cssi/SciSciGPT-SciSciNet huggingface dataset:
Learning Bayesian network structure from correlation-immune data

Searching the complete space of possible Bayesian networks is intractable for problems of interesting size, so Bayesian network structure learning algorithms, such as the commonly used Sparse Candidate algorithm, employ heuristics. However, these heuristics also restrict the types of relationships that can be learned exclusively from data. They are unable to learn relationships that exhibit "correlation-immunity", such as parity. To learn Bayesian networks in the presence of correlation-immune relationships, we extend the Sparse Candidate algorithm with a technique called "skewing". This technique uses the observation that relationships that are correlation-immune under a specific input distribution may not be correlation-immune under another, sufficiently different distribution. We show that by extending Sparse Candidate with this technique we are able to discover relationships between random variables that are approximately correlation-immune, with a significantly lower computational cost than the alternative of considering multiple parents of a node at a time.
INTERPOLATING SEQUENCES ON THE BIDISK

We give a characterization of interpolating sequences for bounded analytic functions on the bidisk.
Norm-inflation results for the BBM equation

Abstract Considered here is the periodic initial-value problem for the regularized long-wave (BBM) equation u t + u x + u u x − u x x t = 0 . Adding to previous work in the literature, it is shown here that for any s 0 , there is smooth initial data that is small in the L 2 -based Sobolev spaces H s , but the solution emanating from it becomes arbitrarily large in arbitrarily small time. This so called norm inflation result has as a consequence the previously determined conclusion that this problem is ill-posed in these negative-norm spaces.
Free Brownian motion and free convolution semigroups: multiplicative case

Abstract. We consider μ, ν a pair of probability measures on the unit circle such that Σλ(ην(z)) = z/ημ(z). We prove that the same type of equation holds for any t ≥ 0 when we replace ν by ν λt and μ by Mt(μ), where λt is the analogue of the normal distribution with respect to and Mt is the map defined by Arizmendi and Hasebe. These equations are a multiplicative analogue of equations studied by Belinschi and Nica. In order to achieve this result, we study infinite divisibility of measures associated with subordination functions in free Brownian motion and free convolution semigroups. We use the modified S-transform introduced by Raj Rao and Speicher to deal with the case that ν has zero mean. The same type of the result holds for convolutions on the positive real line. We also obtain some regularity properties for the free multiplicative analogue of the normal distributions.
Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers

This paper improves state-of-the-art visual object trackers that use online adaptation. Our core contribution is an offline meta-learning-based method to adjust the initial deep networks used in online adaptation-based tracking. The meta learning is driven by the goal of deep networks that can quickly be adapted to robustly model a particular target in future frames. Ideally the resulting models focus on features that are useful for future frames, and avoid overfitting to background clutter, small parts of the target, or noise. By enforcing a small number of update iterations during meta-learning, the resulting networks train significantly faster. We demonstrate this approach on top of the high performance tracking approaches: tracking-by-detection based MDNet [1] and the correlation based CREST [2]. Experimental results on standard benchmarks, OTB2015 [3] and VOT2016 [4], show that our meta-learned versions of both trackers improve speed, accuracy, and robustness.
The isoperimetric spectrum of finitely presented groups

The isoperimeric spectrum consists of all real positive numbers $\alpha$ such that $O(n^\alpha)$ is the Dehn function of a finitely presented group. In this note we show how a recent result of Olshanskii completes the description of the isoperimetric spectrum modulo the celebrated Computer Science conjecture (and one of the seven Millennium Problems) $\mathbf{P=NP}$ and even a formally weaker conjecture.
The impacts of three flamelet burning regimes in nonlinear combustion dynamics

Abstract Axisymmetric simulations of a liquid rocket engine are performed using a delayed detached-eddy-simulation (DDES) turbulence model with the Compressible Flamelet Progress Variable (CFPV) combustion model. Three different pressure instability domains are simulated: completely unstable, semi-stable, and fully stable. The different instability domains are found by varying the combustion chamber and oxidizer post length. Laminar flamelet solutions with a detailed chemical mechanism are examined. The β probability density function (PDF) for the mixture fraction and Dirac δ PDF for both the pressure and the progress variable are used. A coupling mechanism between the volumetric Heat Release Rate (HRR) and the pressure in an unstable cycle is demonstrated. Local extinction and reignition are investigated for all the instability domains using the full S-curve approach. A monotonic decrease in the amount of local extinctions and reignitions occurs when pressure oscillation amplitude becomes smaller. The flame index is used to distinguish between the premixed and non-premixed burning mode in different stability domains. An additional simulation of the unstable pressure oscillation case using only the stable flamelet burning branch of the S-curve is performed. Better agreement with experiments in terms of pressure oscillation amplitude is found when the full S-curve is used.
Quantum ultrametrics on AF algebras and the Gromov-Hausdorff propinquity

We construct quantum metric structures on unital AF algebras with a faithful tracial state, and prove that for such metrics, AF algebras are limits of their defining inductive sequences of finite dimensional C*-algebras for the quantum propinquity. We then study the geometry, for the quantum propinquity, of three natural classes of AF algebras equipped with our quantum metrics: the UHF algebras, the Effros-Shen AF algebras associated with continued fraction expansions of irrationals, and the Cantor space, on which our construction recovers traditional ultrametrics. We also exhibit several compact classes of AF algebras for the quantum propinquity and show continuity of our family of Lip-norms on a fixed AF algebra. Our work thus brings AF algebras into the realm of noncommutative metric geometry.
Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent

Recent work has demonstrated the effectiveness of gradient descent for directly recovering the factors of low-rank matrices from random linear measurements in a globally convergent manner when initialized properly. However, the performance of existing algorithms is highly sensitive in the presence of outliers that may take arbitrary values. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against outliers, where the truncation is performed to rule out the contributions of samples that deviate significantly from the {\em sample median} of measurement residuals adaptively in each iteration. We demonstrate that, when initialized in a basin of attraction close to the ground truth, the proposed algorithm converges to the ground truth at a linear rate for the Gaussian measurement model with a near-optimal number of measurements, even when a constant fraction of the measurements are arbitrarily corrupted. In addition, we propose a new truncated spectral method that ensures an initialization in the basin of attraction at slightly higher requirements. We finally provide numerical experiments to validate the superior performance of the proposed approach.
Degrees of freedom of spatial self-interference suppression for in-band full-duplex with inter-node interference

We study a three-node network with a full-duplex base-station communicating with one uplink and one downlink half-duplex node. In this network, both self-interference and inter-node interference are present. We use an antenna-theory-based channel model to study the spatial degrees of freedom of such network, and study if and how much does spatial isolation outperforms time-division (i.e. half-duplex) counterpart. Using degrees of freedom analysis, we show that spatial isolation outperforms time division, unless the angular spread of the objects that scatters to the intended users is overlapped by the spread of objects that back scatters to the receivers.
Multiple bracket function, Stirling number, and Lah number identities

The author has constructed multiple analogues of several families of combinatorial numbers in a recent article, including the bracket symbol, and the Stirling numbers of the first and second kind. In the present paper, a multiple analogue of another sequence, the Lah numbers, is developed, and certain associated identities and significant properties of all these sequences are constructed.
Joint Frequency Regulation and Economic Dispatch Using Limited Communication

We study the performance of a decentralized integral control scheme for joint power grid frequency regulation and economic dispatch. We show that by properly designing the controller gains, after a power flow perturbation, the control achieves near-optimal economic dispatch while recovering the nominal frequency, without requiring any communication. We quantity the gap between the controllable power generation cost under the decentralized control scheme and the optimal cost, based on the DC power flow model. Moreover, we study the tradeoff between the cost and the convergence time, by adjusting parameters of the control scheme.Communication between generators reduces the convergence time. We identify key communication links whose failures have more significant impacts on the performance of a distributed power grid control scheme that requires information exchange between neighbors.
Optimizing ROOT IO For Analysis

The ROOT I/O (RIO) subsystem is foundational to most HEP experiments - it provides a file format, a set of APIs/semantics, and a reference implementation in C++. It is often found at the base of an experiment's framework and is used to serialize the experiment's data; in the case of an LHC experiment, this may be hundreds of petabytes of files! Individual physicists will further use RIO to perform their end-stage analysis, reading from intermediate files they generate from experiment data. RIO is thus incredibly flexible: it must serve as a file format for archival (optimized for space) and for working data (optimized for read speed). To date, most of the technical work has focused on improving the former use case. We present work designed to help improve RIO for analysis. We analyze the real-world impact of a compression algorithm LZ4 to decrease decompression times (and the corresponding cost in disk space). We introduce new APIs that read RIO data in bulk, removing the per-event overhead of a C++ function call. We compare the performance with the existing RIO APIs for simple structure data and show how this can be complimentary with efforts to improve the parallelism of the RIO stack.
Cross-domain Semantic Parsing via Paraphrasing

Existing studies on semantic parsing mainly focus on the in-domain setting. We formulate cross-domain semantic parsing as a domain adaptation problem: train a semantic parser on some source domains and then adapt it to the target domain. Due to the diversity of logical forms in different domains, this problem presents unique and intriguing challenges. By converting logical forms into canonical utterances in natural language, we reduce semantic parsing to paraphrasing, and develop an attentive sequence-to-sequence paraphrase model that is general and flexible to adapt to different domains. We discover two problems, small micro variance and large macro variance, of pre-trained word embeddings that hinder their direct use in neural networks, and propose standardization techniques as a remedy. On the popular Overnight dataset, which contains eight domains, we show that both cross-domain training and standardized pre-trained word embeddings can bring significant improvement.
POINTS ON HYPERBOLAS AT RATIONAL DISTANCE

Richard Guy asked for the largest set of points which can be placed in the plane so that their pairwise distances are rational numbers. In this article, we consider such a set of rational points restricted to a given hyperbola. To be precise, for rational numbers a, b, c, and d such that the quantity D = (ad - bc)/(2a2) is defined and non-zero, we consider rational distance sets on the conic section axy + bx + cy + d = 0. We show that, if the elliptic curve Y2 = X3 - D2X has infinitely many rational points, then there are infinitely many sets consisting of four rational points on the hyperbola such that their pairwise distances are rational numbers. We also show that any rational distance set of three such points can always be extended to a rational distance set of four such points.
Small Satellite Constellation Separation using Linear Programming based Differential Drag Commands

We study the optimal control of an arbitrarily large constellation of small satellites operating in low Earth orbit. Simulating the lack of on-board propulsion, we limit our actuation to the use of differential drag maneuvers to make in-plane changes to the satellite orbits. We propose an efficient method to separate a cluster of satellites into a desired constellation shape while respecting actuation constraints and maximizing the operational life of the constellation. By posing the problem as a linear program, we determine the optimal drag commands for each satellite on a daily basis with a shrinking-horizon model predictive control approach. We then apply this control strategy to a nonlinear orbital dynamics simulation with a simple, varying atmospheric density model. We demonstrate the ability to separate a cluster of 100+ satellites, starting at the same initial conditions in a circular low Earth orbit, into an equally-spaced, single-plane constellation (with a relative angular separation error tolerance of one-tenth a degree). The constellation separation task can be executed in 71 days, a time frame that is competitive for the state-of-the-practice. We show that there exists a trade-off between minimizing the time used to conduct the separation maneuvers and maximizing the operational life of the constellation. Finally, we show that the control strategy can be used not only to initially acquire but also to maintain the constellation throughout its total lifetime.
Bak-Sneppen backwards

We study the backwards Markov chain for the Bak–Sneppen model of biological evolution and derive its corresponding reversibility equations. We show that, in contrast to the forwards Markov chain, t...
Optimal surviving strategy for drifted Brownian motions with absorption

We study the “Up the River” problem formulated by Aldous (2002), where a unit drift is distributed among a finite collection of Brownian particles on $\mathbb{R}_{+}$, which are annihilated once they reach the origin. Starting $K$ particles at $x=1$, we prove Aldous’ conjecture [Aldous (2002)] that the “push-the-laggard” strategy of distributing the drift asymptotically (as $K\to\infty$) maximizes the total number of surviving particles, with approximately $\frac{4}{\sqrt{\pi}}\sqrt{K}$ surviving particles. We further establish the hydrodynamic limit of the particle density, in terms of a two-phase partial differential equation (PDE) with a moving boundary, by utilizing certain integral identities and coupling techniques.
Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem.

In this paper, we develop a Bayesian evidence maximization framework to solve the sparse non-negative least squares (S-NNLS) problem. We introduce a family of probability densities referred to as the rectified Gaussian scale mixture (R-GSM) to model the sparsity enforcing prior distribution for the solution. The R-GSM prior encompasses a variety of heavy-tailed densities such as the rectified Laplacian and rectified Student's t-distributions with a proper choice of the mixing density. We utilize the hierarchical representation induced by the R-GSM prior and develop an evidence maximization framework based on the expectation-maximization (EM) algorithm. Using the EM based method, we estimate the hyper-parameters and obtain a point estimate for the solution. We refer to the proposed method as rectified sparse Bayesian learning (R-SBL). We provide four R-SBL variants that offer a range of options for computational complexity and the quality of the E-step computation. These methods include the Markov chain Monte Carlo EM, linear minimum mean-square-error estimation, approximate message passing, and a diagonal approximation. Using numerical experiments, we show that the proposed R-SBL method outperforms existing S-NNLS solvers in terms of both signal and support recovery performance, and is also very robust against the structure of the design matrix.
Twisted modules and pseudo-endomorphisms

We exhibit a connection between two constructions of twisted modules for a general vertex operator algebra with respect to inner automorphisms. We also study pseudo-derivations, pseudo-endomorphisms, and twist deformations of ordinary modules by pseudo-endomorphisms.
TASI Lectures on Scattering Amplitudes

These lectures are a brief introduction to scattering amplitudes. We begin with a review of basic kinematical concepts like the spinor helicity formalism, followed by a tutorial on bootstrapping tree-level scattering amplitudes. Afterwards, we discuss on-shell recursion relations and soft theorems, emphasizing their broad applicability to gravity, gauge theory, and effective field theories. Lastly, we report on some of the new field theoretic structures which have emerged from the on-shell picture, focusing primarily on color-kinematics duality.
Bayesian spatial binary classification

Abstract In analyses of spatially-referenced data, researchers often have one of two goals: to quantify relationships between a response variable and covariates while accounting for residual spatial dependence or to predict the value of a response variable at unobserved locations. In this second case, when the response variable is categorical, prediction can be viewed as a classification problem. Many classification methods either ignore response-variable/covariate relationships and rely only on spatially proximate observations for classification, or they ignore spatial dependence and use only the covariates for classification. The Bayesian spatial generalized linear (mixed) model offers a tool to accommodate both spatial and covariate sources of information in classification problems. In this paper, we formally define spatial classification rules based on these models. We also take a close look at two of these models that have been proposed in the literature, namely the probit versions of the spatial generalized linear model (SGLM) and the Bayesian spatial generalized linear mixed model (SGLMM). We describe the implications of the seemingly slight differences between these models for spatial classification and explore the issue of robustness to model misspecification through a simulation study. We also provide an overview of alternatives to the SGLM/SGLMM-based classifiers and illustrate the various methods using satellite-derived land cover data from Southeast Asia.
An efficient Monte Carlo algorithm for determining the minimum energy structures of metallic grain boundaries

Abstract Sampling minimum energy grain boundary (GB) structures in the five-dimensional crystallographic phase space can provide much-needed insight into how GB crystallography affects various interfacial properties. However, the complexity and number of parameters involved often limits the extent of this exploration to a small set of interfaces. In this article, we present a fast Monte Carlo scheme for generating zero-Kelvin, low energy GB structures in the five-dimensional crystallographic phase space. The Monte Carlo trial moves include removal and insertion of atoms in the GB region, which create a diverse set of GB configurations and result in a rapid convergence to the low energy structure. We have validated the robustness of this approach by simulating over 1184 tilt, twist, and mixed character GBs in both fcc (Aluminum and Nickel) and bcc ( α -Iron) metallic systems.
Leveraging Weakly Supervised Data to Improve End-to-end Speech-to-text Translation

End-to-end Speech Translation (ST) models have many potential advantages when compared to the cascade of Automatic Speech Recognition (ASR) and text Machine Translation (MT) models, including lowered inference latency and the avoidance of error compounding. However, the quality of end-to-end ST is often limited by a paucity of training data, since it is difficult to collect large parallel corpora of speech and translated transcript pairs. Previous studies have proposed the use of pre-trained components and multi-task learning in order to benefit from weakly supervised training data, such as speech-to-transcript or text-to-foreign-text pairs. In this paper, we demonstrate that using pre-trained MT or text-to-speech (TTS) synthesis models to convert weakly supervised data into speech-to-translation pairs for ST training can be more effective than multi-task learning. Furthermore, we demonstrate that a high quality end-to-end ST model can be trained using only weakly supervised datasets, and that synthetic data sourced from unlabeled monolingual text or speech can be used to improve performance. Finally, we discuss methods for avoiding overfitting to synthetic speech with a quantitative ablation study.
State-of-the-Art Flash Chips for Dosimetry Applications

In this paper we show that state-of-the-art commercial off-the-shelf Flash memory chip technology (20 nm technology node with multi-level cells) is quite sensitive to ionizing radiation. We find that the fail-bit count in these Flash chips starts to increase monotonically with gamma or X-ray dose at 100 rad(SiO 2 ). Significantly more fail bits are observed in X-ray irradiated devices, most likely due to dose enhancement effects due to high-Z back-end-of-line materials. These results show promise for dosimetry application.
Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls

Large-scale databases of human activity in social media have captured scientific and policy attention, producing a flood of research and discussion. This paper considers methodological and conceptual challenges for this emergent field, with special attention to the validity and representativeness of social media big data analyses. Persistent issues include the over-emphasis of a single platform, Twitter, sampling biases arising from selection by hashtags, and vague and unrepresentative sampling frames. The socio-cultural complexity of user behavior aimed at algorithmic invisibility (such as subtweeting, mock-retweeting, use of “screen captures” for text, etc.) further complicate interpretation of big data social media. Other challenges include accounting for field effects, i.e. broadly consequential events that do not diffuse only through the network under study but affect the whole society. The application of network methods from other fields to the study of human social activity may not always be appropriate. The paper concludes with a call to action on practical steps to improve our analytic capacity in this promising, rapidly-growing field.
Thermo- and Photoacoustic Tomography with Variable Speed and Planar Detectors

We analyze the mathematical model of multiwave tomography with a variable speed with integrating measurements on planes tangent to a sphere surrounding the source. We prove sharp uniqueness and stability estimates with full and partial data and propose a time reversal algorithm which recovers the visible singularities.
Message Passing for Analysis and Resilient Design of Self-Healing Interdependent Cyber-Physical Networks

Coupling cyber and physical systems gives rise to numerous engineering challenges and opportunities. An important challenge is the contagion of failure from one system to another, that can lead to large scale cascading failures. On the other hand, self-healing ability emerges as a valuable opportunity where the overlay cyber network can cure failures in the underlying physical network. To capture both self-healing and contagion, we introduce a factor graph representation of inter-dependent cyber-physical systems in which factor nodes represent various node functionalities and the edges capture the interactions between the nodes. We develop a message passing algorithm to study the dynamics of failure propagation and healing in this representation. Through applying a fixed point analysis to this algorithm, we investigate the network reaction to initial disruptions. Our analysis provides simple yet critical guidelines for choosing network parameters to achieve resiliency against cascading failures.
Spectrum identification using a dynamic Bayesian network model of tandem mass spectra

Shotgun proteomics is a high-throughput technology used to identify unknown proteins in a complex mixture. At the heart of this process is a prediction task, the spectrum identification problem, in which each fragmentation spectrum produced by a shotgun proteomics experiment must be mapped to the peptide (protein subsequence) which generated the spectrum. We propose a new algorithm for spectrum identification, based on dynamic Bayesian networks, which significantly outperforms the de-facto standard tools for this task: SEQUEST and Mascot.
Electroweak vacuum stability in classically conformal \(B-L\) extension of the standard model

We consider the minimal U(1) $$_{B-L}$$ extension of the standard model (SM) with the classically conformal invariance, where an anomaly-free U(1) $$_{B-L}$$ gauge symmetry is introduced along with three generations of right-handed neutrinos and a U(1) $$_{B-L}$$ Higgs field. Because of the classically conformal symmetry, all dimensional parameters are forbidden. The $$B-L$$ gauge symmetry is radiatively broken through the Coleman–Weinberg mechanism, generating the mass for the $$U(1)_{B-L}$$ gauge boson ( $$Z^\prime $$ boson) and the right-handed neutrinos. Through a small negative coupling between the SM Higgs doublet and the $$B-L$$ Higgs field, the negative mass term for the SM Higgs doublet is generated and the electroweak symmetry is broken. In this model context, we investigate the electroweak vacuum instability problem in the SM. It is well known that in the classically conformal U(1) $$_{B-L}$$ extension of the SM, the electroweak vacuum remains unstable in the renormalization group analysis at the one-loop level. In this paper, we extend the analysis to the two-loop level, and perform parameter scans. We identify a parameter region which not only solve the vacuum instability problem, but also satisfy the recent ATLAS and CMS bounds from search for $$Z^\prime $$ boson resonance at the LHC Run-2. Considering self-energy corrections to the SM Higgs doublet through the right-handed neutrinos and the $$Z^\prime $$ boson, we derive the naturalness bound on the model parameters to realize the electroweak scale without fine-tunings.
Statistical Analyses of Hydrophobic Interactions: A Mini-Review

This review focuses on the striking recent progress in solving for hydrophobic interactions between small inert molecules. We discuss several new understandings. First, the inverse temperature phenomenology of hydrophobic interactions, i.e., strengthening of hydrophobic bonds with increasing temperature, is decisively exhibited by hydrophobic interactions between atomic-scale hard sphere solutes in water. Second, inclusion of attractive interactions associated with atomic-size hydrophobic reference cases leads to substantial, nontrivial corrections to reference results for purely repulsive solutes. Hydrophobic bonds are weakened by adding solute dispersion forces to treatment of reference cases. The classic statistical mechanical theory for those corrections is not accurate in this application, but molecular quasi-chemical theory shows promise. Finally, because of the masking roles of excluded volume and attractive interactions, comparisons that do not discriminate the different possibilities face an inte...
Entity-Aware Language Model as an Unsupervised Reranker.

In language modeling, it is difficult to incorporate entity relationships from a knowledge-base. One solution is to use a reranker trained with global features, in which global features are derived from n-best lists. However, training such a reranker requires manually annotated n-best lists, which is expensive to obtain. We propose a method based on the contrastive estimation method that alleviates the need for such data. Experiments in the music domain demonstrate that global features, as well as features extracted from an external knowledge-base, can be incorporated into our reranker. Our final model, a simple ensemble of a language model and reranker, achieves a 0.44% absolute word error rate improvement over an LSTM language model on the blind test data.
Assortative Matching in Africa : Evidence from Rural Mozambique, Cote d'Ivoire, and Malawi

This paper documents novel evidence of positive assortative matching in African marriage markets along cognitive and socio-emotional skills, time and risk preferences, and education, using data from rural Mozambique, Cote d'Ivoire, and Malawi.
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

Caching is envisioned to play a critical role in next-generation content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning (RL) framework is put forth. To handle the large continuous state space, a scalable deep RL approach is pursued. The novel approach relies on a hyper-deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learn-and-adapt to unknown policies of leaf nodes as well as spatio-temporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dot- product self-attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canonical Transformer grows quadratically with sequence length L, making directly modeling long time series infeasible. In order to solve these two issues, we first propose convolutional self-attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism. Then, we propose LogSparse Transformer with only O(L(log L)^2) memory cost, improving forecasting accuracy for time series with fine granularity and strong long-term dependencies under constrained memory budget. Our experiments on both synthetic data and real- world datasets show that it compares favorably to the state-of-the-art.
Human myosin 1e tail but not motor domain replaces fission yeast Myo1 domains to support myosin-I function during endocytosis

In both unicellular and multicellular organisms, long-tailed class I myosins function in clathrin-mediated endocytosis. Myosin 1e (Myo1e) in vertebrates and Myo1 in fission yeast have similar domain organization, yet whether these proteins or their individual protein domains are functionally interchangeable remains unknown. In an effort to assess functional conservation of class I myosins, we tested whether human Myo1e could replace Myo1 in fission yeast Schizosaccharomyces pombe and found that it was unable to substitute for yeast Myo1. To determine if any individual protein domain is responsible for the inability of Myo1e to function in yeast, we created human-yeast myosin-I chimeras. By functionally testing these chimeric myosins in vivo, we concluded that the Myo1e motor domain is unable to function in yeast, even when combined with the yeast Myo1 tail and a full complement of yeast regulatory light chains. Conversely, the Myo1e tail, when attached to the yeast Myo1 motor domain, supports localization to endocytic actin patches and partially rescues the endocytosis defect in myo1Δ cells. Further dissection showed that both the TH1 and TH2-SH3 domains in the human Myo1e tail are required for localization and function of chimeric myosin-I at endocytic sites. Overall, this study provides insights into the role of individual myosin-I domains, expands the utility of fission yeast as a simple model system to study the effects of disease-associated MYO1E mutations, and supports a model of co-evolution between a myosin motor and its actin track.
Pediatric Inpatient Medication Reconciliation Using Emr in a Community Hospital

Introduction According to the Joint Commission, medication reconciliation is the process of comparing a patient’s medication orders to all the medications that the patient was previously taking. Reconciliation is done to avoid medication errors and electronic medical records (EMRs) provide a good medium for the reconciliation process. This study was a quality improvement project aimed at increasing the medication …
Factors influencing thermal solidification of bent-core trimers.

Bent-core trimers are a simple model system for which the competition between crystallization and glass-formation can be tuned by varying a single parameter: the bond angle θ0. Using molecular dynamics simulations, we examine how varying θ0 affects their thermal solidification. By examining trends with θ0, comparing these to the trends in trimers’ jamming phenomenology, and then focusing on the six θ0 that are commensurable with close-packed crystalline order, we obtain three key results: (i) the increase in trimers’ solidification temperature Ts(θ0) as they straighten (as θ0 → 0°) is driven by the same gradual loss of effective configurational freedom that drives athermal trimers’ decreasing ϕJ(θ0) [where ϕJ(θ0) is the packing fraction at jamming]; (ii) θ0 that allow formation of both FCC and HCP order crystallize, while θ0 that only allow formation of HCP order glass-form; and (iii) local cluster-level structure at temperatures slightly above Ts(θ0) is highly predictive of whether trimers will crystallize or glass-form.Bent-core trimers are a simple model system for which the competition between crystallization and glass-formation can be tuned by varying a single parameter: the bond angle θ0. Using molecular dynamics simulations, we examine how varying θ0 affects their thermal solidification. By examining trends with θ0, comparing these to the trends in trimers’ jamming phenomenology, and then focusing on the six θ0 that are commensurable with close-packed crystalline order, we obtain three key results: (i) the increase in trimers’ solidification temperature Ts(θ0) as they straighten (as θ0 → 0°) is driven by the same gradual loss of effective configurational freedom that drives athermal trimers’ decreasing ϕJ(θ0) [where ϕJ(θ0) is the packing fraction at jamming]; (ii) θ0 that allow formation of both FCC and HCP order crystallize, while θ0 that only allow formation of HCP order glass-form; and (iii) local cluster-level structure at temperatures slightly above Ts(θ0) is highly predictive of whether trimers will crystalli...
A linear programming based approach to the Steiner tree problem with a fixed number of terminals

We present a set of integer programs (IPs) for the Steiner tree problem with the property that the best solution obtained by solving all, provides an optimal Steiner tree. Each IP is polynomial in the size of the underlying graph and our main result is that the linear programming (LP) relaxation of each IP is integral so that it can be solved as a linear program. However, the number of IPs grows exponentially with the number of terminals in the Steiner tree. As a consequence, we are able to solve the Steiner tree problem by solving a polynomial number of LPs, when the number of terminals is fixed.
Perturbations to kink-like topological defects in 2D anti de Sitter spacetime

We examine codimension-1 topological defects whose associated worldline is geodesically embedded in AdS2. This discussion extends a previous study of exact analytical solutions to the equations of motion of topological defects in AdSn in a particular limit where the masses of the scalar and gauge field vanish. We study the linear perturbations about the zeroth-order kink-like solution and verify that they are stable. We also discuss general features of the perturbation expansion to all orders.
Relative Hausdorff distance for network analysis

Similarity measures are used extensively in machine learning and data science algorithms. The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced similarity measure for quantifying the closeness of two graphs. In this work we study the effectiveness of RH distance as a tool for detecting anomalies in time-evolving graph sequences. We apply RH to cyber data with given red team events, as well to synthetically generated sequences of graphs with planted attacks. In our experiments, the performance of RH distance is at times comparable, and sometimes superior, to graph edit distance in detecting anomalous phenomena. Our results suggest that in appropriate contexts, RH distance has advantages over more computationally intensive similarity measures.
Asymmetric risk and fuel neutrality in electricity capacity markets

In many liberalized electricity markets, power generators can receive payments for maintaining capacity through capacity markets. These payments help stabilize generator revenues, making investment in capacity more attractive for risk-averse investors when other outlets for risk trading are limited. Here we develop a heuristic algorithm to solve large-scale stochastic equilibrium models describing a competitive market with incomplete risk trading. Introduction of a capacity mechanism has an asymmetric effect on the risk profile of different generation technologies, tilting the resource mix towards those with lower fixed costs and higher operating costs. One implication of this result is that current market structures may be ill-suited to financing low-carbon resources, the most scalable of which have high fixed costs and near-zero operating costs. Development of new risk trading mechanisms to replace or complement current capacity obligations could lead to more efficient outcomes. Capacity markets are designed to ensure availability of sufficient production capacity to meet peak demand. Mays et al. show that these markets favour technologies such as coal and gas by mitigating their capital investment risks while not compensating for operational risks associated with solar and wind energy.
A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions

Instructional videos get high-traffic on video sharing platforms, and prior work suggests that providing time-stamped, subtask annotations (e.g., “heat the oil in the pan”) improves user experiences. However, current automatic annotation methods based on visual features alone perform only slightly better than constant prediction. Taking cues from prior work, we show that we can improve performance significantly by considering automatic speech recognition (ASR) tokens as input. Furthermore, jointly modeling ASR tokens and visual features results in higher performance compared to training individually on either modality. We find that unstated background information is better explained by visual features, whereas fine-grained distinctions (e.g., “add oil” vs. “add olive oil”) are disambiguated more easily via ASR tokens.
Monetary policy and systemic risk-taking in the euro area banking sector

Abstract Available empirical evidence on the significance of the (micro) risk-taking channel of monetary policy is not enough to indicate a threat to financial stability. Evidence of risk-taking with systemic risk implications is necessary. Statistical measures that capture systemic risk in all its forms within a structural factor-augmented vector autoregressive model suggest that conventional and unconventional monetary policies have resulted in systemic risk-taking in the euro area banking sector. Systemic risk has taken the form of an increase in the banking sector’s vulnerability via contagion and interconnectedness. Banks’ balance sheets, however, do not account for the full transmission from (micro) risk taking to systemic risk-taking. The main policy implication is that a persistently accommodative monetary policy may drive a monetary authority with a price stability mandate to consider a possible trade-off with financial stability. At a minimum, coordination between monetary and macro-prudential policies requires serious consideration.
Defining Multiple Configurations of Rubrene on a Ag(100) Surface with 5 Å Spatial Resolution via Ultrahigh Vacuum Tip-Enhanced Raman Spectroscopy

Flexible polycyclic aromatic hydrocarbons (PAHs) have experienced a surge of research with the discovery of nanographenes that can be fabricated on surfaces. The flexibility in σ bonds within aroma...
A Dynamical Study of Extraplanar Diffuse Ionized Gas in NGC 5775

The structure and kinematics of gaseous, disk-halo interfaces are imprinted with the processes that transfer mass, metals, and energy between galactic disks and their environments. We study the extraplanar diffuse ionized gas (eDIG) layer in the interacting, star-forming galaxy NGC 5775 to better understand the consequences of star-formation feedback on the dynamical state of the thick-disk interstellar medium (ISM). Combining emission-line spectroscopy from the Robert Stobie Spectrograph on the Southern African Large Telescope with radio continuum observations from Continuum Halos in Nearby Galaxies - an EVLA Survey, we ask whether thermal, turbulent, magnetic field, and cosmic-ray pressure gradients can stably support the eDIG layer in dynamical equilibrium. This model fails to reproduce the observed exponential electron scale heights of the eDIG thick disk and halo on the northeast ($h_{z,e} = 0.6, 7.5$ kpc) and southwest ($h_{z,e} = 0.8, 3.6$ kpc) sides of the galaxy at $R < 11$ kpc. We report the first definitive detection of an increasing eDIG velocity dispersion as a function of height above the disk. Blueshifted gas along the minor axis at large distances from the midplane hints at a disk-halo circulation and/or ram pressure effects caused by the ongoing interaction with NGC 5774. This work motivates further integral field unit and/or Fabry-Perot spectroscopy of galaxies with a range of star-formation rates to develop a spatially-resolved understanding of the role of star-formation feedback in shaping the kinematics of the disk-halo interface.
Necklaces count polynomial parametric osculants

Abstract We consider the problem of geometrically locally approximating a general complex analytic curve in the plane at a point by the image of a polynomial parametrization t ↦ ( x 1 ( t ) , x 2 ( t ) ) of bidegree ( d 1 , d 2 ) . We show the number of such approximating curves is the number of primitive necklaces on d 1 white beads and d 2 black beads. We show that this number is odd when d 1 = d 2 is squarefree and use this to give a partial solution to a conjecture by Rababah. Our results naturally extend to a generalization regarding hypersurfaces in higher dimensions. There, the number of parametrized curves of multidegree ( d 1 , … , d n ) which optimally osculate a given hypersurface are counted by the number of primitive necklaces with d i beads of color i. The proofs of these results give rise to a numerical homotopy algorithm for computing all multidegree ( d 1 , … , d n ) osculants to a general hypersurface at a point.
Systematic beyond-DFT study of binary transition metal oxides

Various methods going beyond density functional theory (DFT), such as DFT+U, hybrid functionals, meta-GGAs, GW, and DFT-embedded dynamical mean field theory (eDMFT), have been developed to describe the electronic structure of correlated materials, but it is unclear how accurate these methods can be expected to be when applied to a given strongly correlated solid. It is thus of pressing interest to compare their accuracy as they apply to different categories of materials. Here we introduce a novel paradigm in which a chosen set of beyond-DFT methods is systematically and uniformly tested on a chosen class of materials. For a first application, we choose the target materials to be the binary transition metal oxides FeO, CoO, MnO, and NiO in their antiferromagnetic phase and present a head-to-head comparison of spectral properties as computed using the various methods. We also compare with available experimental angle-resolved photoemission spectroscopy (ARPES), inverse-photoemission spectroscopy, and with optical absorption. For the class of compounds studied here, we find that both B3LYP and eDMFT reproduce the experiments quite well, with eDMFT doing best, in particular when comparing with the ARPES data.
mmWave Amplify-and-Forward MIMO Relay Networks With Hybrid Precoding/Combining Design

In this paper, we consider the amplify-and-forward relay networks in mmWave systems and propose a hybrid precoder/combiner design approach. The phase-only RF precoding/combining matrices are first designed to support multi-stream transmission, where we compensate the phase for the eigenmodes of the channel. Then, the baseband precoders/combiners are performed to achieve the maximum mutual information. Based on the data processing inequality for the mutual information, we first jointly design the baseband source and relay nodes to maximize the mutual information before the destination baseband receiver. The proposed low-complexity iterative algorithm for the source and relay nodes is based on the equivalence between the mutual information maximization and the weighted MMSE. After we obtain the optimal precoder and combiner for the source and relay nodes, we implement the MMSE-SIC filter at the baseband receiver to keep the mutual information unchanged, thus obtaining the optimal mutual information for the whole relay system. Simulation results show that our algorithm achieves better performance with lower complexity compared with other algorithms in the literature. In addition, we also propose a robust joint transceiver design for imperfect channel state information.