cssi/SciSciGPT-SciSciNet huggingface dataset:
Stochastic Simulation of Dopamine Neuromodulation for Implementation of Fluorescent Neurochemical Probes in the Striatal Extracellular Space

Imaging the dynamic behavior of neuromodulatory neurotransmitters in the extracelluar space that arise from individual quantal release events would constitute a major advance in neurochemical imaging. Spatial and temporal resolution of these highly stochastic neuromodulatory events requires concurrent advances in the chemical development of optical nanosensors selective for neuromodulators in concert with advances in imaging methodologies to capture millisecond neurotransmitter release. Herein, we develop and implement a stochastic model to describe dopamine dynamics in the extracellular space (ECS) of the brain dorsal striatum to guide the design and implementation of fluorescent neurochemical probes that record neurotransmitter dynamics in the ECS. Our model is developed from first-principles and simulates release, diffusion, and reuptake of dopamine in a 3D simulation volume of striatal tissue. We find that in vivo imaging of neuromodulation requires simultaneous optimization of dopamine nanosensor rev...
Intramolecular Heck cyclization of α-sulfenyl enol triflates. Asymmetric synthesis of a pentacyclic cardenolide precursor having functionality at C-11

Abstract A concise route to the core of complex cardenolides is described. The sequence features use of a sulfone to join enantioenriched A and D ring fragments and also control intramolecular aldolization to generate ring C and an intramolecular Heck cyclization of an α-sulfenyl enol triflate to form the steroid skeleton.
Distorted phosphorus and copper square-planar layers in LaCu1+xP2 and LaCu4P3: synthesis, crystal structure, and physical properties.

Two new lanthanum copper phosphides, LaCu1+xP2 and LaCu4P3, were synthesized from elements. Their crystal structures were determined by means of single-crystal X-ray diffraction. LaCu1+xP2 crystallizes in a complex crystal structure, a derivative of the HfCuSi2 structure type, in the space group Cmmm (No. 65) with unit cell parameters of a = 5.564(3) A, b = 19.96(1) A, c = 5.563(3) A, and Z = 8. Its crystal structure features disordered Cu2xP2 layers alternated with fully ordered PbO-like Cu2P2 layers. The Cu–P layers are separated by La counter-cations. The Cu2xP2 layers are composed of rectangular P4 polyphosphide rings connected by partially occupied Cu atoms. Investigations of the electrical resistivity and Seebeck thermopower for LaCu1+xP2 reveal metallic-type behavior with holes as the main charge carriers. LaCu1+xP2 exhibits unexpectedly low thermal conductivity presumably because of disorder in the Cu2xP2 layers. LaCu4P3 crystallizes in a new structure type, in the tetragonal space group P4/nmm (N...
Imbalanced nucleocytoskeletal connections create common polarity defects in progeria and physiological aging.

Studies of the accelerated aging disorder Hutchinson–Gilford progeria syndrome (HGPS) can potentially reveal cellular defects associated with physiological aging. HGPS results from expression and abnormal nuclear envelope association of a farnesylated, truncated variant of prelamin A called “progerin.” We surveyed the diffusional mobilities of nuclear membrane proteins to identify proximal effects of progerin expression. The mobilities of three proteins—SUN2, nesprin-2G, and emerin—were reduced in fibroblasts from children with HGPS compared with those in normal fibroblasts. These proteins function together in nuclear movement and centrosome orientation in fibroblasts polarizing for migration. Both processes were impaired in fibroblasts from children with HGPS and in NIH 3T3 fibroblasts expressing progerin, but were restored by inhibiting protein farnesylation. Progerin affected both the coupling of the nucleus to actin cables and the oriented flow of the cables necessary for nuclear movement and centrosome orientation. Progerin overexpression increased levels of SUN1, which couples the nucleus to microtubules through nesprin-2G and dynein, and microtubule association with the nucleus. Reducing microtubule-nuclear connections through SUN1 depletion or dynein inhibition rescued the polarity defects. Nuclear movement and centrosome orientation were also defective in fibroblasts from normal individuals over 60 y, and both defects were rescued by reducing the increased level of SUN1 in these cells or inhibiting dynein. Our results identify imbalanced nuclear engagement of the cytoskeleton (microtubules: high; actin filaments: low) as the basis for intrinsic cell polarity defects in HGPS and physiological aging and suggest that rebalancing the connections can ameliorate the defects.
Synthesis and electronic properties of LnRhAsO and LnIrAsO compositions

The synthesis and characterization of the new compositions LnRhAsO (Ln=Ce, Nd) and LnIrAsO (Ln=La, Ce, Nd) are reported. These compounds crystallize in the ZrCuSiAs type structure, isostructural to iron pnictide LnFeAsO materials. Upon substitution of Rh for Fe, both a and c lattice parameters increase relative to 3d transition metal compounds; however, when Ir is substituted for Rh the a-parameter decreases slightly while the c-parameter expands. The decrease in a lattice parameter corresponds to a short metal-metal distance in Ir compounds. CeRhAsO and CeIrAsO compositions show abrupt decreases in resistivity at 7 and 10 K, respectively, coinciding with a small shift in magnetization at the transition temperature. - Graphical abstract: LnIrAsO (Ln=La, Ce, Nd) and LnRhAsO (Ln=Ce, Rh) have been synthesized. These new transition metal oxypnictide compositions are isostructural to LaFeAsO. The 5d Ir compositions demonstrate a shorter metal-metal interaction than the 4d Rh compositions. Highlights: > LnIrAsO (Ln=La, Ce, Nd) and LnRhAsO (Ln=Ce, Nd) have been synthesized. > Ir compositions show a decreased a-parameter and increased c-parameter relative to Rh compositions. > All LnIrAsO and LnRhAsO compositions are metallic while CeIrAsO and CeRhAsO show a sudden drop in resistivity at 10 and 7 K, respectively.
Allylation of donor--acceptor cyclopropanes.

Direct allylation of glycal-derived donor−acceptor cyclopropanes has been achieved with TiCl4 activation followed by addition of allyltrimethylsilane. The α diastereomer is the major product, with selectivities ranging from 3:1 to 10:1 and yields around 80%.
Novel phosphorylation states of the yeast spindle pole body

ABSTRACT Phosphorylation regulates yeast spindle pole body (SPB) duplication and separation and likely regulates microtubule nucleation. We report a phosphoproteomic analysis using tandem mass spectrometry of enriched Saccharomyces cerevisiae SPBs for two cell cycle arrests, G1/S and the mitotic checkpoint, expanding on previously reported phosphoproteomic data sets. We present a novel phosphoproteomic state of SPBs arrested in G1/S by a cdc4-1 temperature-sensitive mutation, with particular focus on phosphorylation events on the γ-tubulin small complex (γ-TuSC). The cdc4-1 arrest is the earliest arrest at which microtubule nucleation has occurred at the newly duplicated SPB. Several novel phosphorylation sites were identified in G1/S and during mitosis on the microtubule nucleating γ-TuSC. These sites were analyzed in vivo by fluorescence microscopy and were shown to be required for proper regulation of spindle length. Additionally, in vivo analysis of two mitotic sites in Spc97 found that phosphorylation of at least one of these sites is required for progression through the cell cycle. This phosphoproteomic data set not only broadens the scope of the phosphoproteome of SPBs, it also identifies several γ-TuSC phosphorylation sites that influence microtubule formation.
Genetic pleiotropy between mood disorders, metabolic, and endocrine traits in a multigenerational pedigree

Bipolar disorder (BD) is a mental disorder characterized by alternating periods of depression and mania. Individuals with BD have higher levels of early mortality than the general population, and a substantial proportion of this is due to increased risk for comorbid diseases. To identify the molecular events that underlie BD and related medical comorbidities, we generated imputed whole-genome sequence data using a population-specific reference panel for an extended multigenerational Old Order Amish pedigree (n = 394), segregating BD and related disorders. First, we investigated all putative disease-causing variants at known Mendelian disease loci present in this pedigree. Second, we performed genomic profiling using polygenic risk scores (PRS) to establish each individual’s risk for several complex diseases. We identified a set of Mendelian variants that co-occur in individuals with BD more frequently than their unaffected family members, including the R3527Q mutation in APOB associated with hypercholesterolemia. Using PRS, we demonstrated that BD individuals from this pedigree were enriched for the same common risk alleles for BD as the general population (β = 0.416, p = 6 × 10−4). Furthermore, we find evidence for a common genetic etiology between BD risk and polygenic risk for clinical autoimmune thyroid disease (p = 1 × 10−4), diabetes (p = 1 × 10−3), and lipid traits such as triglyceride levels (p = 3 × 10−4) in the pedigree. We identify genomic regions that contribute to the differences between BD individuals and unaffected family members by calculating local genetic risk for independent LD blocks. Our findings provide evidence for the extensive genetic pleiotropy that can drive epidemiological findings of comorbidities between diseases and other complex traits.
Palladium-catalyzed intermolecular arylation of functionally-substituted cycloalkenes by aryl iodides

Abstract The paladium-catalyzed, intermolecular arylation of functionally-substituted cycloalkenes by aryl iodides affords a new synthetic route to a variety of arylated cyclic derivatives. The arylation of 2-cyclopenten-1-ol and 2-cyclo-hexen-1-ol provides modest yields of the corresponding 3-arylcycloalkanones, while 2-methyl-2-cyclopenten-1-ol affords a mixture of singly and doubly arylated cyclopentanone products. 1-Acetoxycyclopentene undergoes arylation to produce the corresponding allylic aryl substitution product opening up a new route for the α-arylation of cycloalkanones. Cyclic allylic ethers undergo arylation at both ends of the C-C double bond to generate mixtures of singly arylated vinylic ethers. 1-Cyanocyclopentene reacts with aryl iodides under palladium catalysis to produce 1-cyano-5-arylcyclopentenes in high yield. 2-Methyl-2-cycloalken-1-ones afford modest yields of doubly arylated 2-cycloalken-1-ones in a single step.
Synthesis of 2,5-bis[(3,4-ethylenedioxy)thien-2-yl]- 3-substituted thiophenes.

Synthesis of 2,5-bis[(3,4-ethylenedioxy)thien-2-yl]-3-substituted thiophene monomers afforded compounds with blue emission characteristics and having a quantum yield of 3−5%. Cyclic voltammetry was employed to investigate the electrochemical behavior of the four monomers reported here. Polymer films were deposited by repeated potential cycling. These materials are considered for use in tailoring properties in optoelectronic device applications.
Hetero-Diels–Alder and pyroglutamate approaches to (2S,4R)-2-methylamino-5-hydroxy-4-methylpentanoic acid

Abstract The stereoselective syntheses of fully protected (2 S ,4 R )-2-methylamino-5-hydroxy-4-methylpentanoic acid, a non-coded amino acid of cyclomarin A, and its diastereomer are reported. A pyroglutamate template was employed in the key diastereoselective alkylation used for introducing the 4-methyl stereochemistry. In addition, the first diastereoselective intramolecular hetero-Diels–Alder of a 2-cyano-1-azadiene with an electron deficient dienophile is described.
Intramolecular reactions of benzylic azides with ketones: competition between Schmidt and Mannich pathways.

The Lewis acid-promoted reactions of benzylic azides with ketones can proceed by two major pathways. The azido-Schmidt reaction involves simple addition of azide to the ketone followed by rearrangement and ring expansion. In addition, benzylic azides can undergo prior rearrangement to afford iminium ions that can subsequently participate in a Mannich reaction. A series of ketones containing an α CH2(CH2)nCH(N3)Ph substituent (n = 1−3) was prepared to investigate the dependence of products on ketone ring size and tether length. For all ketones examined, good yields of bicyclic lactams arising from intramolecular Schmidt reaction were obtained when a four-carbon linker was used (n = 1 in the above formulation), but Mannich products predominated for the longer tethers examined (n = 2, 3).
Low degree representations of simple Lie groups

We give upper bounds for the number of irreducible representations of dimension at most n for a compact semisimple Lie group. In particular, we prove that there are at most n irreducible representations of dimension at most n for a simple compact Lie group. We use this prove that the number of conjugacy classes of maximal subgroups of a compact simple Lie group is O(r) where r is the Lie rank. We also give a short proof that the dimensions of the weight spaces of a maximal torus are small relative to the dimension of an irreducible module.
A Simple Steady-State Analysis of Load Balancing Algorithms in the Sub-Halfin-Whitt Regime

This paper studies the steady-state performance of load balancing algorithms in many-server systems. We consider a system with N identical servers with buffer size b-1 such that b = o (√N), in other words, each server can hold at most b jobs, one job in service and b - 1 jobs in buffer.
Regret minimization in behaviorally-constrained zero-sum games

No-regret learning has emerged as a powerful tool for solving extensive-form games. This was facilitated by the counterfactual-regret minimization (CFR) framework, which relies on the instantiation of regret minimizers for simplexes at each information set of the game. We use an instantiation of the CFR framework to develop algorithms for solving behaviorally-constrained (and, as a special case, perturbed in the Selten sense) extensive-form games, which allows us to compute approximate Nash equilibrium refinements. Nash equilibrium refinements are motivated by a major deficiency in Nash equilibrium: it provides virtually no guarantees on how it will play in parts of the game tree that are reached with zero probability. Refinements can mend this issue, but have not been adopted in practice, mostly due to a lack of scalable algorithms. We show that, compared to standard algorithms, our method finds solutions that have substantially better refinement properties, while enjoying a convergence rate that is comparable to that of state-of-the-art algorithms for Nash equilibrium computation both in theory and practice.
A Framework to Utilize DERs’ VAR Resources to Support the Grid in an Integrated T-D System

Increasing penetration of inverter-based distributed energy resources (DERs) opens up interesting opportunities for the transmission systems. We present a hypothesis that the numerous DERs in var control mode can be seen as geographically distributed var devices (mini-SVCs) and if controlled properly, can be exploited to increase system flexibility by providing local var support to the grid as an ancillary service. Based on this premise, a var support framework is proposed in this paper. It utilizes a novel D-OPF formulation for unbalanced three-phase feeders enabling the estimation of the maximum var support that can be provided by the DERs to the grid at different operating points without compromising the distribution network performance. Further, a co-simulation method is developed to investigate the true impact of the proposed DER var support on the grid in an integrated Transmission-Distribution (T-D) system.
On learning finite-state quantum sources

We examine the complexity of learning the distributions produced by finite-state quantum sources. We show how prior techniques for learning hidden Markov models can be adapted to the quantum generator model to find that the analogous state of affairs holds: information-theoretically, a polynomial number of samples suffice to approximately identify the distribution, but computationally, the problem is as hard as learning parities with noise, a notorious open question in computational learning theory.
Exploratory analysis of high-resolution power interruption data reveals spatial and temporal heterogeneity in electric grid reliability

Abstract Modern grid monitoring equipment enables utilities to collect detailed records of power interruptions. These data are aggregated to compute publicly reported metrics describing high-level characteristics of grid performance. The current work explores the depth of insights that can be gained from public data, and the implications of losing visibility into heterogeneity in grid performance through aggregation. We present an exploratory analysis examining three years of high-resolution power interruption data collected by archiving information posted in real-time on the public-facing website of a utility in the Western United States. We report on the size, frequency and duration of individual power interruptions, and on spatio-temporal variability in aggregate reliability metrics. Our results show that metrics of grid performance can vary spatially and temporally by orders of magnitude, revealing heterogeneity that is not evidenced in publicly reported metrics. We show that limited access to granular information presents a substantive barrier to conducting detailed policy analysis, and discuss how more widespread data access could help to answer questions that remain unanswered in the literature to date. Given open questions about whether grid performance is adequate to support societal needs, we recommend establishing pathways to make high-resolution power interruption data available to support policy research.
Robust Interactive Learning

In this paper we propose and study a generalization of the standard active-learning model where a more general type of queries including class conditional queries and mistake queries are allowed. Such queries have been quite useful in applications, but have been lacking theoretical understanding. In this work, we characterize the power of such queries under several well-known noise models. We give nearly tight upper and lower bounds on the number of queries needed to learn both for the general agnostic setting and for the bounded noise model. We further show that our methods can be made adaptive to the (unknown) noise rate, with only negligible loss in query complexity.
Index theory for manifolds with Baas–Sullivan singularities

We study index theory for manifolds with Baas-Sullivan singularities using geometric K-homology with coefficients in a unital C*-algebra. In particular, we define a natural analog of the Baum-Connes assembly map for a torsion-free discrete group in the context of these singular spaces. The cases of singularities modelled on k-points (i.e., z/k-manifolds) and the circle are discussed in detail. In the case of the former, the associated index theorem is related to the Freed-Melrose index theorem; in the case of latter, the index theorem is related to work of Rosenberg.
High-Performance Computing with Quantum Processing Units

The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for functional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well as a loose integration that assumes they cannot. We find that the performance of both approaches is likely to depend on the quantum interconnect that serves to entangle multiple QPUs. We also identify several challenges in assessing QPU performance for HPC, and we consider new metrics that capture the interplay between system architecture and the quantum parallelism underlying computational performance.
Scaling simulation-to-real transfer by learning composable robot skills

We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation-reality gap, we learn a set of diverse policies that are parameterized in a way that makes them easily reusable. This diversity and parameterization of low-level skills allows us to find a transferable policy that is able to use combinations and variations of different skills to solve more complex, high-level tasks. In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a “skill embedding” parameterization which can be used to compose them. Later, we learn high-level policies which actuate the low-level policies via this skill embedding parameterization. The high-level policies encode how and when to reuse the low-level skills together to achieve specific high-level tasks. Importantly, our method learns to control a real robot in joint-space to achieve these high-level tasks with little or no on-robot time, despite the fact that the low-level policies may not be perfectly transferable from simulation to real, and that the low-level skills were not trained on any examples of high-level tasks. We illustrate the principles of our method using informative simulation experiments. We then verify its usefulness for real robotics problems by learning, transferring, and composing free-space and contact motion skills on a Sawyer robot using only joint-space control. We experiment with several techniques for composing pre-learned skills, and find that our method allows us to use both learning-based approaches and efficient search-based planning to achieve high-level tasks using only pre-learned skills.
Bayesian inference in Monte-Carlo tree search

Monte-Carlo Tree Search (MCTS) methods are drawing great interest after yielding breakthrough results in computer Go. This paper proposes a Bayesian approach to MCTS that is inspired by distribution-free approaches such as UCT [13], yet significantly differs in important respects. The Bayesian framework allows potentially much more accurate (Bayes-optimal) estimation of node values and node uncertainties from a limited number of simulation trials. We further propose propagating inference in the tree via fast analytic Gaussian approximation methods: this can make the overhead of Bayesian inference manageable in domains such as Go, while preserving high accuracy of expected-value estimates. We find substantial empirical outperformance of UCT in an idealized bandit-tree test environment, where we can obtain valuable insights by comparing with known ground truth. Additionally we rigorously prove on-policy and off-policy convergence of the proposed methods.
Marcel Grossmann and his contribution to the general theory of relativity

This article reviews the biography of the Swiss mathematician Marcel Grossmann (1878–1936) and his contributions to the emergence of the general theory of relativity. The first part is his biography, while the second part reviews his collaboration with Einstein in Zurich which resulted in the Einstein-Grossmann theory of 1913. This theory is a precursor version of the final theory of general relativity with all the ingredients of that theory except for the correct gravitational field equations. Their collaboration is analyzed in some detail with a focus on the question of exactly what role Grossmann played in it.
Radial Transport and Plasma Heating in Jupiter's Magnetodisc

The ion temperature of the magnetosphere of Jupiter derived from Galileo PLS data was observed to increase by about an order of magnitude from 10 to 40 Jupiter radii. This suggests the presence of heating sources that counteract the adiabatic cooling effect of expanding plasma. There have been different attempts of explaining this phenomena, including a magnetohydrodynamic (MHD) turbulent heating model which is based on flux tube diffusion [Saur, Astrophys. J. Lett., 602, L137, 2004]. We explore an alternate turbulent heating model based on advection, similar to models commonly used in solar wind heating. Based on spectral analysis of Galileo magnetometer (MAG) data, we find that observed MHD turbulence could potentially provide the required heating to explain some of the increase in plasma temperature. This indicates that advection is a more appropriate way to describe radial transport of plasma in the jovian magnetosphere beyond 10 Jupiter radii.
Edge-outer graph embedding and the complexity of the DNA reporter strand problem

Abstract In 2009, Jonoska, Seeman and Wu showed that every graph admits a route for a DNA reporter strand, that is, a closed walk covering every edge either once or twice, in opposite directions if twice, and passing through each vertex in a particular way. This corresponds to showing that every graph has an edge-outer embedding, that is, an orientable embedding with some face that is incident with every edge. In the motivating application, the objective is such a closed walk of minimum length. Here we give a short algorithmic proof of the original existence result, and also prove that finding a shortest length solution is NP-hard, even for 3-connected cubic (3-regular) planar graphs. Independent of the motivating application, this problem opens a new direction in the study of graph embeddings, and we suggest several problems emerging from it.
Ion-dipole interactions are asymptotically unscreened by water in dipolar nanopores, yielding patterned ion distributions.

The permeation, rejection, and transport of electrolytes in water-filled nanopores are critical to ion current gating and desalination processes in synthetic porous membranes and the functions of biological ion channels. While the effects of confinement, pore polarizability, and discrete channel charge sites have been much studied, the potentially dramatic impact of dipole-lined synthetic pores on electrolytes has seldom been addressed. Dipole layers naturally occur on the interior surfaces of certain nanopores, leading to intrinsic preference for cations or anions. This preference can be exploited when the membrane surface is functionalized differently from the pore interior or when there are alternating dipolar/nondipolar stretches inside a long pore. The dipole−ion interaction is asymptotically unscreened by water, leading to ionic, charge segregated, insulating behavior that can block ion transport, and potentially novel current−voltage (I−V) characteristics.
Maximal averages over hypersurfaces and the Newton polyhedron

Abstract Using some resolution of singularities and oscillatory integral methods in conjunction with appropriate damping and interpolation techniques, L p boundedness theorems for p > 2 are obtained for maximal averages over hypersurfaces in R n for n > 2 . These estimates are sharp in various situations, including the convex hypersurfaces of finite line type considered by several authors. As a corollary, we also give a generalization of the result of Sogge and Stein that for some finite p the maximal operator corresponding to a hypersurface whose Gaussian curvature does not vanish to infinite order is bounded on L p for some finite p . Analogous estimates are proven for Fourier transforms of surface measures, and these are sharp for the same hypersurfaces as the maximal operators.
Effect of crystallographic dislocations on the reverse performance of 4H-SiC p-n diodes

Abstract A quantitative study was performed to investigate the impact of crystallographic dislocation defects, including screw dislocation, basal plane dislocation, and threading edge dislocation, and their locations in the active and JTE region, on the reverse performance of 4H-SiC p–n diodes. It was found that higher leakage current in diodes is associated with basal plane dislocations, while lower breakdown voltage is attributed to screw dislocations. The above influence increases in severity when the dislocation is in the active region than in the JTE region. Furthermore, due to the closed-core nature, the impact of threading edge dislocation on the reverse performance of the p–n diodes is less severe than that of other dislocations although its density is much higher.
Conditional Random Fields, Planted Constraint Satisfaction and Entropy Concentration

This paper studies a class of probabilistic models on graphs, where edge variables depend on incident node variables through a fixed probability kernel. The class includes planted constraint satisfaction problems (CSPs), as well as more general structures motivated by coding and community clustering problems. It is shown that under mild assumptions on the kernel and for sparse random graphs, the conditional entropy of the node variables given the edge variables concentrates around a deterministic threshold. This implies in particular the concentration of the number of solutions in a broad class of planted CSPs, the existence of a threshold function for the disassortative stochastic block model, and the proof of a conjecture on parity check codes. It also establishes new connections among coding, clustering and satisfiability.
Cascading Node Failure with Continuous States in Random Geometric Networks

The increasing complexity and interdependency of today's networks highlight the importance of studying network robustness to failure and attacks. Many large-scale networks are prone to cascading effects where a limited number of initial failures (due to attacks, natural hazards or resource depletion) propagate through a dependent mechanism, ultimately leading to a global failure scenario where a substantial fraction of the network loses its functionality. These cascading failure scenarios often take place in networks which are embedded in space and constrained by geometry. Building on previous results on cascading failure in random geometric networks, we introduce and analyze a continuous cascading failure model where a node has an initial continuously-valued state, and fails if the aggregate state of its neighbors falls below a threshold. Within this model, we derive analytical conditions for the occurrence and non-occurrence of cascading node failure, respectively.
Deriving compact laws based on algebraic formulation of a data set

Abstract In various subjects, there exist concise and consistent relationships between input and output parameters. Discovering the relationships, or more precisely compact laws, in a data set is of great interest in many fields, such as physics, chemistry, and finance. In recent years, the field of data discovery has made great progress towards discovering these laws in practice thanks to the success of machine learning. However, machine learning methods relate the input and output data by considering them separately instead of equally. In addition, the analytical approaches to finding the underlying theories governing the data are relatively slow. In this paper, we develop an innovative approach on discovering compact laws. A novel algebraic equation formulation is proposed such that constant determination and candidate equation verification can be explicitly solved with low computational time. This algebraic equation formulation does not distinguish between input and output variables, and converts the problem of deriving meaning from data into solving a linear algebra equation and searching for linear equations that fit the data. We also derive a more efficient search algorithm using finite fields. Rigorous proofs and computational results are presented in validating these methods. The algebraic formulation allows for the search of equation candidates in an explicit mathematical manner. For a certain type of compact theory, our approach assures convergence, with the discovery being computationally efficient and mathematically precise.
Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives

Planning robust executions under uncertainty is a fundamental challenge for building autonomous robots. Partially Observable Markov Decision Processes (POMDPs) provide a standard framework for modeling uncertainty in many applications. In this work, we study POMDPs with safe-reachability objectives, which require that with a probability above some threshold, a goal state is eventually reached while keeping the probability of visiting unsafe states below some threshold. This POMDP formulation is different from the traditional POMDP models with optimality objectives and we show that in some cases, POMDPs with safe-reachability objectives can provide a better guarantee of both safety and reachability than the existing POMDP models through an example. A key algorithmic problem for POMDPs is policy synthesis , which requires reasoning over a vast space of beliefs (probability distributions). To address this challenge, we introduce the notion of a goal-constrained belief space , which only contains beliefs reachable from the initial belief under desired executions that can achieve the given safe-reachability objective. Our method compactly represents this space over a bounded horizon using symbolic constraints, and employs an incremental Satisfiability Modulo Theories (SMT) solver to efficiently search for a valid policy over it. We evaluate our method using a case study involving a partially observable robotic domain with uncertain obstacles. The results show that our method can synthesize policies over large belief spaces with a small number of SMT solver calls by focusing on the goal-constrained belief space.
Alternation acyclic tournaments

Abstract We define a tournament to be alternation acyclic if it does not contain a cycle in which descents and ascents alternate. Using a result by Athanasiadis on hyperplane arrangements, we show that these tournaments are counted by the median Genocchi numbers. By establishing a bijection with objects defined by Dumont, we show that alternation acyclic tournaments in which at least one ascent begins at each vertex, except for the largest one, are counted by the Genocchi numbers of the first kind. Unexpected consequences of our results include a pair of ordinary generating function formulas for the Genocchi numbers of both kinds and a simple model for the normalized median Genocchi numbers.
Convergence of the Z-Bus Method for Three-Phase Distribution Load-Flow with ZIP Loads

This paper derives a set of sufficient conditions guaranteeing that the load-flow problem in unbalanced three-phase distribution networks with wye and delta constant-power, constant-current, and constant-impedance loads (ZIP loads) has a unique solution over a region that can be explicitly calculated from the network parameters. It is also proved that the well-known Z-Bus iterative method is a contraction over the defined region, and hence converges to the unique solution.
Unlabeled Data for Morphological Generation With Character-Based Sequence-to-Sequence Models.

We present a semi-supervised way of training a character-based encoder-decoder recurrent neural network for morphological reinflection—the task of generating one inflected wordform from another. This is achieved by using unlabeled tokens or random strings as training data for an autoencoding task, adapting a network for morphological reinflection, and performing multi-task training. We thus use limited labeled data more effectively, obtaining up to 9.92% improvement over state-of-the-art baselines for 8 different languages.
Small and strong formulations for unions of convex sets from the Cayley embedding

There is often a significant trade-off between formulation strength and size in mixed integer programming. When modeling convex disjunctive constraints (e.g. unions of convex sets), adding auxiliary continuous variables can sometimes help resolve this trade-off. However, standard formulations that use such auxiliary continuous variables can have a worse-than-expected computational effectiveness, which is often attributed precisely to these auxiliary continuous variables. For this reason, there has been considerable interest in constructing strong formulations that do not use continuous auxiliary variables. We introduce a technique to construct formulations without these detrimental continuous auxiliary variables. To develop this technique we introduce a natural non-polyhedral generalization of the Cayley embedding of a family of polytopes and show it inherits many geometric properties of the original embedding. We then show how the associated formulation technique can be used to construct small and strong formulation for a wide range of disjunctive constraints. In particular, we show it can recover and generalize all known strong formulations without continuous auxiliary variables.
On simultaneous two-player combinatorial auctions

We consider the following communication problem: Alice and Bob each have some valuation functions υ1(·) and υ2(·) over subsets of m items, and their goal is to partition the items into [EQUATION] in a way that maximizes the welfare, [EQUATION]. We study both the allocation problem, which asks for a welfare-maximizing partition and the decision problem, which asks whether or not there exists a partition guaranteeing certain welfare, for binary XOS valuations. For interactive protocols with poly(m) communication, a tight 3/4-approximation is known for both [29, 23]. For interactive protocols, the allocation problem is provably harder than the decision problem: any solution to the allocation problem implies a solution to the decision problem with one additional round and log m additional bits of communication via a trivial reduction. Surprisingly, the allocation problem is provably easier for simultaneous protocols. Specifically, we show: • There exists a simultaneous, randomized protocol with polynomial communication that selects a partition whose expected welfare is at least 3/4 of the optimum. This matches the guarantee of the best interactive, randomized protocol with polynomial communication. • For all e > 0, any simultaneous, randomized protocol that decides whether the welfare of the optimal partition is ≥ 1 or ≤ 3/4 − 1/108 + e correctly with probability > 1/2 + 1/poly(m) requires exponential communication. This provides a separation between the attainable approximation guarantees via interactive (3/4) versus simultaneous (≤ 3/4 − 1/108) protocols with polynomial communication. In other words, this trivial reduction from decision to allocation problems provably requires the extra round of communication. We further discuss the implications of our results for the design of truthful combinatorial auctions in general, and extensions to general XOS valuations. In particular, our protocol for the allocation problem implies a new style of truthful mechanisms.
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice

It is well known that weight initialization in deep networks can have a dramatic impact on learning speed. For example, ensuring the mean squared singular value of a network's input-output Jacobian is O(1) is essential for avoiding exponentially vanishing or exploding gradients. Moreover, in deep linear networks, ensuring that all singular values of the Jacobian are concentrated near 1 can yield a dramatic additional speed-up in learning; this is a property known as dynamical isometry. However, it is unclear how to achieve dynamical isometry in nonlinear deep networks. We address this question by employing powerful tools from free probability theory to analytically compute the {\it entire} singular value distribution of a deep network's input-output Jacobian. We explore the dependence of the singular value distribution on the depth of the network, the weight initialization, and the choice of nonlinearity. Intriguingly, we find that ReLU networks are incapable of dynamical isometry. On the other hand, sigmoidal networks can achieve isometry, but only with orthogonal weight initialization. Moreover, we demonstrate empirically that deep nonlinear networks achieving dynamical isometry learn orders of magnitude faster than networks that do not. Indeed, we show that properly-initialized deep sigmoidal networks consistently outperform deep ReLU networks. Overall, our analysis reveals that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning.
Control of Magnetic Microrobot Teams for Temporal Micromanipulation Tasks

In this paper, we present a control framework that allows magnetic microrobot teams to accomplish complex micromanipulation tasks captured by global linear temporal logic (LTL) formulas. To address this problem, we propose an optimal control synthesis method that constructs discrete plans for the robots that satisfy both the assigned tasks as well as proximity constraints between the robots due to the physics of the problem. The proposed algorithm relies on an existing optimal control synthesis approach combined with a novel sampling-based technique to reduce the state-space of the product automaton that is associated with the LTL specifications. The synthesized discrete plans are executed by the microrobots independently using local magnetic fields. Simulation studies show that the proposed algorithm can address large-scale planning problems that cannot be solved using existing optimal control synthesis approaches. Moreover, we present experimental results that also illustrate the potential of the method in practice. To the best of our knowledge, this is the first control framework that allows independent control of teams of magnetic microrobots for temporal micromanipulation tasks.
Decomposition of Reachable Sets and Tubes for a Class of Nonlinear Systems

Reachability analysis provides formal guarantees for performance and safety properties of nonlinear control systems. Here, one aims to compute the backward reachable set (BRS) or tube (BRT)—the set of states from which the system can be driven into a target set at a particular time or within a time interval, respectively. The computational complexity of current approaches scales poorly, making application to high-dimensional systems intractable. We propose a technique that decomposes the dynamics of a general class of nonlinear systems into subsystems which may be coupled through common states, controls, and disturbances. Despite this coupling, BRSs and BRTs can be computed efficiently using our technique without incurring additional approximation errors and without the need for linearizing dynamics or approximating sets as polytopes. Computations of BRSs and BRTs now become orders of magnitude faster, and for the first time BRSs and BRTs for many high-dimensional nonlinear control systems can be computed using the Hamilton–Jacobi formulation. In situations involving bounded adversarial disturbances, our proposed method can obtain slightly conservative results. We demonstrate our theory by numerically computing BRSs and BRTs for several systems, including the six-dimensional Acrobatic Quadrotor using the HJ formulation.
Certifiably optimal low rank factor analysis

Factor Analysis (FA) is a technique of fundamental importance that is widely used in classical and modern multivariate statistics, psychometrics, and econometrics. In this paper, we revisit the classical rank-constrained FA problem which seeks to approximate an observed covariance matrix (Σ) by the sum of a Positive Semidefinite (PSD) low-rank component (θ) and a diagonal matrix (Φ) (with nonnegative entries) subject to Σ - Φ being PSD. We propose a flexible family of rank-constrained, nonlinear Semidefinite Optimization based formulations for this task. We introduce a reformulation of the problem as a smooth optimization problem with convex, compact constraints and propose a unified algorithmic framework, utilizing state of the art techniques in nonlinear optimization to obtain high-quality feasible solutions for our proposed formulation. At the same time, by using a variety of techniques from discrete and global optimization, we show that these solutions are certifiably optimal in many cases, even for problems with thousands of variables. Our techniques are general and make no assumption on the underlying problem data. The estimator proposed herein aids statistical interpretability and provides computational scalability and significantly improved accuracy when compared to current, publicly available popular methods for rank-constrained FA. We demonstrate the effectiveness of our proposal on an array of synthetic and real-life datasets. To our knowledge, this is the first paper that demonstrates how a previously intractable rank-constrained optimization problem can be solved to provable optimality by coupling developments in convex analysis and in global and discrete optimization.
Data Center Server Provision: Distributed Asynchronous Control for Coupled Renewal Systems

This paper considers a cost minimization problem for data centers with $N$ servers and randomly arriving service requests. A central router decides which server to use for each new request. Each server has three types of states (active, idle, and setup) with different costs and time durations. The servers operate asynchronously over their own states and can choose one of multiple sleep modes when idle. We develop an online distributed control algorithm so that each server makes its own decisions. The request queues are bounded and the overall time average cost is near optimal with probability 1. First the algorithm does not need probability information for the arrival rate or job sizes. Finally, an improved algorithm that uses a single queue is developed via a “virtualization” technique, which is shown to provide the same (near optimal) costs. Simulation experiments on a real data center traffic trace demonstrate the efficiency of our algorithm compared with other existing algorithms.
The Alexander polynomial of a rational link

We relate some terms on the boundary of the Newton polygon of the Alexander polynomial Δ(x,y) of a rational link to the number and length of monochromatic twist sites in a particular diagram that w...
A Multi-Class Dispatching and Charging Scheme for Autonomous Electric Mobility On-Demand

Despite the significant advances in vehicle automation and electrification, the next-decade aspirations for massive deployments of autonomous electric mobility on demand (AEMoD) services are still threatened by two major bottlenecks, namely the computational and charging delays. This paper proposes a solution for these two challenges by suggesting the use of fog computing for AEMoD systems, and developing an optimized multi-class charging and dispatching scheme for its vehicles. A queuing model representing the proposed multi-class charging and dispatching scheme is first introduced. The stability conditions of this model and the number of classes that fit the charging capabilities of any given city zone are then derived. Decisions on the proportions of each class vehicles to partially/fully charge, or directly serve customers are then optimized using a stochastic linear program that minimizes the maximum response time of the system. Results show the merits of our proposed model and optimized decision scheme compared to both the always-charge and the equal split schemes.
Unique asymptotics of ancient convex mean curvature flow solutions

We study compact noncollapsed ancient convex solutions to Mean Curvature Flow in $\mathbb{R}^{n+1}$ with $O(1) \times O(n)$ symmetry. We show they all have unique asymptotics as $t \to -\infty$ and we give a precise asymptotic description of these solutions. The asymptotics apply, in particular, to the solutions constructed by White, and Haslhofer and Hershkovits (in the case of those particular solutions the asymptotics were predicted and formally computed by Angenent).