cssi/SciSciGPT-SciSciNet huggingface dataset:
Synthesis of the taxol AB-system by olefination of an A-ring C1 ketone and direct B-ring closure

Abstract Two syntheses of 13-deoxy-taxol C1 ketones are presented. Olefination of these C1 ketones is achieved, indirectly, via either the Meyer-Schuster reaction or by introduction of a vinyl group with vinylcerium dichloride followed by allylic rearrangement. Both methods provide the E-olefin exclusively. Dihydroxylation of these olefins is achieved with catalytic OsO4 and NMNO in moderate yield, providing the taxol-1,2-diol unit with the correct relative stereochemistry. Closure of the B-ring is brought about in excellent yield by displacement of an allylic bromide at C9 by an α-sulfonyl anion at C10.
Applications of Organosulfur Chemistry to Organic Synthesis: Total Synthesis of (+)-Himbeline and (+)-Himbacine

Total syntheses of (+)-himbacine (1) and (+)-himbeline (2) are described. The synthesis involves the preparation of sulfone 38 and aldehyde 42 as single enantiomers followed by coupling of these compounds using a Julia−Lythgoe olefination. The preparation of sulfone 38 features an acid-promoted intramolecular Diels−Alder reaction of an α,β-unsaturated thioester while the synthesis of 42 features a Beak alkylation of piperidine 39.
Phenylated Benzotetraphenes (Dibenzanthracenes) by Nickel-Catalyzed Diphenylacetylene Cycloadditions to Linear [3]Phenylenes

Ni(COD)(PMe3)2 catalyzes the cycloaddition of diphenylacetylene to the four-membered rings of linear [3]phenylene and its 2,3,7,8-tetrakis(trimethylsilyl) derivative. The reaction proceeds via initial formation of the 5,6-diphenylbenzo[3,4]cyclobuta[1,2-b]phenanthrene frame, followed by a competitive second, nonregio­selective alkyne insertion to furnish the corresponding phenyl-­substituted benzo[k]- and -[m]tetraphenes.
COPPER K-EDGE X-RAY ABSORPTION STUDIES OF LA2-XKXCUO4 SUPERCONDUCTORS, BA4NACUO4(CO3)2, AND NDCU2O4

X-ray near-edge absorption spectroscopy (XANES) measurements are reported at the Cu K-edge for La{sub 2-x}K{sub x}CuO{sub 4} superconductors with x = 0.1 and 0.2. Comparisons of the data were made with the absorption spectra of a Cu(II) standard (La{sub 2}CuO{sub 4}), a new Cu(III) standard (Ba{sub 4}NaCuO{sub 4}(CO{sub 3}){sub 2}), and a new mixed-valent Cu(II)/Cu(III) phase (NdCu{sub 2}O{sub 4}). There is a consistent increase in the energy of the main Cu absorption edge in the following series: La{sub 2}CuO{sub 4} < La{sub 1.9}K{sub 0.1}CuO{sub 4} < La{sub 1.8}K{sub 0.2}CuO{sub 4} {approximately} NdCu{sub 2}O{sub 4} < Ba{sub 4}NaCuO{sub 4}(CO{sub 3}){sub 2}. This trend can be attributed to an increase in the average Cu valence and to a decrease in bond length and an increase in covalency. The magnitude of the energy shift from Cu(II) to Cu(III) for the main absorption edge is {approximately}3 eV. There is no measurable change in the energy of the pre-edge feature irrespective of Cu valence. Furthermore, no new features appear at the edge for samples with formal oxidation state between 2 and 3, even for NdCu{sub 2}O{sub 4}, which contains both Cu(II) and Cu(III) in crystallographically distinct sites.
Synthesis of Magnesium Borate (Mg2B2O5) Nanowires by Chemical Vapor Deposition Method

Single-crystal magnesium borate, Mg2B2O5, nanowires were synthesized on MgO substrates using boron triiodide and boric acid vapor (BI3/H3BO3) as precursors and platinum/palladium (Pt/Pd) as catalyst. The conductivity of an individual nanowire was measured.
Palladium-catalyzed reactions of acetoxyenynes with triorganoindium reagents

Abstract The reaction of 1-acetoxy-2,7- and 2,8-enynes with triorganoindium reagents in the presence of 5 mol % palladium catalyst provides cyclic and/or acyclic substitution products depending upon substrate structure. Enynes bearing secondary acetates, quaternary centers, or heteroatoms furnish high yields of carbocyclic or heterocyclic substitution products. NMR studies show that a single trisubstituted alkene stereoisomer is formed in the reaction. A more atom-efficient procedure for the cyclization–substitution process utilizing heteroleptic indium reagents is presented.
Overlaps in brain dynamic functional connectivity between schizophrenia and autism spectrum disorder

Abstract Schizophrenia and autism share some genotipic and phenotypic aspects as connectome miswiring and common cognitive deficits. Currently, there are no medical tests available for either disorders, and diagnostics for both of them include direct reports of relatives and clinical evaluation by a psychiatrist. Despite several medical imaging biomarkers have been proposed in the past, novel effective biomarkers or improvements of the existing ones is still need. This work proposes a dynamic functional connectome analysis combined with machine learning techniques to complement the present diagnostic procedure. We used the moving window technique to locate a set of dynamic functional connectivity states, and then use them as features to classify subjects as autism/schizophrenia or control. Moreover, by using dynamic functional connectivity measures we investigate the question whether those two disorders overlap, namely whether schizophrenia is part of the autism spectrum and which brain region could be involved in both disorders. The results reveal that both static and dynamic functional connectivity can be used to classify subjects with schizophrenia or autism. Lastly, some brain regions show similar functional flexibility in both autism and schizophrenia cohorts giving further possible proofs of their overlaps.
Brominated trimetrexate analogues as inhibitors of pneumocystis carinii and toxoplasma gondii dihydrofolate reductase

Five previously undescribed trimetrexate analogues with bulky 2′-bromo substitution on the phenyl ring were synthesized in order to assess the effect of this structure modification on dihydrofolate reductase inhibition. Condensation of 2-[2-(2-bromo-3,4,5-trimethoxyphenyl)ethyl]-1,l-dicyanopropene with sulfur in the presence of N,N-diethylamine afforded 2-amino-5-(2′-bromo-3′,4′,5′-trimethoxybenzyl)-4-methyl-thiophene-3-carbonitrile (15) and 2-amino-4-[2-(2′-bromo-3′,4′,5′-trimethoxyphenyl)ethyl]thiophene-3-car-bonitrile (16). Further reaction with chloroformamidine hydrochloride converted 15 and 16 into 2,4-diamino-5-(2′-bromo-3′,4′,5′-trimethoxybenzyl)-4-methylthieno[2,3-d]pyrimidine (8a) and 2,4-diamino-4-[2-(2′-bromo-3′,4′,5′-trimethoxyphenyl)ethylthieno[2,3-d]pyrimidine (12) respectively. Other analogues, obtained by reductive coupling of the appropriate 2,4-diaminoquinazoline-6(or 5)-carbonitriles with 2-bromo-3,4,5-trimethoxyaniline, were 2,4-diamino-6-(2′-bromo-3′,4′,5′-trimethoxyanilinomethyl)-5-chloro-quinazoline (9a), 2,4-diamino-5-(2′-bromo-3′,4′,5′-trimethoxyanilinomethyl)quinazoline (10), and 2,4-diamino-6-(2′-bromo-3′,4′,5′-trimethoxyanilinomethyl)quinazoline (11). Enzyme inhibition assays revealed that space-filling 2′-bromo substitution in this limited series of dicyclic 2,4-diaminopyrimidines with a 3′,4′,5′-trimethoxyphenyl side chain and a CH2, CH2CH2, or CH2NH bridge failed to improve species selectivity against either P. carinii or T. gondii dihydrofolate reductase relative to rat liver dihydrofolate reductase.
Total Synthesis of Cyclic Tetrapeptide FR235222, a Potent Immunosuppressant that Inhibits Mammalian Histone Deacetylases

The total synthesis of FR235222, a potent immunosuppressant with in vivo activities, has been achieved. The key steps include assembling its (2S,9R)-2-amino-9-hydroxy-8-oxodecanoic acid residue via an olefin cross-metathesis of a methyl (R)-lactate-derived homoallyl ketone with protected allyl amino acid and constructing its trans-(2R,4S)-4-methylproline unit from protected (R)-pyroglutamic acid in seven steps.
Revealing Scenes by Inverting Structure From Motion Reconstructions

Many 3D vision systems localize cameras within a scene using 3D point clouds. Such point clouds are often obtained using structure from motion (SfM), after which the images are discarded to preserve privacy. In this paper, we show, for the first time, that such point clouds retain enough information to reveal scene appearance and compromise privacy. We present a privacy attack that reconstructs color images of the scene from the point cloud. Our method is based on a cascaded U-Net that takes as input, a 2D multichannel image of the points rendered from a specific viewpoint containing point depth and optionally color and SIFT descriptors and outputs a color image of the scene from that viewpoint. Unlike previous feature inversion methods, we deal with highly sparse and irregular 2D point distributions and inputs where many point attributes are missing, namely keypoint orientation and scale, the descriptor image source and the 3D point visibility. We evaluate our attack algorithm on public datasets and analyze the significance of the point cloud attributes. Finally, we show that novel views can also be generated thereby enabling compelling virtual tours of the underlying scene.
Synthesis and structural characterization of the new compound UEr2O2S3 and the evidence for the old compound U2ErO2S3

Abstract Black square plates of the new compound UEr2O2S3 have been synthesized by the reaction of UOS, Er, and S with an Sb2S3 flux at 1273 K. The structure of UEr2O2S3 has been solved and refined from single-crystal X-ray diffraction data. UEr2O2S3 forms a layered structure with layers of edge-sharing ErS6 octahedra and double layers of disordered (U/Er)O4S4 square antiprisms. Upon reexamination of the previously reported X-ray data for the U3.5+ compound U2ErO2S3 we conclude that it also may be characterized as the U4+ compound UEr2O2S3.
Enantiodifferentiation of multifunctional tertiary alcohols by NMR spectroscopy with a Whelk-O type chiral solvating agent

Abstract A Whelk-O type chiral solvating agent (CSA) allows NMR enantioresolution of multifunctional tertiary alcohols. Crystallographic analysis of the CSA revealed two conformers, which can be expected to coexist in solution. One conformation affords a chiral cleft that can engage a single enantiomer in simultaneous hydrogen bonding, π–π stacking, and CH/π interactions. This chiral recognition process yields nonequivalent NMR signals for the enantiomers of eighteen substrates even when substoichiometric amounts of the CSA are used.
Do Female Local Councilors Improve Women’s Representation?

ABSTRACTTunisia’s 2018 municipal elections, in which a legislated quota was implemented and women won 47 percent of seats, raises questions about whether electing female councilors improves women’s...
Challenges and Opportunities in the Applied Assessment of Student Social and Emotional Learning.

Interest in school-based strategies to support student social and emotional learning (SEL) is strong. Although SEL policies and programs designed to support the development of student competencies ...
Morphological and kinetic studies on hexagonal tungstates

Formation of nanostructured hexagonal alkali tungstates (HTBs) from ammonium metatungstate (AMT) and alkali chlorides has been investigated by a combined strategy encompassing in situ spectroscopic techniques and hydrothermal synthetic studies. They demonstrate how the morphology of the HTBs can be tuned by the straightforward hydrothermal reaction of ammonium metatungstate with the choice of appropriate alkali chlorides. The resulting particle shapes vary from highly anisotropic fibers to multilevel assemblies of hierarchically grown nanorods. Microwave-hydrothermal techniques have further expanded this morphological spectrum. Next, the growth process of alkali HTBs was explored for selected cases by combination of in situ EXAFS and EDXRD investigations. They both show that nucleation starts after a certain induction time and is strongly dependent on the concentration. Though the individual kinetics of alkali HTB formation differ slightly from each other, all alkali cations have a nucleation-controlled g...
Cyclical environments drive variation in life-history strategies: a general theory of cyclical phenology

Cycles, such as seasons or tides, characterize many systems in nature. Overwhelming evidence shows that climate change-driven alterations to environmental cycles—such as longer seasons—are associat...
Fast Estimation of Recombination Rates Using Topological Data Analysis

Accurate estimation of recombination rates is critical for studying the origins and maintenance of genetic diversity. Because the inference of recombination rates under a full evolutionary model is computationally expensive, we developed an alternative approach using topological data analysis (TDA) on genome sequences. We find that this method can analyze datasets larger than what can be handled by any existing recombination inference software, and has accuracy comparable to commonly used model-based methods with significantly less processing time. Previous TDA methods used information contained solely in the first Betti number ( β 1 ) of a set of genomes, which aims to capture the number of loops that can be detected within a genealogy. These explorations have proven difficult to connect to the theory of the underlying biological process of recombination, and, consequently, have unpredictable behavior under perturbations of the data. We introduce a new topological feature, which we call ψ , with a natural connection to coalescent models, and present novel arguments relating β 1 to population genetic models. Using simulations, we show that ψ and β 1 are differentially affected by missing data, and package our approach as TREE (Topological Recombination Estimator). TREE’s efficiency and accuracy make it well suited as a first-pass estimator of recombination rate heterogeneity or hotspots throughout the genome. Our work empirically and theoretically justifies the use of topological statistics as summaries of genome sequences and describes a new, unintuitive relationship between topological features of the distribution of sequence data and the footprint of recombination on genomes.
Learning-induced sequence reactivation during sharp-wave ripples: a computational study

During sleep, memories formed during the day are consolidated in a dialogue between cortex and hippocampus. The reactivation of specific neural activity patterns—replay—during sleep has been observed in both structures and is hypothesized to represent a neuronal substrate of consolidation. In the hippocampus, replay happens during sharp-wave ripple complexes (SWR), when short bouts of excitatory activity in area CA3 induce high-frequency oscillations in area CA1. In particular, recordings of hippocampal cells which spike at a specific location (“place cells”) show that recently learned trajectories are reactivated during CA1 ripples in the following sleep period. Despite the importance of sleep replay, its underlying neural mechanisms are still poorly understood.
Synthesis and Characterization of Manganese-Rich Silicide (α-Mn5Si3, β-Mn5Si3, and β-Mn3Si) Nanowires

The Mn–Si binary phase diagram contains seven distinct equilibrium phases; however, to date, only two phases have been reported in nanowire (NW) morphology. We report the simple reaction of Mn vapor with a Si substrate in a H2 atmosphere to lead to the formation of NW mixtures of MnSi and three new NW phases α-Mn5Si3, β-Mn5Si3, and β-Mn3Si, with the more Mn-rich phases formed at progressively higher temperatures from 800 to 940 °C identified using electron diffraction, together with other structural characterization techniques. Furthermore, the observation of β-Mn5Si3 NWs having the D8m W5Si3 structure type, which is a structure never observed in the Mn–Si system, represents the first report of Mn5Si3 polymorphism. The purposeful use of excess metal species and intentional limitation of vaporous Si species during NW synthesis has led to the first synthesis of manganese-rich silicide NWs.
A One-Pot Double C–H Activation Palladium Catalyzed Route to a Unique Class of Highly Functionalized Thienoisoquinolines

The synthesis of a unique class of highly functionalized 3,4-thienoisoquinolines via an efficient palladium-catalyzed one-pot, regioselective double C–H activation is presented. This class of biologically relevant compounds has been prepared in five steps from commercially available starting materials with overall yields ranging from 27 to 62%. A masked carboxylic acid was used to direct C–H activation to the typically less reactive C4 position. Additionally, the carboxylic acid provides a synthetically useful handle for further functionalization.
The catalytic transformation of thiiranes to cyclic disulfides by tungsten carbonyl complexes

Abstract This report reviews our recent studies of the catalytic transformations of thiiranes and vinylthiiranes by tungsten pentacarbonyl complexes. Whereas the thiranes yield only macrocyclic polydisulfides, the latter reactions yield only the cyclic monodisulfides known as 3,6-dihydro-1,2-dithiins.
Oxygen-Promoted Palladium(II) Catalysis: Facile C(sp2)−C(sp2) Bond Formation via Cross-Coupling of Alkenylboronic Compounds and Olefins

Oxygen-promoted Pd(II) catalysis facilitated the synthesis of conjugated dienes by cross-coupling of alkenylboronic compounds and various olefins including highly substituted alkenes and cyclohexenone. Under mild conditions, these versatile reactions were efficient and highly stereoselective.
Sleep-Dependent Modulation of Metabolic Rate in Drosophila.

Study objectives Dysregulation of sleep is associated with metabolic diseases, and metabolic rate (MR) is acutely regulated by sleep-wake behavior. In humans and rodent models, sleep loss is associated with obesity, reduced metabolic rate, and negative energy balance, yet little is known about the neural mechanisms governing interactions between sleep and metabolism. Methods We have developed a system to simultaneously measure sleep and MR in individual Drosophila, allowing for interrogation of neural systems governing interactions between sleep and metabolic rate. Results Like mammals, MR in flies is reduced during sleep and increased during sleep deprivation suggesting sleep-dependent regulation of MR is conserved across phyla. The reduction of MR during sleep is not simply a consequence of inactivity because MR is reduced ~30 minutes following the onset of sleep, raising the possibility that CO2 production provides a metric to distinguish different sleep states in the fruit fly. To examine the relationship between sleep and metabolism, we determined basal and sleep-dependent changes in MR is reduced in starved flies, suggesting that starvation inhibits normal sleep-associated effects on metabolic rate. Further, translin mutant flies that fail to suppress sleep during starvation demonstrate a lower basal metabolic rate, but this rate was further reduced in response to starvation, revealing that regulation of starvation-induced changes in MR and sleep duration are genetically distinct. Conclusions Therefore, this system provides the unique ability to simultaneously measure sleep and oxidative metabolism, providing novel insight into the physiological changes associated with sleep and wakefulness in the fruit fly.
Stereoselective synthesis of tetrahydropyrans via formal [4 + 2]-cycloaddition: a comparison of allylsilane and crotylsilane.

The reaction of a series of β-(triethylsilyloxy)aldehydes with several allylsilanes and crotyldimethylphenylsilane is described. Aldehydes possessing an α-stereocenter afforded tetrahydropyrans as ...
Stereo- and regioselectivity of Pd0/InI-mediated allylic additions to aldehydes and ketones. In situ generation of allylindium(III) intermediates from N-acylnitroso Diels-Alder cycloadducts and 1-amino-4-acetoxycyclopentenes

Acylnitroso Diels-Alder cycloadduct (11, 37, and 45)- and cyclopentenyl acetate (8 and 9)-derived allylindium(III) species were generated in situ from palladium(0) catalysts and indium(I) iodide, and the stereo- and regiochemistry of their additions to aldehydes and ketones were investigated. Solvent, catalyst, and ionic effects were examined for the reaction of N-acetyl cycloadduct (11) and benzyloxyacetaldehyde (10). The solvent mixture of THF/H(2)O with Pd(OAc)(2).PPh(3) catalysis was found to be optimal. The addition of N-acetyl cycloadduct to aliphatic aldehydes afforded products in good yields and high regio- and stereoselectivity, with the cis-1,4-isomers constituting 90-95% of the products. The reactions with N-Boc (37a) and N-methylcarbamate (37b) cycloadducts also gave the cis-1,4-products predominantly. The same regio- and stereoselectivity applied to the reactions of 4-acetoxy-1-(N-hydroxyphenyacetamido)cyclopentene (8). 4-Acetoxy-1-phenylacetamidocyclopentene (9), however, afforded trans-1,4-products exclusively. Mechanistic speculations involving chelated transition states are described.
A convenient and rapid approach for the synthesis of 1-benzyl-3-heterocyclic pyrazoles

Abstract A variety of 1-benzyl-3-heterocyclic pyrazoles were rapidly assembled by a two-step N-benzylation/Suzuki coupling sequence. A one-pot variation of this sequence is demonstrated.
Precipitation extremes and depth-duration-frequency under internal climate variability

Natural climate variability, captured through multiple initial condition ensembles, may be comparable to the variability caused by knowledge gaps in future emissions trajectories and in the physical science basis, especially at adaptation-relevant scales and projection horizons. The relations to chaos theory, including sensitivity to initial conditions, have caused the resulting variability in projections to be viewed as the irreducible uncertainty component of climate. The multiplier effect of ensembles from emissions-trajectories, multiple-models and initial-conditions contribute to the challenge. We show that ignoring this variability results in underestimation of precipitation extremes return periods leading to maladaptation. However, we show that concatenating initial-condition ensembles results in reduction of hydroclimate uncertainty. We show how this reduced uncertainty in precipitation extremes percolates to adaptation-relevant-Depth-Duration Frequency curves. Hence, generation of additional initial condition ensembles therefore no longer needs to be viewed as an uncertainty explosion problem but as a solution that can lead to uncertainty reduction in assessment of extremes.
Realtime State Estimation with Tactile and Visual Sensing. Application to Planar Manipulation

Accurate and robust object state estimation enables successful object manipulation. Visual sensing is widely used to estimate object poses. However, in a cluttered scene or in a tight workspace, the robot's end-effector often occludes the object from the visual sensor. The robot then loses visual feedback and must fall back on open-loop execution. In this paper, we integrate both tactile and visual input using a framework for solving the SLAM problem, incremental smoothing and mapping (iSAM), to provide a fast and flexible solution. Visual sensing provides global pose information but is noisy in general, whereas contact sensing is local, but its measurements are more accurate relative to the end-effector. By combining them, we aim to exploit their advantages and overcome their limitations. We explore the technique in the context of a pusher-slider system. We adapt iSAM's measurement cost and motion cost to the pushing scenario, and use an instrumented setup to evaluate the estimation quality with different object shapes, on different surface materials, and under different contact modes.
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds

Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating ”soft labels” (e.g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation. Borrowing insights from Wasserstein propagation on graphs [Solomon et al. 2014], we re-formulate the label propagation procedure as a message-passing algorithm, which renders itself naturally to a generalization applicable to hypergraphs through Wasserstein barycenters. Furthermore, in a PAC learning framework, we provide generalization error bounds for propagating one-dimensional distributions on graphs and hypergraphs using 2-Wasserstein distance, by establishing the algorithmic stability of the proposed semisupervised learning algorithm. These theoretical results also shed new lights upon deeper understandings of the Wasserstein propagation on graphs.
Vector approximate message passing for the generalized linear model

The generalized linear model (GLM), where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform output z = Ax, arises in a range of applications such as robust regression, binary classification, quantized compressed sensing, phase retrieval, photon-limited imaging, and inference from neural spike trains. When A is large and i.i.d. Gaussian, the generalized approximate message passing (GAMP) algorithm is an efficient means of MAP or marginal inference, and its performance can be rigorously characterized by a scalar state evolution. For general A, though, GAMP can misbehave. Damping and sequential-updating help to robustify GAMP, but their effects are limited. Recently, a “vector AMP” (VAMP) algorithm was proposed for additive white Gaussian noise channels. VAMP extends AMP's guarantees from i.i.d. Gaussian A to the larger class of rotationally invariant A. In this paper, we show how VAMP can be extended to the GLM. Numerical experiments show that the proposed GLM-VAMP is much more robust to ill-conditioning in A than damped GAMP.
Pattern-Equivariant Cohomology with Integer Coefficients

We relate Kellendonk and Putnam’s pattern-equivariant (PE) cohomology to the inverse-limit structure of a tiling space. This gives a version of PE cohomology with integer coefficients, or with values in any Abelian group. It also provides an easy proof of Kellendonk and Putnam’s original theorem relating PE cohomology to the Cech cohomology of the tiling space. The inverse-limit structure also allows for the construction of a new non-Abelian invariant, the PE representation variety.
Snippets: Tools for teaching on two levels

There are times when the opportunity to create a novel approach to teaching presents itself. This is true of the following innovative approach to teaching difficult concepts using a dual level method to enhance comprehension by undergraduate nursing students. The purpose of this case study was to use short video recordings called Snippets, which were previously recorded by the instructor as a new two-level learning activity to augment nursing students’ grasp of difficult concepts in a conventional didactic classroom, and to determine whether there was a difference in student satisfaction between didactic real-time presentations and didactic recorded 15-to-30 minute sessions with faculty present in the classroom. A mixed methods approach was used. Although there was no statistically significant difference in students’ satisfaction between the two methods of lecture as reported by survey methods, content analysis supported using Snippets as an ‘accessible’ strategy for teaching on two levels to enhance learning. It is useful teaching strategy available to faculty in conventional nursing programs to augment learning for particularly difficult conceptual material.
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications

Major advancements in building general-purpose and customized hardware have been one of the key enablers of versatility and pervasiveness of machine learning models such as deep neural networks. To sustain this ubiquitous deployment of machine learning models and cope with their computational and storage complexity, several solutions such as low-precision representation of model parameters using fixed-point representation and deploying approximate arithmetic operations have been employed. Studying the potency of such solutions in different applications requires integrating them into existing machine learning frameworks for high-level simulations as well as implementing them in hardware to analyze their effects on power/energy dissipation, throughput, and chip area. Lop is a library for design space exploration that bridges the gap between machine learning and efficient hardware realization. It comprises a Python module, which can be integrated with some of the existing machine learning frameworks and implements various customizable data representations including fixed-point and floating-point as well as approximate arithmetic operations. Furthermore, it includes a highly-parameterized Scala module, which allows synthesizing hardware based on the said data representations and arithmetic operations. Lop allows researchers and designers to quickly compare quality of their models using various data representations and arithmetic operations in Python and contrast the hardware cost of viable representations by synthesizing them on their target platforms (e.g., FPGA or ASIC). To the best of our knowledge, Lop is the first library that allows both software simulation and hardware realization using customized data representations and approximate computing techniques.
On a biparameter maximal multilinear operator

Abstract It is well-known that estimates for maximal operators and questions of pointwise convergence are strongly connected. In recent years, convergence properties of so-called ‘non-conventional ergodic averages’ have been studied by a number of authors, including Assani, Austin, Host, Kra, Tao, etc. In particular, much is known regarding convergence in L 2 of these averages, but little is known about pointwise convergence. In this spirit, we consider the pointwise convergence of a particular ergodic average and study the corresponding maximal trilinear operator (over R , thanks to a transference principle). Lacey in [15] and Demeter, Tao, and Thiele in [6] have studied maximal multilinear operators previously; however, the maximal operator we develop has a novel biparameter structure which has not been previously encountered and cannot be estimated using their techniques. We will carve this biparameter maximal multilinear operator using a certain Taylor series and produce non-trivial Holder-type estimates for one of the two “main” terms by treating it as a singular integral, the symbol of which has singular set given by two intersecting planes, similarly to that of the Biest operator, studied by Muscalu, Tao, and Thiele in [24] and [25] .
DEFINABLE SETS IN A HYPERBOLIC GROUP

We give a description of definable sets P = (p1,…,pm) in a free non-abelian group F and in a torsion-free non-elementary hyperbolic group G. As a corollary we show that proper non-cyclic subgroups of F and G are not definable. This answers Malcev's question posed in 1965 for F.
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such dynamic knowledge graphs is not yet well understood. To this end, we present Know-Evolve, a novel deep evolutionary knowledge network that learns non-linearly evolving entity representations over time. The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings. We demonstrate significantly improved performance over various relational learning approaches on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to prior reasoning approaches in multi-relational setting.
Janossy Densities for Unitary Ensembles at the Spectral Edge

For a broad class of unitary ensembles of random matrices, we demonstrate the universal nature of the Janossy densities of eigenvalues near the spectral edge, providing a different formulation of the probability distributions of the limiting second, third, etc. largest eigenvalues of the ensembles in question. The approach is based on a representation of the Janossy densities in terms of a system of orthogonal polynomials, plus the steepest descent method of Deift and Zhou for the asymptotic analysis of the associated Riemann.Hilbert problem.
Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting

Long-lead forecasting for spatio-temporal systems can often entail complex nonlinear dynamics that are difficult to specify it a priori. Current statistical methodologies for modeling these processes are often highly parameterized and thus, challenging to implement from a computational perspective. One potential parsimonious solution to this problem is a method from the dynamical systems and engineering literature referred to as an echo state network (ESN). ESN models use so-called {\it reservoir computing} to efficiently compute recurrent neural network (RNN) forecasts. Moreover, so-called "deep" models have recently been shown to be successful at predicting high-dimensional complex nonlinear processes, particularly those with multiple spatial and temporal scales of variability (such as we often find in spatio-temporal environmental data). Here we introduce a deep ensemble ESN (D-EESN) model. We present two versions of this model for spatio-temporal processes that both produce forecasts and associated measures of uncertainty. The first approach utilizes a bootstrap ensemble framework and the second is developed within a hierarchical Bayesian framework (BD-EESN). This more general hierarchical Bayesian framework naturally accommodates non-Gaussian data types and multiple levels of uncertainties. The methodology is first applied to a data set simulated from a novel non-Gaussian multiscale Lorenz-96 dynamical system simulation model and then to a long-lead United States (U.S.) soil moisture forecasting application.
Covariate adjustment in randomization-based causal inference for 2K factorial designs

Abstract We develop finite-population asymptotic theory for covariate adjustment in randomization-based causal inference for 2 K factorial designs. In particular, we confirm that both the unadjusted and the covariate-adjusted estimators of the factorial effects are asymptotically unbiased and normal, and the latter is more precise than the former.
Character-level convolutional networks for text classification

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.
Active Ranking using Pairwise Comparisons

This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of n objects can be identified by standard sorting methods using n log2 n pairwise comparisons. We are interested in natural situations in which relationships among the objects may allow for ranking using far fewer pairwise comparisons. Specifically, we assume that the objects can be embedded into a d-dimensional Euclidean space and that the rankings reflect their relative distances from a common reference point in ℝd. We show that under this assumption the number of possible rankings grows like n2d and demonstrate an algorithm that can identify a randomly selected ranking using just slightly more than d log n adaptively selected pairwise comparisons, on average. If instead the comparisons are chosen at random, then almost all pairwise comparisons must be made in order to identify any ranking. In addition, we propose a robust, error-tolerant algorithm that only requires that the pairwise comparisons are probably correct. Experimental studies with synthetic and real datasets support the conclusions of our theoretical analysis.
Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

In this paper, we develop a robust economic model predictive controller for the containment of stochastic susceptible-exposed-infected-vigilant (SEIV) epidemic processes, which drives the process to extinction quickly, while minimizing the rate at which control resources are used. The study we present here is significant in that it addresses the problem of efficiently controlling general stochastic epidemic systems without relying on mean-field approximation, which is an important issue in the theory of stochastic epidemic processes. This enables us to provide rigorous convergence guarantees on the stochastic epidemic model itself, improving over the mean-field type convergence results of most prior work. There are two primary technical difficulties addressed in treating this problem: 1) constructing a means of tractably approximating the evolution of the process so that the designed approximation is robust to the modeling error introduced by the applied moment closure and 2) guaranteeing that the designed controller causes the closed-loop system to drive the SEIV process to extinction quickly. As an application, we use the developed framework for optimizing the use of quarantines in containing an SEIV epidemic outbreak.
On a generalization of the generating function for Gegenbauer polynomials

A generalization of the generating function for Gegenbauer polynomials is introduced whose coefficients are given in terms of associated Legendre functions of the second kind. We discuss how our expansion represents a generalization of several previously derived formulae such as Heine's formula and Heine's reciprocal square-root identity. We also show how this expansion can be used to compute hyperspherical harmonic expansions for power-law fundamental solutions of the polyharmonic equation.
Feature encoding in band-limited distributed surveillance systems

Distributed surveillance systems have become popular in recent years due to security concerns. However, transmitting high dimensional data in bandwidth-limited distributed systems becomes a major challenge. In this paper, we address this issue by proposing a novel probabilistic algorithm based on the divergence between the probability distributions of the visual features in order to reduce their dimensionality and thus save the network bandwidth in distributed wireless smart camera networks. We demonstrate the effectiveness of the proposed approach through extensive experiments on two surveillance recognition tasks.
INDICABLE GROUPS AND ENDOMORPHIC PRESENTATIONS

In this paper we look at presentations of subgroups of finitely presented groups with infinite cyclic quotients. We prove that if H is a finitely generated normal subgroup of a finitely presented group G with G/H cyclic, then H has ascending finite endomorphic presentation. It follows that any finitely presented indicable group without free semigroups has the structure of a semidirect product H ⋊ ℤ, where H has finite ascending endomorphic presentation.
Optimal Spectrum Sharing With ARQ-Based Legacy Users Via Chain Decoding

This paper investigates the design of access policies in spectrum sharing networks by exploiting the retransmission protocol of legacy primary users (PUs) to improve the spectral efficiency via opportunistic retransmissions at secondary users (SUs) and chain decoding . The optimal policy maximizing the SU throughput under an interference constraint to the PU and its performance are found in closed form. It is shown that the optimal policy randomizes among three modes: Idle , the SU remains idle over the retransmission window of the PU, to avoid causing interference; Interference Cancellation , the SU transmits only after decoding the PU packet , to improve its own throughput via interference cancellation; and Always Transmit , the SU transmits over the retransmission window of the PU to maximize the future potential of interference cancellation via chain decoding. This structure is exploited to design a stochastic optimization algorithm to facilitate learning and adaptation when the model parameters are unknown or vary over time, based on ARQ feedback from the PU and CSI measurements at the SU receiver. It is shown numerically that, for a 10% interference constraint, the optimal access policy yields 15% improvement over a state-of-the-art scheme without SU retransmissions, and up to $2\times $ gain over a scheme using a non-adaptive access policy instead of the optimal one.
Lower Bounds for Uncentered Maximal Functions in Any Dimension

In this paper we address the following question: given $ p\in (1,\infty)$, $n \geq 1$, does there exists a constant $A(p,n)>1$ such that $\| M f\|_{L^{p}}\geq A(n,p) \| f\|_{L^{p}}$ for any nonnegative $f \in L^{p}(\mathbb{R}^{n})$, where $Mf$ is a maximal function operator defined over the family of shifts and dilates of a centrally symmetric convex body. The inequality fails in general for the centered maximal function operator, but nevertheless we give an affirmative answer to the question for the uncentered maximal function operator and the almost centered maximal function operator. In addition, we also present the Bellman function approach of Melas, Nikolidakis and Stavropoulos to maximal function operators defined over various types of families of sets, and in case of parallelepipeds we will show that $A(n,p)=\left(\frac{p}{p-1}\right)^{1/p}$.
Muse-ing on the Impact of Utterance Ordering on Crowdsourced Emotion Annotations

Emotion recognition algorithms rely on data annotated with high quality labels. However, emotion expression and perception are inherently subjective. There is generally not a single annotation that can be unambiguously declared "correct." As a result, annotations are colored by the manner in which they were collected. In this paper, we conduct crowdsourcing experiments to investigate this impact on both the annotations themselves and on the performance of these algorithms. We focus on one critical question: the effect of context. We present a new emotion dataset, Multimodal Stressed Emotion (MuSE), and annotate the dataset using two conditions: randomized, in which annotators are presented with clips in random order, and contextualized, in which annotators are presented with clips in order. We find that contextual labeling schemes result in annotations that are more similar to a speaker’s own self-reported labels and that labels generated from randomized schemes are most easily predictable by automated systems.
Context-aware Human Intent Inference for Improving Human Machine Cooperation

The ability of human beings to precisely recognize others intents is a significant mental activity in reasoning about actions, such as, what other people are doing and what they will do next. Recent research has revealed that human intents could be inferred by measuring human cognitive activities through heterogeneous body and brain sensors (e.g., sensors for detecting physiological signals like ECG, brain signals like EEG and IMU sensors like accelerometers and gyros etc.). In this proposal, we aim at developing a computational framework for enabling reliable and precise real-time human intent recognition by measuring human cognitive and physiological activities through the heterogeneous body and brain sensors for improving human machine interactions, and serving intentbased human activity prediction.
The rational sectional category of certain universal fibrations

We prove that the (reduced) rational sectional category of the universal fibration with fibre X, for X any space that satisfies a well-known conjecture of Halperin, equals one.
A ubiquitous ~62-Myr periodic fluctuation superimposed on general trends in fossil biodiversity. I. Documentation

We use Fourier analysis and related techniques to investigate the question of periodicities in fossil biodiversity. These techniques are able to identify cycles superimposed on the long-term trends of the Phanerozoic. We review prior results and analyze data previously reduced and published. Joint time-series analysis of various reductions of the Sepkoski Data, Paleobiology Database, and Fossil Record 2 indicate the same periodicity in biodiversity of marine animals at 62 Myr. We have not found this periodicity in the terrestrial fossil record. We have found that the signal strength decreases with time because of the accumulation of apparently “resistant” long-lived genera. The existence of a 62-Myr periodicity despite very different treatment of systematic error, particularly sampling-strength biases, in all three major databases strongly argues for its reality in the fossil record.
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order

Asynchronous parallel optimization received substantial successes and extensive attention recently. One of core theoretical questions is how much speedup (or benefit) the asynchronous parallelization can bring to us. This paper provides a comprehensive and generic analysis to study the speedup property for a broad range of asynchronous parallel stochastic algorithms from the zeroth order to the first order methods. Our result recovers or improves existing analysis on special cases, provides more insights for understanding the asynchronous parallel behaviors, and suggests a novel asynchronous parallel zeroth order method for the first time. Our experiments provide novel applications of the proposed asynchronous parallel zeroth order method on hyper parameter tuning and model blending problems.
Normal Toric Ideals of Low Codimension

Abstract Every normal toric ideal of codimension two is minimally generated by a Grobner basis with squarefree initial monomials. A polynomial time algorithm is presented for checking whether a toric ideal of fixed codimension is normal.
Controlling composition factors of a finite group by its character degree ratio

Abstract For a finite nonabelian group G let rat ( G ) be the largest ratio of degrees of two nonlinear irreducible characters of G . We show that nonabelian composition factors of G are controlled by rat ( G ) in some sense. Specifically, if S different from the simple linear groups PSL 2 ( q ) is a nonabelian composition factor of G , then the order of S and the number of composition factors of G isomorphic to S are both bounded in terms of rat ( G ) . Furthermore, when the groups PSL 2 ( q ) are not composition factors of G , we prove that | G : O ∞ ( G ) | ⩽ rat ( G ) 21 where O ∞ ( G ) denotes the solvable radical of G .
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration

The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that combines the task allocation and motion levels of reasoning: the robotic agent uses Bayesian inference to predict the next goal of its human partner from his or her ongoing motion, and re-plans its own actions in real time. This anticipative adaptation is desirable in many practical scenarios, where humans are unable or unwilling to take on the cognitive overhead required to explicitly communicate their intent to the robot. A behavioral experiment indicates that the combination of goal inference and dynamic task planning significantly improves both objective and perceived performance of the human-robot team. Participants were highly sensitive to the differences between robot behaviors, preferring to work with a robot that adapted to their actions over one that did not.