cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal mass transport kinetic modeling for head and neck DCE-MRI: Initial analysis.

Purpose Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows. Method Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and underwent DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ( Φ F ) and backward flux ( Φ B ). OMT-derived Φ F was compared with the volume transfer constant for CA, K trans , derived from the Extended Tofts Model (ETM). Results The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 ± 0.0091 ( min - 1 ) whereas the mean advective component was 0.0052 ± 0.0086 ( min - 1 ) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67% to 18.76% and 12.76% to 30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ( Φ F ) is comparable with the ETM-derived K trans . Conclusions This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.
The Quantum Theory of Atoms in Molecules in Condensed Charge Density Space

By leveraging the fundamental doctrine of the quantum theory of atoms in molecules — the partitioning of the electron charge density (ρ) into regions bounded by surfaces of zero flux — we map the g...
Are Price-Cost Markups Rising in the United States? A Discussion of the Evidence

A number of recent papers have argued that US firms exert increasing market power, as measured by their markups of price over marginal cost. I review three of the main approaches to estimating economy-wide markups and show that all are based on the hypothesis of firm cost minimization. Yet different assumptions and methods of implementation lead to quite different conclusions regarding the levels and trends of markups. I survey the literature critically and argue that some of the startling findings of steeply rising markups are difficult to reconcile with other evidence and with aggregate data. Existing methods cannot determine whether markups have been stable or whether they have risen modestly over the past several decades. Even relatively small increases in markups are consistent with significant changes in aggregate outcomes, such as the observed decline in labor's share of national income.
MHC genotyping from rhesus macaque exome sequences

Indian rhesus macaque major histocompatibility complex (MHC) variation can influence the outcomes of transplantation and infectious disease studies. Frequently, rhesus macaques are MHC genotyped to identify variants that could account for unexpected results. Since the MHC is only one region in the genome where variation could impact experimental outcomes, strategies for simultaneously profiling variation in the macaque MHC and the remainder of the protein coding genome would be useful. Here we determine MHC class I and class II genotypes using target-capture probes enriched for MHC sequences, a method we term macaque exome sequence (MES) genotyping. For a cohort of 27 Indian rhesus macaques, we describe two methods for obtaining MHC genotypes from MES data and demonstrate that the MHC class I and class II genotyping results obtained with these methods are 98.1% and 98.7% concordant, respectively, with expected MHC genotypes. In contrast, conventional MHC genotyping results obtained by deep sequencing of short multiplex PCR amplicons were only 92.6% concordant with expectations for this cohort.
Bayesian uncertainty quantification and information fusion in CALPHAD-based thermodynamic modeling

Abstract Calculation of phase diagrams is one of the fundamental tools in alloy design—more specifically under the framework of Integrated Computational Materials Engineering. Uncertainty quantification of phase diagrams is the first step required to provide confidence for decision making in property- or performance-based design. As a manner of illustration, a thorough probabilistic assessment of the CALPHAD model parameters is performed against the available data for a Hf–Si binary case study using a Markov Chain Monte Carlo sampling approach. The plausible optimum values and uncertainties of the parameters are thus obtained, which can be propagated to the resulting phase diagram. Using the parameter values obtained from deterministic optimization in a computational thermodynamic assessment tool (in this case Thermo-Calc) as the prior information for the parameter values and ranges in the sampling process is often necessary to achieve a reasonable cost for uncertainty quantification. This brings up the problem of finding an appropriate CALPHAD model with high-level of confidence which is a very hard and costly task that requires considerable expert skill. A Bayesian hypothesis testing based on Bayes’ factors is proposed to fulfill the need of model selection in this case, which is applied to compare four recommended models for the Hf–Si system. However, it is demonstrated that information fusion approaches, i.e., Bayesian model averaging and an error correlation-based model fusion, can be used to combine the useful information existing in all the given models rather than just using the best selected model, which may lack some information about the system being modelled.
LASSO: Large Adaptive optics Survey for Substellar Objects using the new SAPHIRA detector on Robo-AO

We report on initial results from the largest infrared AO direct imaging survey searching for wide orbit (≳ 100 AU) massive exoplanets and brown dwarfs as companions around young nearby stars using Robo-AO at the 2.1-m telescope on Kitt Peak, Arizona. The occurrence rates of these rare substellar companions are critical to furthering our understanding of the origin of planetary-mass companions on wide orbits. The observing efficiency of Robo-AO allows us to conduct a survey an order of magnitude larger than previously possible. We commissioned a low-noise high-speed SAPHIRA near-infrared camera to conduct this survey and report on its sensitivity, performance, and data reduction process.
Applications of fractional calculus in solving Abel-type integral equations: Surface–volume reaction problem

Abstract In this paper we consider a class of partial integro-differential equations of fractional order, motivated by an equation which arises as a result of modeling surface–volume reactions in optical biosensors. We solve these equations by employing techniques from fractional calculus; several examples are discussed. Furthermore, for the first time, we encounter an order of the fractional derivative other than 1 2 in an applied problem. Hence, in this paper we explore the applicability of fractional calculus in real-world applications, further strengthening the true nature of fractional calculus.
The mythos of model interpretability

Supervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world?
MEC-Assisted End-to-End Latency Evaluations for C-V2X Communications

The efficient design of fifth generation (5G) mobile networks is driven by the need to support the dynamic proliferation of several vertical market segments. Considering the automotive sector, different Cellular Vehicle-to-Everything (C- V2X) use cases have been identified by the industrial and research world, referring to infotainment, automated driving and road safety. A common characteristic of these use cases is the need to exploit collective awareness of the road environment towards satisfying performance requirements. One of these requirements is the End-to-End (E2E) latency when, for instance, Vulnerable Road Users (VRUs) inform vehicles about their status (e.g., location) and activity, assisted by the cellular network. In this paper, focusing on a freeway-based VRU scenario, we argue that, in contrast to conventional, remote cloud-based cellular architecture, the deployment of Multi-access Edge Computing (MEC) infrastructure can substantially prune the E2E communication latency. Our argument is supported by an extensive simulation-based performance comparison between the conventional and the MEC-assisted network architecture.
Graph signal processing: Filter design and spectral statistics

Graph signal processing analyzes signals supported on the nodes of a graph by defining the shift operator in terms of a matrix, such as the graph adjacency matrix or Laplacian matrix, related to the structure of the graph. With respect to the graph shift operator, polynomial functions of the shift matrix perform filtering. An application considered in this paper, convergence acceleration filters for distributed average consensus may be viewed as lowpass graph filters periodically applied to the states. Design of graph filters depends on the shift matrix eigendecomposition. Consequently, random graphs present a challenge as this information is often difficult to obtain. Nevertheless, the asymptotic behavior of the shift matrix empirical spectral distribution provides a substitute for suitable random matrix models. This paper employs deterministic approximations for empirical spectral statistics from other works to propose optimization criteria for consensus acceleration filters, evaluating the results through simulation.
Towards the inductive acquisition of temporal knowledge

A method is proposed for the inductive acquisition of temporal classification rules from large number of training instances. The method is designed for the incremental acquisition of uncertain rules. Refinement (specialization and generalization) operators for the temporal domain are given, and various heuristics in rule invocation and incremental rule generation are discussed. The method has potential applications in the acquisition of temporal knowledge in many realtime domains.
A purely homotopy-theoretic proof of the Blakers-Massey Theorem for $n$-cubes

Goodwillie’s proof of the Blakers-Massey Theorem for n- cubes relies on a lemma whose proof invokes transversality. The rest of his proof follows from general facts about cubes of spaces and connectivities of maps. We present a purely homotopytheoretic proof of this lemma. The methods are elementary, using a generalization and modification of an argument originally due to Puppe used to prove the Blakers-Massey Theorem for squares.
Phonon-Polariton Propagation, Guidance, and Control in Bulk and Patterned Thin Film Ferroelectric Crystals

Using time resolved ultrafast spectroscopy, we have demonstrated that the far infrared (FIR) excitations in ferroelectric crystals may be modified through an arsenal of control techniques from the fields of guided waves, geometrical and Fourier optics, and optical pulse shaping. We show that LiNbO3 and LiTaO3 crystals of 10–250 μm thickness behave as slab waveguides for phonon-polaritons, which are admixtures of electromagnetic waves and lattice vibrations, when the polariton wavelength is on the order of or greater than the crystal thickness. Furthermore, we show that ferroelectric crystals are amenable to processing by ultrafast laser ablation, allowing for milling of user-defined patterns designed for guidance and control of phonon-polariton propagation. We have fabricated several functional structures including THz rectangular waveguides, resonators, splitters/couplers, interferometers, focusing reflectors, and diffractive elements. Electric field enhancement has been obtained with the reflective structures, through spatial shaping, of the optical excitation beam used for phonon-polariton generation, and through temporal pulse shaping to permit repetitive excitation of a phonon-polariton resonant cavity.
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challenges to behavioral cloning algorithms. Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost. Therefore, we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function. Though automatic evaluation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that our approach achieves significant improvement in generating more human-like stories than SOTA systems.
A unified approach to self-normalized block sampling

Abstract The inference procedure for the mean of a stationary time series is usually quite different under various model assumptions because the partial sum process behaves differently depending on whether the time series is short or long-range dependent, or whether it has a light or heavy-tailed marginal distribution. In the current paper, we develop an asymptotic theory for the self-normalized block sampling, and prove that the corresponding block sampling method can provide a unified inference approach for the aforementioned different situations in the sense that it does not require the a priori estimation of auxiliary parameters. Monte Carlo simulations are presented to illustrate its finite-sample performance. The R function implementing the method is available from the authors.
Numerical Approximation of Fractional Powers of Regularly Accretive Operators

We study the numerical approximation of fractional powers of accretive operators in this paper. Namely, if A is the accretive operator asso- ciated with an accretive sesquilinear form A(·,·) defined on a Hilbert space V contained in L 2 (), we approximate A −� for � 2 (0,1). The fractional powers are defined in terms of the so-called Balakrishnan integral formula. Given a finite element approximation space VhV, A −� is approximated by A −� hh where Ah is the operator associated with the form A(·,·) restricted to Vh andh is the L 2 ()-projection onto Vh. We first provide error esti- mates for (AA � �h)f in Sobolev norms with index in (0,1) for appropriate f. These results depend on elliptic regularity properties of variational solu- tions involving the form A(·,·) and are valid for the case of less than full elliptic regularity. We also construct and analyze an exponentially convergent SINC quadrature approximation to the Balakrishnan integral defining A � �hf. Fi- nally, the results of numerical computations illustrating the proposed method are given.
Random walks among time increasing conductances: heat kernel estimates

For any graph having a suitable uniform Poincare inequality and volume growth regularity, we establish two-sided Gaussian transition density estimates and parabolic Harnack inequality, for constant speed continuous time random walks evolving via time varying, uniformly elliptic conductances, provided the vertex conductances (i.e. reversing measures), increase in time. Such transition density upper bounds apply for discrete time uniformly lazy walks, with the matching lower bounds holding once the parabolic Harnack inequality is proved.
GPU asynchronous stochastic gradient descent to speed up neural network training

The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision. These results have largely come from computational break throughs of two forms: model parallelism, e.g. GPU accelerated training, which has seen quick adoption in computer vision circles, and data parallelism, e.g. A-SGD, whose large scale has been used mostly in industry. We report early experiments with a system that makes use of both model parallelism and data parallelism, we call GPU A-SGD. We show using GPU A-SGD it is possible to speed up training of large convolutional neural networks useful for computer vision. We believe GPU A-SGD will make it possible to train larger networks on larger training sets in a reasonable amount of time.
Binary Information from Open Clusters Using SEDS (BINOCS) Project: The Dynamical Evolution of the Binary Populations in Cluster Environments

Studying the internal dynamics of stellar clusters is conducted primarily through N-Body simulations. One of the major inputs into N-Body simulations is the binary star frequency and mass distribution, which is currently constrained by relations derived from field binary stars. However to truly understand how clustered environments evolve, binary data from within star clusters is needed including masses. Detailed information on binaries masses, primary and secondary, in star clusters has been limited to date. The primary technique currently available has been radial velocity surveys that are limited in depth. Using previous two-band photometry-based studies that may cover different mass ranges produce potentially discrepant interpretations of the observed binary population. We introduce a new binary detection method, Binary INformation from Open Clusters Using SEDs (BINOCS) that covers the wide mass range needed to improve cluster N-body simulation inputs and comparisons. Using newly-observed multi-wavelength photometric catalogs (0.3 8 microns) of the key open clusters with a range of ages, we can show that the BINOCS method determines accurate binary component masses for unresolved cluster binaries through comparison to available RV-based studies. Using this method, we present results on the dynamical evolution of binaries from 0.4 2.5 solar masses within five prototypical clusters, spaning 30 Myr to 3.5 Gyr, and how the binary populations evolve as a function of mass.
On the moduli space of pairs consisting of a cubic threefold and a hyperplane

Abstract We study the moduli space of pairs ( X , H ) consisting of a cubic threefold X and a hyperplane H in P 4 . The interest in this moduli comes from two sources: the study of certain weighted hypersurfaces whose middle cohomology admit Hodge structures of K3 type and, on the other hand, the study of the singularity O 16 (the cone over a cubic surface). In this paper, we give a Hodge theoretic construction of the moduli space of cubic pairs by relating ( X , H ) to certain “lattice polarized” cubic fourfolds Y. A period map for the pairs ( X , H ) is then defined using the periods of the cubic fourfolds Y. The main result is that the period map induces an isomorphism between a GIT model for the pairs ( X , H ) and the Baily–Borel compactification of some locally symmetric domain of type IV.
Randomness Extraction in AC0 and with Small Locality.

Randomness extractors, which extract high quality (almost-uniform) random bits from biased random sources, are important objects both in theory and in practice. While there have been significant progress in obtaining near optimal constructions of randomness extractors in various settings, the computational complexity of randomness extractors is still much less studied. In particular, it is not clear whether randomness extractors with good parameters can be computed in several interesting complexity classes that are much weaker than P. In this paper we study randomness extractors in the following two models of computation: (1) constant-depth circuits (AC0), and (2) the local computation model. Previous work in these models, such as [Vio05a], [GVW15] and [BG13], only achieve constructions with weak parameters. In this work we give explicit constructions of randomness extractors with much better parameters. As an application, we use our AC0 extractors to study pseudorandom generators in AC0, and show that we can construct both cryptographic pseudorandom generators (under reasonable computational assumptions) and unconditional pseudorandom generators for space bounded computation with very good parameters. Our constructions combine several previous techniques in randomness extractors, as well as introduce new techniques to reduce or preserve the complexity of extractors, which may be of independent interest. These include (1) a general way to reduce the error of strong seeded extractors while preserving the AC0 property and small locality, and (2) a seeded randomness condenser with small locality.
Non-Parametric Adaptation for Neural Machine Translation

Neural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process. In the context of Neural Machine Translation (NMT) this results in poor performance on heterogeneous datasets and on sub-tasks like rare phrase translation. On the other hand, non-parametric approaches are immune to forgetting, perfectly complementing the generalization ability of NMT. However, attempts to combine non-parametric or retrieval based approaches with NMT have only been successful on narrow domains, possibly due to over-reliance on sentence level retrieval. We propose a novel n-gram level retrieval approach that relies on local phrase level similarities, allowing us to retrieve neighbors that are useful for translation even when overall sentence similarity is low. We complement this with an expressive neural network, allowing our model to extract information from the noisy retrieved context. We evaluate our Semi-parametric NMT approach on a heterogeneous dataset composed of WMT, IWSLT, JRC-Acquis and OpenSubtitles, and demonstrate gains on all 4 evaluation sets. The Semi-parametric nature of our approach also opens the door for non-parametric domain adaptation, demonstrating strong inference-time adaptation performance on new domains without the need for any parameter updates.
Strong Converses are Just Edge Removal Properties

This paper explores the relationship between two ideas in the network information theory: edge removal and strong converses. Edge removal properties state that if an edge of small capacity is removed from a network, the capacity region does not change too much. Strong converses state that, for rates outside the capacity region, the probability of error converges to 1 as the blocklength goes to infinity. Various notions of edge removal and strong converse are defined, depending on how edge capacity and error probability scale with blocklength, and relations between them are proved. Each class of strong converse implies a specific class of edge removal. The opposite directions are proved for deterministic networks. Furthermore, a technique based on a novel, causal version of the blowing-up lemma is used to prove that for discrete memoryless networks, the weak edge removal property—that the capacity region changes continuously as the capacity of an edge vanishes—is equivalent to the exponentially strong converse—that outside the capacity region, the probability of error goes to 1 exponentially fast. This result is used to prove exponentially strong converses for several examples, including the discrete two-user interference channel with strong interference, with only a small variation from traditional weak converse proofs.
Resource allocation in cellular systems for applications with random parameters

In this paper, we conduct a study to optimize resource allocation for adaptive real-time and delay-tolerant applications in cellular systems. To represent the user applications via several devices and equipment, sigmoidal-like and logarithmic utility functions are used. A fairness proportional utility functions policy approach is used to allocate the resources among the user equipment (UE)s in a utility percentage form in order to ensure a minimum level of customer satisfaction for all the subscribers. Meanwhile, the priority of resources allocation is given to the real-time applications. We measure the effect of the stochastic variations of the adaptive real-time applications on the optimal rate allocation process and compare the results of the deterministic and stochastic systems. Our work is distinct from the other resource allocation optimization works in that we use the bisection method besides convex optimization techniques to optimally allocate rates, and we present the adaptive real-time applications in a stochastic form. We use Microsoft Visual Basic for Applications with Arena Simulation Software interface to simulate the optimization problem. Finally, we present our optimization algorithm results.
Effective Log-Free Zero Density Estimates for Automorphic L-Functions and the Sato–Tate Conjecture

Let $K/\mathbb{Q}$ be a number field. Let $\pi$ and $\pi^\prime$ be cuspidal automorphic representations of $\mathrm{GL}_d(\mathbb{A}_K)$ and $\mathrm{GL}_{d^\prime}(\mathbb{A}_K)$, and suppose that either both $d$ and $d'$ are at most 2 or at least one of $\pi$ and $\pi^\prime$ is self-dual. When $d=d^\prime=2$, we prove an unconditional and effective log-free zero density estimate for the Rankin-Selberg $L$-function $L(s,\pi\otimes\pi^\prime,K)$. For other choices of $d$ and $d^\prime$, we obtain similar results assuming that either $\pi$ or $\pi^\prime$ satisfies the generalized Ramanujan conjecture. With these density estimates, we make effective the Hoheisel phenomenon of Moreno regarding primes in short intervals and extend it to the context of the Sato-Tate conjecture; additionally, we bound the least prime in the Sato-Tate conjecture in analogy with Linnik's theorem on the least prime in an arithmetic progression. When $K=\mathbb{Q}$, we also prove an effective log-free density estimate for $L(s,\pi\otimes\pi^\prime,\mathbb{Q})$ averaged over twists by Dirichlet characters. With this second density estimate, we prove an averaged form of the prime number theorem in short intervals for $L(s,\pi\otimes\tilde{\pi},\mathbb{Q})$ when $\pi$ is a cuspidal automorphic representation of $\mathrm{GL}_2(\mathbb{A}_{\mathbb{Q}})$.
Flows on Honeycombs and Sums of Littlewood-Richardson Tableaux

Suppose \mu and \mu' are two partitions. We will let \mu \oplus \mu' denote the "direct sum" of the partitions, defined as the sorted partition made of the parts of $\mu$ and $\mu'$. In this paper, we define a summation operation on two Littlewood-Richardson fillings of type (\mu, \nu;\lambda) and (\mu', \nu';\lambda'), which results in a Littlewood-Richardson filling of type (\mu\oplus \mu', \nu\oplus \nu' ;\lambda\oplus \lambda'). We give an algorithm to produce the sum, and show that it terminates in a Littlewood-Richardson filling by defining a bijection between a Littlewood-Richardson filling and a flow on a honeycomb, and then showing that the overlay of the two honeycombs of appropriate type corresponds to the sum of the two fillings.
Scalable Breadth-First Search on a GPU Cluster

On a GPU cluster, the ratio of high computing power to communication bandwidth makes scaling breadth-first search (BFS) on a scale-free graph extremely challenging. By separating high and low out-degree vertices, we present an implementation with scalable computation and a model for scalable communication for BFS and direction-optimized BFS. Our communication model uses global reduction for high-degree vertices, and point-to-point transmission for low-degree vertices. Leveraging the characteristics of degree separation, we reduce the graph size to one third of the conventional edge list representation. With several other optimizations, we observe linear weak scaling as we increase the number of GPUs, and achieve 259.8 GTEPS on a scale-33 Graph500 RMAT graph with 124 GPUs on the latest CORAL early access system.
True contextuality in a psychophysical experiment

Abstract Recent crowdsourcing experiments have shown that true contextuality of the kind found in quantum mechanics can also be present in human behavior. In these experiments simple human choices were aggregated over large numbers of respondents, with each respondent dealing with a single context (set of questions asked). In this paper we present experimental evidence of contextuality in individual human behavior, in a psychophysical experiment with repeated presentations of visual stimuli in randomly varying conteXts (arrangements of stimuli). The analysis is based on the Contextuality-by-Default (CbD) theory whose relevant aspects are reviewed in the paper. CbD allows one to detect contextuality in the presence of direct influences, i.e., when responses to the same stimuli have different distributions in different contexts. The experiment presented is also the first one in which contextuality is demonstrated for responses that are not dichotomous, with five options to choose among. CbD requires that random variables representing such responses be dichotomized before they are subjected to contextuality analysis. A theorem says that a system consisting of all possible dichotomizations of responses has to be contextual if these responses violate a certain condition, called nominal dominance. In our experiment nominal dominance was violated in all data sets, with very high statistical reliability established by bootstrapping.
Attention is All you Need

The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.
Hunting rabbits on the hypercube

Abstract We explore the Hunters and Rabbits game on the hypercube. In the process, we find the solution for all classes of graphs with an isoperimetric nesting property and find the exact hunter number of Q n to be 1 + ∑ i = 0 n − 2 i ⌊ i ∕ 2 ⌋ . In addition, we extend results to the situation where we allow the rabbit to not move between shots.
HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step.

In this article, we develop and analyze a homotopy continuation method, referred to as HONES , for solving the sequential generalized projections in Online Newton Step, as well as the generalized problem known as sequential standard quadratic programming. HONES is fast, tuning-free, error-free (up to machine error) and adaptive to the solution sparsity. This is confirmed by both careful theoretical analysis and extensive experiments on both synthetic and real data.
Equilibrium strategies for time-inconsistent stochastic switching systems

An optimal control problem is considered for a stochastic differential equation containing a state-dependent regime switching, with a recursive cost functional. Due to the non-exponential discounting in the cost functional, the problem is time-inconsistent in general. Therefore, instead of finding a global optimal control (which is not possible), we look for a time-consistent (approximately) locally optimal equilibrium strategy. Such a strategy can be represented through the solution to a system of partial differential equations, called an equilibrium Hamilton–Jacob–Bellman (HJB) equation which is constructed via a sequence of multi-person differential games. A verification theorem is proved and, under proper conditions, the well-posedness of the equilibrium HJB equation is established as well.
Remarks on quantum symmetric algebras

Abstract We examine the quantum symmetric and exterior algebras of finite-dimensional U q ( g ) -modules first systematically studied by Berenstein and Zwicknagl, and we resolve some of their questions. We show that the difference (in the Grothendieck group) between the quantum symmetric and exterior cubes of a finite-dimensional module is the same as it is classically. This implies a certain “numerical Koszul duality” between the algebras. Furthermore, we show that quantum symmetric algebras are “commutative” in an appropriate sense, and moreover that they possess a universal mapping property with respect to such commutative algebras. We make extensive use of the coboundary structure on the module category.
Adaptive sequential optimization with applications to machine learning

A framework is introduced for solving a sequence of slowly changing optimization problems, including those arising in regression and classification applications, using optimization algorithms such as stochastic gradient descent (SGD). The optimization problems change slowly in the sense that the minimizers change at either a fixed or bounded rate. A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples needed from the distributions underlying each problem in order to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. Experiments with synthetic and real data are used to confirm that this approach performs well.
Topic modeling on health journals with regularized variational inference

Topic modeling enables exploration and compact representation of a corpus. The CaringBridge (CB) dataset is a massive collection of journals written by patients and caregivers during a health crisis. Topic modeling on the CB dataset, however, is challenging due to the asynchronous nature of multiple authors writing about their health journeys. To overcome this challenge we introduce the Dynamic Author-Persona topic model (DAP), a probabilistic graphical model designed for temporal corpora with multiple authors. The novelty of the DAP model lies in its representation of authors by a persona --- where personas capture the propensity to write about certain topics over time. Further, we present a regularized variational inference algorithm, which we use to encourage the DAP model's personas to be distinct. Our results show significant improvements over competing topic models --- particularly after regularization, and highlight the DAP model's unique ability to capture common journeys shared by different authors.
Development of a Swarm UAV Simulator Integrating Realistic Motion Control Models for Disaster Operations

Simulation environments for Unmanned Aerial Vehicles (UAVs) can be very useful for prototyping user interfaces and training personnel that will operate UAVs in the real world. The realistic operation of such simulations will only enhance the value of such training. In this paper, we present the integration of a model-based waypoint navigation controller into the Reno Rescue Simulator for the purposes of providing a more realistic user interface in simulated environments. We also present potential uses for such simulations, even for real-world operation of UAVs.
The q-Hahn PushTASEP

We introduce the $q$-Hahn PushTASEP --- an integrable stochastic interacting particle system which is a 3-parameter generalization of the PushTASEP, a well-known close relative of the TASEP (Totally Asymmetric Simple Exclusion Process). The transition probabilities in the $q$-Hahn PushTASEP are expressed through the $_4\phi_3$ basic hypergeometric function. Under suitable limits, the $q$-Hahn PushTASEP degenerates to all known integrable (1+1)-dimensional stochastic systems with a pushing mechanism. One can thus view our new system as a pushing counterpart of the $q$-Hahn TASEP introduced by Povolotsky (2013). We establish Markov duality relations and contour integral formulas for the $q$-Hahn PushTASEP. In a $q\to 1$ limit of our process we arrive at a random recursion which, in a special case, appears to be similar to the inverse-Beta polymer model. However, unlike in recursions for Beta polymer models, the weights (i.e., the coefficients of the recursion) in our model depend on the previous values of the partition function in a nontrivial manner.
A proof of some conjectures of Mao on partition rank inequalities

Based on work of Atkin and Swinnerton-Dyer on partition rank difference functions, and more recent work of Lovejoy and Osburn, Mao has proved several inequalities between partition ranks modulo 10, and additional results modulo 6 and 10 for the \(M_2\) rank of partitions without repeated odd parts. Mao conjectured some additional inequalities. We prove some of Mao’s rank inequality conjectures for both the rank and the \(M_2\) rank modulo 10 using elementary methods.
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction

We consider a crowdsourcing model in which n workers are asked to rate the quality of n items previously generated by other workers. An unknown set of $\alpha n$ workers generate reliable ratings, while the remaining workers may behave arbitrarily and possibly adversarially. The manager of the experiment can also manually evaluate the quality of a small number of items, and wishes to curate together almost all of the high-quality items with at most an fraction of low-quality items. Perhaps surprisingly, we show that this is possible with an amount of work required of the manager, and each worker, that does not scale with n: the dataset can be curated with $\tilde{O}(1/\beta\alpha\epsilon^4)$ ratings per worker, and $\tilde{O}(1/\beta\epsilon^2)$ ratings by the manager, where $\beta$ is the fraction of high-quality items. Our results extend to the more general setting of peer prediction, including peer grading in online classrooms.
THE ALGEBRAIC CONCORDANCE ORDER OF A KNOT

The algebraic concordance group contains elements of order two, four, and of infinite order. Elements of infinite order are detected by the signature function. This paper develops computable invariants to simplify the computation of the order of torsion classes. The results are applied to determine the algebraic orders of all prime knots of 12 or fewer crossings.
Square-tiled surfaces and rigid curves on moduli spaces

Abstract We study the algebro-geometric aspects of Teichmuller curves parameterizing square-tiled surfaces with two applications. (a) There exist infinitely many rigid curves on the moduli space of hyperelliptic curves. They span the same extremal ray of the cone of moving curves. Their union is a Zariski dense subset. Hence they yield infinitely many rigid curves with the same properties on the moduli space of stable n -pointed rational curves for even n . (b) The limit of slopes of Teichmuller curves and the sum of Lyapunov exponents for the Teichmuller geodesic flow determine each other, which yields information about the cone of effective divisors on the moduli space of curves.
Complex Interpolation Between Hilbert, Banach and Operator Spaces

Motivated by a question of Vincent Lafforgue, the author studies the Banach spaces X satisfying the following property: there is a function \varepsilon\to \Delta_X(\varepsilon) tending to zero with \varepsilon>0 such that every operator T\colon \ L_2\to L_2 with \|T\|\le \varepsilon that is simultaneously contractive (i.e., of norm \le 1) on L_1 and on L_\infty must be of norm \le \Delta_X(\varepsilon) on L_2(X). The author shows that \Delta_X(\varepsilon) \in O(\varepsilon^\alpha) for some \alpha>0 if X is isomorphic to a quotient of a subspace of an ultraproduct of \theta-Hilbertian spaces for some \theta>0 (see Corollary 6.7), where \theta-Hilbertian is meant in a slightly more general sense than in the author's earlier paper (1979).
A Bayesian probability calculus for density matrices

One of the main concepts in quantum physics is a density matrix, which is a symmetric positive definite matrix of trace one. Finite probability distributions can be seen as a special case when the density matrix is restricted to be diagonal. We develop a probability calculus based on these more general distributions that includes definitions of joints, conditionals and formulas that relate these, including analogs of the Theorem of Total Probability and various Bayes rules for the calculation of posterior density matrices. The resulting calculus parallels the familiar "conventional" probability calculus and always retains the latter as a special case when all matrices are diagonal. Whereas the conventional Bayesian methods maintain uncertainty about which model has the highest data likelihood, the generalization maintains uncertainty about which unit direction has the largest variance. Surprisingly the bounds also generalize: as in the conventional setting we upper bound the negative log likelihood of the data by the negative log likelihood of the MAP estimator.
Noether’s problem for orientation p-subgroups of symmetric groups

We give a positive solution to Noether’s rationality problem for certain index p subgroups of the p-Sylow subgroups of symmetric groups.
Size-Noise Tradeoffs in Generative Networks

This paper investigates the ability of generative networks to convert their input noise distributions into other distributions. Firstly, we demonstrate a construction that allows ReLU networks to increase the dimensionality of their noise distribution by implementing a ``space-filling'' function based on iterated tent maps. We show this construction is optimal by analyzing the number of affine pieces in functions computed by multivariate ReLU networks. Secondly, we provide efficient ways (using polylog$(1/\epsilon)$ nodes) for networks to pass between univariate uniform and normal distributions, using a Taylor series approximation and a binary search gadget for computing function inverses. Lastly, we indicate how high dimensional distributions can be efficiently transformed into low dimensional distributions.
Toroidal automorphic forms for function fields

The space of toroidal automorphic forms was introduced by Zagier in 1979. Let F be a global field. An automorphic form on GL(2) is toroidal if it has vanishing constant Fourier coefficients along all embedded non-split tori. The interest in this space stems from the fact (amongst others) that an Eisenstein series of weight s is toroidal if s is a non-trivial zero of the zeta function, and thus a connection with the Riemann hypothesis is established. In this paper, we concentrate on the function field case. We show the following results. The (n −1)-th derivative of a non-trivial Eisenstein series of weight s and Hecke character x is toroidal if and only if L(x, s+1/2) vanishes in s to order at least n (for the “only if” part we assume that the characteristic of F is odd). There are no non-trivial toroidal residues of Eisenstein series. The dimension of the space of derivatives of unramified Eisenstein series equals h(g −1)+1 if the characteristic is not 2; in characteristic 2, the dimension is bounded from below by this number. Here g is the genus and h is the class number of F. The space of toroidal automorphic forms is an admissible representation and every irreducible subquotient is tempered.
State space modeling of inverter based microgrids considering distributed secondary voltage control

Appropriate control of high penetration renewable energies in power systems requires a complete modeling of the system. In this paper, a comprehensive state space modeling of voltage source inverters, networks and loads are studied. We have modeled a secondary voltage control that utilizes the nominal set points of the output voltage as the input for the controller design. A distributed control algorithm only requiring a sparse communication between neighboring distributed generators (DGs) is used. The proposed secondary voltage model is applied on a microgrid with three DGs and two loads to verify the results.
Rate of Convergence in the Breuer-Major Theorem via Chaos Expansions

AbstractWe show new estimates for the total variation and Wasserstein distances in the framework of the Breuer-Major theorem. The results are based on the combination of Stein’s method for normal a...
Sensor Array Design Through Submodular Optimization

We consider the problem of far-field sensing by means of a sensor array. Traditional array geometry design techniques are agnostic to prior information about the far-field scene. However, in many applications such priors are available and may be utilized to design more efficient array topologies. We formulate the problem of array geometry design with scene prior as one of finding a sampling configuration that enables efficient inference, which turns out to be a combinatorial optimization problem. While generic combinatorial optimization problems are NP-hard and resist efficient solvers, we show how for array design problems the theory of submodular optimization may be utilized to obtain efficient algorithms that are guaranteed to achieve solutions within a constant approximation factor from the optimum. We leverage the connection between array design problems and submodular optimization and port several results of interest. We demonstrate efficient methods for designing arrays with constraints on the sensing aperture, as well as arrays respecting combinatorial placement constraints. This novel connection between array design and submodularity suggests the possibility for utilizing other insights and techniques from the growing body of literature on submodular optimization in the field of array design.
Cosmetic surgeries on knots in $S^3$

Two Dehn surgeries on a knot are called purely cosmetic, if they yield manifolds that are homeomorphic as oriented manifolds. Suppose there exist purely cosmetic surgeries on a knot in S^3, we show that the two surgery slopes must be the opposite of each other. One ingredient of our proof is a Dehn surgery formula for correction terms in Heegaard Floer homology.
A remarkable sequence of integers

Abstract A survey of properties of a sequence of coefficients appearing in the evaluation of a quartic definite integral is presented. These properties are of analytical, combinatorial and number-theoretical nature.
Enhancing Multiuser MIMO Through Opportunistic D2D Cooperation

We propose a cellular architecture that combines multiuser MIMO downlink with opportunistic use of unlicensed Industrial, Scientific, and Medical Radio (ISM) bands to establish device-to-device (D2D) cooperation. The architecture consists of a physical-layer cooperation scheme based on forming downlink virtual MIMO channels through D2D relaying, and a novel resource allocation strategy for such D2D-enabled networks. We prove the approximate optimality of the physical-layer scheme, and demonstrate that such cooperation boosts the effective $ \mathsf {SNR}$ of the weakest user in the system, especially in the many-user regime, due to multiuser diversity. To harness this physical-layer scheme, we formulate the cooperative user scheduling and the relay selection problem using the network utility maximization framework. For such a cooperative network, we propose a novel utility metric that jointly captures fairness in throughput and the cost of relaying in the system. We propose a joint user scheduling and relay selection algorithm, which we prove to be asymptotically optimal. We study the architecture through system-level simulations over a wide range of scenarios. The highlight of these simulations is an approximately $6x$ improvement in data rate for cell-edge (bottom fifth-percentile) users (over the state-of-the-art) while still improving the overall throughput, and considering various system constraints.