cssi/SciSciGPT-SciSciNet huggingface dataset:
One-Pot Catalytic Enantio- and Diastereoselective Syntheses of anti-, syn-cis-Disubstituted, and syn-Vinyl Cyclopropyl Alcohols

Highly enantio- and diastereoselective methods for the synthesis of a variety of cyclopropyl alcohols are reported. These methods represent the first one-pot approaches to syn-vinyl cyclopropyl alcohols, syn-cis-disubstituted cyclopropyl alcohols, and anti-cyclopropyl alcohols from achiral precursors. The methods begin with enantioselective C−C bond formations promoted by a MIB-based zinc catalyst to generate allylic alkoxide intermediates. The intermediates are then subjected to in situ alkoxide-directed cyclopropanation to provide cyclopropyl alcohols. In the synthesis of vinyl cyclopropyl alcohols, hydroboration of enynes is followed by transmetalation of the resulting dienylborane to zinc to provide dienylzinc reagents. Enantioselective addition to aldehydes generates the requisite dienyl zinc alkoxides, which are then subjected to in situ cyclopropanation to furnish vinyl cyclopropyl alcohols. Cyclopropanation occurs at the double bond allylic to the alkoxide. Using this method, syn-vinylcyclopropyl ...
Unexpected migration and oxidative cyclization of substituted 2-acetophenone triflates under basic conditions: synthetic and mechanistic insights.

Oxidative ring closure of alkyl-substituted 2-hydroxyacetophenone trifluoromethanesulfonate esters (triflates) occurs upon exposure to base in anaerobic DMF at 20−90 °C. Alkyl substitution is required for ring closure. A migrated enol triflate product forms at lower temperature in high yield via migration of the trifluoromethanesulfonate in the unsubstituted and monoalkyl-substituted cases. The alkyl-substituted enol triflates also enter into the benzofuran-3-one ring-forming process under thermal cyclization conditions. Potential mechanistic pathways are evaluated.
The synthesis of two novel fused thienopyridopyridazines

Two previously unknown thienopyridopyridazine ring systems, thieno[2',3':5,6]pyrido[2,3-d]pyridazine-5,8,9(4H,6H,7H)- trione (4) and thieno[3',2':5,6]pyrido[2,3-d]pyridazine-4,5,8(6H,7H,9H)- trione (10) were synthesized and their chlorination reactions with phosphorus oxychloride were investigated
Total synthesis of (+)- and (-)-duryne: a potent anticancer agent from the marine sponge Cribrochalina dura. Establishment of the central double bond geometry and the absolute configuration of the chiral centers.

Duryne is a C30 polyacetylenic alcohol with C2 symmetry. Despite its potent cytotoxicity, its central double bond geometry and the absolute configuration of the chiral centers were not determined. ...
Synthesis and structure of tolyporphin A O,O-diacetate.

The revised structure of tolyporphin A O,O-diacetate (2b) was synthesized by assembling fragments 4, 5, and 12. The synthetic substance was found to be identical to the O,O-diacetate derived from natural tolyporphin A in every respect, thus establishing the relative and absolute configurations of this natural product.
pH-mediated inhibition of a bumble bee parasite by an intestinal symbiont.

Gut symbionts can augment resistance to pathogens by stimulating host-immune responses, competing for space and nutrients, or producing antimicrobial metabolites. Gut microbiota of social bees, which pollinate many crops and wildflowers, protect hosts against diverse infections and might counteract pathogen-related bee declines. Bumble bee gut microbiota, and specifically abundance of Lactobacillus ‘Firm-5’ bacteria, can enhance resistance to the trypanosomatid parasite Crithidia bombi. However, the mechanism underlying this effect remains unknown. We hypothesized that the Firm-5 bacterium Lactobacillus bombicola, which produces lactic acid, inhibits C. bombi via pH-mediated effects. Consistent with our hypothesis, L. bombicola spent medium inhibited C. bombi growth via reduction in pH that was both necessary and sufficient for inhibition. Inhibition of all parasite strains occurred within the pH range documented in honey bees, though sensitivity to acidity varied among strains. Spent medium was slightly more potent than HCl, d- and l-lactic acids for a given pH, suggesting that other metabolites also contribute to inhibition. Results implicate symbiont-mediated reduction in gut pH as a key determinant of trypanosomatid infection in bees. Future investigation into in vivo effects of gut microbiota on pH and infection intensity would test the relevance of these findings for bees threatened by trypanosomatids.
Amino acids as precursors for N-unsubstituted fulleropyrrolidine derivatives

Abstract The formation of azomethine ylides 3 directly from aminoacids and aldehydes and their addition to C 60 is reported. The method provides a new route to N-unsubstituted fulleropyrrolidines 4a–4g .
Magnetic Moment Enhancement at Transition Metal and Rare-Earth Metal Surfaces

Magnetism at surface represents an area of great interest in surface physics because, as expected, the lowered symmetry and coordination number offer a variety of opportunities for inducing new and exotic phenomena and so hold out the promise of new device applications. Here we review the prediction of local spin density functional ab initio electronic structure calculations which have played a key role in the development of this exciting field by providing a clearer understanding of the experimental observations and predicting new systems with desired properties
Molecular aspects of the transport and toxicity of ochratoxin a.

Ochratoxins are a class of naturally occurring compounds produced by several fungi. The most toxic is ochratoxin A (OTA), and occurrence of some human nephropathies and tumors correlate with enhanced OTA exposure. In this Account, the following areas are examined: molecular details of the binding of OTA to human ser-um albumin (HSA), the influences of binding to HSA on the trans-port of OTA across epithelial cell membranes by organic anion transport proteins, the oxidative activation of OTA, and the for-mation of OTA adducts with biological molecules. These studies are beginning to provide a detailed chemical model for the trans-port, accumulation, and genotoxic and carcinogenic effects of OTA.
Syntheses and Crystal Structures of Three Copper Tellurides: BaDyCuTe3, K1.5Dy2Cu2.5Te5, and Acentric K0.5Ba0.5DyCu1.5Te3

Three compounds, BaDyCuTe3, K1.5Dy2Cu2.5Te5, and K0.5Ba0.5DyCu1.5Te3, were synthesized from the reaction at 850 °C of the pure elements with fluxes such as BaCl2 or a eutectic mixture of LiCl/KCl. ...
A Catalog of Spectra, Albedos, and Colors of Solar System Bodies for Exoplanet Comparison

Abstract We present a catalog of spectra and geometric albedos, representative of the different types of solar system bodies, from 0.45 to 2.5 μm. We analyzed published calibrated, uncalibrated spe...
Photoinduced Bergman cycloaromatization of imidazole-fused enediynes

Abstract A series of 4,5-bis-(alkyn-1-yl)imidazoles—‘imidazole-fused’ enediynes—were synthesized and their reactivities in photoinduced Bergman cycloaromatization reactions were determined. The more conformationally rigid analogues gave cycloaromatized products in good yields upon irradiation (450 W low-pressure mercury lamp, ambient temperature). A bicyclic analogue ( 3 ) was shown to cleave supercoiled plasmid DNA.
Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP.

Membrane proteins are underrepresented in structural databases, which has led to a lack of computational tools and the corresponding inappropriate use of tools designed for soluble proteins. For membrane proteins, lipid accessibility is an essential property. Although programs are available for sequence-based prediction of lipid accessibility and structure-based identification of solvent-accessible surface area, the latter does not distinguish between water accessible and lipid accessible residues in membrane proteins. Here we present mp_lipid_acc, the first method to identify lipid accessible residues from the protein structure, implemented in the RosettaMP framework and available as a webserver. Our method uses protein structures transformed in membrane coordinates, for instance from PDBTM or OPM databases, and a defined membrane thickness to classify lipid accessibility of residues. mp_lipid_acc is applicable to both α-helical and β-barrel membrane proteins of diverse architectures with or without water-filled pores and uses a concave hull algorithm for surface-residue classification. We further provide a manually curated benchmark dataset that can be used for further method development. We present a novel tool to classify lipid accessibility from the protein structure, which is applicable to proteins of diverse architectures and achieves prediction accuracies of 90% on a manually curated database. mp_lipid_acc is part of the Rosetta software suite, available at www.rosettacommons.org . The webserver is available at http://rosie.graylab.jhu.edu/mp_lipid_acc/submit and the benchmark dataset is available at http://tinyurl.com/mp-lipid-acc-dataset .
Vagal nerve stimulation triggers widespread responses and alters large-scale functional connectivity in the rat brain

Vagus nerve stimulation (VNS) is a therapy for epilepsy and depression. However, its efficacy varies and its mechanism remains unclear. Prior studies have used functional magnetic resonance imaging (fMRI) to map brain activations with VNS in human brains, but have reported inconsistent findings. The source of inconsistency is likely attributable to the complex temporal characteristics of VNS-evoked fMRI responses that cannot be fully explained by simplified response models in the conventional model-based analysis for activation mapping. To address this issue, we acquired 7-Tesla blood oxygenation level dependent fMRI data from anesthetized Sprague-Dawley rats receiving electrical stimulation at the left cervical vagus nerve. Using spatially independent component analysis, we identified 20 functional brain networks and detected the network-wise activations with VNS in a data-driven manner. Our results showed that VNS activated 15 out of 20 brain networks, and the activated regions covered >76% of the brain volume. The time course of the evoked response was complex and distinct across regions and networks. In addition, VNS altered the strengths and patterns of correlations among brain networks relative to those in the resting state. The most notable changes in network-network interactions were related to the limbic system. Together, such profound and widespread effects of VNS may underlie its unique potential for a wide range of therapeutics to relieve central or peripheral conditions.
Regulation of harvester ant foraging as a closed-loop excitable system

Ant colonies regulate activity in response to changing conditions without using centralized control. Desert harvester ant colonies forage for seeds, and regulate foraging to manage a tradeoff between spending and obtaining water. Foragers lose water while outside in the dry air, but ants obtain water by metabolizing the fats in the seeds they eat. Previous work shows that the rate at which an outgoing forager leaves the nest depends on its recent rate of brief antennal contacts with incoming foragers carrying food. We examine how this process can yield foraging rates that are robust to uncertainty and responsive to temperature and humidity across minute-to-hour timescales. To explore possible mechanisms, we develop a low-dimensional analytical model with a small number of parameters that captures observed foraging behavior. The model uses excitability dynamics to represent response to interactions inside the nest and a random delay distribution to represent foraging time outside the nest. We show how feedback from outgoing foragers returning to the nest stabilizes the incoming and outgoing foraging rates to a common value determined by the volatility of available foragers. The model exhibits a critical volatility above which there is sustained foraging at a constant rate and below which foraging stops. To explain how foraging rates adjust to temperature and humidity, we propose that foragers modify their volatility after they leave the nest and become exposed to the environment. Our study highlights the importance of feedback in the regulation of foraging activity and shows how modulation of volatility can explain how foraging activity responds to conditions and varies across colonies. Our model elucidates the role of feedback across many timescales in collective behavior, and may be generalized to other systems driven by excitable dynamics, such as neuronal networks.
Researching candidate sites for a carbon storage complex in the Central Appalachian Basin, USA

Abstract The purpose of the Central Appalachian Basin-Carbon Storage Assurance Facility Enterprise Integrated Prefeasibility Project was to identify candidate sites in eastern Ohio for a storage complex capable of storing 50 million tonnes. Carbon capture and storage (CCS) will be essential in the Appalachian Basin, which includes coal-fired power plants, natural gas processing, refineries, chemical plants, and natural gas power. The project team investigated Cambrian-Ordovician Age sandstones and carbonates collocated near depleted oilfields, where enhanced oil recovery could provide a stepping stone for developing a storage hub. Feasible routes for linking sources to sinks via regional pipelines were assessed. The sub-basinal analysis demonstrated significant potential storage capacity in both deep saline reservoirs and depleted oil and gas fields. The project definition analysis revealed the project footprint would be reasonable and only two wells would be needed for injection of CO 2 . Project economics illustrated a need for both government and private investment in the absence of a regulatory mandate. Ohio also lacks a comprehensive policy for long-term liability and subsurface storage rights, which could be addressed during pilot testing. Developing qualified sites within two selected areas for large-scale deployment of CCS appears feasible and the study helped to define future research needs.
Microstructural integrity of white matter moderates an association between childhood adversity and adult trait anger.

Amongst a number of negative life sequelae associated with childhood adversity is the later expression of a higher dispositional tendency to experience anger and frustration to a wide range of situations (i.e., trait anger). We recently reported that an association between childhood adversity and trait anger is moderated by individual differences in both threat-related amygdala activity and executive control-related dorsolateral prefrontal cortex (dlPFC) activity, wherein individuals with relatively low amygdala and high dlPFC activity do not express higher trait anger even when having experienced childhood adversity. Here, we examine possible structural correlates of this functional dynamic using diffusion magnetic resonance imaging data from 647 young adult men and women volunteers. Specifically, we tested whether the degree of white matter microstructural integrity as indexed by fractional anisotropy modulated the association between childhood adversity and trait anger. Our analyses revealed that higher microstructural integrity of multiple pathways was associated with an attenuated link between childhood adversity and adult trait anger. Amongst these pathways was the uncinate fasciculus (UF; ΔR 2 = 0.01), which not only provides a major anatomical link between the amygdala and prefrontal cortex but also is associated with individual differences in regulating negative emotion through top-down cognitive reappraisal. These findings suggest that higher microstructural integrity of distributed white matter pathways including but not limited to the UF may represent an anatomical foundation serving to buffer against the expression of childhood adversity as later trait anger, which is itself associated with multiple negative health outcomes.
A new stereoselective route to (2S, 3S, 8S, 9S, 4E, 6E)-3-amino-9-methoxy-2, 6, 8-trimethyl-10-phenyldeca-4, 6-dienoic acid (Adda)

N-Boc-Adda has been prepared in 15 steps and 9% overall yield from the readily available alcohol, 3-pentyne-2-ol, employing a route that includes two Claisen rearrangements.
Massarilactones A and B: novel secondary metabolites from the freshwater aquatic fungus Massarina tunicata

Abstract Massarilactones A and B ( 1 and 2 ) have been isolated from cultures of the freshwater aquatic fungus Massarina tunicata. The structures, including absolute stereochemistry, were determined by X-ray diffraction analysis of their bis(4-bromobenzoate) derivatives.
Optimal control strategies for inhibition of protein aggregation

Protein aggregation has been implicated in many medical disorders, including Alzheimer’s and Parkinson’s diseases. Potential therapeutic strategies for these diseases propose the use of drugs to inhibit specific molecular events during the aggregation process. However, viable treatment protocols require balancing the efficacy of the drug with its toxicity, while accounting for the underlying events of aggregation and inhibition at the molecular level. To address this key problem, we combine here protein aggregation kinetics and control theory to determine optimal protocols that prevent protein aggregation via specific reaction pathways. We find that the optimal inhibition of primary and fibril-dependent secondary nucleation require fundamentally different drug administration protocols. We test the efficacy of our approach on experimental data for the aggregation of the amyloid-β(1-42) peptide of Alzheimer’s disease in the model organism Caenorhabditis elegans . Our results pose and answer the question of the link between the molecular basis of protein aggregation and optimal strategies for inhibiting it, opening up avenues for the design of rational therapies to control pathological protein aggregation.
Usage of and attitudes about green tea extract and Epigallocathechin-3-gallate (EGCG) as a therapy in individuals with Down syndrome.

Abstract Objective Usage of and views concerning alternative therapies in the DS community are not well documented. Some positive effects of green tea extracts (GTE) containing Epigallocathechin-3-gallate (EGCG) have been reported in individuals with DS and DS mouse models, but minimal improvements or detrimental effects of pure EGCG treatment have been reported in DS mouse models. Given the uncertainty about the effectiveness of these supplements, the goal of this study was to determine the relative prevalence of and attitudes about GTE/EGCG treatments among DS caregivers. Methods An anonymous survey about attitudes and usage of GTE/EGCG in individuals with DS was completed by caregivers of these individuals. Results GTE/EGCG treatment was provided by 18% of responding caregivers who were mostly younger, highly educated, and utilized scientific sources and other parents to influence their decision to use GTE/EGCG. Individuals with DS who received GTE/EGCG were characterized as less severely disabled. Most caregivers who did not give GTE/EGCG reported concerns about potential side effects and lack of effectiveness. Few caregivers consulted with medical providers about GTE/EGCG usage. Conclusions These results demonstrate a need for communication between caregivers, medical providers, and scientists about potential benefits and risks for adverse effects of GTE, EGCG, and other nutritional supplements in individuals with DS.
Interactive fingerprinting codes and the hardness of preventing false discovery

We show an essentially tight bound on the number of adaptively chosen statistical queries that a computationally efficient algorithm can answer accurately given n samples from an unknown distribution. A statistical query asks for the expectation of a predicate over the underlying distribution, and an answer to a statistical query is accurate if it is “close” to the correct expectation over the distribution. This question was recently studied by Dwork et al. [DFH+ 15], who showed how to answer Ω(n2) queries efficiently, and also by Hardt and Ullman [HU14], who showed that answering O(n3) queries is hard. We close the gap between the two bounds and show that, under a standard hardness assumption, there is no computationally efficient algorithm that, given n samples from an unknown distribution, can give valid answers to O(n2) adaptively chosen statistical queries. An implication of our results is that computationally efficient algorithms for answering arbitrary, adaptively chosen statistical queries may as well be differentially private. We obtain our results using a new connection between the problem of answering adaptively chosen statistical queries and a combinatorial object called an interactive fingerprinting code [FT01]. In order to optimize our hardness result, we give a new Fourier-analytic approach to analyzing fingerprinting codes that is simpler, more flexible, and yields better parameters than previous constructions.
Sequential Nonparametric Tests for a Change in Distribution: An Application to Detecting Radiological Anomalies

We propose a sequential nonparametric test for detecting a change in distribution, based on windowed Kolmogorov–Smirnov statistics. The approach is simple, robust, highly computationally efficient,...
Asymptotics of ground state degeneracies in quiver quantum mechanics

We study the growth of the ground state degeneracy in the Kronecker model of quiver quantum mechanics. This is the simplest quiver with two gauge groups and bifundamental matter elds, and appears universally in the context of BPS state counting in fourdimensionalN = 2 systems. For large ranks, the ground state degeneracy is exponential with slope a modular function that we are able to compute at integral values of its argument. We also observe that the exponential of the slope is an algebraic number and determine its associated algebraic equation explicitly in several examples. The speed of growth of the degeneracies, together with various physical features of the bound states, suggests a dual string interpretation.
Model-driven data collection for biological systems

For biological experiments aiming at calibrating models with unknown parameters, a good experimental design is crucial, especially for those subject to various constraints, such as financial limitations, time consumption and physical practicability. In this paper, we discuss a sequential experimental design based on information theory for parameter estimation and apply it to two biological systems. Two specific issues are addressed in the proposed applications, namely the determination of the optimal sampling time and the optimal choice of observable. The optimal design, either sampling time or observable, is achieved by an information-theoretic sensitivity analysis. It is shown that this is equivalent with maximizing the mutual information and contrasted with non-adaptive designs, this information theoretic strategy provides the fastest reduction of uncertainty.
Self-Shrinkers to the Mean Curvature Flow asymptotic to Isoparametric Cones

In this paper we construct an end of a self-similar shrinking solution of the mean curvature flow asymptotic to an isoparametric cone C and lying outside of C. We call a cone C in $R^{n+1}$ an isoparametric cone if C is the cone over a compact embedded isoparametric hypersurface $\Gamma \subset S^n$. The theory of isoparametic hypersurfaces is extremely rich and there are infinitely many distinct classes of examples, each with infinitely many members.
Competitive Diffusion in Social Networks: Quality or Seeding?

In this paper, we study a strategic model of marketing and product consumption in social networks. We consider two firms in a market competing to maximize the consumption of their products. Each firm has a limited budget which can be invested on the quality of the product or spent on seeding key individuals in the network to incentivize others to consume more of their product. After the decision of firms, agents choose their consumptions following a myopic best response dynamics which results in a local, linear update for their consumption decision. We characterize the unique Nash equilibrium of the game between firms and study the effect of the budgets as well as the network structure on the optimal allocation. We show that at the equilibrium, firms invest more budget on quality when their budgets are close to each other. However, as the gap between budgets widens, competition in qualities becomes less effective and firms spend more of their budget on seeding. We also show that given equal budget of firms, if the seeding budget is nonzero for a balanced graph, it will also be nonzero for any other graph, and if the seeding budget is zero for a star graph it will be zero for any other graph as well. As a practical extension, we then consider a similar setup for two products that are already being offered with some preset qualities in a network. At some point in time, firms learn about the network structure and decide to utilize a limited budget to mount their market share by either improving the quality or new seeding some agents to incline consumers toward their products. We show that the optimal budget allocation in this case simplifies to a threshold strategy. Interestingly, we derive similar results to those of the original problem, in which preset qualities simulate the role that budgets had in the original setup
Entropy of real rational surface automorphisms

We compare real and complex dynamics for automorphisms of rational surfaces that are obtained by lifting some quadratic birational maps of the plane. In particular, we show how to exploit the exist...
Are clusterings of multiple data views independent

In the Pioneer 100 (P100) Wellness Project, multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor personalized health recommendations to each subgroup. It is tempting to cluster the participants using all of the data types and timepoints, in order to fully exploit the available information. However, clustering the participants based on multiple data views implicitly assumes that a single underlying clustering of the participants is shared across all data views. If this assumption does not hold, then clustering the participants using multiple data views may lead to spurious results. In this article, we seek to evaluate the assumption that there is some underlying relationship among the clusterings from the different data views, by asking the question: are the clusters within each data view dependent or independent? We develop a new test for answering this question, which we then apply to clinical, proteomic, and metabolomic data, across two distinct timepoints, from the P100 study. We find that while the subgroups of the participants defined with respect to any single data type seem to be dependent across time, the clustering among the participants based on one data type (e.g. proteomic data) appears not to be associated with the clustering based on another data type (e.g. clinical data).
A Nonparametric Bayesian Approach for Spoken Term Detection by Example Query.

State of the art speech recognition systems use data-intensive context-dependent phonemes as acoustic units. However, these approaches do not translate well to low resourced languages where large amounts of training data is not available. For such languages, automatic discovery of acoustic units is critical. In this paper, we demonstrate the application of nonparametric Bayesian models to acoustic unit discovery. We show that the discovered units are correlated with phonemes and therefore are linguistically meaningful. We also present a spoken term detection (STD) by example query algorithm based on these automatically learned units. We show that our proposed system produces a P@N of 61.2% and an EER of 13.95% on the TIMIT dataset. The improvement in the EER is 5% while P@N is only slightly lower than the best reported system in the literature.
Supermajority sentiment detection with external influence in large social networks

In a large social network whose members harbor binary sentiments towards an issue, we investigate the asymptotic accuracy of sentiment detection. We model the user sentiments by an Ising Markov random field model and allow the user sentiments to be biased by an external influence. We consider a general supermajority sentiment detection problem and show that the detection accuracy is affected by the network structure, its parameters, as well as the external influence level.
Off-The-Grid Model Based Deep Learning (O-Modl)

We introduce a model based off-the grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies is the learning of non-linear annihilation relations in Fourier space. We rely on a model based framework, which allows us to use a significantly smaller deep network, compared to direct approaches that also learn how to invert the forward model. Preliminary comparisons against image domain MoDL approach demonstrates the potential of the off-the-grid formulation. The main benefit of the proposed scheme compared to structured low-rank methods is the quite significant reduction in computational complexity.
Chaotic crystallography: how the physics of information reveals structural order in materials

We review recent progress in applying information-theoretic and computation-theoretic measures to describe material structure that transcends previous methods based on exact geometric symmetries. We discuss the necessary theoretical background for this new toolset and show how the new techniques detect and describe novel material properties. We discuss how the approach relates to well known crystallographic practice and examine how it provides novel interpretations of familiar structures. Throughout, we concentrate on disordered materials that, while important, have received less attention both theoretically and experimentally than those with either periodic or aperiodic order.
Multiple testing with the structure‐adaptive Benjamini–Hochberg algorithm

In multiple‐testing problems, where a large number of hypotheses are tested simultaneously, false discovery rate (FDR) control can be achieved with the well‐known Benjamini–Hochberg procedure, which a(0,1]dapts to the amount of signal in the data, under certain distributional assumptions. Many modifications of this procedure have been proposed to improve power in scenarios where the hypotheses are organized into groups or into a hierarchy, as well as other structured settings. Here we introduce the ‘structure‐adaptive Benjamini–Hochberg algorithm’ (SABHA) as a generalization of these adaptive testing methods. The SABHA method incorporates prior information about any predetermined type of structure in the pattern of locations of the signals and nulls within the list of hypotheses, to reweight the p‐values in a data‐adaptive way. This raises the power by making more discoveries in regions where signals appear to be more common. Our main theoretical result proves that the SABHA method controls the FDR at a level that is at most slightly higher than the target FDR level, as long as the adaptive weights are constrained sufficiently so as not to overfit too much to the data—interestingly, the excess FDR can be related to the Rademacher complexity or Gaussian width of the class from which we choose our data‐adaptive weights. We apply this general framework to various structured settings, including ordered, grouped and low total variation structures, and obtain the bounds on the FDR for each specific setting. We also examine the empirical performance of the SABHA method on functional magnetic resonance imaging activity data and on gene–drug response data, as well as on simulated data.
3D Human Pose Estimation on a Configurable Bed from a Pressure Image

Robots have the potential to assist people in bed, such as in healthcare settings, yet bedding materials like sheets and blankets can make observation of the human body difficult for robots. A pressure-sensing mat on a bed can provide pressure images that are relatively insensitive to bedding materials. However, prior work on estimating human pose from pressure images has been restricted to 2D pose estimates and flat beds. In this work, we present two convolutional neural networks to estimate the 3D joint positions of a person in a configurable bed from a single pressure image. The first network directly outputs 3D joint positions, while the second outputs a kinematic model that includes estimated joint angles and limb lengths. We evaluated our networks on data from 17 human participants with two bed configurations: supine and seated. Our networks achieved a mean joint position error of 77 mm when tested with data from people outside the training set, outperforming several baselines. We also present a simple mechanical model that provides insight into ambiguity associated with limbs raised off of the pressure mat, and demonstrate that Monte Carlo dropout can be used to estimate pose confidence in these situations. Finally, we provide a demonstration in which a mobile manipulator uses our network's estimated kinematic model to reach a location on a person's body in spite of the person being seated in a bed and covered by a blanket.
A scalable framework for spatiotemporal analysis of location-based social media data

In the past several years, social media (e.g., Twitter and Facebook) has been experiencing a spectacular rise and popularity, and becoming a ubiquitous discourse for content sharing and social networking. With the widespread of mobile devices and location-based services, social media typically allows users to share whereabouts of daily activities (e.g., check-ins and taking photos), and thus strengthens the roles of social media as a proxy to understand human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for ecient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity proles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive ow mapping interface (including both single-source and multiple-source ow mapping) is developed to allow real-time, and interactive visual exploration of movement dynamics in massive location-based social media at multiple scales.
Analysis and Prediction of JND-Based Video Quality Model

The just-noticeable-difference (JND) visual perception property has received much attention in characterizing human subjective viewing experience of compressed video. In this work, we quantity the JND-based video quality assessment model using the satisfied user ratio (SUR) curve, and show that the SUR model can be greatly simplified since the JND points of multiple subjects for the same content in the VideoSet can be well modeled by the normal distribution. Then, we design an SUR prediction method with video quality degradation features and masking features and use them to predict the first, second and the third JND points and their corresponding SUR curves. Finally, we verify the performance of the proposed SUR prediction method with different configurations on the VideoSet. The experimental results demonstrate that the proposed SUR prediction method achieves good performance in various resolutions with the mean absolute error (MAE) of the SUR smaller than 0.05 on average.
Iterated sumsets and subsequence sums

Abstract Let G ≅ Z / m 1 Z × … × Z / m r Z be a finite abelian group with 1 m 1 | … | m r = exp ⁡ ( G ) . The Kemperman Structure Theorem characterizes all subsets A , B ⊆ G satisfying | A + B | | A | + | B | and has been extended to cover the case when | A + B | ≤ | A | + | B | . Utilizing these results, we provide a precise structural description of all finite subsets A ⊆ G with | n A | ≤ ( | A | + 1 ) n − 3 when n ≥ 3 (also when G is infinite), in which case many of the pathological possibilities from the case n = 2 vanish, particularly for large n ≥ exp ⁡ ( G ) − 1 . The structural description is combined with other arguments to generalize a subsequence sum result of Olson asserting that a sequence S of terms from G having length | S | ≥ 2 | G | − 1 must either have every element of G representable as a sum of | G | -terms from S or else have all but | G / H | − 2 of its terms lying in a common H-coset for some H ≤ G . We show that the much weaker hypothesis | S | ≥ | G | + exp ⁡ ( G ) suffices to obtain a nearly identical conclusion, where for the case H is trivial we must allow all but | G / H | − 1 terms of S to be from the same H-coset. The bound on | S | is improved for several classes of groups G, yielding optimal lower bounds for | S | . We also generalize Olson's result for | G | -term subsums to an analogous one for n-term subsums when n ≥ exp ⁡ ( G ) , with the bound likewise improved for several special classes of groups.
AN ABSTRACT MORIMOTO THEOREM FOR GENERALIZED F-STRUCTURES

We abstract Morimoto's construction of complex structures on product manifolds to pairs of certain generalized $F$-structures on manifolds that are not necessarily global products. As applications we characterize invariant generalized complex structures on product manifolds in which one factor is a Lie group and we generalize a theorem of Blair, Ludden and Yano on Hermitian bicontact manifolds.
Joint estimation and localization in sensor networks

This paper addresses the problem of collaborative estimation and tracking of dynamic phenomena via a wireless sensor network. A distributed linear estimator (i.e., a type of a distributed Kalman filter) is derived. We prove that the filter is mean square consistent when estimating static phenomena. In large sensor networks, it is common that the sensors do not have good knowledge of their locations, which affects the estimation procedure. Unlike existing approaches for target tracking, we investigate the performance of our filter when the sensor poses need to be estimated by an auxiliary localization procedure. A distributed Jacobi algorithm is used to localize the sensors from noisy relative measurements. We prove strong convergence guarantees for the localization method and in turn for the joint localization and target estimation approach. The performance of our algorithms is demonstrated in simulation on environmental monitoring and vehicle tracking tasks.
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition

The goal of continuous emotion recognition is to assign an emotion value to every frame in a sequence of acoustic features. We show that incorporating long-term temporal dependencies is critical for continuous emotion recognition tasks. To this end, we first investigate architectures that use dilated convolutions. We show that even though such architectures outperform previously reported systems, the output signals produced from such architectures undergo erratic changes between consecutive time steps. This is inconsistent with the slow moving ground-truth emotion labels that are obtained from human annotators. To deal with this problem, we model a downsampled version of the input signal and then generate the output signal through upsampling. Not only does the resulting downsampling/upsampling network achieve good performance, it also generates smooth output trajectories. Our method yields the best known audio-only performance on the RECOLA dataset.
Plurisubharmonic domination in Banach spaces

Abstract We show that if X is a Banach space with a Schauder basis, Ω ⊂ X is a pseudoconvex open subset, and u : Ω → ( − ∞ , ∞ ) is a locally bounded function, then there is a continuous plurisubharmonic function w : Ω → ( − ∞ , ∞ ) with u ( x ) ⩽ w ( x ) for all x ∈ Ω . This has many applications to analytic cohomology of complex Banach manifolds.
LP Bounds for Rate-Distortion With Variable Side Information

We consider a rate-distortion problem with side information at multiple decoders. Several upper and lower bounds have been proposed for this general problem or special cases of it. We provide an upper bound for general instances of this problem, which takes the form of a linear program, by utilizing random binning and simultaneous decoding techniques [1] and compare it with the existing bounds. We also provide a lower bound for the general problem, which was inspired by a linear-programming lower bound for index coding, and show that it subsumes most of the lower bounds in literature. Using these upper and lower bounds, we explicitly characterize the rate-distortion function of a problem that can be seen as a Gaussian analogue of the “odd-cycle” index coding problem.
Grade Prediction with Temporal Course-wise Influence.

There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective fields of study. In this paper, we present a novel approach for analyzing historical educational records from a large, public university to perform next-term grade prediction; i.e., to estimate the grades that a student will get in a course that he/she will enroll in the next term. Accurate next-term grade prediction holds the promise for better student degree planning, personalized advising and automated interventions to ensure that students stay on track in their chosen degree program and graduate on time. We present a factorization-based approach called Matrix Factorization with Temporal Course-wise Influence that incorporates course-wise influence effects and temporal effects for grade prediction. In this model, students and courses are represented in a latent "knowledge" space. The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space. Course-wise influence is considered as an additional factor in the grade prediction. Our experimental results show that the proposed method outperforms several baseline approaches and infer meaningful patterns between pairs of courses within academic programs.
A comparison of methods for cascade prediction

Information cascades exist in a wide variety of platforms on Internet. A very important real-world problem is to identify which information cascades can "go viral". A system addressing this problem can be used in a variety of applications including public health, marketing and counter-terrorism. As a cascade can be considered as compound of the social network and the time series. However, in related literature where methods for solving the cascade prediction problem were proposed, the experimental settings were often limited to only a single metric for a specific problem formulation. Moreover, little attention was paid to the run time of those methods. In this paper, we first formulate the cascade prediction problem as both classification and regression. Then we compare three categories of cascade prediction methods: centrality based, feature based and point process based. We carry out the comparison through evaluation of the methods by both accuracy metrics and run time. The results show that feature based methods can outperform others in terms of prediction accuracy but suffer from heavy overhead especially for large datasets. While point process based methods can also run into issue of long run time when the model can not well adapt to the data. This paper seeks to address issues in order to allow developers of systems for social network analysis to select the most appropriate method for predicting viral information cascades.
Viviani Polytopes and Fermat Points.

Given a set of oriented hyperplanes ℘ = {p1, ‖, pk} in ℝn, define v : ℝn ℝ by v(X) = the sum of the signed distances from X to p1, ‖, pk, for any point X ∈ Rℝn. We give a simple geometric charact...
Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective

This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain. The key observation is that, for a mixture oftwo sources, with their magnitudes accurately estimated and under a geometric constraint, the absolute phase difference between each source and the mixture can be uniquely determined; in addition, the source phases at each time-frequency $({\text{T}} - {{\Gamma }})$ unit can be narrowed down to only two candidates. To pick the right candidate, we propose three algorithms based on iterative phase reconstruction, group delay estimation, and phase-difference sign prediction. State-of-the-art results are obtained on the publicly available wsj0-2mix and 3 mix corpus.
Error bounds for sparse classifiers in high-dimensions

We prove an L2 recovery bound for a family of sparse estimators defined as minimizers of some empirical loss functions -- which include hinge loss and logistic loss. More precisely, we achieve an upper-bound for coefficients estimation scaling as (k*/n)\log(p/k*): n,p is the size of the design matrix and k* the dimension of the theoretical loss minimizer. This is done under standard assumptions, for which we derive stronger versions of a cone condition and a restricted strong convexity. Our bound holds with high probability and in expectation and applies to an L1-regularized estimator and to a recently introduced Slope estimator, which we generalize for classification problems. Slope presents the advantage of adapting to unknown sparsity. Thus, we propose a tractable proximal algorithm to compute it and assess its empirical performance. Our results match the best existing bounds for classification and regression problems.
An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of “just-in-time” injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption.