cssi/SciSciGPT-SciSciNet huggingface dataset:
Balancing Conservative and Disruptive Growth in the Voter Model

We are concerned with how the implementation of growth determines the expected number of state-changes in a growing self-organizing process. With this problem in mind, we examine two versions of the voter model on a one-dimensional growing lattice. Our main result asserts that the expected number of state-changes before an absorbing state is found can be controlled by balancing the conservative and disruptive forces of growth. This is because conservative growth preserves the self-organization of the voter model as it searches for an absorbing state, whereas disruptive growth undermines this self-organization. In particular, we focus on controlling the expected number of state-changes as the rate of growth tends to zero or infinity in the limit. These results illustrate how growth can affect the costs of self-organization and so are pertinent to the physics of growing active matter.
CausalX: Causal eXplanations and Block Multilinear Factor Analysis

By adhering to the dictum, “No causation without manipulation (treatment, intervention)”, cause and effect data analysis represents changes in observed data in terms of changes in the causal factors. When causal factors are not amenable for active manipulation in the real world due to current technological limitations or ethical considerations, a counterfactual approach performs an intervention on the model of data formation. In the case of object representation or activity (temporal object) representation, varying object parts is generally unfeasible whether they be spatial and/or temporal. Multilinear algebra, the algebra of higher order tensors, is a suitable and transparent framework for disentangling the causal factors of data formation. Learning a part-based intrinsic causal factor representations in a multilinear framework requires applying a set of interventions on a part-based multilinear model. We propose a unified multilinear model of wholes and parts. We derive a hierarchical block multilinear factorization, the $M$ -mode Block SVD, that computes a disentangled representation of the causal factors by optimizing simultaneously across the entire object hierarchy. Given computational efficiency considerations, we introduce an incremental bottom-up computational alternative, the Incremental $M$ -mode Block SVD, that employs the lower level abstractions, the part representations, to represent the higher level of abstractions, the parent wholes. This incremental computational approach may also be employed to update the causal model parameters when data becomes available incrementally. The resulting object representation is an interpretable combinatorial choice of intrinsic causal factor representations related to an object's recursive hierarchy of wholes and parts that renders object recognition robust to occlusion and reduces training data requirements.
Automatic Microprocessor Performance Bug Detection

Processor design validation and debug is a difficult and complex task, which consumes the lion’s share of the design process. Design bugs that affect processor performance rather than its functionality are especially difficult to catch, particularly in new microarchitectures. This is because, unlike functional bugs, the correct processor performance of new microarchitectures on complex, long-running benchmarks is typically not deterministically known. Thus, when performance benchmarking new microarchitectures, performance teams may assume that the design is correct when the performance of the new microarchitecture exceeds that of the previous generation, despite significant performance regressions existing in the design. In this work we present a two-stage, machine learning-based methodology that is able to detect the existence of performance bugs in microprocessors. Our results show that our best technique detects 91.5% of microprocessor core performance bugs whose average IPC impact across the studied applications is greater than 1% versus a bug-free design with zero false positives. When evaluated on memory system bugs, our technique achieves 100% detection with zero false positives. Moreover, the detection is automatic, requiring very little performance engineer time.
Association and Other Forms of Positive Dependence for Feller Evolution Systems

We prove characterizations of positive dependence for a general class of time-inhomogeneous Markov processes called Feller evolution processes (FEPs) and for jump-FEPs. General FEPs can be analyzed...
On Misspecification in Prediction Problems and Robustness via Improper Learning

We study probabilistic prediction games when the underlying model is misspecified, investigating the consequences of predicting using an incorrect parametric model. We show that for a broad class of loss functions and parametric families of distributions, the regret of playing a "proper" predictor -- one from the putative model class -- relative to the best predictor in the same model class has lower bound scaling at least as $\sqrt{\gamma n}$, where $\gamma$ is a measure of the model misspecification to the true distribution in terms of total variation distance. In contrast, using an aggregation-based (improper) learner, one can obtain regret $d \log n$ for any underlying generating distribution, where $d$ is the dimension of the parameter; we exhibit instances in which this is unimprovable even over the family of all learners that may play distributions in the convex hull of the parametric family. These results suggest that simple strategies for aggregating multiple learners together should be more robust, and several experiments conform to this hypothesis.
A Comparative Study of Infinite Elements for Two-Dimensional Electromagnetic Scattering Analysis

Recently, a new type of infinite elements was proposed as an accurate and efficient mesh truncation method for the finite element analysis of open-region acoustic scattering. These infinite elements are based on the radial expansion of the scattered field and are used to model the scattered field outside a separable surface characterized by a constant radial coordinate (such as a spherical or a spheroidal surface in three dimensions). In this paper, two-dimensional (2-D) elliptic infinite elements are formulated and implemented for the finite element analysis of 2-D time-harmonic electromagnetic scattering, and their performance is studied by comparison with the finite element solution using conventional absorbing boundary conditions (ABCs). Numerical results are presented to demonstrate that the new type of infinite elements is indeed more accurate than both the first- and second-order ABCs.
Automatic Registration and Clustering of Time Series

Clustering of time series data exhibits a number of challenges not present in other settings, notably the problem of registration (alignment) of observed signals. Typical approaches include pre-registration to a user-specified template or time warping approaches which attempt to optimally align series with a minimum of distortion. For many signals obtained from recording or sensing devices, these methods may be unsuitable as a template signal is not available for pre-registration, while the distortion of warping approaches may obscure meaningful temporal information. We propose a new method for automatic time series alignment within a clustering problem. Our approach, Temporal Registration using Optimal Unitary Transformations (TROUT), is based on a novel dissimilarity measure between time series that is easy to compute and automatically identifies optimal alignment between pairs of time series. By embedding our new measure in a optimization formulation, we retain well-known advantages of computational and statistical performance. We provide an efficient algorithm for TROUT-based clustering and demonstrate its superior performance over a range of competitors.
The Role of Working Memory in Program Tracing

Program tracing, or mentally simulating a program on concrete inputs, is an important part of general program comprehension. Programs involve many kinds of virtual state that must be held in memory, such as variable/value pairs and a call stack. In this work, we examine the influence of short-term working memory (WM) on a person’s ability to remember program state during tracing. We first confirm that previous findings in cognitive psychology transfer to the programming domain: people can keep about 7 variable/value pairs in WM, and people will accidentally swap associations between variables due to WM load. We use a restricted focus viewing interface to further analyze the strategies people use to trace through programs, and the relationship of tracing strategy to WM. Given a straight-line program, we find half of our participants traced a program from the top-down line-by-line (linearly), and the other half start at the bottom and trace upward based on data dependencies (on-demand). Participants with an on-demand strategy made more WM errors while tracing straight-line code than with a linear strategy, but the two strategies contained an equal number of WM errors when tracing code with functions. We conclude with the implications of these findings for the design of programming tools: first, programs should be analyzed to identify and refactor human-memory-intensive sections of code. Second, programming environments should interactively visualize variable metadata to reduce WM load in accordance with a person’s tracing strategy. Third, tools for program comprehension should enable externalizing program state while tracing.
What helped, and what did not?: An Evaluation of the Strategies to Improve Continuous Integration

Continuous integration (CI) is a widely used practice in modern software engineering. Unfortunately, it is also an expensive practice --- Google and Mozilla estimate their CI systems in millions of dollars. There are a number of techniques and tools designed to or having the potential to save the cost of CI or expand its benefit - reducing time to feedback. However, their benefits in some dimensions may also result in drawbacks in others. They may also be beneficial in other scenarios where they are not designed to help. In this paper, we perform the first exhaustive comparison of techniques to improve CI, evaluating 14 variants of 10 techniques using selection and prioritization strategies on build and test granularity. We evaluate their strengths and weaknesses with 10 different cost and time-to-feedback saving metrics on 100 real-world projects. We analyze the results of all techniques to understand the design decisions that helped different dimensions of benefit. We also synthesized those results to lay out a series of recommendations for the development of future research techniques to advance this area.
Oxygen delivery systems for adults in Sub-Saharan Africa: A scoping review.

Background Respiratory diseases are the leading cause of death and disability worldwide. Oxygen is an essential medicine used to treat hypoxemia from respiratory diseases. However, the availability and utilization of oxygen delivery systems for adults in sub-Saharan Africa is not well-described. We aim to identify and describe existing data around oxygen availability and provision for adults in sub-Saharan Africa, determine knowledge or research gaps, and make recommendations for future research and capacity building. Methods We systematically searched four databases for articles on April 22, 2020, for variations of keywords related to oxygen with a focus on countries in sub-Saharan Africa. Inclusion criteria were studies that included adults and addressed hypoxemia assessment or outcome, oxygen delivery mechanisms, oxygen availability, oxygen provision infrastructure, and oxygen therapy and outcomes. Results 35 studies representing 22 countries met inclusion criteria. Availability of oxygen delivery systems ranged from 42%-94% between facilities, with wide variability in the consistency of availability. There was also wide reported prevalence of hypoxemia, with most studies focusing on specific populations. In facilities where oxygen is available, health care workers are ill-equipped to identify adult patients with hypoxemia, provide oxygen to those who need it, and titrate or discontinue oxygen appropriately. Oxygen concentrators were shown to be the most cost-effective delivery system in areas where power is readily available. Conclusions There is a substantial need for building capacity for oxygen delivery throughout sub-Saharan Africa. Addressing this critical issue will require innovation and a multi-faceted approach of developing infrastructure, better equipping facilities, and health care worker training.
Noble gas bubbles in bcc metals: Ab initio-based theory and kinetic Monte Carlo modeling

Abstract Understanding the interactions of noble gases with metals is of fundamental importance for the design of radiation-resistant structural materials for fission and fusion nuclear reactors. Here we present a unified theory for describing the energetics of He, Ne, Ar, and Kr bubbles in bcc metals in group 5B (V, Nb, Ta), 6B (Cr, Mo, W) and 8B (Fe). Our predictive analytical model is based on the effective-medium and isotropic elasticity theories, and is parameterized using density functional theory (DFT) calculations of small gas-vacancy clusters. By performing kinetic Monte Carlo (KMC) simulations driven by our analytical model, we have predicted the lifetimes of noble gas bubbles and their coarsening by Ostwald ripening. Our most notable finding is the exceptionally higher thermal stability of Ne, Ar and Kr bubbles than He bubbles in bcc metals, conferring them outstanding resistance to Ostwald ripening. The physical origin of the unexpected stability of bubbles formed by large noble gas atoms has been further elucidated. Our theoretical finding is consistent with the experimental observation of He gas bubble superlattice (GBS) coarsening under thermal annealing, and provides new insights on the exceptional stability of fission GBS in bcc U-Mo up to a high homologous temperature of 0.78. The present calculated results also compare favorably with the existing thermal helium desorption spectrometry experiments in the literature.
Transfinite sequences of topologies, descriptive complexity, and approximating equivalence relations

We introduce the notion of filtration between topologies and study its stabilization properties. Descriptive set theoretic complexity plays a role in this study. Filtrations lead to natural transfinite sequences approximating a given equivalence relation. We investigate those.
Prediction of right heart failure after left ventricular assist implantation: external validation of the EUROMACS right-sided heart failure risk score.

AIMS Prediction of right heart failure (RHF) after left ventricular assist device (LVAD) implant remains a challenge. The EUROMACS right-sided heart failure (EUROMACS-RHF) risk score was proposed as a prediction tool for post-LVAD RHF but lacks from large external validation. The aim of our study was to externally validate the score. METHODS AND RESULTS From January 2007 to December 2017, 878 continuous-flow LVADs were implanted at three tertiary centres. We calculated the EUROMACS-RHF score in 662 patients with complete data. We evaluated its predictive performance for early RHF defined as either (i) need for short- or long-term right-sided circulatory support, (ii) continuous inotropic support for ≥14 days, or (iii) nitric oxide for ≥48 h post-operatively. Right heart failure occurred in 211 patients (32%). When compared with non-RHF patients, pre-operatively they had higher creatinine, bilirubin, right atrial pressure, and lower INTERMACS class (P < 0.05); length of stay and in-hospital mortality were higher. Area under the ROC curve for RHF prediction of the EUROMACS-RHF score was 0.64 [95% confidence interval (CI) 0.60-0.68]. Reclassification of patients with RHF was significantly better when applying the EUROMACS-RHF risk score on top of previous published scores. Patients in the high-risk category had significantly higher in-hospital and 2-year mortality [hazard ratio: 1.64 (95% CI 1.16-2.32) P = 0.005]. CONCLUSION In an external cohort, the EUROMACS-RHF had limited discrimination predicting RHF. The clinical utility of this score remains to be determined.
The Law and Economics of Proportionality in Discovery

This Article analyzes the proportionality standard in discovery. Many believe the renewed emphasis on this standard contained in the 2015 Amendments to the Federal Rules of Civil Procedure has the potential to infuse litigation practice with considerably more attention to questions related to the costs and benefits of discovery. We discuss the history and rationale of proportionality’s inclusion in Rule 26, adopting an analytical framework that focuses on how costs and benefits can diverge in litigation generally, and discovery in particular. Finally, we use this framework to understand the mechanics and challenges involved in deploying the six factors included in the proportionality standard. Throughout, we emphasize that the proportionality standard requires both difficult-to-answer positive questions and unavoidably normative judgments.
AI-guided discovery of the invariant host response to viral pandemics.

Abstract Background Coronavirus Disease 2019 (Covid-19) continues to challenge the limits of our knowledge and our healthcare system. Here we sought to define the host immune response, a.k.a, the “cytokine storm” that has been implicated in fatal COVID-19 using an AI-based approach. Method Over 45,000 transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a ‘seed’ gene; ACE2 was rationalized because it encodes the receptor that facilitates the entry of SARS-CoV-2 (the virus that causes COVID-19) into host cells. An AI-based approach was used to explore the utility of the signature in navigating the uncharted territory of Covid-19, setting therapeutic goals, and finding therapeutic solutions. Findings The 166-gene signature was surprisingly conserved across all vi ral p andemics, including COVID-19, and a subset of 20-genes classified disease severity, inspiring the nomenclatures ViP and severe-ViP signatures, respectively. The ViP signatures pinpointed a paradoxical phenomenon wherein lung epithelial and myeloid cells mount an IL15 cytokine storm, and epithelial and NK cell senescence and apoptosis determine severity/fatality. Precise therapeutic goals could be formulated; these goals were met in high-dose SARS-CoV-2-challenged hamsters using either neutralizing antibodies that abrogate SARS-CoV-2•ACE2 engagement or a directly acting antiviral agent, EIDD-2801. IL15/IL15RA were elevated in the lungs of patients with fatal disease, and plasma levels of the cytokine prognosticated disease severity. Interpretation The ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool to rapidly assess disease severity and vet candidate drugs. Funding This work was supported by the National Institutes for Health (NIH) [grants CA151673 and GM138385 (to DS) and AI141630 (to P.G), DK107585–05S1 (SD) and AI155696 (to P.G, D.S and S.D), U19-AI142742 (to S.C, CCHI: Cooperative Centers for Human Immunology)]; Research Grants Program Office (RGPO) from the University of California Office of the President (UCOP) (R00RG2628 & R00RG2642 to P.G, D.S and S.D); the UC San Diego Sanford Stem Cell Clinical Center (to P.G, D.S and S.D); LJI Institutional Funds (to S.C); the VA San Diego Healthcare System Institutional funds (to L.C.A). GDK was supported through The American Association of Immunologists Intersect Fellowship Program for Computational Scientists and Immunologists. One sentence summary The host immune response in COVID-19.
Discrepancy minimization via a self-balancing walk

We study discrepancy minimization for vectors in ℝn under various settings. The main result is the analysis of a new simple random process in high dimensions through a comparison argument. As corollaries, we obtain bounds which are tight up to logarithmic factors for online vector balancing against oblivious adversaries, resolving several questions posed by Bansal, Jiang, Singla, and Sinha (STOC 2020), as well as a linear time algorithm for logarithmic bounds for the Komlos conjecture.
Brauer groups and Galois cohomology of commutative ring spectra

In this paper we develop methods for classifying Baker, Richter, and Szymik's Azumaya algebras over a commutative ring spectrum, especially in the largely inaccessible case where the ring is nonconnective. We give obstruction-theoretic tools, constructing and classifying these algebras and their automorphisms with Goerss–Hopkins obstruction theory, and give descent-theoretic tools, applying Lurie's work on , previously studied by Mathew and Stojanoska.
HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture

Recently Homomorphic Encryption (HE) is used to implement Privacy-Preserving Neural Networks (PPNNs) that perform inferences directly on encrypted data without decryption. Prior PPNNs adopt mobile network architectures such as SqueezeNet for smaller computing overhead, but we find naively using mobile network architectures for a PPNN does not necessarily achieve shorter inference latency. Despite having less parameters, a mobile network architecture typically introduces more layers and increases the HE multiplicative depth of a PPNN, thereby prolonging its inference latency. In this paper, we propose a \textbf{HE}-friendly privacy-preserving \textbf{M}obile neural n\textbf{ET}work architecture, \textbf{HEMET}. Experimental results show that, compared to state-of-the-art (SOTA) PPNNs, HEMET reduces the inference latency by $59.3\%\sim 61.2\%$, and improves the inference accuracy by $0.4 \% \sim 0.5\%$.
Substantial drag reduction in turbulent flow using liquid-infused surfaces

Experiments are presented that demonstrate how liquid-infused surfaces can reduce turbulent drag significantly in Taylor–Couette flow. The test liquid was water, and the test surface was composed of square microscopic grooves measuring to , filled with alkane liquids with viscosities from 0.3 to 1.4 times that of water. We achieve drag reduction exceeding 35 %, four times higher than previously reported for liquid-infused surfaces in turbulent flow. The level of drag reduction increased with viscosity ratio, groove width, fluid area fraction and Reynolds number. The optimum groove width was given by .
Optimizing fitness-for-use of differentially private linear queries

In practice, differentially private data releases are designed to support a variety of applications. A data release is fit for use if it meets target accuracy requirements for each application. In ...
Separated trailing-edge flow at a transonic Mach number

Results from an experimental investigation of a small-scale separated flow at a sharp trailing edge are presented. The separated flow was a result of sustained adverse pressure gradients. Experiments were carried out at a transonic Mach number, without any shock wave in the flow, and at a high Reynolds number. Measurements include surface pressure distributions, mean velocity, turbulent shear stress, turbulent kinetic energy, and some streamwise turbulence intensity profiles in the region from just upstream of separation to downstream into the near-wake, following wake-closure. The results show noticeable changes in the mean and turbulent quantities in the separated region although the wall adverse pressure gradients are relatively weak. Turbulent normal stress becomes important in the vicinity of separation and in the separated zone with regard to both mean-flow development and turbulence-energy production. The experimental data are used to assess the performance of two boundary-layer calculation methods.
Discovery of 13 New Orbital Periods for Classical Novae

I report new orbital periods (P) for 13 classical novae, based on light curves from TESS, AAVSO, and other public archives. These new nova periods now constitute nearly one-seventh of all known nova periods. Five of my systems have P>1 day, which doubles the number of such systems that must have evolved companion stars. (This is simply because ground-based time series have neither the coverage nor the stability required to discover these small-amplitude long periods.) V1016 Sgr has a rare P below the period gap, and suddenly becomes useful for current debates on evolution of novae. Five of the novae (FM Cir, V399 Del, V407 Lup, YZ Ret, and V549 Vel) have the orbital modulations in the tail of the eruption after the transition phase. Soon after the transition, YZ Ret shows a unique set of aperiodic diminishing oscillations, plus YZ Ret shows two highly-significant transient periods, 1.1% and 4.5% longer than the orbital period, much like for the superhump phenomenon. I also report an optical 591.27465 second periodicity for V407 Lup, which is coherent and must be tied to the white dwarf spin period. The new orbital periods in days are 0.1883907 +- 0.0000048 for V1405 Cas, 3.4898 +- 0.0072 for FM Cir, 0.162941 +- 0.000060 for V339 Del, 3.513 +- 0.020 for V407 Lup, 1.32379 +- 0.00048 for V2109 Oph, 3.21997 +- 0.00039 for V392 Per, 0.1628714 +- 0.0000110 for V598 Pup, 0.1324539 +- 0.0000098 for YZ Ret, 0.07579635 +- 0.00000017 for V1016 Sgr, 7.101 +- 0.016 for V5583 Sgr, 0.61075 +- 0.00071 for V1534 Sco, 0.40319 +- 0.00005 for V549 Vel, and 0.146501 +- 0.000058 for NQ Vul.
Probabilistic Voltage Sensitivity based Preemptive Voltage Monitoring in Unbalanced Distribution Networks

With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel voltage monitoring approach based on low-complexity, data-driven probabilistic voltage sensitivity analysis. The usefulness of this work is not only in predicting voltage violations in unbalanced distribution grids, but also in opening up the door for optimal voltage control. Using system data and forecasts, the proposed approach predicts the distribution of system node voltages which is then used to to identify nodes that may violate the nominal operational limits with high probability. The method is tested on the IEEE 37 node distribution system considering integrated distributed solar energy sources. The method is validated against the classic load flow based method and offers over 95% accuracy in predicting voltage violations.
Quotients of the Gordian and H(2)-Gordian graphs

The Gordian graph and H(2)-Gordian graphs of knots are abstract graphs whose vertex sets represent isotopy classes of unoriented knots, and whose edge sets record whether pairs of knots are related...
On linear time decidability of differential privacy for programs with unbounded inputs

We introduce an automata model for describing interesting classes of differential privacy mechanisms/algorithms that include known mechanisms from the literature. These automata can model algorithms whose inputs can be an unbounded sequence of real-valued query answers. We consider the problem of checking whether there exists a constant d such that the algorithm described by these automata are d∈-differentially private for all positive values of the privacy budget parameter ∈. We show that this problem can be decided in time linear in the automaton's size by identifying a necessary and sufficient condition on the underlying graph of the automaton. This paper's results are the first decidability results known for algorithms with an unbounded number of query answers taking values from the set of reals.
Littlewood complexes for symmetric groups

We construct a complex $\mathcal{L}_\bullet^\lambda$ resolving the irreducible representations $\mathcal{S}^{\lambda[n]}$ of the symmetric groups $S_n$ by representations restricted from $GL_n(k)$. This construction lifts to $\mathrm{Rep}(S_\infty)$, where it yields injective resolutions of simple objects. It categorifies stable Specht polynomials, and allows us to understand evaluations of these polynomials for all $n$.
Feasibility Governor for Linear Model Predictive Control

This paper introduces the Feasibility Governor (FG): an add-on unit that enlarges the region of attraction of Model Predictive Control by manipulating the reference to ensure that the underlying optimal control problem remains feasible. The FG is developed for linear systems subject to polyhedral state and input constraints. Offline computations using polyhedral projection algorithms are used to construct the feasibility set. Online implementation relies on the solution of a convex quadratic program that guarantees recursive feasibility. The closed-loop system is shown to satisfy constraints, achieve asymptotic stability, and exhibit zero-offset tracking.
Accelerators for critical experiments involving single-particle upset in solid-state microcircuits

Charged-particle interactions in microelectronic circuit chips (integrated circuits) present a particularly insidious problem for solid-state electronic systems due to the generation of soft errors or single-particle event upset (SEU) by either cosmic rays or other radiation sources. Particle accelerators are used to provide both light and heavy ions in order to assess the propensity of integrated circuit chips for SEU. Critical aspects of this assessment involve the ability to analytically model SEU for the prediction of error rates in known radiation environments. In order to accurately model SEU, the measurement and prediction of energy deposition in the form of an electron-hole plasma generated along an ion track is of paramount importance. This requires the use of accelerators which allow for ease in both energy control (change of energy) and change of ion species. This and other aspects of ion-beam control and diagnostics (e.g., uniformity and flux) are of critical concern for the experimental verification of theoretical SEU models.
A Persistent Invasive Phenotype in Post-Hypoxic Tumor Cells is Revealed by Novel Fate-Mapping and Computational Modeling

Hypoxia is a critical factor in solid tumors that has been associated with cancer progression and aggressiveness. We recently developed a hypoxia-fate mapping system that allowed the tracing of post-hypoxic cells within a tumor for the first time. This novel approach, based on an oxygen-dependent fluorescent switch, made the investigation of the post-hypoxic phenotype possible. The system allowed us to measure key biological features such as oxygen distribution, cell proliferation and migration. Using this data, we developed a computational model to investigate the motility and phenotypic persistence of hypoxic and post-hypoxic cells during tumor progression. The behavior of hypoxic and post-hypoxic cells was defined by phenotypic persistence time, cell movement bias and the fraction of cells that respond to an enhanced migratory stimulus. Our studies revealed that post-hypoxic cells have an enhanced persistent migratory phenotype that promotes the formation of invasive structures (“plumes”) expanding towards the oxygenated tumor regions. This work combined advanced cell tracking and imaging techniques with mathematical modeling, and revealed for the first time that a persistent invasive migratory phenotype that develops under hypoxic conditions enhances their escape into non-hypoxic tumor regions to invade the surrounding tissue.
Evolutionarily Stable (Mis)specifications: Theory and Applications

We introduce an evolutionary framework to evaluate competing (mis)specifications in strategic situations, focusing on which misspecifications can persist over a correct specification. Agents with heterogeneous specifications coexist in a society and repeatedly match against random opponents to play a stage game. They draw Bayesian inferences about the environment based on personal experience, so their learning depends on the distribution of specifications and matching assortativity in the society. One specification is evolutionarily stable against another if, whenever sufficiently prevalent, its adherents obtain higher expected objective payoffs than their counterparts. The learning channel leads to novel stability phenomena compared to frameworks where the heritable unit of cultural transmission is a single belief instead of a specification (i.e., set of feasible beliefs). We apply the framework to linear-quadratic-normal games where players receive correlated signals but possibly misperceive the information structure. The correct specification is not evolutionarily stable against a correlational error, whose direction depends on matching assortativity. As another application, the framework also endogenizes coarse analogy classes in centipede games. The full paper can be found at https://kevinhe.net/papers/theory_evolution.pdf
Geometric Rates of Convergence for Kernel-based Sampling Algorithms

The rate of convergence of weighted kernel herding (WKH) and sequential Bayesian quadrature (SBQ), two kernel-based sampling algorithms for estimating integrals with respect to some target probability measure, is investigated. Under verifiable conditions on the chosen kernel and target measure, we establish a near-geometric rate of convergence for target measures that are nearly atomic. Furthermore, we show these algorithms perform comparably to the theoretical best possible sampling algorithm under the maximum mean discrepancy. An analysis is also conducted in a distributed setting. Our theoretical developments are supported by empirical observations on simulated data as well as a real world application.
Using Side Channel Information and Artificial Intelligence for Malware Detection

Cybersecurity continues to be a difficult issue for society especially as the number of networked systems grows. Techniques to protect these systems range from rules-based to artificial intelligence-based intrusion detection systems and anti-virus tools. These systems rely upon the information contained in network packets and downloaded executables to function. Side channel information leaked from hardware has been shown to reveal secret information in systems such as encryption keys. Computers provide many side channels such as temperature, access rates, operational frequencies, and voltages that can provide insight into what is running on a system. This work demonstrates that this side channel information can be used to detect malware running on a computing platform without access to the code involved.
Distributed and time-varying primal-dual dynamics via contraction analysis

In this note, we provide an overarching analysis of primal-dual dynamics associated to linear equality-constrained optimization problems using contraction analysis. For the well-known standard version of the problem: we establish convergence under convexity and the contracting rate under strong convexity. Then, for a canonical distributed optimization problem, we use partial contractivity to establish global exponential convergence of its primal-dual dynamics. As an application, we propose a new distributed solver for the least-squares problem with the same convergence guarantees. Finally, for time-varying versions of both centralized and distributed primal-dual dynamics, we exploit their contractive nature to establish bounds on their tracking error. To support our analyses, we introduce novel results on contraction theory.
Data-driven fractional subgrid-scale modeling for scalar turbulence: A nonlocal LES approach

Abstract Filtering the passive scalar transport equation in the large-eddy simulation (LES) of turbulent transport gives rise to the closure term corresponding to the unresolved scalar flux. Understanding and respecting the statistical features of subgrid-scale (SGS) flux is a crucial point in robustness and predictability of the LES. In this work, we investigate the intrinsic nonlocal behavior of the SGS passive scalar flux through studying its two-point statistics obtained from the filtered direct numerical simulation (DNS) data for passive scalar transport in homogeneous isotropic turbulence (HIT). Presence of long-range correlations in true SGS scalar flux urges to go beyond the conventional local closure modeling approaches that fail to predict the non-Gaussian statistical features of turbulent transport in passive scalars. Here, we propose an appropriate statistical model for microscopic SGS motions by taking into account the filtered Boltzmann transport equation (FBTE) for passive scalar. In FBTE, we approximate the filtered equilibrium distribution with an α-stable Levy distribution that essentially incorporates a power-law behavior to resemble the observed nonlocal statistics of SGS scalar flux. Generic ensemble-averaging of such FBTE lets us formulate a continuum level closure model for the SGS scalar flux appearing in terms of fractional-order Laplacian that is inherently nonlocal. Through a data-driven approach, we infer the optimal version of our SGS model using the high-fidelity data for the two-point correlation function between the SGS scalar flux and filtered scalar gradient, and sparse linear regression. In an a priori test, the optimal fractional-order model yields a promising performance in reproducing the probability distribution function (PDF) of the SGS dissipation of the filtered scalar variance compared to its true PDF obtained from the filtered DNS data.
The level, slope, and curve factor model for stocks

Abstract I develop a method to extract only the priced factors from stock returns. The first step estimates expected returns based on firm characteristics. The second step uses the estimated expected returns to form portfolios. The last step uses principal component analysis to extract factors from the portfolio returns. The procedure isolates and emphasizes the comovement across assets that is related to expected returns as opposed to firm characteristics. It produces three factors–level, slope, and curve–which perform as well or better than other leading models. The methodology performs well in out-of-sample tests. The new factors have macroeconomic risk interpretations.
Modeling stem cell nucleus mechanics using confocal microscopy

Nuclear mechanics is emerging as a key component of stem cell function and differentiation. While changes in nuclear structure can be visually imaged with confocal microscopy, mechanical characterization of the nucleus and its sub-cellular components require specialized testing equipment. A computational model permitting cell-specific mechanical information directly from confocal and atomic force microscopy of cell nuclei would be of great value. Here, we developed a computational framework for generating finite element models of isolated cell nuclei from multiple confocal microscopy scans and simple atomic force microscopy (AFM) tests. Confocal imaging stacks of isolated mesenchymal stem cells were converted into finite element models and siRNA-mediated Lamin A/C depletion isolated chromatin and Lamin A/C structures. Using AFM-measured experimental stiffness values, a set of conversion factors were determined for both chromatin and Lamin A/C to map the voxel intensity of the original images to the element stiffness, allowing the prediction of nuclear stiffness in an additional set of other nuclei. The developed computational framework will identify the contribution of a multitude of sub-nuclear structures and predict global nuclear stiffness of multiple nuclei based on simple nuclear isolation protocols, confocal images and AFM tests.
NMR chemical shift assignments of RNA oligonucleotides to expand the RNA chemical shift database

RNAs play myriad functional and regulatory roles in the cell. Despite their significance, three-dimensional structure elucidation of RNA molecules lags significantly behind that of proteins. NMR-based studies are often rate-limited by the assignment of chemical shifts. Automation of the chemical shift assignment process can greatly facilitate structural studies, however, accurate chemical shift predictions rely on a robust and complete chemical shift database for training. We searched the Biological Magnetic Resonance Data Bank (BMRB) to identify sequences that had no (or limited) chemical shift information. Here, we report the chemical shift assignments for 12 RNA hairpins designed specifically to help populate the BMRB.
Intensity Estimation for Poisson Process With Compositional Noise

Intensity estimation for Poisson processes is a classical problem and has been extensively studied over the past few decades. Practical observations, however, often contain compositional noise, i.e. a nonlinear shift along the time axis, which makes standard methods not directly applicable. The key challenge is that these observations are not aligned, and registration procedures are required for successful estimation. In this paper, we propose an alignment-based framework for positive intensity estimation. We first show that the intensity function is area-preserved with respect to compositional noise. Such a property implies that the time warping is only encoded in the normalized intensity, or density, function. Then, we decompose the estimation of the intensity by the product of the estimated total intensity and estimated density. The estimation of the density relies on a metric which measures the phase difference between two density functions. An asymptotic study shows that the proposed estimation algorithm provides a consistent estimator for the normalized intensity. We then extend the framework to estimating non-negative intensity functions. The success of the proposed estimation algorithms is illustrated using two simulations. Finally, we apply the new framework in a real data set of neural spike trains, and find that the newly estimated intensities provide better classification accuracy than previous methods.
Three-dimensional pose discrimination in natural images of humans

Author(s): Zhu, Hongru; Yuille, Alan; Kersten, Daniel | Abstract: Perceiving 3D structure in natural images is an immense computational challenge for the visual system. While many previous studies focused on rigid 3D objects, we applied a novel method on a common set of non-rigid objects—static images of the human body in the natural world. We investigated to what extent human ability to interpret 3D poses in natural images depends on pose typicality and viewpoint informativeness. We tested subjects on matching natural pose images with synthetic body images of the same poses given viewpoint changes. We found that performance for typical poses was measurably better than atypical poses; however, we found no significant difference between informative and less informative viewpoints. Results suggested that human ability to interpret 3D poses depends on pose typicality but not viewpoint informativeness. Further comparisons of 2D and 3D pose matching models suggested that humans probably use prior knowledge of 3D pose structures.
Measurements of the Hubble Constant: Tensions in Perspective*

Measurement of the distances to nearby galaxies have improved rapidly in recent decades. The ever-present challenge is to reduce systematic effects, especially as greater distances are probed, and the uncertainties become larger. In this paper, we combine several recent calibrations of the Tip of the Red Giant Branch (TRGB) method. These calibrations are internally self-consistent at the 1% level. New Gaia Early Data Release 3 (EDR3) data provide an additional consistency check, at a (lower) 5% level of accuracy, a result of the well-documented Gaia angular covariance bias. The updated TRGB calibration applied to a distant sample of Type Ia supernovae from the Carnegie Supernova Project results in a value of the Hubble constant of Ho = 69.8 $\pm$ 0.6 (stat) $\pm$ 1.6 (sys) km/s/Mpc. No statistically significant difference is found between the value of Ho based on the TRGB and that determined from measurements of the cosmic microwave background. The TRGB results are also consistent to within 2$\sigma$ with the SHoES and Spitzer plus HST Key Project Cepheid calibrations. The TRGB results alone do not demand additional new physics beyond the standard Lambda-CDM cosmological model. They have the advantage of simplicity of the underlying physics (the core He flash) and small systematic uncertainties (from extinction, metallicity and crowding). Finally, the strengths and weaknesses of both the TRGB and Cepheids are reviewed, and prospects for addressing the current discrepancy with future Gaia, HST and JWST observations are discussed. Resolving this discrepancy is essential for ascertaining if the claimed tension in Ho between the locally-measured and the CMB-inferred value is physically motivated.
Hyperthreads in holographic spacetimes

We generalize bit threads to hyperthreads in the context of holographic spacetimes. We define a “k-thread” to be a hyperthread which connects k different boundary regions and posit that it may be considered as a unit of k-party entanglement. Using this new object, we show that the contribution of hyperthreads to calculations of holographic entanglement entropy are generically finite. This is accomplished by constructing a surface whose area determines their maximum allowed contribution. We also identify surfaces whose area is proportional to the maximum number of k-threads, motivating a possible measure of multipartite entanglement. We use this to make connections to the current understanding of multipartite entanglement in holographic spacetimes.
A Plug-and-Play Type Field-Deployable Bio-Agent-Free Salicylic Acid Sensing System

Salicylic acid (SA) is a primary phytohormone released in response to stress (particularly biotic infections) in plants. Monitoring SA levels in plants may provide a way for early crop disease detection and application of effective measures, resulting in higher agricultural efficiency. Additionally, SA is an important chemical in the pharmaceutical and healthcare industry due to its analgesic and anti-inflammatory properties. Developing a fast and accurate way for monitoring SA levels in human serum can have a life-saving impact for patients suffering from overdosing and/or mis-dosing. In this work, we present a low-cost, portable, and field-deployable electrochemical SA sensing system aimed towards achieving the above-mentioned goals. The developed sensor consists of a plug-and-play type device equipped with specially designed high accuracy sensing electronics and a novel procedure for robust data analysis. The developed sensor exhibits excellent linearity and sensitivity and selectivity. The practical applicability of the developed sensor was also demonstrated by measuring SA levels in real samples with good accuracy.
The Volo Command in Roman Comedy

The contribution aims to show how commands and requests with volo (and nolo), e.g. volo te facere, “I want you to do [this]”, are constrained by social variables, specifically the relationship of hearer to speaker. There are 181 relevant tokens gathered from the corpus of extant Roman comedy. First, three kinds of volo command are distinguished and discussed: volo+bald infinitive, volo+accusativus cum infinitivo, and volo+finite clause with subjunctive. Second, the following are demonstrated: (1.) volo+AcI and volo+subjunctive typically convey peremptory commands; (2.) volo+bald infinitive “prefers” requests compared to the former two expressions; (3.) all three expressions tend to be used in contexts where the speaker enjoys greater authority than the hearer. To conclude, it is suggested that Plautus may use the form to support the authoritative stance of characters in Amphitruo, Casina and Captivi.