cssi/SciSciGPT-SciSciNet huggingface dataset:
Endosymbiotic Bacteria Are Prevalent and Diverse in Agricultural Spiders

Maternally inherited bacterial endosymbionts are common in arthropods, but their distribution and prevalence are poorly characterized in many host taxa. Initial surveys have suggested that vertically transmitted symbionts may be particularly common in spiders (Araneae). Here, we used diagnostic PCR and high-throughput sequencing to evaluate symbiont infection in 267 individual spiders representing 14 species (3 families) of agricultural spiders. We found 27 operational taxonomic units (OTUs) that are likely endosymbiotic, including multiple strains of Wolbachia, Rickettsia, and Cardinium, which are all vertically transmitted and frequently associated with reproductive manipulation of arthropod hosts. Additional strains included Rickettsiella, Spiroplasma, Rhabdochlamydia, and a novel Rickettsiales, all of which could range from pathogenic to mutualistic in their effects upon their hosts. Seventy percent of spider species had individuals that tested positive for one or more endosymbiotic OTUs, and specimens frequently contained multiple symbiotic strain types. The most symbiont-rich species, Idionella rugosa, had eight endosymbiotic OTUs, with as many as five present in the same specimen. Individual specimens within infected spider species had a variety of symbiotypes, differing from one another in the presence or absence of symbiotic strains. Our sample included both starved and unstarved specimens, and dominant bacterial OTUs were consistent per host species, regardless of feeding status. We conclude that spiders contain a remarkably diverse symbiotic microbiota. Spiders would be an informative group for investigating endosymbiont population dynamics in time and space, and unstarved specimens collected for other purposes (e.g., food web studies) could be used, with caution, for such investigations.
Spectra of Overlapping Wishart Matrices and the Gaussian Free Field

Consider a doubly-infinite array of i.i.d. centered variables with moment conditions, from which one can extract a finite number of rectangular, overlapping submatrices, and form the corresponding Wishart matrices. We show that under basic smoothness assumptions, centered linear eigenstatistics of such matrices converge jointly to a Gaussian vector with an interesting covariance structure. This structure, which is similar to those appearing in [A. Borodin, Clt for spectra of submatrices of wigner random matrices, Mosc. Math. J. 14(1) (2014) 29–38; A. Borodin and V. Gorin, General beta Jacobi corners process and the Gaussian free field, preprint (2013), arXiv:1305.3627; T. Johnson and S. Pal, Cycles and eigenvalues of sequentially growing random regular graphs, Ann. Probab. 42(4) (2014) 1396–1437], can be described in terms of the height function, and leads to a connection with the Gaussian Free Field on the upper half-plane. Finally, we generalize our results from univariate polynomials to a special class...
A Novel Method for Detecting Extended Sources with VERITAS

The most commonly used techniques for estimating the background contribution in IACT data analysis are the ring background model and the reflected region methods. However, these two techniques are poorly suited for analyses of sources with extensions comparable to the detector’s field of view (greater than ∼1◦). Nearby pulsar wind nebulae, supernova remnants interacting with molecular clouds, and dark matter signatures from galaxy clusters are just a few potentially highly extended source classes. A three dimensional maximum likelihood analysis is in development that seeks to resolve this issue for data from the VERITAS telescopes. The technique incorporates relevant instrument response functions to model the distribution of detected gammaray like events in two spatial dimensions. Additionally, we incorporate a third dimension based on a gamma-hadron discriminating parameter. The inclusion of this third dimension significantly improves the sensitivity of the technique to highly extended sources. We present this promising technique as well as systematic studies demonstrating its potential for revealing sources of large extent in VERITAS data.
Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments

ABSTRACTObserving system simulation experiments (OSSEs) have been widely used as a rigorous and cost-effective way to guide development of new observing systems, and to evaluate the performance of ...
Rates of estimation for determinantal point processes.

Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been proposed, but surprisingly the basic properties of the maximum likelihood estimator (MLE) have received little attention. In this paper, we study the local geometry of the expected log-likelihood function to prove several rates of convergence for the MLE. We also give a complete characterization of the case where the MLE converges at a parametric rate. Even in the latter case, we also exhibit a potential curse of dimensionality where the asymptotic variance of the MLE is exponentially large in the dimension of the problem.
Human-AI Learning Performance in Multi-Armed Bandits

People frequently face challenging decision-making problems in which outcomes are uncertain or unknown. Artificial intelligence (AI) algorithms exist that can outperform humans at learning such tasks. Thus, there is an opportunity for AI agents to assist people in learning these tasks more effectively. In this work, we use a multi-armed bandit as a controlled setting in which to explore this direction. We pair humans with a selection of agents and observe how well each human-agent team performs. We find that team performance can beat both human and agent performance in isolation. Interestingly, we also find that an agent's performance in isolation does not necessarily correlate with the human-agent team's performance. A drop in agent performance can lead to a disproportionately large drop in team performance, or in some settings can even improve team performance. Pairing a human with an agent that performs slightly better than them can make them perform much better, while pairing them with an agent that performs the same can make them them perform much worse. Further, our results suggest that people have different exploration strategies and might perform better with agents that match their strategy. Overall, optimizing human-agent team performance requires going beyond optimizing agent performance, to understanding how the agent's suggestions will influence human decision-making.
Decentralized optimal control for connected automated vehicles at intersections including left and right turns

In prior work, we addressed the problem of optimally controlling on line connected and automated vehicles crossing two adjacent intersections in an urban area to minimize fuel consumption while achieving maximal throughput without any explicit traffic signaling and without considering left and right turns. In this paper, we extend the solution of this problem to account for left and right turns under hard safety constraints. Furthermore, we formulate and solve another optimization problem to minimize a measure of passenger discomfort while the vehicle turns at the intersection and we investigate the associated tradeoff between minimizing fuel consumption and passenger discomfort.
An introduction to artificial prediction markets for classification

Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learning of conditional probability estimators. The artificial prediction market is a novel method for fusing the prediction information of features or trained classifiers, where the fusion result is the contract price on the possible outcomes. The market can be trained online by updating the participants' budgets using training examples. Inspired by the real prediction markets, the equations that govern the market are derived from simple and reasonable assumptions. Efficient numerical algorithms are presented for solving these equations. The obtained artificial prediction market is shown to be a maximum likelihood estimator. It generalizes linear aggregation, existent in boosting and random forest, as well as logistic regression and some kernel methods. Furthermore, the market mechanism allows the aggregation of specialized classifiers that participate only on specific instances. Experimental comparisons show that the artificial prediction markets often outperform random forest and implicit online learning on synthetic data and real UCI data sets. Moreover, an extensive evaluation for pelvic and abdominal lymph node detection in CT data shows that the prediction market improves adaboost's detection rate from 79.6% to 81.2% at 3 false positives/volume.
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

In several recently proposed stochastic optimization methods (e.g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-moment estimators requires memory equal to the number of parameters. For the case of neural network weight matrices, we propose maintaining only the per-row and per-column sums of these moving averages, and estimating the per-parameter second moments based on these sums. We demonstrate empirically that this method produces similar results to the baseline. Secondly, we show that adaptive methods can produce larger-than-desired updates when the decay rate of the second moment accumulator is too slow. We propose update clipping and a gradually increasing decay rate scheme as remedies. Combining these methods and dropping momentum, we achieve comparable results to the published Adam regime in training the Transformer model on the WMT 2014 English-German machine translation task, while using very little auxiliary storage in the optimizer. Finally, we propose scaling the parameter updates based on the scale of the parameters themselves.
e-SAFE: Secure, Efficient and Forensics-Enabled Access to Implantable Medical Devices

To facilitate monitoring and management, modern Implantable Medical Devices (IMDs) are often equipped with wireless capabilities, which raise the risk of malicious access to IMDs. Although schemes are proposed to secure the IMD access, some issues are still open. First, pre-sharing a long-term key between a patient’s IMD and a doctor’s programmer is vulnerable since once the doctor’s programmer is compromised, all of her patients suffer; establishing a temporary key by leveraging proximity gets rid of pre-shared keys, but as the approach lacks real authentication, it can be exploited by nearby adversaries or through man-in-the-middle attacks. Second, while prolonging the lifetime of IMDs is one of the most important design goals, few schemes explore to lower the communication and computation overhead all at once. Finally, how to safely record the commands issued by doctors for the purpose of forensics, which can be the last measure to protect the patients’ rights, is commonly omitted in the existing literature. Motivated by these important yet open problems, we propose an innovative scheme e-SAFE, which significantly improves security and safety, reduces the communication overhead and enables IMD-access forensics. We present a novel lightweight compressive sensing based encryption algorithm to encrypt and compress the IMD data simultaneously, reducing the data transmission overhead by over 50% while ensuring high data confidentiality and usability. Furthermore, we provide a suite of protocols regarding device pairing, dual-factor authentication, and accountability-enabled access. The security analysis and performance evaluation show the validity and efficiency of the proposed scheme.
Newton polytopes and symmetric Grothendieck polynomials

Abstract Symmetric Grothendieck polynomials are inhomogeneous versions of Schur polynomials that arise in combinatorial K-theory. A polynomial has saturated Newton polytope (SNP) if every lattice point in the polytope is an exponent vector. We show that the Newton polytopes of these Grothendieck polynomials and their homogeneous components have SNP. Moreover, the Newton polytope of each homogeneous component is a permutahedron. This addresses recent conjectures of C. Monical–N. Tokcan–A. Yong and of A. Fink–K. Meszaros–A. St. Dizier in this special case.
Real-Time Brain Machine Interaction via Social Robot Gesture Control

Brain-Machine Interaction (BMI) system motivates interesting and promising results in forward/feedback control consistent with human intention. It holds great promise for advancements in patient care and applications to neurorehabilitation. Here, we propose a novel neurofeedback-based BCI robotic platform using a personalized social robot in order to assist patients having cognitive deficits through bilateral rehabilitation and mental training. For initial testing of the platform, electroencephalography (EEG) brainwaves of a human user were collected in real time during tasks of imaginary movements. First, the brainwaves associated with imagined body kinematics parameters were decoded to control a cursor on a computer screen in training protocol. Then, the experienced subject was able to interact with a social robot via our real-time BMI robotic platform. Corresponding to subject's imagery performance, he/she received specific gesture movements and eye color changes as neural-based feedback from the robot. This hands-free neurofeedback interaction not only can be used for mind control of a social robot's movements, but also sets the stage for application to enhancing and recovering mental abilities such as attention via training in humans by providing real-time neurofeedback from a social robot.
A Unified Stochastic Hybrid System Approach to Aggregate Modeling of Responsive Loads

Aggregate load modeling is of fundamental importance for systematic analysis and design of various demand response strategies. Instead of keeping track of the trajectories of individual loads, the aggregate modeling problem focuses on characterizing the density evolution of the load population. Most existing models are only applicable to thermostatically controlled loads (TCLs) with first-order linear dynamics. This paper develops a unified aggregate modeling approach that can be used for general TCLs as well as deferrable loads. We propose a deterministic hybrid system model to describe individual load dynamics under demand response rules, and develop a general stochastic hybrid system (SHS) model to capture the population dynamics. We also derive a set of partial differential equations (PDEs) that govern the probability density evolution of the SHS. Our results cannot be obtained using the existing SHS tools in the literature as the proposed SHS model involves both random and deterministic switchings with general switching surfaces in multidimensional domains. The derived PDE model includes many existing aggregate load modeling results as special cases and can be used in many other realistic modeling scenarios that have not been studied in the literature.
Structured perfect Bayesian equilibrium in infinite horizon dynamic games with asymmetric information

In dynamic games with asymmetric information structure, the widely used concept of equilibrium is perfect Bayesian equilibrium (PBE). This is expressed as a strategy and belief pair that simultaneously satisfy sequential rationality and belief consistency. Unlike symmetric information dynamic games, where subgame perfect equilibrium (SPE) is the natural equilibrium concept, to date there does not exist a universal algorithm that decouples the interdependence of strategies and beliefs over time in calculating PBE. In this paper we find a subset of PBE for an infinite horizon discounted reward asymmetric information dynamic game. We refer to it as Structured PBE or SPBE; in SPBE, any agents' strategy depends on the public history only through a common public belief and on private history only through the respective agents' latest private information (his private type). The public belief acts as a summary of all the relevant past information and it's dimension does not increase with time. The motivation for this comes the common information approach proposed in Nayyar et al. (2013) for solving decentralized team (non-strategic) resource allocation problems with asymmetric information. We calculate SPBE by solving a single-shot fixed-point equation and a corresponding forward recursive algorithm. We demonstrate our methodology by means of a public goods example.
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization

Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis. Such algorithms have been widely studied for structured sparse models where the sparsity information is very specific, e.g., convex sparsity-inducing norms or $\ell^0$-norm. However, these norms cannot be directly applied to the problem of complex (non-convex) graph-structured sparsity models, which have important application in disease outbreak and social networks, etc. In this paper, we propose a stochastic gradient-based method for solving graph-structured sparsity constraint problems, not restricted to the least square loss. We prove that our algorithm enjoys a linear convergence up to a constant error, which is competitive with the counterparts in the batch learning setting. We conduct extensive experiments to show the efficiency and effectiveness of the proposed algorithms.
Flexible interpretations: a computational model for dynamic uncertainty assessment

The investigations reported in this paper center on the process of dynamic uncertainty assessment during interpretation tasks in real domain. In particular, we are interested here in the nature of the control structure of computer programs that can support multiple interpretation and smooth transitions between them, in real time. Each step of the processing involves the interpretation of one input item and the appropriate re-establishment of the system's confidence of the correctness of its interpretation(s).
Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data

Receiver operating characteristic curves are widely used as a measure of accuracy of diagnostic tests and can be summarised using the area under the receiver operating characteristic curve (AUC). Often, it is useful to construct a confidence interval for the AUC; however, because there are a number of different proposed methods to measure variance of the AUC, there are thus many different resulting methods for constructing these intervals. In this article, we compare different methods of constructing Wald-type confidence interval in the presence of missing data where the missingness mechanism is ignorable. We find that constructing confidence intervals using multiple imputation based on logistic regression gives the most robust coverage probability and the choice of confidence interval method is less important. However, when missingness rate is less severe (e.g. less than 70%), we recommend using Newcombe's Wald method for constructing confidence intervals along with multiple imputation using predictive mean matching.
Exact triangle for fibered Dehn twists

We use quilted Floer theory to generalize Seidel’s long exact sequence in symplectic Floer theory to fibered Dehn twists. We then apply the sequence to construct versions of the Floer and Khovanov–Rozansky exact triangles in Lagrangian Floer theory of moduli spaces of bundles.
A duality principle for groups

The duality principle for Gabor frames states that a Gabor sequence obtained by a time–frequency lattice is a frame for L2(Rd) if and only if the associated adjoint Gabor sequence is a Riesz sequence. We prove that this duality principle extends to any dual pairs of projective unitary representations of countable groups. We examine the existence problem of dual pairs and establish some connection with classification problems for II1 factors. While in general such a pair may not exist for some groups, we show that such a dual pair always exists for every subrepresentation of the left regular unitary representation when G is an abelian infinite countable group or an amenable ICC group. For free groups with finitely many generators, the existence problem of such a dual pair is equivalent to the well-known problem about the classification of free group von Neumann algebras.
Instrumental variables as bias amplifiers with general outcome and confounding

Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all pretreatment covariates. However, when there is unmeasured confounding between the treatment and outcome, estimators adjusting for some pretreatment covariate might have greater bias than estimators without adjusting for this covariate. This kind of covariate is called a bias amplifier, and includes instrumental variables that are independent of the confounder, and affect the outcome only through the treatment. Previously, theoretical results for this phenomenon have been established only for linear models. We fill in this gap in the literature by providing a general theory, showing that this phenomenon happens under a wide class of models satisfying certain monotonicity assumptions. We further show that when the treatment follows an additive or multiplicative model conditional on the instrumental variable and the confounder, these monotonicity assumptions can be interpreted as the signs of the arrows of the causal diagrams.
Online Object Tracking, Learning and Parsing with And-Or Graphs

This paper presents a method, called AOGTracker , for simultaneously tracking, learning and parsing (TLP) of unknown objects in video sequences with a hierarchical and compositional And-Or graph (AOG) representation. The TLP method is formulated in the Bayesian framework with a spatial and a temporal dynamic programming (DP) algorithms inferring object bounding boxes on-the-fly. During online learning, the AOG is discriminatively learned using latent SVM [1] to account for appearance (e.g., lighting and partial occlusion) and structural (e.g., different poses and viewpoints) variations of a tracked object, as well as distractors (e.g., similar objects) in background. Three key issues in online inference and learning are addressed: (i) maintaining purity of positive and negative examples collected online, (ii) controling model complexity in latent structure learning, and (iii) identifying critical moments to re-learn the structure of AOG based on its intrackability. The intrackability measures uncertainty of an AOG based on its score maps in a frame. In experiments, our AOGTracker is tested on two popular tracking benchmarks with the same parameter setting: the TB-100/50/CVPR2013 benchmarks [2] , [3] , and the VOT benchmarks [4] —VOT 2013, 2014, 2015 and TIR2015 (thermal imagery tracking). In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network [5] , [6] . In the latter, our AOGTracker outperforms all other trackers in VOT2013 and is comparable to the state-of-the-art methods in VOT2014, 2015 and TIR2015.
Falconer distance problem, additive energy and Cartesian products

A celebrated result due to Wolff says if E is a compact subset of R, then the Lebesgue measure of the distance set ∆(E) = {|x − y| : x, y ∈ E} is positive if the Hausdorff dimension of E is greater than 4 3 . In this paper we improve the 4 3 barrier by a small exponent for Cartesian products. In higher dimensions, also in the context of Cartesian products, we reduce Erdogan’s d 2 + 1 3 exponent to d 2 2d−1 . The proof uses a combination of Fourier analysis and additive comibinatorics.
Integrated facility location and production scheduling in multi-generation energy systems

Abstract In this paper, we investigate the energy system design problems with the multi-generation technologies, i.e., simultaneous generation of multiple types of energy. Our results illustrate the economic value of multi-generation technologies to reduce spatio-temporal demand uncertainty by risk pooling both within and across different facilities.
Three new results on continuation criteria for the 3D relativistic Vlasov-Maxwell system

Abstract We consider continuation criteria for the three-dimensional relativistic Vlasov–Maxwell system. When the particle density, f ( t , x , p ) , is compactly supported at t = 0 , we prove ‖ p 0 18 5 r − 1 + β f ‖ L t ∞ L x r L p 1 ≲ 1 , where 1 ≤ r ≤ 2 and β > 0 is arbitrarily small, is a continuation criteria. Our continuation criteria is an improvement in the 1 ≤ r ≤ 2 range to the previously best known criteria ‖ p 0 4 r − 1 + β f ‖ L t ∞ L x r L 1 p ≲ 1 due to Kunze [7] . We also consider continuation criteria when f ( 0 , x , p ) has noncompact support. In this regime, Luk–Strain [9] proved that ‖ p 0 θ f ‖ L x 1 L p 1 ≲ 1 is a continuation criteria for θ > 5 . We improve this result to θ > 3 . Finally, we build on another result by Luk–Strain [8] . The authors proved boundedness of momentum support on a fixed two-dimensional plane is a sufficient continuation criteria. We prove the same result even if the plane varies continuously in time.
On spreading sequences and asymptotic structures

In the first part of the paper we study the structure of Banach spaces with a conditional spreading basis. The geometry of such spaces exhibit a striking resemblance to the geometry of James' space. Further, we show that the averaging projections onto subspaces spanned by constant coefficient blocks with no gaps between supports are bounded. As a consequence, every Banach space with a spreading basis contains a complemented subspace with an unconditional basis. This gives an affirmative answer to a question of H. Rosenthal. The second part contains two results on Banach spaces $X$ whose asymptotic structures are closely related to $c_0$ and do not contain a copy of $\ell_1$: i) Suppose $X$ has a normalized weakly null basis $(x_i)$ and every spreading model $(e_i)$ of a normalized weakly null block basis satisfies $\|e_1-e_2\|=1$. Then some subsequence of $(x_i)$ is equivalent to the unit vector basis of $c_0$. This generalizes a similar theorem of Odell and Schlumprecht, and yields a new proof of the Elton-Odell theorem on the existence of infinite $(1+\varepsilon)$-separated sequences in the unit sphere of an arbitrary infinite dimensional Banach space. ii) Suppose that all asymptotic models of $X$ generated by weakly null arrays are equivalent to the unit vector basis of $c_0$. Then $X^*$ is separable and $X$ is asymptotic-$c_0$ with respect to a shrinking basis $(y_i)$ of $Y\supseteq X$.
Making a circulant 2-qubit entangling gate

We present a way to physically realize a circulant 2-qubit entangling gate in the Kauffman–Jones version of SU(2) Chern–Simons theory at level 4. Our approach uses qubit and qutrit ancillas, braids, fusions and interferometric measurements. Our qubit is formed by four anyons of topological charges 1221. Among other 2-qubit entangling gates which we generate in the present work, we produce in particular the circulant gate CEG=14I+i34J−34J2+i34J3, where J denotes the permutation matrix associated with the cycle (1432) and I denotes the identity matrix.
What’s Going On in Neural Constituency Parsers? An Analysis

A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity. The goal of this work is to analyze the extent to which information provided directly by the model structure in classical systems is still being captured by neural methods. To this end, we propose a high-performance neural model (92.08 F1 on PTB) that is representative of recent work and perform a series of investigative experiments. We find that our model implicitly learns to encode much of the same information that was explicitly provided by grammars and lexicons in the past, indicating that this scaffolding can largely be subsumed by powerful general-purpose neural machinery.
Inferring the Composition of Disintegrating Planet Interiors from Dust Tails with Future James Webb Space Telescope Observations

Disintegrating planets allow for the unique opportunity to study the composition of the interiors of small, hot, rocky exoplanets because the interior is evaporating and that material is condensing into dust, which is being blown away and then transiting the star. Their transit signal is dominated by dusty effluents forming a comet-like tail trailing the host planet (or leading it, in the case of K2-22b), making these good candidates for transmission spectroscopy. To assess the ability of such observations to diagnose the dust composition, we simulate the transmission spectra from 5 to 14 μm for the planet tail assuming an optically thin dust cloud comprising a single dust species with a constant column density scaled to yield a chosen visible transit depth. We find that silicate resonant features near 10 μm can produce transit depths that are at least as large as those in the visible. For the average transit depth of 0.55% in the Kepler band for K2-22b, the features in the transmission spectra can be as large as 1%, which is detectable with the James Webb Space Telescope (JWST) MIRI low-resolution spectrograph in a single transit. The detectability of compositional features is easier with an average grain size of 1 μm despite features being more prominent with smaller grain sizes. We find most features are still detectable for transit depths of ~0.3% in the visible range. If more disintegrating planets are found with future missions such as the space telescope TESS, follow-up observations with JWST can explore the range of planetary compositions.
HLA class I binding prediction via convolutional neural networks.

Motivation Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and non-self cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases. Results We apply machine learning techniques from the natural language processing (NLP) domain to address the task of MHC-peptide binding prediction. More specifically, we introduce a new distributed representation of amino acids, name HLA-Vec, that can be used for a variety of downstream proteomic machine learning tasks. We then propose a deep convolutional neural network architecture, name HLA-CNN, for the task of HLA class I-peptide binding prediction. Experimental results show combining the new distributed representation with our HLA-CNN architecture achieves state-of-the-art results in the majority of the latest two Immune Epitope Database (IEDB) weekly automated benchmark datasets. We further apply our model to predict binding on the human genome and identify 15 genes with potential for self binding. Availability and implementation Codes to generate the HLA-Vec and HLA-CNN are publicly available at: https://github.com/uci-cbcl/HLA-bind . Contact xhx@ics.uci.edu. Supplementary information Supplementary data are available at Bioinformatics online.
Regression Adjustments for Estimating the Global Treatment Effect in Experiments with Interference

Standard estimators of the global average treatment effect can be biased in the presence of interference. This paper proposes regression adjustment estimators for removing bias due to interference in Bernoulli randomized experiments. We use a fitted model to predict the counterfactual outcomes of global control and global treatment. Our work differs from standard regression adjustments in that the adjustment variables are constructed from functions of the treatment assignment vector, and that we allow the researcher to use a collection of any functions correlated with the response, turning the problem of detecting interference into a feature engineering problem. We characterize the distribution of the proposed estimator in a linear model setting and connect the results to the standard theory of regression adjustments under SUTVA. We then propose an estimator that allows for flexible machine learning estimators to be used for fitting a nonlinear interference functional form. We propose conducting statistical inference via bootstrap and resampling methods, which allow us to sidestep the complicated dependences implied by interference and instead rely on empirical covariance structures. Such variance estimation relies on an exogeneity assumption akin to the standard unconfoundedness assumption invoked in observational studies. In simulation experiments, our methods are better at debiasing estimates than existing inverse propensity weighted estimators based on neighborhood exposure modeling. We use our method to reanalyze an experiment concerning weather insurance adoption conducted on a collection of villages in rural China.
Constructive Regularization of the Random Matrix Norm

We study the structure of $$n \times n$$ random matrices with centered i.i.d. entries having only two finite moments. In the recent joint work with R. Vershynin, we have shown that the operator norm of such matrix A can be reduced to the optimal order $$O(\sqrt{n})$$ with high probability by zeroing out a small submatrix of A, but did not describe the structure of this “bad” submatrix nor provide a constructive way to find it. In the current paper, we give a very simple description of a small “bad” subset of entries. We show that it is enough to zero out a small fraction of the rows and columns of A with largest $$L_2$$ norms to bring the operator norm of A to the almost optimal order $$O(\sqrt{n \log \log n})$$ , under additional assumption that the matrix entries are symmetrically distributed. As a corollary, we also obtain a constructive procedure to find a small submatrix of A that one can zero out to achieve the same norm regularization. The main component of the proof is the development of techniques extending constructive regularization approaches known for the Bernoulli matrices (from the works of Feige and Ofek, and Le, Levina and Vershynin) to the considerably broader class of heavy-tailed random matrices.
A parametrix for the fundamental solution of the Klein–Gordon equation on asymptotically de Sitter spaces

Abstract In this paper we construct a parametrix for the forward fundamental solution of the wave and Klein–Gordon equations on asymptotically de Sitter spaces without caustics. We use this parametrix to obtain asymptotic expansions for solutions of ( □ − λ ) u = f and to obtain a uniform L p estimate for a family of bump functions traveling to infinity.
There is no (75,32,10,16) strongly regular graph

Abstract We show that there is no ( 75 , 32 , 10 , 16 ) strongly regular graph. The result is obtained by a mix of algebraic and computational approaches. The main idea is to build large enough induced structure and apply the star complement technique. Our result implies that there is no partial geometry pg ( 4 , 7 , 2 ) and no regular two-graph with 76 vertices, from which it follows that there also exists no strongly regular graph with parameters ( 76 , 35 , 18 , 14 ) . In order to solve this classification problem we also develop an efficient algorithm for the problem of finding a maximal clique in a graph.
Exploring architectures, data and units for streaming end-to-end speech recognition with RNN-transducer

We investigate training end-to-end speech recognition models with the recurrent neural network transducer (RNN-T): a streaming, all-neural, sequence-to-sequence architecture which jointly learns acoustic and language model components from transcribed acoustic data. We explore various model architectures and demonstrate how the model can be improved further if additional text or pronunciation data are available. The model consists of an ‘encoder’, which is initialized from a connectionist temporal classification-based (CTC) acoustic model, and a ‘decoder’ which is partially initialized from a recurrent neural network language model trained on text data alone. The entire neural network is trained with the RNN-T loss and directly outputs the recognized transcript as a sequence of graphemes, thus performing end-to-end speech recognition. We find that performance can be improved further through the use of sub-word units ('wordpieces') which capture longer context and significantly reduce substitution errors. The best RNN-T system, a twelve-layer LSTM encoder with a two-layer LSTM decoder trained with 30,000 wordpieces as output targets achieves a word error rate of 8.5% on voice-search and 5.2% on voice-dictation tasks and is comparable to a state-of-the-art baseline at 8.3% on voice-search and 5.4% voice-dictation.
Alexandrov spaces with large volume growth

Abstract Let ( X , d ) be an n-dimensional Alexandrov space whose Hausdorff measure H n satisfies a condition giving the metric measure space ( X , d , H n ) a notion of having nonnegative Ricci curvature. We examine the influence of large volume growth on these spaces and generalize some classical arguments from Riemannian geometry showing that when the volume growth is sufficiently large, then ( X , d , H n ) has finite topological type.
Scalable Factorized Hierarchical Variational Autoencoder Training

Deep generative models have achieved great success in unsupervised learning with the ability to capture complex nonlinear relationships between latent generating factors and observations. Among them, a factorized hierarchical variational autoencoder (FHVAE) is a variational inference-based model that formulates a hierarchical generative process for sequential data. Specifically, an FHVAE model can learn disentangled and interpretable representations, which have been proven useful for numerous speech applications, such as speaker verification, robust speech recognition, and voice conversion. However, as we will elaborate in this paper, the training algorithm proposed in the original paper is not scalable to datasets of thousands of hours, which makes this model less applicable on a larger scale. After identifying limitations in terms of runtime, memory, and hyperparameter optimization, we propose a hierarchical sampling training algorithm to address all three issues. Our proposed method is evaluated comprehensively on a wide variety of datasets, ranging from 3 to 1,000 hours and involving different types of generating factors, such as recording conditions and noise types. In addition, we also present a new visualization method for qualitatively evaluating the performance with respect to the interpretability and disentanglement. Models trained with our proposed algorithm demonstrate the desired characteristics on all the datasets.
Setting the threshold for high throughput detectors: A mathematical approach for ensembles of dynamic, heterogeneous, probabilistic anomaly detectors

Cyber operations now manage a high volume of heterogeneous log data. Anomaly Detection (AD) in such operations involves multiple (e.g., per IP, per data type) ensembles of detectors modeling heterogeneous characteristics (e.g., rate, size, type) often with adaptive online models producing alerts in near real time. Because of the high data volume, setting the threshold for each detector in such a system is an essential yet underdeveloped configuration issue that, if slightly mistuned, can leave the system useless, either producing a myriad of alerts (and flooding downstream systems) or giving none. In this work, we build on the foundations of Ferragut et al. to provide a set of rigorous results for understanding the relationship between threshold values and alert quantities for probabilistic detectors. This informs an algorithm for setting the threshold of multiple, heterogeneous, possibly dynamic detectors completely a priori, in principle. Indeed, if the underlying distribution of the incoming data is known, the algorithm provides provably manageable thresholds. If the distribution is unknown (poorly estimated), our analysis gives insight into how the model distribution differs from the actual distribution, indicating refitting is necessary. We provide empirical experiments, regulating the alert rate of a system with ≈2,500 adaptive detectors scoring over 1.5M events in 5 hours of timestamps. Further, we demonstrate on real network data and detection framework of Harshaw et al. the alternative case, demonstrating that the inability to regulate alerts indicates how the detection model is not a good fit to the data.
On Schauder estimates for a class of nonlocal fully nonlinear parabolic equations

We obtain Schauder estimates for a class of concave fully nonlinear nonlocal parabolic equations of order $$\sigma \in (0,2)$$ with rough and non-symmetric kernels. We also prove that the solution to a translation invariant equation with merely bounded data is $$C^\sigma $$ in x variable and $$\Lambda ^1$$ in t variable, where $$\Lambda ^1$$ is the Zygmund space. From these results, we can derive the corresponding results for nonlocal elliptic equations with rough and non-symmetric kernels, which are new even in this case.
Invariant Gibbs measure evolution for the radial nonlinear wave equation on the 3D ball

Abstract We establish new global well-posedness results along Gibbs measure evolution for the nonlinear wave equation posed on the unit ball in R 3 via two distinct approaches. The first approach invokes the method established in the works Bourgain (1994, 1996) [3] , [4] , [5] based on a contraction-mapping principle and applies to a certain range of nonlinearities. The second approach allows to cover the full range of nonlinearities admissible to treatment by Gibbs measure, working instead with a delicate analysis of convergence properties of solutions. The method of the second approach is quite general, and we shall give applications to the nonlinear Schrodinger equation on the unit ball in subsequent works Bourgain and Bulut (2013) [10] , [11] .
A Finite Time Analysis of Temporal Difference Learning with Linear Function Approximation

Temporal difference learning (TD) is a simple iterative algorithm widely used for policy evaluation in Markov reward processes. Bhandari et al. prove finite time convergence rates for TD learning w...
Solving Set Cover with Pairs Problem using Quantum Annealing.

Here we consider using quantum annealing to solve Set Cover with Pairs (SCP), an NP-hard combinatorial optimization problem that plays an important role in networking, computational biology, and biochemistry. We show an explicit construction of Ising Hamiltonians whose ground states encode the solution of SCP instances. We numerically simulate the time-dependent Schrodinger equation in order to test the performance of quantum annealing for random instances and compare with that of simulated annealing. We also discuss explicit embedding strategies for realizing our Hamiltonian construction on the D-wave type restricted Ising Hamiltonian based on Chimera graphs. Our embedding on the Chimera graph preserves the structure of the original SCP instance and in particular, the embedding for general complete bipartite graphs and logical disjunctions may be of broader use than that the specific problem we deal with.
The Dynamics of Vector-Borne Relapsing Diseases

Abstract In this paper, we describe the dynamics of a vector-borne relapsing disease, such as tick-borne relapsing fever, using the methods of compartmental models. After some motivation and model description we provide a proof of a conjectured general form of the reproductive ratio R0, which is the average number of new infections produced by a single infected individual. A disease free equilibrium undergoes a bifurcation at R 0 = 1 and we show that for an arbitrary number of relapses it is a transcritical bifurcation with a single branch of endemic equilibria that is locally asymptotically stable for R0 sufficiently close to 1. Furthermore, we show there is no backwards bifurcation. We then show that these results can be extended to variants of the model with an example that allows for variation in the number of relapses before recovery. Finally, we discuss implications of our results and directions for future research.
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.
Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility

We provide formal definitions of degree of blameworthiness and intention relative to an epistemic state (a probability over causal models and a utility function on outcomes). These, together with a definition of actual causality, provide the key ingredients for moral responsibility judgments. We show that these definitions give insight into commonsense intuitions in a variety of puzzling cases from the literature.
Tautological classes and smooth bundles over BSU(2)

For a Lie group G and a smooth manifold W, we study the difference between smooth actions of G on W and bundles over the classifying space of G with fiber W and structure group Diff(W). In particular, we exhibit smooth manifold bundles over BSU(2) that are not induced by an action. The main tool for reaching this goal is a technical result that gives a constraint for the values of tautological classes pulled back to the cohomology of BSU(2) along a map induced by an action.