cssi/SciSciGPT-SciSciNet huggingface dataset:
Transcriptional repression by ApiAP2 factors is central to chronic toxoplasmosis

Tachyzoite to bradyzoite development in Toxoplasma is marked by major changes in gene expression resulting in a parasite that expresses a new repertoire of surface antigens hidden inside a modified parasitophorous vacuole called the tissue cyst. The factors that control this important life cycle transition are not well understood. Here we describe an important transcriptional repressor mechanism controlling bradyzoite differentiation that operates in the tachyzoite stage. The ApiAP2 factor, AP2IV-4, is a nuclear factor dynamically expressed in late S phase through mitosis/cytokinesis of the tachyzoite cell cycle. Remarkably, deletion of the AP2IV-4 locus resulted in the expression of a subset of bradyzoite-specific proteins in replicating tachyzoites that included tissue cyst wall components BPK1, MCP4, CST1 and the surface antigen SRS9. In the murine animal model, the mis-timing of bradyzoite antigens in tachyzoites lacking AP2IV-4 caused a potent inflammatory monocyte immune response that effectively eliminated this parasite and prevented tissue cyst formation in mouse brain tissue. Altogether, these results indicate that suppression of bradyzoite antigens by AP2IV-4 during acute infection is required for Toxoplasma to successfully establish a chronic infection in the immune-competent host.
Synthesis of 2,2′‐bipyridyl‐type compounds via the suzuki‐miyaura cross‐coupling reaction

2,2′-Bipyridyl-type compounds may be prepared by Suzuki-Miyaura coupling of a 2-pyridylboronic ester with 2-haloazines and -azoles. Ten examples are presented with yields of 47 to 84%. Both arylbromides and arylchlorides undergo the coupling, but the reaction is sensitive to ring substitution adjacent to the halogen.
Automated Electrochemical Synthesis and Photoelectrochemical Characterization of Zn1-xCoxO Thin Films for Solar Hydrogen Production

High-throughput electrochemical methods have been developed for the investigation of Zn1-xCoxO films for photoelectrochemical hydrogen production from water. A library of 120 samples containing 27 different compositions (0 <= x <= 0.068) was synthesized by automated serial electrochemical deposition. High-throughput photoelectrochemical screening revealed improved solar hydrogen production for the cobalt-doped films, with Zn0.956Co0.044O exhibiting a 4-fold improvement over pure ZnO with no external bias. Flat-band potential, bias-dependent photocurrent, and action spectra were also measured automatically with the high-throughput screening system. The 200-nm-thick films were subsequently characterized by numerous techniques, including SEM, XRD, XPS, and UV-vis, which show that the depositions are well-controlled. Zn/Co stoichiometry in the films was controlled by the ratio of the Zn and Co precursors in each deposition bath. All films exhibited the wurtzite structure typical of pure ZnO, and the Co2+ appears to substitute Zn2+, forming a single-phase solid solution. Band gaps of the solid solutions were systematically lower than the 3.2-eV band gap typical of ZnO.
Chemical vapor deposition of gallium sulfide thin films

The compound [Ga(S-i-Pr) 2 (μ-S-i-Pr)]2, a thermally stable solid with a low melting point, was used as a single-source precursor to gallium sulfide films in a low-pressure chemical vapor deposition process. Film depositions were carried out at substrate temperatures in the range 350-610 °C. The films were determined to have a Ga 2 S 3 stoichiometry by Rutherford backscattering and energy-dispersive X-ray spectrometries. X-ray diffraction studies showed that the films deposited on glass, silicon, and YSZ (111) substrates were composed of γ-Ga 2 S 3 , α-Ga 2 S 3 , and highly oriented γ-Ga 2 S 3 , respectively.
The homologous conjugate addition of thiols to electron-deficient cyclopropanes catalyzed by a calcium(II) complex

Abstract The synthesis of γ-sulfanyl malonates was achieved through the addition of thiols to electron deficient cyclopropanes. These reactions are catalyzed by calcium acetylacetonate, Ca(acac) 2 . A variety of electron rich and electron deficient thiols were added without the need for prior activation or exogenous base. The thiol additions to donor–acceptor cyclopropanes bearing electron-rich and electron-deficient aromatic and heteroaromatic groups proceeded in good to excellent yields.
Palladium-Catalyzed Decarboxylative Cross-Coupling of α-Oxocarboxylic Acids and Their Derivatives

The development of palladium(II)-catalyzed decarboxylative cross-coupling of α-oxocarboxylic acids and their derivatives is summarized in this account. Acetanilides, 2-phenyl-pyridines, and benzoic acids were found to be suitable substrates for direct acylation through decarboxylative cross-coupling with α-oxo­carboxylic acids. Potassium aryl trifluoroborates were also transformed into ketones, amides, and esters with α-oxocarboxylic acids, oxamic acids, and oxalate monoesters, respectively, in modified catalytic systems. 1 Introduction 2 Palladium-Catalyzed Decarboxylative Cross-Coupling Through C–H Bond Functionalization 2.1 Direct ortho-Acylation of Acetanilides 2.2 Direct Acylation of 2-Phenylpyridines 2.3 Direct ortho-Acylation of Benzoic Acids 3 Transformation of Potassium Aryl Trifluoroborates into Ketones, Esters, and Amides 3.1 Formation of Aryl Ketones from Potassium Aryl Trifluoroborates 3.2 Preparation of Aryl Amides and Esters from Potassium Aryl Trifluoroborates 3.3 Mechanistic Studies 4 Conclusions and Outlook
Diruthenium σ-Alkynyl Compounds: A New Class of Conjugated Organometallics

Reviewed herein are key aspects of the recent progress in the chemistry of σ-alkynyl compounds of Ru2(LL)4, where LL is an N,N‘-bidentate bridging ligand. Synthetic methodologies for both the mono-alkynyl Ru2(II,III) and bis-alkynyl Ru2(III,III) compounds have been thoroughly investigated. The basic electronic properties of these Ru2(II,III) and Ru2(III,III) compounds were elucidated on the basis of solution voltammetry and absorption spectroscopy, which revealed rich redox characteristics and small energy gaps ranging from 1.2 to 1.6 eV. Facile electron transfer across the polyyn-diyl chains was demonstrated on the basis of Ru2(ap)4 type compounds (ap is 2-anilinopyridinate), and a very small electronic decay constant (β) of 0.063 A-1 was determined. Also demonstrated was the long-range electronic coupling between Fc−Fc+ termini bridged by a −(C⋮C)m−Ru2(DMBA)4−(C⋮C)n− fragment (DMBA is N,N‘-dimethylbenzamidinate), and high hole mobility across the Ru2(III,III) unit was inferred. Illustrating the potentia...
Potassium hydride in paraffin: a useful base for Williamson ether synthesis

Abstract A procedure for the preparation of potassium hydride in paraffin from potassium metal has been developed. The KH (P) so produced proved to be an efficient base for the Williamson ether synthesis.
Investigation of the behavior of arenediazonium salts with olefins in BmimPF6

The palladium-catalyzed reactions of olefins with arenediazonium salts in ionic liquids were investigated. For methyl acrylate and methyl acrylonitrile, normal Heck cross-coupling products are obtained in good yields. However, highly selective dimerization products are formed in excellent yields for styrenes. The catalyst system can be recycled.
Zirconium and hafnium hydrogen monothiophosphates, H2Zr(PO3S)2 and H2Hf(PO3S)2. Syntheses and selective ion-exchange properties of sulfur-containing analogues of H2M(PO4)2 (M = Zr, Hf).

The reactions between aqueous solutions of M4+ (M = Zr, Hf) and PO3S3- each result in the precipitation of a white gel that can be dried to a powder. Elemental analysis results for the white polycrystalline product yield a stoichiometry of H2M(PO3S)2. These new compounds are characterized by thermal analysis (DSC, TG-MS), vibrational spectroscopy (FT-IR, FT-Raman), 31P MAS NMR spectroscopy, energy-dispersive spectroscopy (EDS), and powder X-ray diffraction (XRD). On the basis of the characterizations and the results of trialkylamine intercalation experiments, we conclude that the H2M(PO3S)2 compounds have a layered structure that is likely similar to that of alpha-H2Zr(PO4)2.H2O. The interlayer spacing for both H2M(PO3S)2 compounds, determined by XRD, is approximately 9.4 A. Our characterization results suggest that the sulfur atom of each PO3S3- group is preferentially pointed into the interlayer region of the compound and is protonated. Two of the many potentially interesting properties of H2Zr(PO3S)2, its ion-exchange capacity and selectivity, are investigated. H2Zr(PO3S)2 is demonstrated to be an effective and recyclable ion-exchange material for both Zn2+(aq) and Cd2+(aq). Mass balance experiments indicate that the removal of Cd2+(aq) and Zn2+(aq) ions by solid H2Zr(PO3S)2 occurs by an ion-exchange process. Ion exchange results in the formation of the new compounds H0.2Cd0.9Zr(PO3S)2 and H0.50Zn0.75Zr(PO3S)2. The extraction of metal ions is monitored by XRD, vibrational spectroscopy, and elemental analysis. H2Zr(PO3S)2 reversibly intercalates Zn2+(aq) ions through three complete cycles of intercalation and deintercalation without any loss of ion-exchange capacity.
An improved catalyst for the cyclization/hydrosilylation of functionalized 1,6-dienes employing dimethylphenylsilane

A 1:1 mixture of (Me4phen)Pd(Me)Cl [Me4phen=3,4,7,8-tetramethyl-1,10-phenanthroline] and NaBAr4 [Ar=3,5-C6H3(CF3)2] catalyzed the cyclization/hydrosilylation of functionalized 1,6-dienes with dimethylphenyl-silane within minutes at room temperature to form silylated cyclopentanes in good yield with high trans-selectivity.
Apoptosis in snowflake yeast: novel trait, or side effect of toxic waste?

Recent experiments evolving de novo multicellularity in yeast have found that large cluster-forming genotypes also exhibit higher rates of programmed cell death (apoptosis). This was previously interpreted as the evolution of a simple form of cellular division of labour: apoptosis results in the scission of cell-cell connections, allowing snowflake yeast to produce proportionally smaller, faster-growing propagules. Through spatial simulations, Duran-Nebreda and Sole (J. R. Soc. Interface 12, 20140982 (doi:10.1073/pnas.1115323109)) develop the novel null hypothesis that apoptosis is not an adaptation, per se, but is instead caused by the accumulation of toxic metabolites in large clusters. Here we test this hypothesis by synthetically creating unicellular derivatives of snowflake yeast through functional complementation with the ancestral ACE2 allele. We find that multicellular snowflake yeast with elevated apoptosis exhibit a similar rate of apoptosis when cultured as single cells. We also show that larger snowflake yeast clusters tend to contain a greater fraction of older, senescent cells, which may explain why larger clusters of a given genotype are more apoptotic. Our results show that apoptosis is not caused by side effects of spatial structure, such as starvation or waste product accumulation, and are consistent with the hypothesis that elevated apoptosis is a trait that co-evolves with large cluster size.
Synthesis and anti‐HIV activity of substituted 1,2,4‐triazolo‐thiophene derivatives

A new series of substituted 1,2,4-triazoles bearing thiophene molecules 8 and 10 has been synthesized from cycloaddition of thiophene 3-and 2-carbonitriles 5 and 9, respectively, with the reactive cumulene intermediates 4. The newly synthesized products have been evaluated for their anti-HIV activity. © 2007 Wiley Periodicals, Inc. Heteroatom Chem 18:443–448, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/hc.20319
Synthesis of tri-and tetrasubstituted imidazoles

A reliable sequence that allows for regiospecific incorporation of four alkyl substituents on an imidazole ring has been developed. This procedure involves the addition of a substituted amino alcohol to a thioamide and subsequent oxidation with PDC. Unlike many imidazole syntheses, acid-sensitive functionality is tolerated given the mild conditions.
Catalytic Lewis acid phosphorylation with pyrophosphates

Abstract We report a method for the Lewis acid catalyzed phosphorylation of alcohols with pyrophosphates. Ti(O t Bu) 4 was found to be the most effective catalyst in the phosphorylation of both primary and secondary alcohols with tetrabenzylpyrophosphate, providing conversions between 54% and >98% and isolated yields between 50% and 97%. Other pyrophosphates with orthogonal protecting groups were synthesized and screened to validate the generality of the approach. This study will describe how benzyl, methyl, ethyl, allyl, and o -nitrobenzyl pyrophosphates are all effective phosphorylating agents under Lewis acid catalysis.
Synthesis, purification and spectroscopic characterization of potential impurities of hexamethylmelamine

The syntheses and spectroscopic properties (ir, 1 H nmr, 13 C nmr, uv and ms) of pure samples of 2-chloro-4,6-bis(dimethylamino)-s-triazine 1, 4,6-dichloro-2-dimethylamino-s-triazine 2, 4,6-bis(dimethylamino)-s-triazin-2(1H)-one 3, 4-chloro-6-dimethylamino-s-triazin-2(1H)-one 4, 6-dimethylamino-s-triazine-2,4(1H,3H)- dione 5, and 2,4,6-tris(dimethylamino)-s-triazine (altretamine, HMM) are reported. Evidence for enol-keto equilibria are also presented for 3, in which the enol form exhibits as an H-bonded dimer structure similar to the dimer of organic carboxylic acids
Pressure-induced formation of molecular B2X4(μ-X)2 (X = Cl, Br, I) species.

Boron(III) halides (BX3, where X = F, Cl, Br, I) at ambient pressure conditions exist as strictly monomeric, trigonal-planar molecules. Using correlated ab initio calculations, the three heavier ha...
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations. Computational modelling of chemical systems requires a balance between accuracy and computational cost. Here the authors use transfer learning to develop a general purpose neural network potential that approaches quantum-chemical accuracy for reaction thermochemistry, isomerization, and drug-like molecular torsions.
Derivations of the Core Functions of the Maximum Entropy Theory of Ecology

The Maximum Entropy Theory of Ecology (METE), is a theoretical framework of macroecology that makes a variety of realistic ecological predictions about how species richness, abundance of species, metabolic rate distributions, and spatial aggregation of species interrelate in a given region. In the METE framework, "ecological state variables" (representing total area, total species richness, total abundance, and total metabolic energy) describe macroecological properties of an ecosystem. METE incorporates these state variables into constraints on underlying probability distributions. The method of Lagrange multipliers and maximization of information entropy (MaxEnt) lead to predicted functional forms of distributions of interest. We demonstrate how information entropy is maximized for the general case of a distribution, which has empirical information that provides constraints on the overall predictions. We then show how METE's two core functions are derived. These functions, called the "Spatial Structure Function" and the "Ecosystem Structure Function" are the core pieces of the theory, from which all the predictions of METE follow (including the Species Area Relationship, the Species Abundance Distribution, and various metabolic distributions). Primarily, we consider the discrete distributions predicted by METE. We also explore the parameter space defined by the METE's state variables and Lagrange multipliers. We aim to provide a comprehensive resource for ecologists who want to understand the derivations and assumptions of the basic mathematical structure of METE.
Valuation Bias and Limits to Nudges

Because of limits to arbitrage, it is reasonable for sell-side analysts’ target prices to deviate from their corresponding estimates of future fundamental value. Although this inequality does not imply that sell-side analysts should provide biased estimates of fundamental value, many do. According to the author, a key contributor to this bias is making an unwarranted assumption that in the long run firms can be expected to earn more than their cost of capital. Biased estimates of fundamental value can harm long-term investors. At the same time, the author explains in this article that there are simple nudges that can help avoid the bias. Nevertheless, limits to nudges associated with buy-side catering prevent their application. The magnitude of the associated bias can be substantial for the stocks of large firms that dominate the market. Notably, sell-side analysts’ upwardly biased estimated fundamental values for these stocks lie in the vicinity of consensus target prices and market prices. According to the author, this suggests that valuation bias associated with stocks of large firms that dominate the market is consistent with intermittent overvaluation of these stocks and the overall market. TOPICS:Fundamental equity analysis, portfolio management/multi-asset allocation
Peer Financial Distress and Individual Leverage

Using health shocks to identify financial distress situations, I document that peer distress leads to a decline in individual leverage and debt on average. Individual leverage declines by 5.7% and remains deflated for at least five years following peer distress. This decline occurs as individuals borrow less on the intensive margin, pay higher fractions of their debt and save more while their income remains unchanged. As a result, individuals are less likely to default during the period following peer distress. The heterogeneity in responses highlight the role of changes in beliefs and preferences as the underlying mechanism. (JEL D10, D12, D14, D84, H31, R20)Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Derivatives and exceptional poles of the local exterior square L-function for $$GL_m$$

Let $$\pi $$ be an irreducible admissible representation of $$GL_m(F)$$, where F is a non-archimedean local field of characteristic zero. In 1990’s Jacquet and Shalika established an integral representation for the exterior square L-function. We complete, following the method developed by Cogdell and Piatetski-Shapiro, the computation of the local exterior square L-function $$L(s,\pi ,\wedge ^2)$$ via the integral representation in terms of L-functions of supercuspidal representations by a purely local argument. With this result, we show the equality of the local analytic L-functions $$L(s,\pi ,\wedge ^2)$$ via the integral representation for the irreducible admissible representation $$\pi $$ for $$GL_m(F)$$ and the local arithmetic L-functions $$L(s, \wedge ^2(\phi (\pi )))$$ of its Langlands parameter $$\phi (\pi )$$ through local Langlands correspondence.
A Study of EV BMS Cyber Security Based on Neural Network SOC Prediction

Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form of tampering with EV battery's State of Charge (SOC) was explored. A Back Propagation (BP) Neural Network (NN) was trained and tested based on experimental data to estimate SOC of battery under normal operation and cyber-attack scenarios. NeuralWare software was used to run scenarios. Different statistic metrics of the predicted values were compared against the actual values of the specific battery tested to measure the stability and accuracy of the proposed BP network under different operating conditions. The results showed that BP NN was able to capture and detect the false entries due to a cyber-attack on its network.
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning

This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state.
Toward a market model for Bayesian inference

We present a methodology for representing probabilistic relationships in a general-equilibrium economic model. Specifically, we define a precise mapping from a Bayesian network with binary nodes to a market price system where consumers and producers trade in uncertain propositions. We demonstrate the correspondence between the equilibrium prices of goods in this economy and the probabilities represented by the Bayesian network. A computational market model such as this may provide a useful framework for investigations of belief aggregation, distributed probabilistic inference, resource allocation under uncertainty, and other problems of decentralized uncertainty.
Multiview RGB-D Dataset for Object Instance Detection

This paper presents a new multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud. Also, an approach for detection and recognition is presented, which is comprised of two parts: i) a new multi-view 3D proposal generation method and ii) the development of several recognition baselines using AlexNet to score our proposals, which is trained either on crops of the dataset or on synthetically composited training images. Finally, we compare the performance of the object proposals and a detection baseline to the Washington RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset is more challenging for object detection and recognition. The dataset is available at: http://cs.gmu.edu/~robot/gmu-kitchens.html.
On mean ergodic convergence in the Calkin algebras

In this paper, we give a geometric characterization of mean ergodic convergence in the Calkin algebras for Banach spaces that have the bounded compact approximation property.
On Fence Patrolling by Mobile Agents

Suppose that a fence needs to be protected (perpetually) by $k$ mobile agents with maximum speeds $v_1,\ldots,v_k$ so that no point on the fence is left unattended for more than a given amount of time. The problem is to determine if this requirement can be met, and if so, to design a suitable patrolling schedule for the agents. Alternatively, one would like to find a schedule that minimizes the idle time , that is, the longest time interval during which some point is not visited by any agent. We revisit this problem, introduced by Czyzowicz et al. (2011), and discuss several strategies for the cases where the fence is an open and a closed curve, respectively. In particular: (i) we disprove a conjecture by Czyzowicz et al. regarding the optimality of their algorithm ${\mathcal A}_2$ for unidirectional patrolling of a closed fence; (ii) we present a schedule with a lower idle time for patrolling an open fence, improving an earlier result of Kawamura and Kobayashi.
Distributed Optimization of Multi-Beam Directional Communication Networks

We formulate an optimization problem for maximizing the data rate of a common message transmitted from nodes within an airborne network broadcast to a central station receiver while maintaining a set of intranetwork rate demands. Assuming that the network has full-duplex links with multi-beam directional capability, we obtain a convex multi-commodity flow problem and use a distributed augmented Lagrangian algorithm to solve for the optimal flows associated with each beam in the network. For each augmented Lagrangian iteration, we propose a scaled gradient projection method to minimize the local Lagrangian function that incorporates the local topology of each node in the network. Simulation results show fast convergence of the algorithm in comparison to simple distributed primal dual methods and highlight performance gains over standard minimum distance-based routing.
The periodic Cauchy problem of the modified Hunter-Saxton equation

We prove that the periodic initial value problem for the modified Hunter-Saxton equation is locally well-posed for initial data in the space of continuously differentiable functions on the circle and in Sobolev spaces $$H^s (\mathbb{T})$$ when s > 3/2. We also study the analytic regularity (both in space and time variables) of this problem and prove a Cauchy-Kowalevski type theorem. Our approach is to rewrite the equation and derive the estimates which permit application of o.d.e. techniques in Banach spaces. For the analytic regularity we use a contraction argument on an appropriate scale of Banach spaces to obtain analyticity in both time and space variables.
AN EMPIRICAL METHOD FOR IMPROVING THE QUALITY OF RXTE PCA SPECTRA

United States. National Aeronautics and Space Administration (Hubble Fellowship Grant HST-HF-51315.01)
Enumeration of points, lines, planes, etc.

One of the earliest results in enumerative combinatorial geometry is the following theorem of de Bruijn and Erdős: Every set of points $E$ in a projective plane determines at least $\lvert E \rvert$ lines, unless all the points are contained in a line. The result was extended to higher dimensions by Motzkin and others, who showed that every set of points $E$ in a projective space determines at least $\lvert E \rvert$ hyperplanes, unless all the points are contained in a hyperplane. Let $E$ be a spanning subset of an $r$-dimensional vector space. We show that, in the partially ordered set of subspaces spanned by subsets of $E$, there are at least as many $(r-p)$-dimensional subspaces as there are $p$-dimensional subspaces, for every $p$ at most $\frac{1}{2} r$. This confirms the “top-heavy” conjecture by Dowling and Wilson for all matroids realizable over some field. The proof relies on the decomposition theorem package for $\ell$-adic intersection complexes.
Training Deep Networks to be Spatially Sensitive

In many computer vision tasks, for example saliency prediction or semantic segmentation, the desired output is a foreground map that predicts pixels where some criteria is satisfied. Despite the inherently spatial nature of this task commonly used learning objectives do not incorporate the spatial relationships between misclassified pixels and the underlying ground truth. The Weighted F-measure, a recently proposed evaluation metric, does reweight errors spatially, and has been shown to closely correlate with human evaluation of quality, and stably rank predictions with respect to noisy ground truths (such as a sloppy human annotator might generate). However it suffers from computational complexity which makes it intractable as an optimization objective for gradient descent, which must be evaluated thousands or millions of times while learning a model's parameters. We propose a differentiable and efficient approximation of this metric. By incorporating spatial information into the objective we can use a simpler model than competing methods without sacrificing accuracy, resulting in faster inference speeds and alleviating the need for pre/post-processing. We match (or improve) performance on several tasks compared to prior state of the art by traditional metrics, and in many cases significantly improve performance by the weighted F-measure.
Diversity-promoting Bayesian learning of latent variable models

In learning latent variable models (LVMs), it is important to effectively capture infrequent patterns and shrink model size without sacrificing modeling power. Various studies have been done to "diversify" a LVM, which aim to learn a diverse set of latent components in LVMs. Most existing studies fall into a frequentist-style regularization framework, where the components are learned via point estimation. In this paper, we investigate how to "diversify" LVMs in the paradigm of Bayesian learning, which has advantages complementary to point estimation, such as alleviating overfitting via model averaging and quantifying uncertainty. We propose two approaches that have complementary advantages. One is to define diversity-promoting mutual angular priors which assign larger density to components with larger mutual angles based on Bayesian network and von Mises-Fisher distribution and use these priors to affect the posterior via Bayes rule. We develop two efficient approximate posterior inference algorithms based on variational inference and Markov chain Monte Carlo sampling. The other approach is to impose diversity-promoting regularization directly over the post-data distribution of components. These two methods are applied to the Bayesian mixture of experts model to encourage the "experts" to be diverse and experimental results demonstrate the effectiveness and efficiency of our methods.
Estimating Mutual Information for Discrete-Continuous Mixtures

Estimation of mutual information from observed samples is a basic primitive in machine learning, useful in several learning tasks including correlation mining, information bottleneck, Chow-Liu tree, and conditional independence testing in (causal) graphical models. While mutual information is a quantity well-defined for general probability spaces, estimators have been developed only in the special case of discrete or continuous pairs of random variables. Most of these estimators operate using the 3H -principle, i.e., by calculating the three (differential) entropies of X, Y and the pair (X,Y). However, in general mixture spaces, such individual entropies are not well defined, even though mutual information is. In this paper, we develop a novel estimator for estimating mutual information in discrete-continuous mixtures. We prove the consistency of this estimator theoretically as well as demonstrate its excellent empirical performance. This problem is relevant in a wide-array of applications, where some variables are discrete, some continuous, and others are a mixture between continuous and discrete components.
A Chasm Between Identity and Equivalence Testing with Conditional Queries

A recent model for property testing of probability distributions [CFGM13, CRS15] enables tremendous savings in the sample complexity of testing algorithms, by allowing them to condition the sampling on subsets of the domain. In particular, Canonne, Ron, and Servedio [CRS15] showed that, in this setting, testing identity of an unknown distribution D (i.e., whether D = D∗ for an explicitly known D∗) can be done with a constant number of samples, independent of the support size n – in contrast to the required √ n in the standard sampling model. However, it was unclear whether the same held for the case of testing equivalence, where both distributions are unknown. Indeed, while Canonne, Ron, and Servedio [CRS15] established a polylog(n)-query upper bound for equivalence testing, very recently brought down to O(log logn) by Falahatgar et al. [FJO+15], whether a dependence on the domain size n is necessary was still open, and explicitly posed by Fischer at the Bertinoro Workshop on Sublinear Algorithms [Sublinear.info, Problem 66]. In this work, we answer the question in the positive, showing that any testing algorithm for equivalence must make Ω (√ log logn ) queries in the conditional sampling model. Interestingly, this demonstrates an intrinsic qualitative gap between identity and equivalence testing, absent in the standard sampling model (where both problems have sampling complexity nΘ(1)). Turning to another question, we investigate the complexity of support size estimation. We provide a doubly-logarithmic upper bound for the adaptive version of this problem, generalizing work of Ron and Tsur [RT14] to our weaker model. We also establish a logarithmic lower bound for the non-adaptive version of this problem. This latter result carries on to the related problem of non-adaptive uniformity testing, an exponential improvement over previous results that resolves an open question of Chakraborty, Fischer, Goldhirsh, and Matsliah [CFGM13]. ∗EECS, MIT. Email: jayadev@csail.mit.edu. Research supported by grant from MITEI-Shell program. †Columbia University. Email: ccanonne@cs.columbia.edu. Research supported by NSF CCF-1115703 and NSF CCF-1319788. ‡EECS, MIT. Email: g@csail.mit.edu.
The Bar-Natan theory splits

We show that over the binary field &#x1d53d;2, the Bar-Natan perturbation of Khovanov homology splits as the direct sum of its two reduced theories, which we also prove are isomorphic. This extends Shumakovitch’s analogous result for ordinary Khovanov homology, without the perturbation.
Learning Visual Predictive Models of Physics for Playing Billiards

Abstract: The ability to plan and execute goal specific actions in varied, unexpected settings is a central requirement of intelligent agents. In this paper, we explore how an agent can be equipped with an internal model of the dynamics of the external world, and how it can use this model to plan novel actions by running multiple internal simulations ("visual imagination"). Our models directly process raw visual input, and use a novel object-centric prediction formulation based on visual glimpses centered on objects (fixations) to enforce translational invariance of the learned physical laws. The agent gathers training data through random interaction with a collection of different environments, and the resulting model can then be used to plan goal-directed actions in novel environments that the agent has not seen before. We demonstrate that our agent can accurately plan actions for playing a simulated billiards game, which requires pushing a ball into a target position or into collision with another ball.
Principal stratification analysis using principal scores

Summary Practitioners are interested in not only the average causal effect of a treatment on the outcome but also the underlying causal mechanism in the presence of an intermediate variable between the treatment and outcome. However, in many cases we cannot randomize the intermediate variable, resulting in sample selection problems even in randomized experiments. Therefore, we view randomized experiments with intermediate variables as semiobservational studies. In parallel with the analysis of observational studies, we provide a theoretical foundation for conducting objective causal inference with an intermediate variable under the principal stratification framework, with principal strata defined as the joint potential values of the intermediate variable. Our strategy constructs weighted samples based on principal scores, defined as the conditional probabilities of the latent principal strata given covariates, without access to any outcome data. This principal stratification analysis yields robust causal inference without relying on any model assumptions on the outcome distributions. We also propose approaches to conducting sensitivity analysis for violations of the ignorability and monotonicity assumptions: the very crucial but untestable identification assumptions in our theory. When the assumptions required by the classical instrumental variable analysis cannot be justified by background knowledge or cannot be made because of scientific questions of interest, our strategy serves as a useful alternative tool to deal with intermediate variables. We illustrate our methodologies by using two real data examples and find scientifically meaningful conclusions.
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

Given a partial description like “she opened the hood of the car,” humans can reason about the situation and anticipate what might come next (”then, she examined the engine”). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88%), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research.
Likelihood Ratio Gradient Estimation for Steady-State Parameters

We consider a discrete-time Markov chain Φ on a general state-space X, whose transition probabilities are parameterized by a real-valued vector θ. Under the assumption that Φ is geometrically ergod...
Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach

We consider the problem of functional principal component analysis for correlated functional data. In particular, we focus on a separable covariance structure and consider irregularly and possibly sparsely observed sample trajectories. By observing that under the sparse measurements setting, the empirical covariance of pre-smoothed sample trajectories is a highly biased estimator along the diagonal, we propose to modify the empirical covariance by estimating the diagonal and off-diagonal parts of the covariance kernel separately. We prove that under a separable covariance structure, this method can consistently estimate the eigenfunctions of the covariance kernel. We also quantify the role of the correlation in the L2 risk of the estimator, and show that under a weak correlation regime, the risk achieves the optimal nonparametric rate when the number of measurements per curve is bounded.
Interdiction of a Markovian Evader

Shortest path network interdiction is a combinatorial optimization problem on an activity network arising in a number of important security-related applications. It is classically formulated as a bilevel maximin problem representing an "interdictor" and an "evader". The evader tries to move from a source node to the target node along a path of the least cost while the interdictor attempts to frustrate this motion by cutting edges or nodes. The interdiction objective is to find the optimal set of edges to cut given that there is a finite interdiction budget and the interdictor must move first. We reformulate the interdiction problem for stochastic evaders by introducing a model in which the evader follows a Markovian random walk guided by the least-cost path to the target. This model can represent incomplete knowledge about the evader, and the resulting model is a nonlinear 0-1 optimization problem. We then introduce an optimization heuristic based on betweenness centrality that can rapidly find high-quality interdiction solutions by providing a global view of the network.
Pullback invariants of Thurston maps

Associated to a Thurston map f : S2 ! S2 with postcritical set P are several di↵erent invariants obtained via pullback: a relation SP f SP on the set SP of free homotopy classes of curves in S2 \P , a linear operator f : R[SP ]! R[SP ] on the free R-module generated by SP , a virtual endomorphism f : PMod(S2, P ) 99K PMod(S2, P ) on the pure mapping class group, an analytic self-map f : T (S2, P )! T (S2, P ) of an associated Teichmuller space, and an analytic self-correspondence X Y 1 : M(S2, P ) ◆ M(S2, P ) of an associated moduli space. Viewing these associated maps as invariants of f , we investigate relationships between their properties. 1
A CLT for the third integrated moment of Brownian local time increments

Let $\{L^{x}_{t} ; (x,t)\in \mathbb{R}^{1}\times \mathbb{R}^{1}_{+}\}$ denote the local time of Brownian motion. Our main result is to show that for each fixed t \[ {\int\! (L^{x+h}_{t}- L^{x}_{t})^{3} dx\over h^{2}} \stackrel{\mathcal{L}}{\Longrightarrow}\sqrt{ 192}\left( \int\! (L^{x}_{t})^{3} dx\right)^{1/2}\,\,\eta \] as h → 0, where η is a normal random variable with mean zero and variance one, that is independent of $L^{x}_{t}$. This generalizes our previous result for the second moment. We also explain why our approach will not work for higher moments.
Improved stability of whole brain surface parcellation with multi-atlas segmentation

Whole brain segmentation and cortical surface parcellation are essential in understanding the brain’s anatomicalfunctional relationships. Multi-atlas segmentation has been regarded as one of the leading segmentation methods for the whole brain segmentation. In our recent work, the multi-atlas technique has been adapted to surface reconstruction using a method called Multi-atlas CRUISE (MaCRUISE). The MaCRUISE method not only performed consistent volumesurface analyses but also showed advantages on robustness compared with the FreeSurfer method. However, a detailed surface parcellation was not provided by MaCRUISE, which hindered the region of interest (ROI) based analyses on surfaces. Herein, the MaCRUISE surface parcellation (MaCRUISEsp) method is proposed to perform the surface parcellation upon the inner, central and outer surfaces that are reconstructed from MaCRUISE. MaCRUISEsp parcellates inner, central and outer surfaces with 98 cortical labels respectively using a volume segmentation based surface parcellation (VSBSP), following a topological correction step. To validate the performance of MaCRUISEsp, 21 scanrescan magnetic resonance imaging (MRI) T1 volume pairs from the Kirby21 dataset were used to perform a reproducibility analyses. MaCRUISEsp achieved 0.948 on median Dice Similarity Coefficient (DSC) for central surfaces. Meanwhile, FreeSurfer achieved 0.905 DSC for inner surfaces and 0.881 DSC for outer surfaces, while the proposed method achieved 0.929 DSC for inner surfaces and 0.835 DSC for outer surfaces. Qualitatively, the results are encouraging, but are not directly comparable as the two approaches use different definitions of cortical labels.
Conley Conjecture Revisited

We show that whenever a closed symplectic manifold admits a Hamiltonian diffeomorphism with finitely many simple periodic orbits, the manifold has a spherical homology class of degree two with positive symplectic area and positive integral of the first Chern class. This theorem encompasses all known cases of the Conley conjecture (symplectic CY and negative monotone manifolds) and also some new ones (e.g., weakly exact symplectic manifolds with non-vanishing first Chern class). The proof hinges on a general Lusternik-Schnirelmann type result that, under some natural additional conditions, the sequence of mean spectral invariants for the iterations of a Hamiltonian diffeomorphism never stabilizes. We also show that for the iterations of a Hamiltonian diffeomorphism with finitely many periodic orbits the sequence of action gaps between the "largest" and the "smallest" spectral invariants remains bounded and, as consequence, establish some new cases of the $C^\infty$-generic existence of infinitely many simple periodic orbits.
The Knight Move Conjecture is false

The Knight Move Conjecture claims that the Khovanov homology of any knot decomposes as direct sums of some "knight move" pairs and a single "pawn move" pair. This is true for instance whenever the Lee spectral sequence from Khovanov homology to Q^2 converges on the second page, as it does for all alternating knots and knots with unknotting number at most 2. We present a counterexample to the Knight Move Conjecture. For this knot, the Lee spectral sequence admits a nontrivial differential of bidegree (1,8).
Exponential decay of eigenfunctions in many-body type scattering with second-order perturbations

Abstract We show the exponential decay of eigenfunctions of second-order geometric many-body type Hamiltonians at non-threshold energies. Moreover, in the case of first-order and small second-order perturbations we show that there are no eigenfunctions with positive energy.
The Gamma question for many-one degrees

Abstract A set A is coarsely computable with density r ∈ [ 0 , 1 ] if there is an algorithm for deciding membership in A which always gives a (possibly incorrect) answer, and which gives a correct answer with density at least r. To any Turing degree a we can assign a value Γ T ( a ) : the minimum, over all sets A in a, of the highest density at which A is coarsely computable. The closer Γ T ( a ) is to 1, the closer a is to being computable. Andrews, Cai, Diamondstone, Jockusch, and Lempp noted that Γ T can take on the values 0, 1/2, and 1, but not any values in strictly between 1/2 and 1. They asked whether the value of Γ T can be strictly between 0 and 1/2. This is the Gamma question. Replacing Turing degrees by many-one degrees, we get an analogous question, and the same arguments show that Γ m can take on the values 0, 1/2, and 1, but not any values strictly between 1/2 and 1. We will show that for any r ∈ [ 0 , 1 / 2 ] , there is an m-degree a with Γ m ( a ) = r . Thus the range of Γ m is [ 0 , 1 / 2 ] ∪ { 1 } . Benoit Monin has recently announced a solution to the Gamma question for Turing degrees. Interestingly, his solution gives the opposite answer: the only possible values of Γ T are 0, 1/2, and 1.
Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices

We consider the problem of learning the preferences of a heterogeneous population by observing choices from an assortment of products, ads, or other offerings. Our observation model takes a form common in assortment planning applications: each arriving customer is offered an assortment consisting of a subset of all possible offerings; we observe only the assortment and the customer's single choice. In this paper we propose a mixture choice model with a natural underlying low-dimensional structure, and show how to estimate its parameters. In our model, the preferences of each customer or segment follow a separate parametric choice model, but the underlying structure of these parameters over all the models has low dimension. We show that a nuclear-norm regularized maximum likelihood estimator can learn the preferences of all customers using a number of observations much smaller than the number of item-customer combinations. This result shows the potential for structural assumptions to speed up learning and improve revenues in assortment planning and customization. We provide a specialized factored gradient descent algorithm and study the success of the approach empirically.