in a non-polar or a polar phase. Since catalyst and ligand loading can be at a low mole percent level on the polymer, the polymer’s intrinsic solubility can be maintained with a variety of ligands and catalysts making such separation strategies general. Catalysis with such soluble polymer-supported species can either be carried out under biphasic or monophasic conditions. In the former case, aqueous/organic, fluorous/organic or other biphasic regimes can be used. In the later case, thermomorphic or latent biphasic conditions can be employed. In either case, catalyst separation and recovery can be accomplished simply without using significant quantities of additional solvent.
Synthesis of highly functionalized furanones via aldol reaction of 3-silyloxyfurans.

The stereoselective aldol reaction of 3-silyloxyfurans with aldehydes in the presence of a Lewis acid is described. N-Bromosuccinimide (NBS)-mediated cyclization of the aldol product leads to the formation of the 2,7-dioxa-bicyclo[2.2.1]heptan-3-one ring system, which represents the formal product of hetero Diels−Alder reaction of the furan with the aldehyde.
Structure and reactivity of hydrocarbon radical cations

Publisher Summary This chapter discusses recent computational studies of radical cations in the context of qualitative concepts of classical physical organic chemistry. It demonstrates how such basic concepts, such as conjugation and electronic state or even more fundamental notions of structure, bonding, and mechanism, lead to new and interesting effects in radical cation chemistry that are quite different than what is usually expected in the chemistry of neutral compounds. The chapter also discusses how these effects need to be taken into consideration to understand the chemistry of radical cations. It concentrates on computational results from the laboratory, and discusses experimental data only in the context of the calculational data. The differences in structure and reactivity between neutral, closed-shell organic compounds and their radical cation counterparts as well as the generally low activation energies indicate the potential for developing new synthetic methodology or to uncover biochemical reaction mechanisms that use electron transfer catalysis.
Towards an open data framework for body sensor networks supporting bluetooth low energy

Major companies, healthcare professionals, the military, and other scientists and innovators are now sensing that fitness and health data from wearable biosensors will likely provide new discoveries and insights into physiological, cognitive, and emotional health status of an individual. Having the ability to collect, process, and correlate data simultaneously from a set of heterogonous biosensor sources may be a key factor in informing the development of new technologies for reducing health risks, improving health status, and possibly preventing and predicting disease. The challenge in achieving this is getting easy access to heterogeneous data from a set of disparate sensors in a single, integrated wearable monitoring system. Often times, the data recorded by commercial biosensing devices are contained within each manufacturer's proprietary platform. Summary data is available for some devices as free downloads or included only in annual premium memberships. Access to raw measurements is generally unavailable, especially from a custom developed application that may include prototype biosensors. In this paper, we explore key ideas on how to leverage the design features of Bluetooth Low Energy to ease the integration of disparate biosensors at the sensor communication layer. This component is intended to fit into a larger, multi-layered, open data framework that can provide additional data management and analytics capabilities for consumers and scientists alike at all the layers of a data access model which is typically employed in a body sensor network system.
New silicon-mediated ring expansion of n-sized conjugated cycloalkenones into homoallylic n+3 lactones

Abstract Silicon nucleophilic β-addition to various 2-cycloalkenones, followed ultimately by mild and rapid α-alkylation of the corresponding cycloalkanone enolates using diverse epoxides and BF3·OEt2, produces useful γ-lactols and γ-hydroxyketones. Hypervalent iodine-promoted oxidative fragmentation then yields regiospecifically unsaturated, 3-atom ring expanded, 8–10 membered homoallylic lactones with good control of alkene geometry.
A Hydrolysis-Resistant Terminal ME Bond (M = Nb; E = S, Se: M = Ta; E = S) in a Chalcogenido-Substituted Mixed-Metal Polyoxoanion

New examples of terminal group 5 chalcogenido-substituted polytungstates with the Keggin [PW{sub 11}O{sub 39}ME]{sup 4-} (M = Nb, Ta; E = S, Se) and Lindquist [W{sub 5}O{sub 18}NbSe]{sup 3-} structures have been prepared by oxo/chalcogenido ligand substitution reactions. The compounds and their reactions with H{sub 2}O in nonaqueous solution were characterized by IR and {sup 17}O, {sup 31}P, {sup 183}W, and {sup 77}Se NMR spectroscopy. The terminal group 5 metal-chalcogenido bond, found previously to be very susceptible to hydrolysis, is hydrolysis resistant for the Keggin ion series (M = Nb; E = S, Se: M = Ta; E = S).
SubClonal Hierarchy Inference from Somatic Mutations: Automatic Reconstruction of Cancer Evolutionary Trees from Multi-region Next Generation Sequencing

Recent improvements in next-generation sequencing of tumor samples and the ability to identify somatic mutations at low allelic fractions have opened the way for new approaches to model the evolution of individual cancers. The power and utility of these models is increased when tumor samples from multiple sites are sequenced. Temporal ordering of the samples may provide insight into the etiology of both primary and metastatic lesions and rationalizations for tumor recurrence and therapeutic failures. Additional insights may be provided by temporal ordering of evolving subclones--cellular subpopulations with unique mutational profiles. Current methods for subclone hierarchy inference tightly couple the problem of temporal ordering with that of estimating the fraction of cancer cells harboring each mutation. We present a new framework that includes a rigorous statistical hypothesis test and a collection of tools that make it possible to decouple these problems, which we believe will enable substantial progress in the field of subclone hierarchy inference. The methods presented here can be flexibly combined with methods developed by others addressing either of these problems. We provide tools to interpret hypothesis test results, which inform phylogenetic tree construction, and we introduce the first genetic algorithm designed for this purpose. The utility of our framework is systematically demonstrated in simulations. For most tested combinations of tumor purity, sequencing coverage, and tree complexity, good power (≥ 0.8) can be achieved and Type 1 error is well controlled when at least three tumor samples are available from a patient. Using data from three published multi-region tumor sequencing studies of (murine) small cell lung cancer, acute myeloid leukemia, and chronic lymphocytic leukemia, in which the authors reconstructed subclonal phylogenetic trees by manual expert curation, we show how different configurations of our tools can identify either a single tree in agreement with the authors, or a small set of trees, which include the authors' preferred tree. Our results have implications for improved modeling of tumor evolution and the importance of multi-region tumor sequencing.
Stereoselective Synthesis of Freidinger Lactams Using Oxaziridines Derived from Amino Acids.

Conformationally restrained dipeptidyl lactams are building blocks for the synthesis of peptidomimetics, including Freidinger lactams (Figure 1). Few synthetic methodologies toward such moieties allow for incorporation of a stereodefined substituent on the ring nitrogen (i.e., corresponding to an amino acid side chain). Enantiopure Freidinger lactams were obtained by (1) condensation of (S)-tert-butoxycarbonyl (Boc)-protected 2-aminocycloalkanones with commercially available α-amino esters, (2) oxidation of the resulting imines with m-CPBA to give spirocyclic oxaziridines, and (3) photorearrangement. Conformational analyses of seven- and eight-membered dipeptidyl lactams by NMR and by X-ray crystallography are described. The utility of this chemistry was illustrated by the synthesis of potential inhibitors of angiotensin converting enzyme (ACE).
2-(1,3-Benzodithiol-2-ylidene)-1,3-benzoxathiole (dibenzo-oxatrithiafulvalene, DBOTTF): The first tetraheterofulvalene containing oxygen

Abstract The synthesis and characterization of 2-(1,3-benzodithiol-2-ylidene)-1,3-benzoxathiole (dibenzo-oxatrithiafulvalene, DBOTTF), the first example of a tetraheterofulvalene containing oxygen within the fulvalene moiety, are described. Redox potentials of DBOTTF are compared with the other known dibenzotetraheterofulvalene derivatives.
Application of glycodiversification: expedient synthesis and antibacterial evaluation of a library of kanamycin B analogues.

The expedient synthesis of a library of kanamycin B analogues is reported. The revealed SAR will guide future designs in developing kanamycin-type aminoglycoside antibiotics against drug-resistant bacteria.
Biradicals/zwitterions from enallene-isonitriles. Formal [4 + 1] cycloadditions leading to 11H-indeno[1,2-b]quinoline and related compounds.

1,3-Prototropic rearrangement of the benzannulated enyne-isonitriles to the corresponding enallene-isonitriles followed by cycloaromatization generated the putative quinoline biradicals/zwitterions. A subsequent intramolecular radical−radical coupling or electrophilic aromatic substitution then gave the formal [4 + 1] cycloaddition adducts leading to 11H-indeno[1,2-b]quinoline and related compounds.
Ruddlesden-Popper Lnn+1NinO3n+1 nickelates: structure and properties

Abstract In the past decade intense research activity in the area of the so-called Ruddlesden-Poper phases, especially the nickelates, has been motivated by the similarity of their structure and many of their electronic properties to the high temperature cuprate superconducting compounds. In the past few years these activities have been further intensified by the discovery of colossal magnetoresistance in the Ruddlesden-Popper manganates.
Matrix metalloproteinases: Regulation and biological functions

Remodeling of the extracellular matrix occurs during many physiological and pathological conditions. The matrix metalloproteinases (MMPs) are a family of zinc-dependent proteases that are responsible for the proteolytic degradation of number of extracellular matrix (ECM) components. Regulation of MMP activity both at transcriptional and translational levels modulates the degradation of ECM components. Although most MMPs have similar regulatory mechanisms, certain MMPs have unique mechanims of activation at the cell surface. Understanding the processes that regulate the expression of MMPs is important since an imbalance between MMPs and their natural inhibitors, tissue inhibitors of metalloproteinses, leads to a pathological situation such as cancer. In this review, we will briefly discuss various MMPs, their structure, regulation and function.
SELECTIVE DEPROTECTION OF ALKYL VS. ARYL SILYL ETHERS

Abstract Alkyl silyl ethers, in particular t-butyldimethylsilyl derivatives, can be selectively cleaved in high yields over aryl silyl ethers using small percentages of I 2 in MeOH at ambient temperatures.
Control of magnetic dipole emission with surface plasmon polaritons

The spontaneous emission of weak magnetic dipoles located in the vicinity of a plasmonic metal is analyzed theoretically and studied experimentally using Eu3+ ions in organic matrices as a spectroscopic probe. We show that close distances to metal are preferable for magnetic light enhancement experiments, as magnetic dipoles show weaker quenching than electric dipoles while the efficiencies of emission to surface plasmon polaritons are comparable for the both types of dipoles. The possibility of magnetic emission enhancement with SPPs in simple flat geometry is discussed.
Defending non-Bayesian learning against adversarial attacks

This paper addresses the problem of non-Bayesian learning over multi-agent networks, where agents repeatedly collect partially informative observations about an unknown state of the world, and try to collaboratively learn the true state out of m alternatives. We focus on the impact of adversarial agents on the performance of consensus-based non-Bayesian learning, where non-faulty agents combine local learning updates with consensus primitives. In particular, we consider the scenario where an unknown subset of agents suffer Byzantine faults—agents suffering Byzantine faults behave arbitrarily. We propose two learning rules. In our learning rules, each non-faulty agent keeps a local variable which is a stochastic vector over the m possible states. Entries of this stochastic vector can be viewed as the scores assigned to the corresponding states by that agent. We say a non-faulty agent learns the underlying truth if it assigns one to the true state and zeros to the wrong states asymptotically.
VehSense: Slippery Road Detection Using Smartphones

This paper investigates a new application of vehicular sensing: detecting and reporting the slippery road conditions. We describe a system and associated algorithm to monitor vehicle skidding events using smartphones and OBD-II (On board Diagnostics) adapters. This system, which we call the VehSense, gathers data from smartphone inertial sensors and vehicle wheel speed sensors, and processes the data to monitor slippery road conditions in real-time. Specifically, two speed readings are collected: 1) ground speed, which is estimated by vehicle acceleration and rotation, and 2) wheel speed, which is retrieved from the OBD-II interface. The mismatch between these two speeds is used to infer a skidding event. Without tapping into vehicle manufactures' proprietary data (e.g., antilock braking system), VehSense is compatible with most of the passenger vehicles, and thus can be easily deployed. We evaluate our system on snow-covered roads at Buffalo, and show that it can detect vehicle skidding effectively.
Practical inexact proximal quasi-Newton method with global complexity analysis

Recently several methods were proposed for sparse optimization which make careful use of second-order information (Hsieh et al. in Sparse inverse covariance matrix estimation using quadratic approximation. In: NIPS, 2011; Yuan et al. in An improved GLMNET for l1-regularized logistic regression and support vector machines. National Taiwan University, Taipei City, 2011; Olsen et al. in Newton-like methods for sparse inverse covariance estimation. In: NIPS, 2012; Byrd et al. in A family of second-order methods for convex l1-regularized optimization. Technical report, 2012) to improve local convergence rates. These methods construct a composite quadratic approximation using Hessian information, optimize this approximation using a first-order method, such as coordinate descent and employ a line search to ensure sufficient descent. Here we propose a general framework, which includes slightly modified versions of existing algorithms and also a new algorithm, which uses limited memory BFGS Hessian approximations, and provide a novel global convergence rate analysis, which covers methods that solve subproblems via coordinate descent.
2-Cycles on higher Fano hypersurfaces

Let \(X_d\) be a smooth hypersurface of degree d in \(\mathbb {P}_{\mathbb {C}}^n\). Suppose that the Fano variety \(\mathrm {F(X_d)}\) of lines of \(X_d\) is smooth. We prove that the Griffiths group \(\mathrm {Griff}_1(\mathrm {F}(\mathrm {X_d}))\) of \(\mathrm {F(X_d)}\) is trivial if the hypersurface \(\mathrm {X_d}\) is of 2-Fano type. As a result, we give a positive answer to a question of Professor Voisin about the first Griffiths groups of Fano varieties in some cases. Base on this result, we prove that \(\mathrm {CH}_2(\mathrm {X_d})=\mathbb {Z}\) for a smooth 3-Fano hypersurface \(\mathrm {X_d}\subseteq \mathbb {P}^n_{\mathbb {C}}\) with smooth Fano variety of lines.
Modeling wave dark matter in dwarf spheroidal galaxies

This paper studies a model of dark matter called wave dark matter (also known as scalar field dark matter and boson stars). Wave dark matter describes dark matter as a scalar field which satisfies the Einstein-Klein-Gordon equations. These equations rely on a fundamental constant (also known as the "mass term" of the Klein-Gordon equation), which can be interpreted physically as a characteristic frequency of the scalar field. In this work, we compare the wave dark matter model to observations to obtain an estimate of . Specifically, we compare the mass profiles of spherically symmetric static states of wave dark matter to certain Burkertmass profiles recently shown to predict well the velocity dispersion profiles of the eight classical dwarf spheroidal galaxies. We outline a procedure for estimating in these circumstances and show that under precise assumptions the value of can be bounded above by 1000 yr−1. We also show that a reasonable working value for this constant is = 50 yr−1.
Bone remodeling as a spatial evolutionary game

Bone remodeling is a complex process involving cell-cell interactions, biochemical signaling and mechanical stimuli. Early models of the biological aspects of remodeling were non-spatial and focused on the local dynamics at a fixed location in the bone. Several spatial extensions of these models have been proposed, but they generally suffer from two limitations: first, they are not amenable to analysis and are computationally expensive, and second, they neglect the role played by bone-embedded osteocytes. To address these issues, we developed a novel model of spatial remodeling based on the principles of evolutionary game theory. The analytically tractable framework describes the spatial interactions between zones of bone resorption, bone formation and quiescent bone, and explicitly accounts for regulation of remodeling by bone-embedded, mechanotransducing osteocytes. Using tools from the theory of interacting particle systems we systematically classified the different dynamic regimes of the spatial model and identified regions of parameter space that allow for global coexistence of resorption, formation and quiescence, as observed in physiological remodeling. In coexistence scenarios, three-dimensional simulations revealed the emergence of sponge-like bone clusters. Comparison between spatial and non-spatial dynamics revealed substantial differences and suggested a stabilizing role of space. Our findings emphasize the importance of accounting for spatial structure and bone-embedded osteocytes when modeling the process of bone remodeling. Thanks to the lattice-based framework, the proposed model can easily be coupled to a mechanical model of bone loading.
Eye in a Disk: eyeIntegration Human Pan-Eye and Body Transcriptome Database Version 1.0.

Purpose We develop an accessible and reliable RNA sequencing (RNA-seq) transcriptome database of healthy human eye tissues and a matching reactive web application to query gene expression in eye and body tissues. Methods We downloaded the raw sequence data for 1375 RNA-seq samples across 54 tissues in the Genotype-Tissue Expression (GTEx) project as a noneye reference set. We then queried several public repositories to find all healthy, nonperturbed, human eye-related tissue RNA-seq samples. The 916 eye and 1375 GTEx samples were sent into a Snakemake-based reproducible pipeline we wrote to quantify all known transcripts and genes, removes samples with poor sequence quality and mislabels, normalizes expression values across each tissue, perform 882 differential expression tests, calculate GO term enrichment, and output all as a single SQLite database file: the Eye in a Disk (EiaD) dataset. Furthermore, we rewrote the web application eyeIntegration (available in the public domain at https://eyeIntegration.nei.nih.gov) to display EiaD. Results The new eyeIntegration portal provides quick visualization of human eye-related transcriptomes published to date by database version, gene/transcript, 19 eye tissues, and 54 body tissues. As a test of the value of this unified pan-eye dataset, we showed that fetal and organoid retina are highly similar at a pan-transcriptome level, but display distinct differences in certain pathways and gene families, such as protocadherin and HOXB family members. Conclusions The eyeIntegration v1.0 web app serves the pan-human eye and body transcriptome dataset, EiaD. This offers the eye community a powerful and quick means to test hypotheses on human gene and transcript expression across 54 body and 19 eye tissues.
Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds

Approximate dynamic programming is a popular method for solving large Markov decision processes. This paper describes a new class of approximate dynamic programming (ADP) methods--distributionally robust ADP--that address the curse of dimensionality by minimizing a pessimistic bound on the policy loss. This approach turns ADP into an optimization problem, for which we derive new mathematical program formulations and analyze its properties. DRADP improves on the theoretical guarantees of existing ADP methods--|it guarantees convergence and L1 norm-based error bounds. The empirical evaluation of DRADP shows that the theoretical guarantees translate well into good performance on benchmark problems.
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget. While many existing algorithms simplify networks based on the number of MACs or weights, optimizing those indirect metrics may not necessarily reduce the direct metrics, such as latency and energy consumption. To solve this problem, NetAdapt incorporates direct metrics into its adaptation algorithm. These direct metrics are evaluated using empirical measurements, so that detailed knowledge of the platform and toolchain is not required. NetAdapt automatically and progressively simplifies a pre-trained network until the resource budget is met while maximizing the accuracy. Experiment results show that NetAdapt achieves better accuracy versus latency trade-offs on both mobile CPU and mobile GPU, compared with the state-of-the-art automated network simplification algorithms. For image classification on the ImageNet dataset, NetAdapt achieves up to a 1.7\(\times \) speedup in measured inference latency with equal or higher accuracy on MobileNets (V1&V2).
Harmonic morphisms and hyperelliptic graphs

We study harmonic morphisms of graphs as a natural discrete analogue of holomorphic maps between Riemann surfaces. We formulate a graph-theoretic analogue of the classical Riemann-Hurwitz formula, study the functorial maps on Jacobians and harmonic 1-forms induced by a harmonic morphism, and present a discrete analogue of the canonical map from a Riemann surface to projective space. We also discuss several equivalent formulations of the notion of a hyperelliptic graph, all motivated by the classical theory of Riemann surfaces. As an application of our results, we show that for a two-edge-connected graph G that is not a cycle, there is at most one involution on G for which the quotient is a tree. We also show that the number of spanning trees in a graph G is even if and only if G admits a nonconstant harmonic morphism to the graph B 2 consisting of two vertices connected by two edges. Finally, we use the Riemann-Hurwitz formula and our results on hyperelliptic graphs to classify all hyperelliptic graphs having no Weierstrass points.
Controlling shear jamming in dense suspensions via the particle aspect ratio.

Dense suspensions of particles in a liquid exhibit rich, non-Newtonian behaviors such as shear thickening (ST) and shear jamming (SJ). ST has been widely studied and is known to be enhanced by increasing the particles' frictional interactions and also by making their shape more anisotropic. SJ however has only recently been understood to be a distinct phenomenon and, while the role of interparticle friction has been investigated, the role of particle anisotropy in controlling the SJ regime has remained unknown. To address this we here synthesize silica particles for use in water/glycerol suspensions. This pairing of hydrogen-bonding particle surfaces and suspension solvent has been shown to elicit SJ with spherical particles. We then vary particle aspect ratio from Γ = 1 (spheres) to Γ = 11 (slender rods), and perform rheological measurements to determine the effect of particle anisotropy on the onset of shear jamming. We also show that the effect on the precursor to SJ, discontinuous shear thickening (DST), is consistent with prior work. We find that increasing aspect ratio significantly reduces ϕm, the minimum particle packing fraction at which SJ can be observed, to values as low ϕm = 33% for Γ = 11. The ability to fix the properties of the solvated particle surfaces, and thus the particle interactions at contact, while varying shape anisotropy, yields fundamental insights about the SJ capabilities of suspensions and provides a framework to rationally design and tune these behaviors.
Need-Based Communication for Smart Grid: When to Inquire Power Price?

In smart grid, a home appliance can adjust its power consumption level according to the realtime power price obtained from communication channels. Most studies on smart grid do not consider the cost of communications which cannot be ignored in many situations. Therefore, the total cost in smart grid should be jointly optimized with the communication cost. In this paper, a probabilistic mechanism of locational margin price (LMP) is applied and a model for the stochastic evolution of the underlying load which determines the power price is proposed. Based on this framework of power price, the problem of determining when to inquire the power price is formulated as a Markov decision process and the corresponding elements, namely the action space, system state and reward function, are defined. Dynamic programming is then applied to obtain the optimal strategy. A simpler myopic approach is proposed by comparing the cost of communications and the penalty incurred by using the old value of power price. Numerical results show the significant performance gain of the optimal strategy of price inquiry, as well as the near-optimality of the myopic approach.
Trapezoidal Numbers, Divisor Functions, and a Partition Theorem of Sylvester

A partition of a positive integer n is a representation of n as a sum of a finite number of positive integers (called parts). A trapezoidal number is a positive integer that has a partition whose parts are a decreasing sequence of consecutive integers, or, more generally, whose parts form a finite arithmetic progression. This paper reviews the relation between trapezoidal numbers, partitions, and the set of divisors of a positive integer. There is also a complete proof of a theorem of Sylvester that produces a stratification of the partitions of an integer into odd parts and partitions into disjoint trapezoids.
Boolean Functions: Noise Stability, Non-Interactive Correlation, and Mutual Information

Let $T_{\epsilon}$ be the noise operator acting on Boolean functions $f:\{0,1\}^{n}\rightarrow\{0,1\}$ , where $\epsilon\in[0,1/2]$ is the noise parameter. Given $p > 1$ and the mean $\mathbb{E}f$ , which Boolean function $f$ maximizes the p-th moment $\mathbb{E}(T_{\epsilon}f)^{p_{?}}$ Our findings are: in the low noise scenario, i.e., $\epsilon$ is small, the maximum is achieved by the lexicographical function; in the high noise scenario, i.e., $\epsilon$ is close to 1/2, the maximum is achieved by Boolean functions with the maximal degree-1 Fourier weight; and when $p$ is an integer, the maximum is achieved by some monotone function, and in particular, among balanced Boolean functions, the maximum is achieved by any function which is 0 on all strings with fewer than $n/2\ 1$ ,s when $p$ is large enough. Our results recover Mossel and O'Donnell's results about the problem of non-interactive correlation distillation, and confirm a conjecture of Courtade and Kumar on the most informative Boolean function in the low noise and high noise regimes. We also observe that Courtade and Kumar's conjecture is equivalent to that the dictator function maximizes $\mathbb{E}(T_{\epsilon}f)^{p}$ for $p$ close to 1.
On a clustering criterion for dependent observations

Abstract A univariate clustering criterion for stationary processes satisfying a β -mixing condition is proposed extending the work of Bharath et al. (2013) to the dependent setup. The approach is characterized by an alternative sample criterion function based on truncated partial sums which renders the framework amenable to various interesting extensions for which limit results for partial sums are available. Techniques from empirical process theory for mixing sequences play a vital role in the arguments employed in the proofs of the limit theorems.
Characterizing Graphs Of Maximum Principal Ratio

The principal ratio of a connected graph, denoted γ(G), is the ratio of the maximum and minimum entries of its Perron eigenvector. Cioaba and Gregory (2007) conjectured that the graph on n vertices maximizing γ(G) is a kite graph, that is, a complete graph with a pendant path. In this paper, their conjecture is proved
Finite Uniform Bisimulations for Linear Systems With Finite Input Alphabets

We consider discrete-time systems with linear dynamics and a finite input alphabet. We propose a sufficient condition for the existence of finite uniform bisimulations, and an algorithm that terminates when the condition holds and constructs the corresponding equivalence classes. We derive a stronger sufficient condition for the construction of arbitrarily many equivalence classes, together with a corresponding constructive algorithm. Finally, we derive a necessary condition for the existence of finite uniform bisimulations. All our conditions are topological in nature.
On the complexity of testing attainment of the optimal value in nonlinear optimization

We prove that unless $$\hbox {P}=\hbox {NP}$$ , there exists no polynomial time (or even pseudo-polynomial time) algorithm that can test whether the optimal value of a nonlinear optimization problem where the objective and constraints are given by low-degree polynomials is attained. If the degrees of these polynomials are fixed, our results along with previously-known “Frank–Wolfe type” theorems imply that exactly one of two cases can occur: either the optimal value is attained on every instance, or it is strongly NP-hard to distinguish attainment from non-attainment. We also show that testing for some well-known sufficient conditions for attainment of the optimal value, such as coercivity of the objective function and closedness and boundedness of the feasible set, is strongly NP-hard. As a byproduct, our proofs imply that testing the Archimedean property of a quadratic module is strongly NP-hard, a property that is of independent interest to the convergence of the Lasserre hierarchy. Finally, we give semidefinite programming (SDP)-based sufficient conditions for attainment of the optimal value, in particular a new characterization of coercive polynomials that lends itself to an SDP hierarchy.
Simplex QP-based methods for minimizing a conic quadratic objective over polyhedra

We consider minimizing a conic quadratic objective over a polyhedron. Such problems arise in parametric value-at-risk minimization, portfolio optimization, and robust optimization with ellipsoidal objective uncertainty; and they can be solved by polynomial interior point algorithms for conic quadratic optimization. However, interior point algorithms are not well-suited for branch-and-bound algorithms for the discrete counterparts of these problems due to the lack of effective warm starts necessary for the efficient solution of convex relaxations repeatedly at the nodes of the search tree. In order to overcome this shortcoming, we reformulate the problem using the perspective of the quadratic function. The perspective reformulation lends itself to simple coordinate descent and bisection algorithms utilizing the simplex method for quadratic programming, which makes the solution methods amenable to warm starts and suitable for branch-and-bound algorithms. We test the simplex-based quadratic programming algorithms to solve convex as well as discrete instances and compare them with the state-of-the-art approaches. The computational experiments indicate that the proposed algorithms scale much better than interior point algorithms and return higher precision solutions. In our experiments, for large convex instances, they provide up to 22x speed-up. For smaller discrete instances, the speed-up is about 13x over a barrier-based branch-and-bound algorithm and 6x over the LP-based branch-and-bound algorithm with extended formulations. The software that was reviewed as part of this submission was given the Digital Object identifier https://doi.org/10.5281/zenodo.1489153 .
A Study of the Matrix Carleson Embedding Theorem with Applications to Sparse Operators

Abstract In this paper, we study the dyadic Carleson Embedding Theorem in the matrix weighted setting. We provide two new proofs of this theorem, which highlight connections between the matrix Carleson Embedding Theorem and both maximal functions and H 1 -BMO duality. Along the way, we establish boundedness results about maximal functions associated to matrix A 2 weights and duality results concerning H 1 and BMO sequence spaces in the matrix setting. As an application, we then use this Carleson Embedding Theorem to show that if S is a sparse operator, then the operator norm of S on L 2 ( W ) satisfies ‖ S ‖ L 2 ( W ) → L 2 ( W ) ≲ [ W ] A 2 3 2 , for every matrix A 2 weight W.
Learning Implicitly Recurrent CNNs Through Parameter Sharing

We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates. Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks. Compared to traditional CNNs, we demonstrate substantial parameter savings on standard image classification tasks, while maintaining accuracy. Our simple parameter sharing scheme, though defined via soft weights, in practice often yields trained networks with near strict recurrent structure; with negligible side effects, they convert into networks with actual loops. Training these networks thus implicitly involves discovery of suitable recurrent architectures. Though considering only the design aspect of recurrent links, our trained networks achieve accuracy competitive with those built using state-of-the-art neural architecture search (NAS) procedures. Our hybridization of recurrent and convolutional networks may also represent a beneficial architectural bias. Specifically, on synthetic tasks which are algorithmic in nature, our hybrid networks both train faster and extrapolate better to test examples outside the span of the training set.
A zero-one Law for improvements to Dirichlet's Theorem

We give an integrability condition on a function $\psi$ guaranteeing that for almost all (or almost no) $x\in\mathbb{R}$, the system $|qx-p|\leq \psi(t)$, $|q|<t$ is solvable in $p\in \mathbb{Z}$, $q\in \mathbb{Z}\smallsetminus \{0\}$ for sufficiently large $t$. Along the way, we characterize such $x$ in terms of the growth of their continued fraction entries, and we establish that Dirichlet's Approximation Theorem is sharp in a very strong sense. Higher-dimensional generalizations are discussed at the end of the paper.
Cohomology of the Hilbert scheme of points on a surface with values in representations of tautological bundles

Let X[n] be the Hilbert scheme of n points on the smooth quasi-projective surface X, and let L[n] be the tautological bundle on X[n] naturally associated to the line bundle L on X. As a corollary of Haiman's results, we express the image Φ(L[n]) of the tautological bundle L[n] for the Bridgeland-King-Reid equivalence Φ:Db(X[n])→DSnb(Xn) in terms of a complex CL• of Sn-equivariant sheaves in DSnb(Xn) and we characterize the image Φ(L[n]⊗⋅⋅⋅⊗L[n]) in terms of the hyperderived spectral sequence E1p,q associated to the derived k-fold tensor power of the complex CL•. The study of the Sn-invariants of this spectral sequence allows us to get the derived direct images of the double tensor power and of the general k-fold exterior power of the tautological bundle for the Hilbert-Chow morphism, providing Danila-Brion-type formulas in these two cases. This easily yields the computation of the cohomology of X[n] with values in L[n]⊗L[n] and ΛkL[n]
Correcting camera shake by incremental sparse approximation

The problem of deblurring an image when the blur kernel is unknown remains challenging after decades of work. Recently there has been rapid progress on correcting irregular blur patterns caused by camera shake, but there is still much room for improvement. We propose a new blind deconvolution method using incremental sparse edge approximation to recover images blurred by camera shake. We estimate the blur kernel first from only the strongest edges in the image, then gradually refine this estimate by allowing for weaker and weaker edges. Our method competes with the benchmark de-blurring performance of the state-of-the-art while being significantly faster and easier to generalize.
Replays of spatial memories suppress topological fluctuations in cognitive map

The spiking activity of the hippocampal place cells plays a key role in producing and sustaining an internalized representation of the ambient space-a cognitive map. These cells do not only exhibit location-specific spiking during navigation, but also may rapidly replay the navigated routs through endogenous dynamics of the hippocampal network. Physiologically, such reactivations are viewed as manifestations of "memory replays" that help to learn new information and to consolidate previously acquired memories by reinforcing synapses in the parahippocampal networks. Below we propose a computational model of these processes that allows assessing the effect of replays on acquiring a robust topological map of the environment and demonstrate that replays may play a key role in stabilizing the hippocampal representation of space.
The vanishing conjecture for maps of Tor and derived splinters

We say an excellent local domain $(S,n)$ satisfies the vanishing conditions for maps of Tor, if for every $A\to R\to S$ with $A$ regular and $A\to R$ module-finite torsion-free extension, and every $A$-module $M$, the map $Tor^A_i(M, R)\to Tor_i^A(M, S)$ vanishes for every $i\geq 1$. Hochster-Huneke's conjecture (theorem in equal characteristic) thus states that regular rings satisfy such vanishing conditions. The main theorem of this paper shows that, in equal characteristic, rings that satisfy the vanishing conditions for maps of Tor are exactly derived splinters in the sense of Bhatt. In particular, rational singularities in characteristic $0$ satisfy the vanishing conditions. This greatly generalizes Hochster-Huneke's result and Boutot's theorem. Moreover, this leads to a new (and surprising) characterization of rational singularities in terms of splittings in module-finite extensions.
Reachable Set Estimation and Safety Verification for Piecewise Linear Systems with Neural Network Controllers

In this work, the reachable set estimation and safety verification problems for a class of piecewise linear systems equipped with neural network controllers are addressed. The neural network is considered to consist of Rectified Linear Unit (ReLU) activation functions. A layer-by-layer approach is developed for the output reachable set computation of ReLU neural networks. The computation is formulated in the form of a set of manipulations for a union of polytopes. Based on the output reachable set for neural network controllers, the output reachable set for a piecewise linear feedback control system can be estimated iteratively for a given finite-time interval. With the estimated output reachable set, the safety verification for piecewise linear systems with neural network controllers can be performed by checking the existence of intersections of unsafe regions and output reach set. A numerical example is presented to illustrate the effectiveness of our approach.
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent

A major challenge in understanding the generalization of deep learning is to explain why (stochastic) gradient descent can exploit the network architecture to find solutions that have good generalization performance when using high capacity models. We find simple but realistic examples showing that this phenomenon exists even when learning linear classifiers --- between two linear networks with the same capacity, the one with a convolutional layer can generalize better than the other when the data distribution has some underlying spatial structure. We argue that this difference results from a combination of the convolution architecture, data distribution and gradient descent, all of which are necessary to be included in a meaningful analysis. We analyze of the generalization performance as a function of data distribution and convolutional filter size, given gradient descent as the optimization algorithm, then interpret the results using concrete examples. Experimental results show that our analysis is able to explain what happens in our introduced examples.
Automatic quantitative morphological analysis of interacting galaxies

Abstract The large number of galaxies imaged by digital sky surveys reinforces the need for computational methods for analyzing galaxy morphology. While the morphology of most galaxies can be associated with a stage on the Hubble sequence, the morphology of galaxy mergers is far more complex due to the combination of two or more galaxies with different morphologies and the interaction between them. Here we propose a computational method based on unsupervised machine learning that can quantitatively analyze morphologies of galaxy mergers and associate galaxies by their morphology. The method works by first generating multiple synthetic galaxy models for each galaxy merger, and then extracting a large set of numerical image content descriptors for each galaxy model. These numbers are weighted using Fisher discriminant scores, and then the similarities between the galaxy mergers are deduced using a variation of Weighted Nearest Neighbor analysis such that the Fisher scores are used as weights. The similarities between the galaxy mergers are visualized using phylogenies to provide a graph that reflects the morphological similarities between the different galaxy mergers, and thus quantitatively profile the morphology of galaxy mergers.
Consolidation of freshly deposited cohesive and noncohesive sediment: Particle-resolved simulations

Mud is widely present in the environment and it can bind contaminants or nutrients. Since particles that make up mud are small and sticky, it is difficult to perform precise measurements. To this end, we present a numerical framework, which yields results that have so far been impossible to obtain.
Comparison Techniques for Competing Brownian Particles

Consider a finite system of Brownian particles on the real line. Each particle has drift and diffusion coefficients depending on its current rank relative to other particles, as in Karatzas et al. (Ann I H Poincare-PR 52(1):323–354, 2016). We prove some comparison results for these systems. As an example, we show that if we remove a few particles from the top, then the gaps between adjacent particles become stochastically larger, the local times of collision between adjacent particles become stochastically smaller, and the remaining particles shift upward, in the sense of stochastic ordering.