cssi/SciSciGPT-SciSciNet huggingface dataset:
The product formula in unitary deformation K-theory

For nitely generated groups G and H, we prove that there is a weak equivalence KG^ku KH ' K(G H) of ku-algebra spectra, where K denotes the \unitary deformation K-theory" functor. Additionally, we give spectral sequences for computing the homotopy groups of KG and HZ^ku KG in terms of connective K-theory and homology of spaces of G-representations. The underlying goal of many programs in algebraic K-theory is to understand the algebraic K-groups of a eld F as being built from the K-groups of the algebraic closure of the eld, together with the action of the absolute Galois group. Specically , Carlsson's program (see (2)) is to construct a model for the algebraic K-theory spectrum using the Galois group and the K-theory spectrum of the algebraic closure F. In some specic instances, the absolute Galois group of the eld F is explicitly the pronite completion ^ G of a discrete group G. (For example, the absolute Galois group of the eld k(z) of rational func- tions, where k is an algebraically closed of characteristic zero, is the pronite completion of a free group.) In the case where F contains an algebraically closed subeld, the pronite completion of a \deformation K-theory" spectrum KG is conjecturally equivalent to the pronite completion of the algebraic K-theory spectrum KF . Additionally, it would be advantageous for this description to be compatible with the motivic spectral sequence. This deformation K- theory spectrum has an Atiyah-Hirzebruch spectral sequence arising from a spectrum level ltration. The ltration quotients are spectra built from isomorphism classes of representations of the group. It is hoped that this ltration is related to the motivic spectral sequence, and that this relation would give a greater understanding of the rela- tionships between Milnor K-theory, Galois cohomology, and the repre- sentation theory of the Galois group. We now outline the construction of deformation K-theory. To a nitely generated group G one associates the categoryC of nite dimen- sional unitary representations of G, with morphisms being equivariant isometric isomorphisms. Elementary methods of representation theory
An Introduction to Matrix Concentration Inequalities

Random matrices now play a role in many areas of theoretical, applied,and computational mathematics. Therefore, it is desirable to have toolsfor studying random matrices that are flexible, easy to use, and powerful.Over the last fifteen years, researchers have developed a remarkablefamily of results, called matrix concentration inequalities, that achieveall of these goals.This monograph offers an invitation to the field of matrix concentrationinequalities. It begins with some history of random matrix theory;it describes a flexible model for random matrices that is suitablefor many problems; and it discusses the most important matrix concentrationresults. To demonstrate the value of these techniques, thepresentation includes examples drawn from statistics, machine learning,optimization, combinatorics, algorithms, scientific computing, andbeyond.
Average size of 2-Selmer groups of elliptic curves over function fields

Employing a geometric setting inspired by the proof of the Fundamental Lemma, we study some counting problems related to the average size of 2-Selmer groups and hence obtain an estimate for it.
Identifying system-wide early warning signs of instability in stochastic power systems

Prior research has shown that spectral decomposition of the reduced power flow Jacobian (RPFJ) can yield participation factors that describe the extent to which particular buses contribute to particular spectral components of a power system. Research has also shown that both variance and autocorrelation of time series voltage data tend to increase as a power system with stochastically fluctuating loads approaches certain critical transitions. This paper presents evidence suggesting that a system's participation factors predict the relative bus voltage variance values for all nodes in a system. As a result, these participation factors can be used to filter, weight, and combine real time PMU data from various locations dispersed throughout a power network in order to develop coherent measures of global voltage stability. This paper first describes the method of computing the participation factors. Next, two potential uses of the participation factors are given: (1) predicting the relative bus voltage variance magnitudes, and (2) locating generators at which the autocorrelation of voltage measurements clearly indicate proximity to critical transitions. The methods are tested using both analytical and numerical results from a dynamic model of a 2383-bus test case.
Positive definite Hankel matrix completions and Hamburger moment completions

Abstract In this paper we give solutions to Hamburger moment problems with missing entries. The problem of completing partial positive sequences is considered. The main result is a characterization of positive definite completable patterns, namely patterns that guarantee the existence of Hamburger moment completion of a partial positive definite sequence. Moreover, several patterns which are not positive definite completable are given.
High-dimensional Ising model selection with Bayesian information criteria

We consider the use of Bayesian information criteria for selection of the graph underlying an Ising model. In an Ising model, the full conditional distributions of each variable form logistic regression models, and variable selection techniques for regression allow one to identify the neighborhood of each node and, thus, the entire graph. We prove high-dimensional consistency results for this pseudo-likelihood approach to graph selection when using Bayesian information criteria for the variable selection problems in the logistic regressions. The results pertain to scenarios of sparsity, and following related prior work the information criteria we consider incorporate an explicit prior that encourages sparsity.
Lipschitz Constants To Curve Complexes For Punctured Surfaces

Abstract Given a surface S g , n there is a map s y s : T g , n → C g , n where T g , n is the Teichmuller space with the Teichmuller metric, C g , n is the curve complex with the standard metric, and d C g , n ( s y s ( X ) , s y s ( Y ) ) ≤ K d T g , n ( X , Y ) + C . We give asymptotic bounds for the minimal value of K which we denote K g , n ≍ 1 log ⁡ ( | χ g , n | ) for sequences of surfaces with fixed genus and sequences of surfaces where the genus is a rational multiple of the punctures. This generalizes work of Gadre, Hironaka, Kent, and Leininger where they give the same asymptotic bounds for closed surfaces.
Learning Language Games through Interaction

We introduce a new language learning setting relevant to building adaptive natural language interfaces. It is inspired by Wittgenstein’s language games: a human wishes to accomplish some task (e.g., achieving a certain configuration of blocks), but can only communicate with a computer, who performs the actual actions (e.g., removing all red blocks). The computer initially knows nothing about language and therefore must learn it from scratch through interaction, while the human adapts to the computer’s capabilities. We created a game called SHRDLURN in a blocks world and collected interactions from 100 people playing it. First, we analyze the humans’ strategies, showing that using compositionality and avoiding synonyms correlates positively with task performance. Second, we compare computer strategies, showing that modeling pragmatics on a semantic parsing model accelerates learning for more strategic players.
PARABANK: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-Constrained Neural Machine Translation.

We present PARABANK, a large-scale English paraphrase dataset that surpasses prior work in both quantity and quality. Following the approach of PARANMT (Wieting and Gimpel, 2018), we train a Czech-English neural machine translation (NMT) system to generate novel paraphrases of English reference sentences. By adding lexical constraints to the NMT decoding procedure, however, we are able to produce multiple high-quality sentential paraphrases per source sentence, yielding an English paraphrase resource with more than 4 billion generated tokens and exhibiting greater lexical diversity. Using human judgments, we also demonstrate that PARABANK’s paraphrases improve over PARANMT on both semantic similarity and fluency. Finally, we use PARABANK to train a monolingual NMT model with the same support for lexically-constrained decoding for sentence rewriting tasks.
Subjectivity, Bayesianism, and causality

"Subjectivity" is studied by comparing Lacanian and Bayesian probability theory.Causality is explained as arising from a two-player game with imperfect information.A measure-theoretic model of the interactive subject is introduced. Display Omitted Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty. Its defining property is the interpretation of probabilities as degrees of belief in propositions about the state of the world relative to an inquiring subject. This essay examines the notion of subjectivity by drawing parallels between Lacanian theory and Bayesian probability theory, and concludes that the latter must be enriched with causal interventions to model agency. The central contribution of this work is an abstract model of the subject that accommodates causal interventions in a measure-theoretic formalisation. This formalisation is obtained through a game-theoretic Ansatz based on modelling the inside and outside of the subject as an extensive-form game with imperfect information between two players. Finally, I illustrate the expressiveness of this model with an example of causal induction.
Measuring the superconducting coherence length in thin films using a two-coil experiment

We present measurements of the superconducting coherence length $\ensuremath{\xi}$ in thin ($d\ensuremath{\le}100\ensuremath{-}\AA{}$) films of MoGe and Nb using a combination of linear and nonlinear mutual inductance techniques. As the alternating current in the drive coil is increased at a fixed temperature, we see a crossover from linear to nonlinear coupling to the pickup coil, consistent with the unbinding of vortex-antivortex pairs as the peak pair momentum nears $\ensuremath{\hbar}/\ensuremath{\xi}$ and the unbinding barrier vanishes. We compare measurements of $\ensuremath{\xi}$ performed by this mutual inductance technique to values determined from the films' upper critical fields, thereby confirming the applicability of a recent calculation of the upper limit on a vortex-free state in our experiment.
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog.

A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision! In this paper, using a Task & Talk reference game between two agents as a testbed, we present a sequence of ‘negative’ results culminating in a ‘positive’ one – showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge ‘naturally’,despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.
SafetyNets: verifiable execution of deep neural networks on an untrusted cloud

Inference using deep neural networks is often outsourced to the cloud since it is a computationally demanding task. However, this raises a fundamental issue of trust. How can a client be sure that the cloud has performed inference correctly? A lazy cloud provider might use a simpler but less accurate model to reduce its own computational load, or worse, maliciously modify the inference results sent to the client. We propose SafetyNets, a framework that enables an untrusted server (the cloud) to provide a client with a short mathematical proof of the correctness of inference tasks that they perform on behalf of the client. Specifically, SafetyNets develops and implements a specialized interactive proof (IP) protocol for verifiable execution of a class of deep neural networks, i.e., those that can be represented as arithmetic circuits. Our empirical results on three- and four-layer deep neural networks demonstrate the run-time costs of SafetyNets for both the client and server are low. SafetyNets detects any incorrect computations of the neural network by the untrusted server with high probability, while achieving state-of-the-art accuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognition tasks (75.22%).
Introduction to the non-asymptotic analysis of random matrices.

This is a tutorial on some basic non-asymptotic methods and concepts in random matrix theory. The reader will learn several tools for the analysis of the extreme singular values of random matrices with independent rows or columns. Many of these methods sprung off from the development of geometric functional analysis since the 1970's. They have applications in several fields, most notably in theoretical computer science, statistics and signal processing. A few basic applications are covered in this text, particularly for the problem of estimating covariance matrices in statistics and for validating probabilistic constructions of measurement matrices in compressed sensing. These notes are written particularly for graduate students and beginning researchers in different areas, including functional analysts, probabilists, theoretical statisticians, electrical engineers, and theoretical computer scientists.
Financial market development and firm investment in tax avoidance: Evidence from credit default swap market

Abstract Lenders reduce their monitoring efforts after hedging their credit risk exposure through credit default swap (CDS) contracts, which are akin to insurance against borrowers’ adverse credit events. In this study, we examine whether, upon observing the reduced lender monitoring following CDS trading, shareholders demand that borrowing firms invest in more aggressive tax planning strategies, which were previously constrained by risk-averse lenders. Using a difference-in-differences design that exploits the variation in timing of the inception of CDS trading, we document that borrowers exhibit greater tax avoidance after the inception of CDS trading. Consistent with shareholders stepping up their demands post-CDS, we find that the increase in tax avoidance is stronger for (i) firms with more powerful and influential shareholders, as measured by dedicated institutional investors, (ii) firms with stronger shareholder defenders, as measured by board independence and board size, and (iii) firms with more strategic default options. We also find that borrowers with higher levels of tax avoidance are less likely to file for bankruptcy in the future. Our findings are robust to a battery of sensitivity checks, including controlling for cost of debt and endogeneity, as well as using alternative measures of tax avoidance.
Unique Reconstruction of Coded Strings From Multiset Substring Spectra

The problem of reconstructing strings from their substring spectra has a long history and in its most simple incarnation asks for determining under which conditions the spectrum uniquely determines the string. We study the problem of coded string reconstruction from multiset substring spectra, where the strings are restricted to lie in some codebook. In particular, we consider binary codebooks that allow for unique string reconstruction and propose a new method, termed repeat replacement , to create the codebook. Our contributions include algorithmic solutions for repeat replacement and constructive redundancy bounds for the underlying coding schemes. We also consider extensions of the problem to noisy settings in which substrings are compromised by burst and random errors. The study is motivated by applications in DNA-based data storage systems that use high throughput readout sequencers.
Test Score Gap Robustness to Scaling : The Scale Transformation Command

Social scientists frequently rely on the cardinal comparability of test scores to assess achievement gaps between population subgroups and their evolution over time. This approach has been criticized due to the ordinal nature of test scores and the sensibility of results to order-preserving transformations, which are theoretically plausible. Bond and Lang (2013) document the sensitivity of measured ability to scaling choices and develop a method to assess the robustness of changes in ability over time to scaling choices. This paper presents the scale transformation command, which expands the Bond and Lang method to more general cases and optimizes their algorithm to work with large data sets. The program assesses the robustness of an achievement gap between two subgroups to any arbitrary choice of scale by finding bounds for the original gap estimation. Additionally, the program finds scale transformations that are very likely and unlikely to benchmark against the results obtained. Finally, the program also allows the user to measure how much gap growth coefficients change when including controls in their specifications.
L'evolved: autonomous and ubiquitous utilities as smart agents

Ubiquitous computing has been focusing on creating smart agents that are submerged into everyday environments, however, recent development on physical computing is demanding a shift from calm computing to a physically engaging form. Computing is no more limited to increasing our comfort through passive and pervasive deployment, they can now be created as being more actively intermeshed into our physical tasks. We present L'evolved, autonomous ubiquitous utilities that assist in user tasks through active physical participation. They not only dynamically adapt to individual user needs, but also work in close tandem with the users. By harnessing drone technology, we realize the design and implementation of a flying lamp and a desk that affords free motions and computational controls. Through specific use scenarios of those exemplary utilities, we show how this new form of smart agents promises various new ways of interacting with our physical environments.
Ergodicity and Accuracy of Optimal Particle Filters for Bayesian Data Assimilation

Data assimilation refers to the methodology of combining dynamical models and observed data with the objective of improving state estimation. Most data assimilation algorithms are viewed as approximations of the Bayesian posterior (filtering distribution) on the signal given the observations. Some of these approximations are controlled, such as particle filters which may be refined to produce the true filtering distribution in the large particle number limit, and some are uncontrolled, such as ensemble Kalman filter methods which do not recover the true filtering distribution in the large ensemble limit. Other data assimilation algorithms, such as cycled 3DVAR methods, may be thought of as controlled estimators of the state, in the small observational noise scenario, but are also uncontrolled in general in relation to the true filtering distribution. For particle filters and ensemble Kalman filters it is of practical importance to understand how and why data assimilation methods can be effective when used with a fixed small number of particles, since for many large-scale applications it is not practical to deploy algorithms close to the large particle limit asymptotic. In this paper, the authors address this question for particle filters and, in particular, study their accuracy (in the small noise limit) and ergodicity (for noisy signal and observation) without appealing to the large particle number limit. The authors first overview the accuracy and minorization properties for the true filtering distribution, working in the setting of conditional Gaussianity for the dynamics-observation model. They then show that these properties are inherited by optimal particle filters for any fixed number of particles, and use the minorization to establish ergodicity of the filters. For completeness we also prove large particle number consistency results for the optimal particle filters, by writing the update equations for the underlying distributions as recursions. In addition to looking at the optimal particle filter with standard resampling, they derive all the above results for (what they term) the Gaussianized optimal particle filter and show that the theoretical properties are favorable for this method, when compared to the standard optimal particle filter.
A result on polynomials derived via graph theory

Summary. We present an example of a result in graph theory that is used to obtain a result in another branch of mathematics. More precisely, we show that the isomorphism of certain directed graphs ...
Macroeconomic Tail Risks and Asset Prices

I document that dividend growth and returns on the aggregate U.S. stock market are more correlated with consumption growth in bad economic times. In a consumption-based asset pricing model with a generalized disappointment averse investor and small, IID consumption shocks, this feature results in a realistic equity premium despite low risk aversion. The model is consistent with the main facts about stock market risk premia inferred from equity index options, remains tightly parameterized, and allows for analytical solutions for asset prices. An extension with non-IID dynamics accounts for excess volatility and return predictability while preserving the model's consistency with option moments.
Natural genetic variation screen in Drosophila identifies Wnt signaling, mitochondrial metabolism, and redox homeostasis genes as modifiers of apoptosis

Apoptosis is the primary cause of degeneration in a number of neuronal, muscular, and metabolic disorders. These diseases are subject to a great deal of phenotypic heterogeneity in patient populations, primarily due to differences in genetic variation between individuals. This creates a barrier to effective diagnosis and treatment. Understanding how genetic variation influences apoptosis could lead to the development of new therapeutics and better personalized treatment approaches. In this study, we examine the impact of the natural genetic variation in the Drosophila Genetic Reference Panel (DGRP) on two models of apoptosis-induced retinal degeneration: overexpression of p53 or reaper (rpr). We identify a number of known apoptotic, neural, and developmental genes as candidate modifiers of degeneration. We also use Gene Set Enrichment Analysis (GSEA) to identify pathways that harbor genetic variation that impact these apoptosis models, including Wnt signaling, mitochondrial metabolism, and redox homeostasis. Finally, we demonstrate that many of these candidates have a functional effect on apoptosis and degeneration. These studies provide a number of avenues for modifying genes and pathways of apoptosis-related disease.
Aquatic Macrophytes are Seasonally Important Dietary Resources for Moose

Moose (Alces alces) are generalist herbivores, but are important aquatic-terrestrial ecotone specialists. Aquatic macrophytes are a high-quality food source for moose during summer, but the importance of aquatic food sources to the moose diet is difficult to study. We used stable isotope analysis of carbon and nitrogen from moose hooves and forage (terrestrial plants, aquatic macrophytes, and arboreal lichen) to assess the diet of moose at Isle Royale National Park, Michigan, USA, using Bayesian mixing models. We also evaluated the isotopic variability along chronologies of serially sampled hooves. Overall, our mixing models indicate that 13%–27% of the summer moose diet was aquatic in origin. Among moose that died during winter, body condition was impaired and hoof δ15N was higher where aquatic habitats were sparse. Although isotope chronologies preserved in hooves could significantly enhance our understanding of ungulate foraging ecology, interpretation of such chronologies is presently limited by our lack of knowledge pertaining to hoof growth rate and seasonal growth variability related to age and health. Distinct isotopic values among terrestrial plants, aquatic macrophytes, and arboreal lichens indicate that continued methodological advances in stable isotope ecology will lead to more precise estimates of the contribution of aquatic feeding to moose population dynamics and other ungulates.
Kinematics associated with treadmill walking in Rett syndrome.

Background and purpose Individuals with Rett syndrome suffer from severely impaired cognitive and motor performance. Current movement-related therapeutic programs often include traditional physical therapy activities and assisted treadmill walking routines for those individuals who are ambulatory. However, there are no quantitative reports of kinematic gait parameters obtained during treadmill walking. The purpose of this research was to characterize the kinematic patterns of the lower limbs during treadmill walking as speed was slowly increased. Methods Seventeen independently ambulatory females diagnosed with a methyl-CpG-binding protein 2 gene mutation walked on a motorized treadmill while joint kinematics were obtained by a camera-based motion capture system and analysis software. Results Stride times progressively decreased as treadmill speeds increased. There were significant main effects of speed on sagittal knee and hip ranges of motion and hip velocity. There were large joint asymmetries and variance values relative to other ambulatory patient populations, although variance values decreased as walking speed increased. Conclusions The results indicate that individuals with Rett syndrome can adapt their kinematic gait patterns in response to increasing treadmill speed, but only within a narrow range of speeds. We suggest that treadmill training for ambulatory individuals with Rett syndrome may promote improved walking kinematics and possibly provide overall health benefits.Implications for rehabilitationWalking is an activity that can counter the negative impacts of the sedentary lifestyle of many individuals with disabilities, including those individuals with Rett syndrome.Documentation of the lower limb kinematic patterns displayed during walking by ambulatory females with Rett syndrome can be used by clinicians to evaluate their patients' gait performance in response to therapeutic and pharmacological interventions designed to promote walking.The ability to adapt to increases in treadmill speed suggests that a training program of treadmill walking may be effective in promoting improved gait performance in individuals with Rett syndrome.
Cell Communication Network Factor 4 (CCN4/WISP1) Shifts Melanoma Cells from a Fragile Proliferative State to a Resilient Metastatic State.

Cellular communication network factor 4 (CCN4/WISP1) is a secreted matricellular protein that stimulates metastasis in multiple malignancies but has an unclear impact on phenotypic changes in melanoma. Recent data using cells edited via a double-nickase CRISPR/Cas9 approach suggest that CCN4/WISP1 stimulates invasion and metastasis of melanoma cells. While these data also suggest that loss of CCN4/WISP1 increases cell proliferative, the CRISPR approach used may be an alternative explanation rather than the loss of gene function. To test whether CCN4/WISP1 also influences the proliferative phenotype of melanoma cells, we used mouse melanoma models and knocked out Ccn4 using a homology-directed repair CRISPR/Cas9 system to generate pools of Ccn4-knockout cells. The resulting edited cell pools were compared to parental cell lines using an ensemble of in vitro and in vivo assays. In vitro assays using knockout pools supported previous findings that CCN4/WISP1 promoted an epithelial–mesenchymal-like transition in melanoma cells and stimulated invasion and metastasis. While Ccn4 knockout also enhanced cell growth in optimal 2D culture conditions, the knockout suppressed certain cell survival signaling pathways and rendered cells less resistant to stress conditions. Tumor cell growth assays at sub-optimal conditions in vitro, quantitative analysis of tumor growth assays in vivo, and transcriptomics analysis of human melanoma cell lines were also used to quantify changes in phenotype and generalize the findings. In addition to stimulating invasion and metastasis of melanoma cells, the results suggested that CCN4/WISP1 repressed cell growth and simultaneously enhanced cell survival.
Coverage Analysis for Energy-Harvesting UAV-Assisted mmWave Cellular Networks

In this paper, we jointly consider the downlink simultaneous wireless information and power transfer (SWIPT) and uplink information transmission in unmanned aerial vehicle (UAV)-assisted millimeter wave (mmWave) cellular networks, in which the user equipment (UE) locations are modeled using Poisson cluster processes (e.g., Thomas cluster processes or Matern cluster processes). Distinguishing features of mmWave communications, such as different path loss models for line-of-sight (LOS) and non-LOS (NLOS) links and directional transmissions are taken into account. In the downlink phase, the association probability, and energy coverages of different tier UAVs and ground base stations (GBSs) are investigated. Moreover, we define a successful transmission probability to jointly present the energy and signal-to-interference-plus-noise ratio (SINR) coverages and provide general expressions. In the uplink phase, we consider the scenario that each UAV receives information from its own cluster member UEs. We determine the Laplace transform of the interference components and characterize the uplink SINR coverage. In addition, we formulate the average uplink throughput, with the goal to identify the optimal time division multiplexing between the donwlink and uplink phases. Through numerical results we investigate the impact of key system parameters on the performance. We show that the network performance is improved when the cluster size becomes smaller. In addition, we analyze the optimal height of UAVs, optimal power splitting value and optimal time division multiplexing that maximizes the network performance.
Existence and uniqueness of the Karcher mean on unital C⁎-algebras

Abstract The Karcher mean on the cone Ω of invertible positive elements of the C ⁎ -algebra B ( E ) of bounded operators on a Hilbert space E has recently been extended to a contractive barycentric map on the space of L 1 -probability measures on Ω. In this paper we first show that the barycenter satisfies the Karcher equation and then establish the uniqueness of the solution. Next we establish that the Karcher mean is real analytic in each of its coordinates, and use this fact to show that both the Karcher mean and Karcher barycenter map exist and are unique on any unital C ⁎ -algebra. The proof depends crucially on a recent result of the author giving a converse of the inverse function theorem.
scGEAToolbox: a Matlab toolbox for single-cell RNA sequencing data analysis.

MOTIVATION Single-cell RNA sequencing (scRNA-seq) technology has revolutionized the way research is done in biomedical sciences. It provides an unprecedented level of resolution across individual cells for studying cell heterogeneity and gene expression variability. Analyzing scRNA-seq data is challenging though, due to the sparsity and high dimensionality of the data. RESULTS I developed scGEAToolbox-a Matlab toolbox for scRNA-seq data analysis. It contains a comprehensive set of functions for data normalization, feature selection, batch correction, imputation, cell clustering, trajectory/pseudotime analysis, and network construction, which can be combined and integrated to building custom workflow. While most of the functions are implemented in native Matlab, wrapper functions are provided to allow users to call the "third-party" tools developed in Matlab or other languages. Furthermore, scGEAToolbox is equipped with sophisticated graphical user interfaces (GUIs) generated with App Designer, making it an easy-to-use application for quick data processing. AVAILABILITY https://github.com/jamesjcai/scGEAToolbox. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Euclidean Distance Degree of Projective Varieties

We give a positive answer to a conjecture of Aluffi-Harris on the computation of the Euclidean distance degree of a possibly singular projective variety in terms of the local Euler obstruction function.
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs

We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training. This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours. We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage. In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks.
Protein expression of prenyltransferase subunits in postmortem schizophrenia dorsolateral prefrontal cortex

The pathophysiology of schizophrenia includes altered neurotransmission, dysregulated intracellular signaling pathway activity, and abnormal dendritic morphology that contribute to deficits of synaptic plasticity in the disorder. These processes all require dynamic protein–protein interactions at cell membranes. Lipid modifications target proteins to membranes by increasing substrate hydrophobicity by the addition of a fatty acid or isoprenyl moiety, and recent evidence suggests that dysregulated posttranslational lipid modifications may play a role in multiple neuropsychiatric disorders, including schizophrenia. Consistent with these emerging findings, we have recently reported decreased protein S-palmitoylation in schizophrenia. Protein prenylation is a lipid modification that occurs upstream of S-palmitoylation on many protein substrates, facilitating membrane localization and activity of key intracellular signaling proteins. Accordingly, we hypothesized that, in addition to palmitoylation, protein prenylation may be abnormal in schizophrenia. To test this, we assayed protein expression of the five prenyltransferase subunits (FNTA, FNTB, PGGT1B, RABGGTA, and RABGGTB) in postmortem dorsolateral prefrontal cortex from patients with schizophrenia and paired comparison subjects (n = 13 pairs). We found decreased levels of FNTA (14%), PGGT1B (13%), and RABGGTB (8%) in schizophrenia. To determine whether upstream or downstream factors may be driving these changes, we also assayed protein expression of the isoprenoid synthases FDPS and GGPS1 and prenylation-dependent processing enzymes RCE and ICMT. We found these upstream and downstream enzymes to have normal protein expression. To rule out effects from chronic antipsychotic treatment, we assayed FNTA, PGGT1B, and RABGGTB in the cortex from rats treated long-term with haloperidol decanoate and found no change in the expression of these proteins. Given the role prenylation plays in localization of key signaling proteins found at the synapse, these data offer a potential mechanism underlying abnormal protein–protein interactions and protein localization in schizophrenia.
Receptors, circuits and neural dynamics for prediction

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While theoretical and computational frameworks for prediction exist, circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling and machine learning, we show that trial history and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual prediction task. Predictive beta feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, a NMDA receptor blocker - but not the control drug dexmedetomidine - perturbed predictions, their representation in higher-order cortex, and feedback to posterior cortex. This study suggests predictions depend on alpha activity in higher-order cortex, beta feedback and NMDA receptors, and ketamine blocks access to learned predictive information.
Debiasing community detection: the importance of lowly connected nodes

Community detection is an important task in social network analysis, allowing us to identify and understand the communities within the social structures provided by the network. However, many community detection approaches either fail to assign low-degree (or lowly connected) users to communities, or assign them to trivially small communities that prevent them from being included in analysis. In this work we investigate how excluding these users can bias analysis results. We then introduce an approach that is more inclusive for lowly connected users by incorporating them into larger groups. Experiments show that our approach outperforms the existing state-of-the-art in terms of F1 and Jaccard similarity scores while reducing the bias towards low-degree users.
IL-1-conferred gene expression pattern in ERα+ BCa and AR+ PCa cells is intrinsic to ERα- BCa and AR- PCa cells and promotes cell survival.

Breast (BCa) and prostate (PCa) cancers are hormone receptor (HR)-driven cancers. Thus, BCa and PCa patients are given therapies that reduce hormone levels or directly block HR activity; but most patients eventually develop treatment resistance. We have previously reported that interleukin-1 (IL-1) inflammatory cytokine downregulates ERα and AR mRNA in HR-positive (HR+) BCa and PCa cell lines, yet the cells can remain viable. Additionally, we identified pro-survival proteins and processes upregulated by IL-1 in HR+ BCa and PCa cells, that are basally high in HR− BCa and PCa cells. Therefore, we hypothesize that IL-1 confers a conserved gene expression pattern in HR+ BCa and PCa cells that mimics conserved basal gene expression patterns in HR− BCa and PCa cells to promote HR-independent survival and tumorigenicity. We performed RNA sequencing (RNA-seq) for HR+ BCa and PCa cell lines exposed to IL-1 and for untreated HR− BCa and PCa cell lines. We confirmed expression patterns of select genes by RT-qPCR and used siRNA and/or drug inhibition to silence select genes in the BCa and PCa cell lines. Finally, we performed Ingenuity Pathway Analysis (IPA) and used the gene ontology web-based tool, GOrilla, to identify signaling pathways encoded by our RNA-seq data set. We identified 350 genes in common between BCa and PCa cells that are induced or repressed by IL-1 in HR+ cells that are, respectively, basally high or low in HR− cells. Among these genes, we identified Sequestome-1 (SQSTM1/p62) and SRY (Sex-Determining Region Y)-Box 9 (SOX9) to be essential for survival of HR− BCa and PCa cell lines. Analysis of publicly available data indicates that p62 and SOX9 expression are elevated in HR-independent BCa and PCa sublines generated in vitro, suggesting that p62 and SOX9 have a role in acquired hormone receptor independence and treatment resistance. We also assessed HR− cell line viability in response to the p62-targeting drug, verteporfin, and found that verteporfin is cytotoxic for HR− cell lines. Our 350 gene set can be used to identify novel therapeutic targets and/or biomarkers conserved among acquired (e.g. due to inflammation) or intrinsic HR-independent BCa and PCa.
Expression and Purification of Protease-Activated Receptor 4 (PAR4) and Analysis with Histidine Hydrogen–Deuterium Exchange

Protease-activated receptors (PARs) are G-protein-coupled receptors that are activated by proteolysis of the N-terminus, which exposes a tethered ligand that interacts with the receptor. Numerous studies have focused on the signaling pathways mediated by PARs. However, the structural basis for initiation of these pathways is unknown. Here, we describe a strategy for the expression and purification of PAR4. This is the first PAR family member to be isolated without stabilizing modifications for biophysical studies. We monitored PAR4 activation with histidine hydrogen-deuterium exchange. PAR4 has nine histidines that are spaced throughout the protein, allowing a global view of solvent accessible and nonaccessible regions. Peptides containing each of the nine His residues were used to determine the t1/2 for each His residue in apo or thrombin-activated PAR4. The thrombin-cleaved PAR4 exhibited a 2-fold increase (p > 0.01) in t1/2 values observed for four histidine residues (His180, His229, His240, and His380), demonstrating that these regions have decreased solvent accessibility upon thrombin treatment. In agreement, thrombin-cleaved PAR4 also was resistant to thermolysin digestion. In contrast, the rate of thermolysin proteolysis following stimulation with the PAR4 activation peptide was the same as that of unstimulated PAR4. Further analysis showed the C-terminus is protected in thrombin-activated PAR4 compared to uncleaved or agonist peptide-treated PAR4. The studies described here are the first to examine the tethered ligand activation mechanism for a PAR family member biophysically and shed light on the overall conformational changes that follow activation of PARs by a protease.
Controlling Structural Bias in Intrinsically Disordered Proteins Using Solution Space Scanning.

Intrinsically disordered proteins or regions (IDRs) differ from their well-folded counterparts by lacking a stable tertiary state. Instead, IDRs exist in an ensemble of conformations and often poss...
Hierarchical Reinforcement Learning for Quadruped Locomotion

Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors. To solve these problems, we introduce a hierarchical framework that can automatically learn to decompose complex locomotion tasks. A high-level policy issues commands in the form of a latent vector and also selects for how long the low-level policy will execute the latent command. Concurrently, the low-level policy uses the latent command and only the robot’s on-board sensors to control the robot’s actuators. Our approach allows the high-level policy to run at a lower frequency than the low-level one. We test our framework on a path-following task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as low-level skills are needed for this task. We then show efficient adaptation of the trained policy to new tasks by transfer of the trained low-level policy. Finally, we validate the policies on a real quadruped robot. To the best of our knowledge, this is the first application of end-to-end hierarchical learning to a real robotic locomotion task.
An Efficient Sum Query Algorithm for Distance-Based Locally Dominating Functions

In this paper, we consider the following sum query problem: Given a point set P in $${\mathbb {R}}^d$$ , and a distance-based function f(p, q) (i.e., a function of the distance between p and q) satisfying some general properties, the goal is to develop a data structure and a query algorithm for efficiently computing a $$(1+\epsilon )$$ -approximate solution to the sum $$\sum _{p \in P} f(p,q)$$ for any query point $$q \in {\mathbb {R}}^d$$ and any small constant $$\epsilon >0$$ . Existing techniques for this problem are mainly based on some core-set techniques which often have difficulties to deal with functions with local domination property. Based on several new insights to this problem, we develop in this paper a novel technique to overcome these encountered difficulties. Our algorithm is capable of answering queries with high success probability in time no more than $${\tilde{O}}_{\epsilon ,d}(n^{0.5 + c})$$ , and the underlying data structure can be constructed in $${\tilde{O}}_{\epsilon ,d}(n^{1+c})$$ time for any $$c>0$$ , where the hidden constant has only polynomial dependence on $$1/\epsilon$$ and d. Our technique is simple and can be easily implemented for practical purpose.
Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures

Modern neural network-based algorithms are able to produce highly accurate depth estimates from stereo image pairs, nearly matching the reliability of measurements from more expensive depth sensors. However, this accuracy comes with a higher computational cost since these methods use network architectures designed to compute and process matching scores across all candidate matches at all locations, with floating point computations repeated across a match volume with dimensions corresponding to both space and disparity. This leads to longer running times to process each image pair, making them impractical for real-time use in robots and autonomous vehicles. We propose a new stereo algorithm that employs a significantly more efficient network architecture. Our method builds an initial match cost volume using traditional matching costs that are fast to compute, and trains a network to estimate disparity from this volume. Crucially, our network only employs per-pixel and two-dimensional convolution operations: to summarize the local match information at each location as a lowdimensional feature vector, and to spatially process these "cost-signature" features to produce a dense disparity map. Experimental results on KITTI show that our method delivers competitive accuracy at significantly higher speeds— running at 48 frames per second on a modern GPU.
Ultrasonic Velocities In Laboratory-Formed Gas Hydrate-Bearing Sediments

Gas Hydrates are widely distributed in the near sur face at high latitudes and many ocean bottoms. Our laboratory measurements help calibrate the seismic and well log data that are used to ascertain the in situ distribution of hydrates. Ultrasonic velocities are measured in unconsolidated sand specimen under thermobaric conditions comparable to those found in shallow oceanic or permafrost region s, i.e. in the gas hydrate stability zone. Compressional-wave (p-wave) velocity data in dry Ottawa Sand samples subjected to a confining pressure that from 2 to 20 MPa range from about 700 to 1500 m/s. Gas hydrates were then formed a partially saturated Ott awa sand sample. As expected, this hydrate formation resulted in a dramatic increase i n p-wave velocities from 700 m/s in the partially water saturated sample to 2100 m/s in the hydrate-bearing specimen. The hydrate effectively cements the sand such that nine hours after entering the hydrate stability region, the recorded waveforms remained c onstant even as the confining pressure was increased by more than 10 MPa.