cssi/SciSciGPT-SciSciNet huggingface dataset:
Temperature-driven narrowing of the insulating gap as a precursor of the insulator-to-metal transition: Implications for the electronic structure of solids.

We present a microscopic picture rationalizing the surprisingly steep decrease in the bandgap with temperature in insulators, crystalline or otherwise. The gap narrowing largely results from fluctuations of long-wavelength optical phonons—when the latter are present—or their disordered analogs if the material is amorphous. We elaborate on this notion to show that possibly with the exception of weakly bound solids made of closed-shell electronic configurations, the existence of an insulating gap or pseudogap in a periodic solid implies that optical phonons must be present, too. This means that in an insulating solid, the primitive cell must have at least two atoms and/or that a charge density wave is present, with the possible exception of weakly bonded solids such as rare-gas or ferromagnetic Wigner crystals. As a corollary, a (periodic) elemental solid held together by nonclosed shell interactions and whose primitive unit contains only one atom will ordinarily be a metal, consistent with observation. Consequences of the present picture for Wigner solids are discussed. A simple field theory of the metal-insulator transition is constructed that directly ties long-wavelength optical vibrations with fluctuations of an order parameter for the metal-insulator transition. The order parameter is shown to have at least two components, yet no Goldstone mode arises as a result of the transition.We present a microscopic picture rationalizing the surprisingly steep decrease in the bandgap with temperature in insulators, crystalline or otherwise. The gap narrowing largely results from fluctuations of long-wavelength optical phonons—when the latter are present—or their disordered analogs if the material is amorphous. We elaborate on this notion to show that possibly with the exception of weakly bound solids made of closed-shell electronic configurations, the existence of an insulating gap or pseudogap in a periodic solid implies that optical phonons must be present, too. This means that in an insulating solid, the primitive cell must have at least two atoms and/or that a charge density wave is present, with the possible exception of weakly bonded solids such as rare-gas or ferromagnetic Wigner crystals. As a corollary, a (periodic) elemental solid held together by nonclosed shell interactions and whose primitive unit contains only one atom will ordinarily be a metal, consistent with observation. Con...
Association reactions of transition metal atoms with simple molecules near room temperature

Abstract An experimental method for studies of association reactions of transition metal atoms with simple molecules near room temperature is described, and results for Cr, Fe, Ni and Cu atom reactions with CO, C2H4 and O2 are discussed. Attention is given to methods for obtaining information on binding energies of 1:1 complexes from kinetic data. The use of unimolecular reaction theory to model association rate coefficients at low pressure is discussed in detail, and several examples are given of estimates of binding energies from such modeling studies. Results for NiC2H4 are in good agreement with theoretical predictions. Progress in this area depends on the availability of information from experiment or theory on structural and electronic properties of association complexes.
Convenient substitution of hydroxypyridines with trifluoroacetaldehyde ethyl hemiacetal

Thermal substitution of 4-hydroxypyridine 1 with trifluoroacetaldehyde ethyl hemiacetal (TFAE) leads to a moderate yield of 3-(1-hydroxy-2,2,2-trifluoroethyl)-4-hydroxypyridine 7 in the presence of a catalytic amount of anhydrous potassium carbonate. Under similar conditions, several α-trifluoromethyl hydroxy-pyridinemethanols 8–15 are easily prepared from 2- or 3-hydroxypyridines 2–6.
Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits.

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.
On the electronic structure required for the uniaxial magnetic properties of the magnetic metal SrCo6O11

On the basis of electronic structure analysis, it is shown that the total moment of SrCo6O11 per formula unit should be close to 6 μB, rather than 4 μB reported experimentally, to explain its uniaxial magnetic properties.
One-pot, catalytic, asymmetric synthesis of polypropionates.

The opening of methylketene dimer, followed by aldol reactions of the resulting enolate, provides a convenient access to syn,syn-dipropionate aldol adducts of a variety of aldehydes. These aldol adducts are useful precursors in the synthesis of complex polypropionates.
Striatal-frontal network activation during voluntary task selection under conditions of monetary reward

During voluntary task selection, a number of internal and external biases may guide such a choice. However, it is not well understood how reward influences task selection when multiple options are possible. To address this issue, we examined brain activation in a voluntary task-switching paradigm while participants underwent fMRI (n = 19). To reinforce the overall goal to choose the tasks randomly, participants were told of a large bonus that they would receive at the end of the experiment for making random task choices. We also examined how occasional, random rewards influenced both task performance and brain activation. We hypothesized that these transient rewards would increase the value of the just-performed task, and therefore bias participants to choose to repeat the same task on the subsequent trial. Contrary to expectations, transient reward had no consistent behavioral effect on subsequent task choice. Nevertheless, the receipt of such rewards did influence activation in brain regions associated with reward processing as well as those associated with goal-directed control. In addition, reward on a prior trial was found to influence activation during task choice on a subsequent trial, with greater activation in a number of executive function regions compared with no-reward trials. We posit that both the random presentation of transient rewards and the overall task bonus for random task choices together reinforced the goal to choose the tasks randomly, which in turn influenced activation in both reward-related regions and those regions involved in abstract goal processing.
Hydrogen-deuterium exchange in dimethylpyridines

Dimethylpyridines undergo deuterium-hydrogen exchange when heated in deuterium oxide containing potassium carbonate at ring positions 2 and 6 when these positions are unsubstituted and at methyl groups located at ring positions 2,4, and 6 exclusively.
Catalytic asymmetric hydrogenation of β-substituted α,β,γ,δ-unsaturated amino acids

Abstract The Rh-DuPHOS and Rh-BPE catalyzed hydrogenation of β-substituted α,β,γ,δ-unsaturated amino acids establishes two contiguous stereogenic centers simultaneously. Both high regioselectivity and good to excellent enantioselectivity have been demonstrated in this process leading to β-branched allyl glycine derivatives. Enamide geometry was found to influence stereoselectivity. Studies aimed at defining the scope and limitations of this process are described.
When do numbers really matter

Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network parameters can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytic results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.
A facile preparation of pyrrolo[3,4-b]indoles

Abstract Reductive ring contraction of dimethyl 5 H -pyridazino[4,5-b]indole-1,4-dicarboxylate produced dimethyl 2,4-dihydropyrrolo[3,4-b]indole-1,3-dicarboxylate.
Using computational theory to constrain statistical models of neural data.

Computational neuroscience is, to first order, dominated by two approaches: the ‘bottom-up’ approach, which searches for statistical patterns in large-scale neural recordings, and the ‘top-down’ approach, which begins with a theory of computation and considers plausible neural implementations. While this division is not clear-cut, we argue that these approaches should be much more intimately linked. From a Bayesian perspective, computational theories provide constrained prior distributions on neural data — albeit highly sophisticated ones. By connecting theory to observation via a probabilistic model, we provide the link necessary to test, evaluate, and revise our theories in a data-driven and statistically rigorous fashion. This review highlights examples of this theory-driven pipeline for neural data analysis in recent literature and illustrates it with a worked example based on the temporal difference learning model of dopamine.
Thermal and catalyzed [3,3]-phosphorimidate rearrangements.

[3,3]-Sigmatropic rearrangements have been widely utilized for the synthesis of structurally complex organic molecules because of the ease with which carbon−carbon bonds are formed in a regio- and stereocontrolled manner. However, there are far fewer [3,3]-rearrangements available for the selective formation of carbon−nitrogen bonds despite the enormous potential of such reactions for the preparation of stereodefined allylic amines. We describe here the scope and mechanism of a [3,3]-rearrangement of allylic phosphorimidates that provides access to stereodefined allylic amines of diverse structure. The reactive intermediate in the reaction, an allylic phosphorimidate, is produced in situ through the combination of readily available starting materials (allylic alcohols, chlorophosphites, and organic azides), rendering the reaction an efficient three-component process. Analogous to other [3,3]-rearrangements, the stereochemistry in an allylic alcohol starting material is transferred with fidelity to the all...
TRINIOBIUM POLYTUNGSTOPHOSPHATES. SYNTHESES, STRUCTURES, CLARIFICATION OF ISOMERISM AND REACTIVITY IN THE PRESENCE OF H2O2

The reaction of K(7)[HNb(6)O(19)], H(2)O(2) and A-Na(9)[PW(9)O(34)] in water followed by treatment with Cs(+) or (n-Bu(4)N)(+) (TBA) affords the corresponding salts of the tris(peroxoniobium) heteropolyanion A,beta-[(NbO(2))(3)PW(9)O(37)](6)(-) (1) in approximately 60% isolated yields. An X-ray structure of the Cs salt, Cs1 (monoclinic P2/c; a = 16.92360(10) A, b = 13.5721(2) A, c = 22.31890(10) A, beta = 92.0460(10) degrees, and Z = 4) confirms the A-type substitution pattern of the three Nb atoms and clarifies the M(3) rotational (Baker-Figgis) isomerism in the Keggin unit as beta. The three terminal eta(2)-O(2)(2)(-) groups on the Nb atoms give 1 an overall symmetry approximating the chiral C(3). These terminal peroxo ligands, and these groups only, thermally decompose when either Cs1 or TBA1 is in solution unless additional H(2)O(2) is present. The peroxo groups can be titrated with triphenylphosphine (2.8 +/- 0.3 peroxide groups found per molecule). Refluxing TBA1 in acetonitrile for 24 h in the presence of base generates the parent heteropolyanion, [Nb(3)PW(9)O(40)](6)(-) (2) in 80% yield after isolation. Treatment of 2 with glacial acetic acid in acetonitrile converts it to [Nb(6)P(2)W(18)O(77)](6)(-) (3) in approximately 100% yield, while treatment of TBA3 with hydroxide converts it back to 2 in high yield. Spectroscopic (FTIR, Raman, (183)W NMR, and (31)P NMR), titrimetric, mass spectrometric (FABMS), and elemental analysis data are all consistent with these formulas. The addition of TBA1 to solutions of alkenes and 33% aqueous peroxide in acetonitrile at reflux results in the generation of the corresponding vicinal diols in high selectivity and yield at high conversion of substrate. Several spectroscopic and kinetics experiments, including a novel one correlating the incubation time of TBA1 under the reaction conditions with the rates of alkene oxidation, establish that TBA1 functions primarily as a catalyst precursor and that much of the catalytic activity is derived from generation of tungstate under the reaction conditions.
All-Si valley-hall photonic topological insulator

We propose an all-Si photonic topological insulator (PTI) that emulates the quantum-valley-Hall (QVH) effect with backscattering-free edge states. Such QVH-PTI has exotic external coupling property to vacuum and can be utilized for designing random resonant time-delay cavities immune to reflections.
Total synthesis of solandelactones E and F, homoeicosanoids from the hydroid Solanderia secunda.

Asymmetric total syntheses of solandelactones E and F confirmed that hydroxyl configuration at C11 in these oxylipins had been misassigned and that the stereochemistry at this center should be reversed. Key steps in the synthesis involved a Nagao asymmetric acetate aldol reaction, a directed Simmons−Smith cyclopropanation, a Holmes−Claisen rearrangement to establish the unsaturated octalactone, and a Nozaki−Hiyama−Kishi coupling to connect two major fragments at C11−C12.
Effective Dynamic Models of Metabolic Networks

Mathematical models of biochemical networks are the useful tools to understand and ultimately predict how cells utilize nutrients to produce valuable products. Hybrid cybernetic models (HCMs) in combination with elementary modes (EMs) are a tool to model cellular metabolism. However, HCM is limited to reduced metabolic networks because of the computational burden of calculating EMs. In this letter, we develop the hybrid cybernetic modeling with flux balance analysis (HCM-FBA) technique, which uses flux balance solutions instead of EMs to dynamically model metabolism. We show that HCM-FBA has comparable performance to HCM for a proof of concept metabolic network and for a reduced anaerobic E. coli network. Next, HCM-FBA is applied to a larger metabolic network of aerobic E. coli metabolism, which was infeasible for HCM (29 FBA modes versus more than 153000 EMs). The global sensitivity analysis further reduces the number of FBA modes required to describe the aerobic E. coli data, while maintaining model fit. Thus, HCM-FBA is a promising alternative to HCM for large networks, where the generation of EMs is infeasible.
Tetrapotassium dicarbonatodioxoperoxouranium(VI) 2.5-hydrate, K4[U(CO3)2O2(O2)].2.5H2O.

The title compound was obtained by reacting UO 2 powder in 2 M K 2 CO 3 with hydrogen peroxide. The compound contains individual [U(CO 3 ) 2 O 2 (O 2 )] 4- ions, which are linked via an extended network of K atoms and hydrogen bonding. The U atom is coordinated to two trans-axial O atoms and six O atoms in the equatorial plane, forming distorted hexagonal bipyramids. The carbonate ligands are bound to the U center in a bidentate manner, with U-O bond distances ranging from 2.438 (5) to 2.488 (5) A. The peroxo group forms a threemembered ring with the U atom, with U-O bond distances of 2.256(6) and 2.240 (6) A. The U=O bond distances of 1.806(5) and 1.817 (5) A, and an O-U-O angle of 175.3 (3) are characteristic of the linear uranyl(VI) unit.
REVO: Resampling of ensembles by variation optimization

Conventional molecular dynamics simulations are incapable of sampling many important interactions in biomolecular systems due to their high dimensionality and rough energy landscapes. To observe rare events and calculate transition rates in these systems, enhanced sampling is a necessity. In particular, the study of ligand-protein interactions necessitates a diverse ensemble of protein conformations and transition states, and for many systems, this occurs on prohibitively long time scales. Previous strategies such as WExplore that can be used to determine these types of ensembles are hindered by problems related to the regioning of conformational space. Here, we propose a novel, regionless, enhanced sampling method that is based on the weighted ensemble framework. In this method, a value referred to as “trajectory variation” is optimized after each cycle through cloning and merging operations. This method allows for a more consistent measurement of observables and broader sampling resulting in the efficient exploration of previously unexplored conformations. We demonstrate the performance of this algorithm with the N-dimensional random walk and the unbinding of the trypsin-benzamidine system. The system is analyzed using conformation space networks, the residence time of benzamidine is confirmed, and a new unbinding pathway for the trypsin-benzamidine system is found. We expect that resampling of ensembles by variation optimization will be a useful general tool to broadly explore free energy landscapes.
Tannakian Formalism Over Fields with Operators

We develop a theory of tensor categories over a field endowed with abstract operators. Our notion of a "field with operators", coming from work of Moosa and Scanlon, includes the familiar cases of differential and difference fields, Hasse-Schmidt derivations, and their combinations. We develop a corresponding Tannakian formalism, describing the category of representations of linear groups defined over such fields. The paper extends the previously know (classical) algebraic and differential algebraic Tannakian formalisms.
On the distributions of infinite server queues with batch arrivals

Queues that feature multiple entities arriving simultaneously are among the oldest models in queueing theory, and are often referred to as “batch” (or, in some cases, “bulk”) arrival queueing systems. In this work, we study the effect of batch arrivals on infinite server queues. We assume that the arrival epochs occur according to a Poisson process, with treatment of both stationary and non-stationary arrival rates. We consider both exponentially and generally distributed service durations, and we analyze both fixed and random arrival batch sizes. In addition to deriving the transient mean, variance, and moment-generating function for time-varying arrival rates, we also find that the steady-state distribution of the queue is equivalent to the sum of scaled Poisson random variables with rates proportional to the order statistics of its service distribution. We do so through viewing the batch arrival system as a collection of correlated sub-queues. Furthermore, we investigate the limiting behavior of the process through a batch scaling of the queue and through fluid and diffusion limits of the arrival rate. In the course of our analysis, we make important connections between our model and the harmonic numbers, generalized Hermite distributions, and truncated polylogarithms.
Learning Visual Servoing with Deep Features and Fitted Q-Iteration

Visual servoing involves choosing actions that move a robot in response to observations from a camera, in order to reach a goal configuration in the world. Standard visual servoing approaches typically rely on manually designed features and analytical dynamics models, which limits their generalization capability and often requires extensive application-specific feature and model engineering. In this work, we study how learned visual features, learned predictive dynamics models, and reinforcement learning can be combined to learn visual servoing mechanisms. We focus on target following, with the goal of designing algorithms that can learn a visual servo using low amounts of data of the target in question, to enable quick adaptation to new targets. Our approach is based on servoing the camera in the space of learned visual features, rather than image pixels or manually-designed keypoints. We demonstrate that standard deep features, in our case taken from a model trained for object classification, can be used together with a bilinear predictive model to learn an effective visual servo that is robust to visual variation, changes in viewing angle and appearance, and occlusions. A key component of our approach is to use a sample-efficient fitted Q-iteration algorithm to learn which features are best suited for the task at hand. We show that we can learn an effective visual servo on a complex synthetic car following benchmark using just 20 training trajectory samples for reinforcement learning. We demonstrate substantial improvement over a conventional approach based on image pixels or hand-designed keypoints, and we show an improvement in sample-efficiency of more than two orders of magnitude over standard model-free deep reinforcement learning algorithms. Videos are available at this http URL .
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models

Author(s): Sedghi, Hanie; Janzamin, Majid; Anandkumar, Anima | Editor(s): Gretton, Arthur; Robert, Christian C | Abstract: We consider the problem of learning mixtures of generalized linear models (GLM) which arise in classification and regression problems. Typical learning approaches such as expectation maximization (EM) or variational Bayes can get stuck in spurious local optima. In contrast, we present a tensor decomposition method which is guaranteed to correctly recover the parameters. The key insight is to employ certain feature transformations of the input, which depend on the input generative model. Specifically, we employ score function tensors of the input and compute their cross-correlation with the response variable. We establish that the decomposition of this tensor consistently recovers the parameters, under mild non-degeneracy conditions. We demonstrate that the computational and sample complexity of our method is a low order polynomial of the input and the latent dimensions.
Some rigidity results for noncompact gradient steady Ricci solitons and Ricci-flat manifolds

Abstract Gradient steady Ricci solitons are natural generalizations of Ricci-flat manifolds. In this article, we prove a curvature gap theorem for gradient steady Ricci solitons with nonconstant potential functions; and a curvature gap theorem for Ricci-flat manifolds, removing the volume growth assumptions in known results.
Restricting cohomology classes to disk and segment configuration spaces

Abstract The configuration space of n labeled disks of radius r inside the unit disk is denoted Conf n , r ( D 2 ) . We study how the cohomology of this space depends on r. In particular, given a cohomology class of Conf n , 0 ( D 2 ) , for which r does its restriction to Conf n , r ( D 2 ) vanish? A related question: given the configuration space Seg n , r ( D 2 ) of n labeled, oriented segments of length r, it has a map to ( S 1 ) n that records the direction of each segment. For which r does this angle map have a continuous section? The paper consists of a collection of partial results, and it contains many questions and conjectures.
Existence of outermost apparent horizons with product of spheres topology

In this paper we construct the first examples of (n + m + 2)dimensional asymptotically. at Riemannian manifolds with non-negative scalar curvature that have outermost minimal hypersurfaces with non-spherical topology for n, m >= 1. The outermost minimal hypersurfaces are, topologically, S-n x Sm+1. In the context of general relativity these hypersurfaces correspond to outermost apparent horizons of black holes.
THE FILTER DICHOTOMY AND MEDIAL LIMITS

The Filter Dichotomy says that every uniform nonmeager filter on the integers is mapped by a finite-to-one function to an ultrafilter. The consistency of this principle was proved by Blass and Laflamme. A medial limit is a universally measurable function from to the unit interval [0, 1] which is finitely additive for disjoint sets, and maps singletons to 0 and ω to 1. Christensen and Mokobodzki independently showed that the Continuum Hypothesis implies the existence of medial limits. We show that the Filter Dichotomy implies that there are no medial limits.
Complete manifolds with bounded curvature and spectral gaps

Abstract We study the spectrum of complete noncompact manifolds with bounded curvature and positive injectivity radius. We give general conditions which imply that their essential spectrum has an arbitrarily large finite number of gaps. In particular, for any noncompact covering of a compact manifold, there is a metric on the base so that the lifted metric has an arbitrarily large finite number of gaps in its essential spectrum. Also, for any complete noncompact manifold with bounded curvature and positive injectivity radius we construct a metric uniformly equivalent to the given one (also of bounded curvature and positive injectivity radius) with an arbitrarily large finite number of gaps in its essential spectrum.
From rational billiards to dynamics on moduli spaces

This short expository note gives an elementary introduction to the study of dynamics on certain moduli spaces, and in particular the recent breakthrough result of Eskin, Mirzakhani, and Mohammadi. We also discuss the context and applications of this result, and connections to other areas of mathematics such as algebraic geometry, Teichmuller theory, and ergodic theory on
Polygenic modeling with bayesian sparse linear mixed models.

Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given dataset one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a “Bayesian sparse linear mixed model” (BSLMM) that includes both these models as special cases. We address several key computational and statistical issues that arise when applying BSLMM, including appropriate prior specification for the hyper-parameters and a novel Markov chain Monte Carlo algorithm for posterior inference. We apply BSLMM and compare it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction. For PVE estimation, we demonstrate that BSLMM combines the advantages of both standard LMMs and sparse regression modeling. For phenotype prediction it considerably outperforms either of the other two methods, as well as several other large-scale regression methods previously suggested for this problem. Software implementing our method is freely available from http://stephenslab.uchicago.edu/software.html.
Exponential bases, Paley–Wiener spaces and applications

Abstract We investigate the connection between translation bases for Paley–Wiener spaces and exponential Fourier bases for a domain. We apply these results to the characterization of vector-valued time–frequency translations of a Paley–Wiener “window” signal.
Constraints on Airy function zeros from quantum-mechanical sum rules

We derive new constraints on the zeros of Airy functions by using the so-called quantum bouncer system to evaluate quantum-mechanical sum rules and perform perturbation theory calculations for the Stark effect. Using commutation and completeness relations, we show how to systematically evaluate sums of the form Sp(n) = ∑k≠n1/(ζk − ζn)p, for natural p > 1, where −ζn is the nth zero of Ai(ζ).
Low regularity conservation laws for integrable PDE

We present a general method for obtaining conservation laws for integrable PDE at negative regularity and exhibit its application to KdV, NLS, and mKdV. Our method works uniformly for these problems posed both on the line and on the circle.
Quantitative methods for comparing different HVAC control schemes

Experimentally comparing the energy usage and comfort characteristics of different controllers in heating, ventilation, and air-conditioning (HVAC) systems is difficult because variations in weather and occupancy conditions preclude the possibility of establishing equivalent experimental conditions across the order of hours, days, and weeks. This paper is concerned with defining quantitative metrics of energy usage and occupant comfort, which can be computed and compared in a rigorous manner that is capable of determining whether differences between controllers are statistically significant in the presence of such environmental fluctuations. Experimental case studies are presented that compare two alternative controllers (a schedule controller and a hybrid system learning-based model predictive controller) to the default controller in a building-wide HVAC system. Lastly, we discuss how our proposed methodology may also be able to quantify the efficiency of other building automation systems.
Permutation Resolutions for Specht Modules of Hecke Algebras

Abstract In Boltje and Hartmann (2011) [BH] , a chain complex was constructed in a combinatorial way which conjecturally is a resolution of the (dual of the) integral Specht module for the symmetric group in terms of permutation modules. In this paper we extend the definition of the chain complex to the integral Iwahori Hecke algebra and prove the same partial exactness results that were proven in the symmetric group case.
Explaining and Harnessing Adversarial Examples

Abstract: Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.
On the space of Kahler potentials

We consider the geodesic equation for the generalized Kahler potential with only mixed second derivatives bounded. We show that given two such generalized Kahler potentials, there is a unique geodesic segment such that for each point on the geodesic, the generalized Kahler potential has uniformly bounded mixed second derivatives (in manifold directions). This generalizes a fundamental theorem of Chen (2000) on the space of Kahler potentials.© 2014 Wiley Periodicals, Inc.
The Price of Fair PCA: One Extra dimension

We investigate whether the standard dimensionality reduction technique of PCA inadvertently produces data representations with different fidelity for two different populations. We show on several real-world data sets, PCA has higher reconstruction error on population A than on B (for example, women versus men or lower- versus higher-educated individuals). This can happen even when the data set has a similar number of samples from A and B. This motivates our study of dimensionality reduction techniques which maintain similar fidelity for A and B. We define the notion of Fair PCA and give a polynomial-time algorithm for finding a low dimensional representation of the data which is nearly-optimal with respect to this measure. Finally, we show on real-world data sets that our algorithm can be used to efficiently generate a fair low dimensional representation of the data.
An earthworm-inspired soft crawling robot controlled by friction

We present the design, fabrication, modeling and feedback control of an earthworm-inspired soft robot that crawls on flat surfaces by actively changing the frictional forces acting on its body. Earthworms are segmented and composed of repeating units called metameres. During crawling, muscles enable these metameres to interact with each other in order to generate peristaltic waves and retractable setae (bristles) produce variable traction. The proposed robot crawls by replicating these two mechanisms, employing pneumatically-powered soft actuators. Using the notion of controllable subspaces, we show that locomotion would be impossible for this robot in the absence of friction. Also, we present a method to generate feasible control inputs to achieve crawling, perform exhaustive numerical simulations of feedforward-controlled locomotion, and describe the synthesis and implementation of suitable real-time friction-based feedback controllers for crawling. The effectiveness of the proposed approach is demonstrated through analysis, simulations and locomotion experiments.
Convex Optimization: Algorithms and Complexity

This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural optimization and stochastic optimization. Our presentation of black-box optimization, strongly influenced by the seminal book of Nesterov, includes the analysis of cutting plane methods, as well as accelerated gradient descent schemes. We also pay special attention to non-Euclidean settings relevant algorithms include Frank-Wolfe, mirror descent, and dual averaging and discuss their relevance in machine learning. We provide a gentle introduction to structural optimization with FISTA to optimize a sum of a smooth and a simple non-smooth term, saddle-point mirror prox Nemirovski's alternative to Nesterov's smoothing, and a concise description of interior point methods. In stochastic optimization we discuss stochastic gradient descent, mini-batches, random coordinate descent, and sublinear algorithms. We also briefly touch upon convex relaxation of combinatorial problems and the use of randomness to round solutions, as well as random walks based methods.
Decentralized demand side management with rooftop PV in residential distribution network

In the past extensive researches have been conducted on demand side management (DSM) program which aims at reducing peak loads and saving electricity cost. In this paper, we propose a framework to study decentralized household demand side management in a residential distribution network which consists of multiple smart homes with schedulable electrical appliances and some rooftop photovoltaic generation units. Each smart home makes individual appliance scheduling to optimize the electric energy cost according to the day-ahead forecast of electricity prices and its willingness for convenience sacrifice. Using the developed simulation model we examine the performance of decentralized household DSM and study their impacts on the distribution network operation and renewable integration, in terms of utilization efficiency of rooftop PV generation, overall voltage deviation, real power loss, and possible reverse power flows.
Multifunctional hyperuniform cellular networks: optimality, anisotropy and disorder

Disordered hyperuniform heterogeneous materials are new, exotic amorphous states of matter that behave like crystals in the manner in which they suppress volume-fraction fluctuations at large length scales, and yet are statistically isotropic with no Bragg peaks. It has recently been shown that disordered hyperuniform dielectric two-dimensional cellular network solids possess complete photonic band gaps comparable in size to photonic crystals, while at the same time maintaining statistical isotropy, enabling waveguide geometries not possible with photonic crystals. Motivated by these developments, we explore other functionalities of various two-dimensional ordered and disordered hyperuniform cellular networks, including their effective thermal or electrical conductivities and elastic moduli. We establish the multifunctionality of a class of such low-density networks by demonstrating that they maximize or virtually maximize the effective conductivities and elastic moduli. This is accomplished using the machinery of homogenization theory, including optimal bounds and cross-property bounds, and statistical mechanics. We have identified ordered and disordered hyperuniform low-weight cellular networks that are multifunctional with respect to transport (e.g., heat dissipation and fluid transport), mechanical and electromagnetic properties, which can be readily fabricated using 3D printing and lithographic technologies.
Adaptive strategies for solving parameterized systems using homotopy continuation

Abstract Three aspects of applying homotopy continuation, which is commonly used to solve parameterized systems of polynomial equations, are investigated. First, for parameterized systems which are homogeneous, we investigate options for performing computations on an adaptively chosen affine coordinate patch. Second, for parameterized systems which are overdetermined, we investigate options for adaptively selecting a well-constrained subsystem to restore numerical stability. Finally, since one is typically interested in only computing real solutions for parameterized problems which arise from applications, we investigate a scheme for heuristically identifying solution paths which appear to be ending at nonreal solutions and truncating them. We demonstrate these three aspects on two problems arising in computer vision.
MAGAN: Aligning Biological Manifolds

It is increasingly common in many types of natural and physical systems (especially biological systems) to have different types of measurements performed on the same underlying system. In such settings, it is important to align the manifolds arising from each measurement in order to integrate such data and gain an improved picture of the system. We tackle this problem using generative adversarial networks (GANs). Recently, GANs have been utilized to try to find correspondences between sets of samples. However, these GANs are not explicitly designed for proper alignment of manifolds. We present a new GAN called the Manifold-Aligning GAN (MAGAN) that aligns two manifolds such that related points in each measurement space are aligned together. We demonstrate applications of MAGAN in single-cell biology in integrating two different measurement types together. In our demonstrated examples, cells from the same tissue are measured with both genomic (single-cell RNA-sequencing) and proteomic (mass cytometry) technologies. We show that the MAGAN successfully aligns them such that known correlations between measured markers are improved compared to other recently proposed models.
On the lower semicontinuity of the ADM mass

The ADM mass, viewed as a functional on the space of asymptotically flat Riemannian metrics of nonnegative scalar curvature, fails to be continuous for many natural topologies. In this paper we prove that lower semicontinuity holds in natural settings: first, for pointed Cheeger--Gromov convergence (without any symmetry assumptions) for $n=3$, and second, assuming rotational symmetry, for weak convergence of the associated canonical embeddings into Euclidean space, for $n \geq 3$. We also apply recent results of LeFloch and Sormani to deal with the rotationally symmetric case, with respect to a pointed type of intrinsic flat convergence. We provide several examples, one of which demonstrates that the positive mass theorem is implied by a statement of the lower semicontinuity of the ADM mass.
Low/High Redshift Classification of Emission Line Galaxies in the HETDEX survey

We discuss different methods to separate high- from low-redshift galaxies based on a combination of spectroscopic and photometric observations. Our baseline scenario is the Hobby-Eberly Telescope Dark Energy eXperiment (HETDEX) survey, which will observe several hundred thousand Lyman Alpha Emitting (LAE) galaxies at 1.9 < z < 3.5, and for which the main source of contamination is [OII]-emitting galaxies at z < 0.5. Additional information useful for the separation comes from empirical knowledge of LAE and [OII] luminosity functions and equivalent width distributions as a function of redshift. We consider three separation techniques: a simple cut in equivalent width, a Bayesian separation method, and machine learning algorithms, including support vector machines. These methods can be easily applied to other surveys and used on simulated data in the framework of survey planning.
A tale of three probabilistic families: Discriminative, descriptive, and generative models

The pattern theory of Grenander is a mathematical framework where patterns are represented by probability models on random variables of algebraic structures. In this paper, we review three families of probability models, namely, the discriminative models, the descriptive models, and the generative models. A discriminative model is in the form of a classifier. It specifies the conditional probability of the class label given the input signal. A descriptive model specifies the probability distribution of the signal, based on an energy function defined on the signal. A generative model assumes that the signal is generated by some latent variables via a transformation. We shall review these models within a common framework and explore their connections. We shall also review the recent developments that take advantage of the high approximation capacities of deep neural networks.
Quantum Unique Ergodicity of Degenerate Eisenstein Series on GL(n)

In the area of quantum chaos, it is of great interest to study the distribution of the $$L^2$$ -mass of eigenfunctions of the Laplacian as eigenvalues tend to infinity. Luo and Sarnak first formulated and proved arithmetic quantum unique ergodicity for the continuous spectrum (spanned by Eisenstein series) of the hyperbolic Laplacian on $$SL(2,\mathbb {Z})\backslash \mathbb {H}$$ by utilizing the sub-convexity bounds of L-functions associated to Maass cusp forms. In this paper, we build on Luo and Sarnak’s method and explore the structure properties of the constant terms of GL(n) Eisenstein series and extend their results to higher ranks. We prove quantum unique ergodicity for a subspace of the continuous spectrum spanned by the degenerate Eisenstein Series on GL(n).
Networks for Joint Affine and Non-Parametric Image Registration

We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model. Specifically, it consists of three stages. In the first stage, a multi-step affine network predicts affine transform parameters. In the second stage, we use a U-Net-like network to generate a momentum, from which a velocity field can be computed via smoothing. Finally, in the third stage, we employ a self-iterable map-based vSVF component to provide a non-parametric refinement based on the current estimate of the transformation map. Once the model is trained, a registration is completed in one forward pass. To evaluate the performance, we conducted longitudinal and cross-subject experiments on 3D magnetic resonance images (MRI) of the knee of the Osteoarthritis Initiative (OAI) dataset. Results show that our framework achieves comparable performance to state-of-the-art medical image registration approaches, but it is much faster, with a better control of transformation regularity including the ability to produce approximately symmetric transformations, and combining affine as well as non-parametric registration.
Analysis of peeling decoder for MET ensembles

The peeling decoder introduced by Luby, et al. allows analysis of LDPC decoding for the binary erasure channel (BEC). For irregular ensembles, they analyze the decoder state as a Markov process and present a solution to the differential equations describing the process mean. Multi-edge type (MET) ensembles allow greater precision through specifying graph connectivity. We generalize the the peeling decoder for MET ensembles and derive analogous differential equations. We offer a new change of variables and solution to the node fraction evolutions in the general (MET) case. This result is preparatory to investigating finite-length ensemble behavior.
Random martingales and localization of maximal inequalities

Abstract Let ( X , d , μ ) be a metric measure space. For ∅ ≠ R ⊆ ( 0 , ∞ ) consider the Hardy–Littlewood maximal operator M R f ( x ) = def sup r ∈ R 1 μ ( B ( x , r ) ) ∫ B ( x , r ) | f | d μ . We show that if there is an n > 1 such that one has the “microdoubling condition” μ ( B ( x , ( 1 + 1 n ) r ) ) ≲ μ ( B ( x , r ) ) for all x ∈ X and r > 0 , then the weak ( 1 , 1 ) norm of M R has the following localization property: ‖ M R ‖ L 1 ( X ) → L 1 , ∞ ( X ) ≍ sup r > 0 ‖ M R ∩ [ r , n r ] ‖ L 1 ( X ) → L 1 , ∞ ( X ) . An immediate consequence is that if ( X , d , μ ) is Ahlfors–David n-regular then the weak ( 1 , 1 ) norm of M R is ≲ n log n , generalizing a result of Stein and Stromberg (1983) [47] . We show that this bound is sharp, by constructing a metric measure space ( X , d , μ ) that is Ahlfors–David n-regular, for which the weak ( 1 , 1 ) norm of M ( 0 , ∞ ) is ≳ n log n . The localization property of M R is proved by assigning to each f ∈ L 1 ( X ) a distribution over random martingales for which the associated (random) Doob maximal inequality controls the weak ( 1 , 1 ) inequality for M R .
The Graph Isomorphism Problem and approximate categories

It is unknown whether two graphs can be tested for isomorphism in polynomial time. A classical approach to the Graph Isomorphism Problem is the d-dimensional Weisfeiler-Lehman algorithm. The d-dimensional WL-algorithm can distinguish many pairs of graphs, but the pairs of non-isomorphic graphs constructed by Cai, Furer and Immerman it cannot distinguish. If d is fixed, then the WL-algorithm runs in polynomial time. We will formulate the Graph Isomorphism Problem as an Orbit Problem: Given a representation V of an algebraic group G and two elements v"1,v"2@?V, decide whether v"1 and v"2 lie in the same G-orbit. Then we attack the Orbit Problem by constructing certain approximate categories C"d, d@?N={1,2,3,...} whose objects include the elements of V. We show that v"1 and v"2 are not in the same orbit by showing that they are not isomorphic in the category C"d for some d@?N. For every d this gives us an algorithm for isomorphism testing. We will show that the WL-algorithms reduce to our algorithms, but that our algorithms cannot be reduced to the WL-algorithms. Unlike the Weisfeiler-Lehman algorithm, our algorithm can distinguish the Cai-Furer-Immerman graphs in polynomial time.