cssi/SciSciGPT-SciSciNet huggingface dataset:
Chang’s conjecture and semiproperness of nonreasonable posets

Let $$\mathbb {Q}$$ denote the poset which adds a Cohen real then shoots a club through the complement of $$\big ( [\omega _2]^\omega \big )^V$$ with countable conditions. We prove that the version of Strong Chang’s conjecture from Todorcevic and Torres-Perez (MLQ Math Log Q 58(4–5):342–347, 2012) implies semiproperness of $$\mathbb {Q}$$ , and that semiproperness of $$\mathbb {Q}$$ —in fact semiproperness of any poset which is sufficiently nonreasonable in the sense of Foreman and Magidor (Ann Pure Appl Log 76(1):47–97, 1995)—implies the version of strong Chang’s conjecture from Woodin (The axiom of determinacy, forcing axioms, and the nonstationary ideal, de Gruyter Series in Logic and its Applications, 2nd ed., vol 1, Walter de Gruyter GmbH & Co. KG, Berlin, 2010) and Todorcevic (Conjectures of Rado and Chang and cardinal arithmetic, Kluwer Acadamic, Dordrecht, 1993). In particular, semiproperness of $$\mathbb {Q}$$ has large cardinal strength, which answers a question of Friedman and Krueger (Trans. Am. Math. Soc. 359(5):2407–2420, 2007). One corollary of our work is that the version of Strong Chang’s Conjecture from Todorcevic and Torres-Perez (MLQ Math Log Q 58(4–5):342–347, 2012) does not imply the existence of a precipitous ideal on $$\omega _1$$ .
Examination and implications of experimental research on investor perceptions

Abstract This paper synthesizes existing experimental research in the area of investor perceptions and offers directions for future research. Investor-related experimental research has grown substantially, especially in the last decade, as it has made valuable contributions in establishing causal links, examining underlying process measures, and examining areas with little available data. Within this review, I examine 121 papers and identify three broad categories that affect investor perceptions: information format, investor features, and disclosure credibility. Information format describes how investors are influenced by information salience, information labeling, reporting and accounting complexity, financial statement recognition, explanatory disclosures, and proposed disclosure changes. Investor features describes investors’ use of heuristics, investor preferences, and the effect of investor experience. Disclosure credibility is influenced by external and internal assurance, management credibility, disclosure characteristics, and management incentives. Using this framework, I summarize the existing research and identify areas that would benefit from additional research.
How did the data extraction business model come to dominate? Changes in the web use ecosystem before mobiles surpassed personal computers

It is widely believed that the spread of data extraction technologies on the Internet has led to the erosion of traditional professional content providers and the transformation of the online media...
Semi-supervised Learning for Acoustic Impedance Inversion

Recent applications of deep learning in the seismic domain have shown great potential in different areas such as inversion and interpretation. Deep learning algorithms, in general, require tremendous amounts of labeled data to train properly. To overcome this issue, we propose a semi-supervised framework for acoustic impedance inversion based on convolutional and recurrent neural networks. Specifically, seismic traces and acoustic impedance traces are modeled as time series. Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for acoustic impedance. The proposed workflow uses well log data to guide the inversion. In addition, it utilizes a learned seismic forward model to regularize the training and to serve as a geophysical constraint for the inversion. The proposed workflow achieves an average correlation of 98% between the estimated and target elastic impedance using 20 AI traces for training.
Conspicuous by Its Absence: Diagnostic Expert Testing Under Uncertainty

We study the problem a diagnostic expert (e.g., a physician) faces when offering a diagnosis to a client (e.g., a patient) and show that information asymmetry about the expert’s diagnostic ability ...
PPAC: A Versatile In-Memory Accelerator for Matrix-Vector-Product-Like Operations

Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. Most existing PIM architectures are either general-purpose but only support atomistic operations, or are specialized to accelerate a single task. We propose the Parallel Processor in Associative Content-addressable memory (PPAC), a novel in-memory accelerator that supports a range of matrix-vector-product (MVP)-like operations that find use in traditional and emerging applications. PPAC is, for example, able to accelerate low-precision neural networks, exact/approximate hash lookups, cryptography, and forward error correction. The fully-digital nature of PPAC enables its implementation with standard-cell-based CMOS, which facilitates automated design and portability among technology nodes. To demonstrate the efficacy of PPAC, we provide post-layout implementation results in 28nm CMOS for different array sizes. A comparison with recent digital and mixed-signal PIM accelerators reveals that PPAC is competitive in terms of throughput and energy-efficiency, while accelerating a wide range of applications and simplifying development.
Axonal localization of the fragile X family of RNA binding proteins is conserved across mammals

Spatial segregation of proteins to neuronal axons arises in part from local translation of mRNAs that are first transported into axons in ribonucleoprotein particles (RNPs), complexes containing mRNAs and RNA binding proteins. Understanding the importance of local translation for a particular circuit requires not only identifying axonal RNPs and their mRNA cargoes, but also whether these RNPs are broadly conserved or restricted to only a few species. Fragile X granules (FXGs) are axonal RNPs containing the fragile X related family of RNA binding proteins along with ribosomes and specific mRNAs. FXGs were previously identified in mouse, rat, and human brains in a conserved subset of neuronal circuits but with species-dependent developmental profiles. Here, we asked whether FXGs are a broadly conserved feature of the mammalian brain and sought to better understand the species-dependent developmental expression pattern. We found FXGs in a conserved subset of neurons and circuits in the brains of every examined species that together include mammalian taxa separated by up to 160 million years of divergent evolution. A developmental analysis of rodents revealed that FXG expression in frontal cortex and olfactory bulb followed consistent patterns in all species examined. In contrast, FXGs in hippocampal mossy fibers increased in abundance across development for most species but decreased across development in guinea pigs and members of the Mus genus, animals that navigate particularly small home ranges in the wild. The widespread conservation of FXGs suggests that axonal translation is an ancient, conserved mechanism for regulating the proteome of mammalian axons.
Learning Latent Fractional dynamics with Unknown Unknowns

Despite significant effort in understanding complex systems (CS), we lack a theory for modeling, inference, analysis and efficient control of time-varying complex networks (TVCNs) in uncertain environments. From brain activity dynamics to microbiome, and even chromatin interactions within the genome architecture, many such TVCNs exhibits a pronounced spatio-temporal fractality. Moreover, for many TVCNs only limited information (e.g., few variables) is accessible for modeling, which hampers the capabilities of analytical tools to uncover the true degrees of freedom and infer the CS model, the hidden states and their parameters. Another fundamental limitation is that of understanding and unveiling of unknown drivers of the dynamics that could sporadically excite the network in ways that straightforward modeling does not work due to our inability to model non-stationary processes. Towards addressing these challenges, in this paper, we consider the problem of learning the fractional dynamical complex networks under unknown unknowns (i.e., hidden drivers) and partial observability (i.e., only partial data is available). More precisely, we consider a generalized modeling approach of TVCNs consisting of discrete-time fractional dynamical equations and propose an iterative framework to determine the network parameterization and predict the state of the system. We showcase the performance of the proposed framework in the context of task classification using real electroencephalogram data.
On the spectral resolution of products of Laplacian eigenfunctions

We study products of eigenfunctions of the Laplacian $-\Delta \phi_{\lambda} = \lambda \phi_{\lambda}$ on compact manifolds. If $\phi_{\mu}, \phi_{\lambda}$ are two eigenfunctions and $\mu \leq \lambda$, then one would perhaps expect their product $\phi_{\mu}\phi_{\lambda}$ to be mostly a linear combination of eigenfunctions with eigenvalue close to $\lambda$. This can faily quite dramatically: on $\mathbb{T}^2$, we see that $$ 2\sin{(n x)} \sin{((n+1) x)} = \cos{(x)} - \cos{( (2n+1) x)} $$ has half of its $L^2-$mass at eigenvalue 1. Conversely, the product $$ \sin{(n x)} \sin{(m y)} \qquad \mbox{lives at eigenvalue} \quad \max{\left\{m^2,n^2\right\}} \leq m^2 + n^2 \leq 2\max{\left\{m^2,n^2\right\}}$$ and the heuristic is valid. We show that the main reason is that in the first example 'the waves point in the same direction': if the heuristic fails and multiplication carries $L^2-$mass to lower frequencies, then $\phi_{\mu}$ and $\phi_{\lambda}$ are strongly correlated at scale $ \sim \lambda^{-1/2}$ (the shorter wavelength) $$ \left\| \int_{M}{ p(t,x,y)( \phi_{\lambda}(y) - \phi_{\lambda}(x))( \phi_{\mu}(y) - \phi_{\mu}(x)) dy} \right\|_{L^2_x} \gtrsim \| \phi_{\mu}\phi_{\lambda}\|_{L^2},$$ where $p(t,x,y)$ is the classical heat kernel and $t \sim \lambda^{-1}$. This turns out to be a fairly fundamental principle and is even valid for the Hadamard product of eigenvectors of a Graph Laplacian.
Systematic Variations of CO Gas Abundance with Radius in Gas-rich Protoplanetary Disks

NASA - Space Telescope Science InstituteSpace Telescope Science InstituteNational Aeronautics & Space Administration (NASA) [HST-HF2-51401.001, HST-HF2-51394.001, HST-HF2-51419.001]; NASANational Aeronautics & Space Administration (NASA) [NAS5-26555]
Cascaded Coded Distributed Computing on Heterogeneous Networks

Coded distributed computing (CDC) introduced by Li et al. in 2015 offers an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. For the more general cascaded CDC, Map computations are repeated at r nodes to significantly reduce the communication load among nodes tasked with computing Q Reduce functions s times. While an achievable cascaded CDC scheme was proposed, it only operates on homogeneous networks, where the storage, computation load and communication load of each computing node is the same. In this paper, we address this limitation by proposing a novel combinatorial design which operates on heterogeneous networks where nodes have varying storage and computing capabilities. We provide an analytical characterization of the computation-communication trade-off and show that it is optimal within a constant factor and could outperform the state-of-the-art homogeneous schemes.
Super-resolution meets machine learning: approximation of measures

The problem of super-resolution in general terms is to recuperate a finitely supported measure $$\mu $$ given finitely many of its coefficients $$\hat{\mu }(k)$$ with respect to some orthonormal system. The interesting case concerns situations, where the number of coefficients required is substantially smaller than a power of the reciprocal of the minimal separation among the points in the support of $$\mu $$. In this paper, we consider the more severe problem of recuperating $$\mu $$ approximately without any assumption on $$\mu $$ beyond having a finite total variation. In particular, $$\mu $$ may be supported on a continuum, so that the minimal separation among the points in the support of $$\mu $$ is 0. A variant of this problem is also of interest in machine learning as well as the inverse problem of de-convolution. We define an appropriate notion of a distance between the target measure and its recuperated version, give an explicit expression for the recuperation operator, and estimate the distance between $$\mu $$ and its approximation. We show that these estimates are the best possible in many different ways. We also explain why for a finitely supported measure the approximation quality of its recuperation is bounded from below if the amount of information is smaller than what is demanded in the super-resolution problem.
Adoption of PBIS within school districts.

Abstract Recent research in implementation science has focused on identifying factors that predict adoption of evidence-based practices in schools. Less attention has focused on examining the adoption of practices within districts. This study included a sequential cohort of 552 districts in 25 U.S. states adopting an evidence-based framework, school-wide positive behavioral interventions and supports (PBIS). We examined schools within districts reporting on PBIS fidelity during their first five years of PBIS initiatives. Latent change score and multi-level growth modeling were used to examine and predict the percent of district adoption of PBIS over time. Results showed a significant increase in the rate of district adoption over the first 4 years of the initiative, with a decrease in growth between years 4 and 5. District size, proportion of students receiving free or reduced lunch, and districts located in cities and towns were found to be significant predictors of the percent of schools adopting PBIS. Implications for future implementation research and strategies for increasing the adoption of PBIS are discussed.
Customer Preference and Station Network in the London Bike-Share System

We study customer preference for the bike-share system in the city of London. We estimate a structural demand model on the station network to learn the preference parameters and use the estimated m...
Value of Information in Probabilistic Logic Programs.

In medical decision making, we have to choose among several expensive diagnostic tests such that the certainty about a patient's health is maximized while remaining within the bounds of resources like time and money. The expected increase in certainty in the patient's condition due to performing a test is called the value of information (VoI) for that test. In general, VoI relates to acquiring additional information to improve decision-making based on probabilistic reasoning in an uncertain system. This paper presents a framework for acquiring information based on VoI in uncertain systems modeled as Probabilistic Logic Programs (PLPs). Optimal decision-making in uncertain systems modeled as PLPs have already been studied before. But, acquiring additional information to further improve the results of making the optimal decision has remained open in this context. We model decision-making in an uncertain system with a PLP and a set of top-level queries, with a set of utility measures over the distributions of these queries. The PLP is annotated with a set of atoms labeled as "observable"; in the medical diagnosis example, the observable atoms will be results of diagnostic tests. Each observable atom has an associated cost. This setting of optimally selecting observations based on VoI is more general than that considered by any prior work. Given a limited budget, optimally choosing observable atoms based on VoI is intractable in general. We give a greedy algorithm for constructing a "conditional plan" of observations: a schedule where the selection of what atom to observe next depends on earlier observations. We show that, preempting the algorithm anytime before completion provides a usable result, the result improves over time, and, in the absence of a well-defined budget, converges to the optimal solution.
Deep convolutional neural networks for uncertainty propagation in random fields

The development of a reliable and robust surrogate model is often constrained by the dimensionality of the problem. For a system with high-dimensional inputs/outputs (I/O), conventional approaches usually use a low-dimensional manifold to describe the high-dimensional system, where the I/O data is first reduced to more manageable dimensions and then the condensed representation is used for surrogate modeling. In this study, we present a new solution scheme for this type of problems based on a deep learning approach. The proposed surrogate is based on a particular network architecture, i.e. the convolutional neural networks. The surrogate architecture is designed in a hierarchical style containing three different levels of model structures, advancing the efficiency and effectiveness of the model in the aspect of training and deploying. To assess the model performance, we carry out uncertainty quantification in a continuum mechanics benchmark problem. Numerical results suggest the proposed model is capable of directly inferring a wide variety of I/O mapping relationships. Uncertainty analysis results obtained via the proposed surrogate have successfully characterized the statistical properties of the output fields compared to the Monte Carlo estimates.
Prescription drug monitoring programs and neonatal outcomes

Abstract Over the last two decades, the number of delivering mothers using or dependent on opiates has increased dramatically, giving rise to a five-fold increase in the proportion of babies born with neonatal abstinence syndrome (NAS). First, the current study documents NAS trends in the United States and their substantial variation across states. Second, it explores the relationship, if any, between the adoption of prescription drug monitoring programs (PDMPs) and reductions in NAS incidence across the United States. We find that the introduction of operational PDMPs reduced NAS incidence in the United States by 10%. We also examined the effects on birth outcomes, infant mortality, and other pregnancy complications and find little evidence of any effect of PDMPs on birth weight, premature births, and infant mortality.
Neogenin-1 distinguishes between myeloid-biased and balanced Hoxb5+ mouse long-term hematopoietic stem cells.

Hematopoietic stem cells (HSCs) self-renew and generate all blood cells. Recent studies with single cell transplants and lineage tracing suggest that adult HSCs are diverse in their reconstitution and lineage potentials. However, prospective isolation of these subpopulations has remained challenging. Here, we identify Neogenin-1 (NEO1) as a unique surface marker on a fraction of mouse HSCs labeled with Hoxb5, a specific reporter of long-term HSCs (LT-HSCs). We show that NEO1+Hoxb5+ LT-HSCs expand with age and respond to myeloablative stress in young mice while NEO1−Hoxb5+ LT-HSCs exhibit no significant change in number. Furthermore, NEO1+Hoxb5+ LT-HSCs are more often in the G2/S cell cycle phase compared to NEO1−Hoxb5+ LT-HSCs in both young and old bone marrow. Upon serial transplantation, NEO1+Hoxb5+ LT-HSCs exhibit myeloid-biased differentiation and reduced reconstitution while NEO1−Hoxb5+ LT-HSCs are lineage-balanced and stably reconstitute recipients. Gene expression analysis reveals erythroid and myeloid priming in the NEO1+ fraction and association of quiescence and self-renewal–related transcription factors with NEO1− LT-HSCs. Finally, transplanted NEO1+Hoxb5+ LT-HSCs rarely generate NEO1−Hoxb5+ LT-HSCs while NEO1−Hoxb5+ LT-HSCs repopulate both LT-HSC fractions. This supports a model in which dormant, balanced NEO1−Hoxb5+ LT-HSCs can hierarchically precede active, myeloid-biased NEO1+Hoxb5+ LT-HSCs.
Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk

Recent advances in scanning transmission electron microscopy (STEM) allow to observe solid-state transformations and reactions in materials induced by thermal stimulus or electron beam on the atomic level. However, despite the rate at which large volumes of data can be generated (sometimes in the gigabyte to terabyte range per single experiment), approaches for the extraction of material-specific knowledge on the kinetics and thermodynamics of these processes are still lacking. One of the critical issues lies in being able to map the evolution of various atomic structures and determine the associated transition probabilities directly from raw experimental data characterized by high levels of noise and missing structural elements. Here, we demonstrate an approach based on the combination of multiple machine learning techniques to study the dynamic behavior of e-beam irradiated Si atoms in the bulk and at the edges of single-layer graphene in STEM experiments. First, a deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. Then, a Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. Finally, the frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the thermodynamics of defect populations and chemical reaction networks from the atomically resolved STEM data. Here, we observe a clear tendency for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene.
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles

Mutually beneficial behavior in repeated games can be enforced via the threat of punishment, as enshrined in game theory’s well-known “folk theorem.” There is a cost, however, to a player for generating these disincentives. In this work, we seek to minimize this cost by computing a “Stackelberg punishment,” in which the player selects a behavior that sufficiently punishes the other player while maximizing its own score under the assumption that the other player will adopt a best response. This idea generalizes the concept of a Stackelberg equilibrium. Known efficient algorithms for computing a Stackelberg equilibrium can be adapted to efficiently produce a Stackelberg punishment. We demonstrate an application of this idea in an experiment involving a virtual autonomous vehicle and human participants. We find that a self-driving car with a Stackelberg punishment policy discourages human drivers from bullying in a driving scenario requiring social negotiation.
On mean curvature flow with forcing

This paper investigates geometric properties and well-posedness of a mean curvature flow with volume-dependent forcing. With the class of forcing which bounds the volume of the evolving set away fr...
Laudatio : Pietro Trimarchi

The text of a Laudatio delivered on September 16, 2006 by Professor Francesco Parisi on the occasion of the awarding of an honorary membership in the European Association of Law and Economics (EALE) to Professor Pietro Trimarchi. This was the first such honorary membership awarded by this association.
Congruences modulo powers of 11 for some eta-quotients

The partition function $$ p_{[1^c11^d]}(n)$$ can be defined using the generating function, $$\begin{aligned} \sum _{n=0}^{\infty }p_{[1^c{11}^d]}(n)q^n=\prod _{n=1}^{\infty }\dfrac{1}{(1-q^n)^c(1-q^{11 n})^d}. \end{aligned}$$In this paper, we prove infinite families of congruences for the partition function $$ p_{[1^c11^d]}(n)$$ modulo powers of 11 for any integers c and d, which generalizes Atkin and Gordon’s congruences for powers of the partition function. The proofs use an explicit basis for the vector space of modular functions of the congruence subgroup $$\Gamma _0(11)$$.
Ridge Regression: Structure, Cross-Validation, and Sketching

We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator? (2) how to correctly use cross-validation to choose the regularization parameter? and (3) how to accelerate computation without losing too much accuracy? We consider the three problems in a unified large-data linear model. We give a precise representation of ridge regression as a covariance matrix-dependent linear combination of the true parameter and the noise. We study the bias of $K$-fold cross-validation for choosing the regularization parameter, and propose a simple bias-correction. We analyze the accuracy of primal and dual sketching for ridge regression, showing they are surprisingly accurate. Our results are illustrated by simulations and by analyzing empirical data.
Enabling remote quantum emission in 2D semiconductors via porous metallic networks.

Here we report how two-dimensional crystal (2DC) overlayers influence the recrystallization of relatively thick metal films and the subsequent synergetic benefits this provides for coupling surface plasmon-polaritons (SPPs) to photon emission in 2D semiconductors. We show that annealing 2DC/Au films on SiO2 results in a reverse epitaxial process where initially nanocrystalline Au films gain texture, crystallographically orient with the 2D crystal overlayer, and form an oriented porous metallic network (OPEN) structure in which the 2DC can suspend above or coat the inside of the metal pores. Both laser excitation and exciton recombination in the 2DC semiconductor launch propagating SPPs in the OPEN film. Energy in-/out- coupling occurs at metal pore sites, alleviating the need for dielectric spacers between the metal and 2DC layer. At low temperatures, single-photon emitters (SPEs) are present across an OPEN-WSe2 film, and we demonstrate remote SPP-mediated excitation of SPEs at a distance of 17 μm. Here, the authors develop a reverse epitaxial process whereby a nanocrystalline Au film becomes highly textured and support a suspended 2D WSe2 overlayer. Surface plasmon polaritons are launched in nanostructured Au by laser excitation and couple remotely to single photon emitters present in WSe2.
Temperature Discontinuity at an Evaporating Water Interface

Evaporative mass flux is governed by the interfacial state of liquid and vapor phases. For closely similar pressures and mass fluxes of liquid water into its own vapor, the discontinuity between in...
Multi-Server Private Information Retrieval with Coded Side Information

In this paper, we study the multi-server setting of the Private Information Retrieval with Coded Side Information (PIR-CSI) problem. In this problem, there are K messages replicated across N servers, and there is a user who wishes to download one message from the servers without revealing any information to any server about the identity of the requested message. The user has a side information which is a linear combination of a subset of M messages in the database. The parameter M is known to all servers in advance, whereas the indices and the coefficients of the messages in the user’s side information are unknown to any server a priori.We focus on a class of PIR-CSI schemes, referred to as server-symmetric schemes, in which the queries/answers to/from different servers are symmetric in structure. We define the rate of a PIR-CSI scheme as its minimum download rate among all problem instances, and define the server-symmetric capacity of the PIR-CSI problem as the supremum of rates over all server-symmetric PIR-CSI schemes. Our main results are as follows: (i) when the side information is not a function of the user’s requested message, the capacity is given by ${\left( {1 + 1/N + \cdots + 1/{N^{\left\lceil {\frac{K}{{M + 1}}} \right\rceil - 1}}} \right)^{ - 1}}$ for any 1 ≤ M ≤ K− 1; and (ii) when the side information is a function of the user’s requested message, the capacity is equal to 1 for M = 2 and M = K, and it is equal to N/(N + 1) for any 3 ≤ M ≤ K− 1. The converse proofs rely on new information-theoretic arguments, and the achievability schemes are inspired by our recently proposed scheme for single-server PIR-CSI as well as the Sun-Jafar scheme for multi-server PIR.
SYMMETRIES,QUANTUM GEOMETRY, AND THE FUNDAMENTAL INTERACTIONS

A generalized Noether's theorem and the operational determination of a physical geometry in quantum physics are used to motivate a quantum geometry consisting of relations between quantum states that are defined by a universal group. Making these relations dynamical implies the nonlocal effect of the fundamental interactions on the wave function, as in the Aharonov–Bohm effect and its generalizations to non-Abelian gauge fields and gravity. The usual space–time geometry is obtained as the classical limit of this quantum geometry using the quantum-state space metric.
A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

Reconstructing components of a genomic mixture from data obtained by means of DNA sequencing is a challenging problem encountered in a variety of applications including single individual haplotyping and studies of viral communities. High-throughput DNA sequencing platforms oversample mixture components to provide massive amounts of reads whose relative positions can be determined by mapping the reads to a known reference genome; assembly of the components, however, requires discovery of the reads' origin – an NP-hard problem that the existing methods struggle to solve with the required level of accuracy. In this paper, we present a learning framework based on a graph auto-encoder designed to exploit structural properties of sequencing data. The algorithm is a neural network which essentially trains to ignore sequencing errors and infers the posterior probabilities of the origin of sequencing reads. Mixture components are then reconstructed by finding consensus of the reads determined to originate from the same genomic component. Results on realistic synthetic as well as experimental data demonstrate that the proposed framework reliably assembles haplotypes and reconstructs viral communities, often significantly outperforming state-of-the-art techniques. Source codes, datasets and supplementary document are available at https://github.com/WuLoli/GAEseq.
Applications of machine learning in decision analysis for dose management for dofetilide.

Background Initiation of the antiarrhythmic medication dofetilide requires an FDA-mandated 3 days of telemetry monitoring due to heightened risk of toxicity within this time period. Although a recommended dose management algorithm for dofetilide exists, there is a range of real-world approaches to dosing the medication. Methods and results In this multicenter investigation, clinical data from the Antiarrhythmic Drug Genetic (AADGEN) study was examined for 354 patients undergoing dofetilide initiation. Univariate logistic regression identified a starting dofetilide dose of 500 mcg (OR 5.0, 95%CI 2.5–10.0, p<0.001) and sinus rhythm at the start of dofetilide loading (OR 2.8, 95%CI 1.8–4.2, p<0.001) as strong positive predictors of successful loading. Any dose-adjustment during loading (OR 0.19, 95%CI 0.12–0.31, p<0.001) and a history coronary artery disease (OR 0.33, 95%CI 0.19–0.59, p<0.001) were strong negative predictors of successful dofetilide loading. Based on the observation that any dose adjustment was a significant negative predictor of successful initiation, we applied multiple supervised approaches to attempt to predict the dose adjustment decision, but none of these approaches identified dose adjustments better than a probabilistic guess. Principal component analysis and cluster analysis identified 8 clusters as a reasonable data reduction method. These 8 clusters were then used to define patient states in a tabular reinforcement learning model trained on 80% of dosing decisions. Testing of this model on the remaining 20% of dosing decisions revealed good accuracy of the reinforcement learning model, with only 16/410 (3.9%) instances of disagreement. Conclusions Dose adjustments are a strong determinant of whether patients are able to successfully initiate dofetilide. A reinforcement learning algorithm informed by unsupervised learning was able to predict dosing decisions with 96.1% accuracy. Future studies will apply this algorithm prospectively as a data-driven decision aid.
Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond

Recursive least-squares algorithms often use forgetting factors as a heuristic to adapt to non-stationary data streams. The first contribution of this paper rigorously characterizes the effect of forgetting factors for a class of online Newton algorithms. For exp-concave and strongly convex objectives, the algorithms achieve the dynamic regret of max{O(log T),O(√TV)}, where V is a bound on the path length of the comparison sequence. In particular, we show how classic recursive least-squares with a forgetting factor achieves this dynamic regret bound. By varying V, we obtain a trade-off between static and dynamic regret. In order to obtain more computationally efficient algorithms, our second contribution is a novel gradient descent step size rule for strongly convex functions. Our gradient descent rule recovers the order optimal dynamic regret bounds described above. For smooth problems, we can also obtain static regret of O(T1-β) and dynamic regret of O(Tβ V*), where β ∈ (0,1) and V* is the path length of the sequence of minimizers. By varying β, we obtain a trade-off between static and dynamic regret.
Concurrence of quantum anomalous Hall and topological Hall effects in magnetic topological insulator sandwich heterostructures.

The quantum anomalous Hall (QAH) effect is a consequence of non-zero Berry curvature in momentum space. The QAH insulator harbours dissipation-free chiral edge states in the absence of an external magnetic field. However, the topological Hall (TH) effect, a hallmark of chiral spin textures, is a consequence of real-space Berry curvature. Here, by inserting a topological insulator (TI) layer between two magnetic TI layers, we realized the concurrence of the TH effect and the QAH effect through electric-field gating. The TH effect is probed by bulk carriers, whereas the QAH effect is characterized by chiral edge states. The appearance of the TH effect in the QAH insulating regime is a consequence of chiral magnetic domain walls that result from the gate-induced Dzyaloshinskii–Moriya interaction and occurs during the magnetization reversal process in the magnetic TI sandwich samples. The coexistence of chiral edge states and chiral spin textures provides a platform for proof-of-concept dissipationless spin-textured spintronic applications. The coexistence of chiral edge states and chiral spin textures in magnetic topological insulator sandwiches provides a platform for proof-of-concept dissipationless spin-textured spintronic applications.
SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain.Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization – one of the main problems affecting other packages in underwater domain – by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness.
Scheduling and Pricing of Energy Generation and Storage in Power Systems

This paper proposes a fundamental model for continuous-time scheduling and marginal pricing of energy generation and storage in day-ahead power systems operation. The paper begins with formulating the economic operation problem of power systems with generating units and energy storage (ES) devices as a continuous-time optimal control problem, where the Lagrange multiplier trajectory associated with the continuous-time power balance constraint is proven to be the marginal price of energy generation and storage. The marginal price is calculated in closed-form, which reveals that in addition to the incremental cost rates of generating units, the marginal price embeds the financial ES charging offers and discharging bids that are defined as incremental charging utility and incremental discharging cost rates. This paper shows that the adjoint function associated with the ES state equation establishes a temporal dependence between the marginal prices during the ES charge and discharge states. A function space-based method is developed to solve the proposed model, which converts the continuous-time problem into a mixed-integer linear programming problem with finite dimensional decision space. The features of the proposed scheduling and pricing models are demonstrated using numerical studies conducted on the IEEE Reliability Test System.
The Role of Oceanic Processes in the Initiation of Boreal Winter Intraseasonal Oscillations Over the Indian Ocean

Observational analyses and a hierarchy of ocean general circulation model (OGCM) experiments were performed to understand the influence of oceanic processes on the warm sea surface temperature anomalies (SSTAs) prior to the convection initiation of boreal winter intraseasonal oscillations (ISOs), including the Madden-Julian Oscillation (MJO), in the equatorial Indian Ocean. We found 39 strong ISOs that passed over the Indian Ocean Warm Pool region during the November-April season of the 2001-2012 period. 17/39 ISO events initiated in the Seychelles-Chagos Thermocline Ridge (SCTR) before propagating eastward; the remaining events initiated in the southern Arabian Sea (6) or Warm Pool (16) regions. The SCTR event set was notable in that it contained more global-scale MJOs (71-76%), as defined by the RMM and OMI indices, than the WP events (25-44%). Additionally, ~24% (44%) of the SCTR (Warm Pool) events were preceded by strong oceanic process-induced SSTAs of similar magnitude to those of shortwave radiative and turbulent heat fluxes. The Arabian Sea events, however, were not associated with statistically significant SSTA signals prior to convection. Based on a mixed layer heat budget analysis, entrainment and upwelling reduction were the dominant oceanic processes contributing to the warming, in contrast with boreal summer, when horizontal advection dominated. We examined several case studies, including primary MJO events, where oceanic Rossby waves were associated with the entrainment and upwelling reduction. Two simple atmospheric boundary layer convergence models revealed that the SSTAs contributed at least half of the total convergence and suggested that the ocean dynamical effect was responsible for the majority of SSTA-forced convergence for those case studies. These results underscore the need for climate prediction models to accurately represent the ocean structure and processes to include the effects of oceanic predictors.
Effect of Daily Teacher Feedback on Subsequent Motivation and Mental Health Outcomes in Fifth Grade Students: a Person-Centered Analysis

Prevention scientists recognize that implementing effective prevention practices and programs responsive to the needs of individuals but based solely upon the findings from variable-centered methods presents several limitations due to numerous risk factors, pathways, and unobserved influences. One such understudied influence that is masked by variable-centered methods, motivation, is a person-level characteristic that influences treatment outcomes. The purpose of this paper is to demonstrate the use of an alternative person-centered approach, group iterative multiple model estimation (GIMME), to model change over time that focuses on the interdependence of daily student motivation levels and teacher feedback and their relations to student outcomes over time. Specifically, we used GIMME to model person level responses to negative teacher feedback regarding students' next day motivational ratings using data from 58 5th grade students participating in a study of the impact of the self-monitoring and regulation training strategy (SMARTS). Results identified a set of SMARTS students whose daily readiness aligned with high rates of self and teacher agreement regarding ongoing performance ratings. However, results identified a group of students whose daily motivation and readiness for change was adversely impacted by negative teacher feedback the day before. For these students, they were more likely than their peers to experience high levels of depression and internalization scores. Motivationally oriented practice suggestions for providing feedback to students who may be sensitive to this type of feedback and research implications of these findings are discussed.
The automorphism group and limit set of a bounded domain I: The finite type case

Abstract For bounded pseudoconvex domains with finite type we give a precise description of the automorphism group: if an orbit of the automorphism group accumulates on at least two different points of the boundary, then the automorphism group has finitely many components and is the almost direct product of a compact group and connected Lie group locally isomorphic to Aut ( B k ) . Further, the limit set is a smooth submanifold diffeomorphic to the sphere of dimension 2 k − 1 . As applications we prove a new finite jet determination theorem and a Tits alternative theorem. The geometry of the Kobayashi metric plays an important role in the paper.
Conservation Voltage Reduction by Coordinating Legacy Devices, Smart Inverters and Battery

Due to market, there is flexibility in the price for power purchase by the utility. At the maximum loading condition the cost of power is high, hence conservation voltage reduction (CVR) is a useful technique in reducing power demand of the distribution system. Further, by installing battery energy storage system (BESS) in the distribution system, power can be stored in the BESS when the price is low and can be utilize to supply the demand when the price is high. In this paper, power conserving and cost saving problems are investigated. As we consider loads in distribution system as voltage dependent loads, the CVR objective is obtained by optimizing the nodal voltages of a feeder towards the lower limit of the ANSI value using legacy devices and smart inverter. In order to solve introduced problems, a MPC-based algorithm is proposed for optimal usage of future information prediction such as load energy consumption profile, PVs generation profiles and TOU electricity tariffs. Moreover, effect of depreciation cost of the BESS is in cost saving is investigated. The proposed method is validated using IEEE-123 node system. It is demonstrated form the results that the proposed method is able to reduce the power consumption and cost of purchased power from substation.
2D Ti3C2Tz MXene Synthesized by Water-free Etching of Ti3AlC2 in Polar Organic Solvents

Summary The first MXene discovered, Ti3C2Tz, was synthesized by etching of the parent MAX phase, Ti3AlC2, in a solution of concentrated hydrofluoric acid. Since then, several other ways of synthesizing MXenes have been reported, such as electrochemical etching in various electrolytes, high-temperature alkali treatment, molten salt synthesis, etc. The majority of these etching methods, however, use water as their main solvent, limiting direct use of MXenes in water-sensitive applications. In this work, we show that it is possible to etch, and delaminate, MXenes in the absence of water, by using organic polar solvents in the presence of ammonium dihydrogen fluoride. We further show that by using this etching method, it is possible to obtain Ti3C2Tz flakes rich in fluorine terminations. We also demonstrate that electrodes made from Ti3C2Tz etched in propylene carbonate resulted in Na-ion battery anodes with double the capacity to those etched in water.
Who Benefits?: Positive Learner Outcomes from Behavioral Analytics of Online Lecture Video Viewing Using ClassTranscribe

Lecture material of a sophomore large-enrollment (N=271) system programming 15-week class was delivered solely online using a new video-based web platform. The platform provided accurate accessible transcriptions and captioning plus a custom text-searchable interface to rapidly find relevant video moments from the entire course. The system logged student searching and viewing behaviors as fine-grained web browser interaction events including full-screen-switching, loss-of-focus, incremental searching events, and continued-video-watching events with the latter at 15-second granularity. Student learning behaviors and findings from three research questions are presented using individual-level performance and interaction data. Firstly, we report on learning outcomes from alternative learning paths that arise from the course's application of Universal Design for Learning principles. Secondly, final exam performance was equal or better to prior semesters that utilized traditional in-person live lectures. Thirdly, learning outcomes of low and high performing students were analyzed independently by grouping students into four quartiles based on their non-final-exam course performance of programming assignments and quizzes. We introduce and justify an empirically-defined qualification threshold for sufficient video minutes viewed for each group. In all quartiles, students who watched an above-threshold of video minutes improved their in-group final exam performance (ranging from +6 % to +14%) with the largest gain for the lowest-performing quartile. The improvement was similar in magnitude for all groups when expressed as a fraction of unrewarded final exam points. Overall, the study presents and evaluates how learner use of online video using ClassTranscribe predicts course performance and positive learning outcomes.