cssi/SciSciGPT-SciSciNet huggingface dataset:
Uniform Regularity for free-boundary navier-stokes equations with surface tension

We study the zero-viscosity limit of free boundary Navier–Stokes equations with surface tension in unbounded domain thus extending the work of Masmoudi and Rousset [Uniform regularity and vanishing...
Trajectory stratification of stochastic dynamics

We present a general mathematical framework for trajectory stratification for simulating rare events. Trajectory stratification involves decomposing trajectories of the underlying process into frag...
On Robustness Analysis of a Dynamic Average Consensus Algorithm to Communication Delay

This paper studies the robustness of a dynamic average consensus algorithm to communication delay over strongly connected and weight-balanced (SCWB) digraphs. Under delay-free communication, the algorithm of interest achieves a practical asymptotic tracking of the dynamic average of the time-varying agents’ reference signals. For this algorithm, in both its continuous-time and discrete-time implementations, we characterize the admissible communication delay range and study the effect of the delay on the rate of convergence and the tracking error bound. Our study also includes establishing a relationship between the admissible delay bound and the maximum degree of SCWB digraphs. We also show that for delays in the admissible bound, for static signals, the algorithms achieve perfect tracking. Moreover, when the interaction topology is a connected undirected graph, we show that the discrete-time implementation is guaranteed to tolerate at least one step delay. Simulations demonstrate our results.
Iterates of generic polynomials and generic rational functions

In 1985, Odoni showed that in characteristic $0$ the Galois group of the $n$-th iterate of the generic polynomial with degree $d$ is as large as possible. That is, he showed that this Galois group is the $n$-th wreath power of the symmetric group $S_d$. We generalize this result to positive characteristic, as well as to the generic rational function. These results can be applied to prove certain density results in number theory, two of which are presented here. This work was partially completed by the late R.W.K. Odoni in an unpublished paper.
A General Analysis of the Convergence of ADMM

We provide a new proof of the linear convergence of the alternating direction method of multipliers (ADMM) when one of the objective terms is strongly convex. Our proof is based on a framework for analyzing optimization algorithms introduced in Lessard et al. (2014), reducing algorithm convergence to verifying the stability of a dynamical system. This approach generalizes a number of existing results and obviates any assumptions about specific choices of algorithm parameters. On a numerical example, we demonstrate that minimizing the derived bound on the convergence rate provides a practical approach to selecting algorithm parameters for particular ADMM instances. We complement our upper bound by constructing a nearly-matching lower bound on the worst-case rate of convergence.
Symmetrization for Embedding Directed Graphs

In this paper, we propose to solve the directed graph embedding problem via a two stage approach: in the first stage, the graph is symmetrized in one of several possible ways, and in the second stage, the so-obtained symmetrized graph is embeded using any state-of-the-art (undirected) graph embedding algorithm. Note that it is not the objective of this paper to propose a new (undirected) graph embedding algorithm or discuss the strengths and weaknesses of existing ones; all we are saying is that whichever be the suitable graph embedding algorithm, it will fit in the above proposed symmetrization framework.
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis

Converting malware into images followed by vision-based deep learning algorithms has shown superior threat detection efficacy compared with classical machine learning algorithms. When malware are visualized as images, visual-based interpretation schemes can also be applied to extract insights of why individual samples are classified as malicious. In this work, via two case studies of dynamic malware classification, we extend the local interpretable model-agnostic explanation algorithm to explain image-based dynamic malware classification and examine its interpretation fidelity. For both case studies, we first train deep learning models via transfer learning on malware images, demonstrate high classification effectiveness, apply an explanation method on the images, and correlate the results back to the samples to validate whether the algorithmic insights are consistent with security domain expertise. In our first case study, the interpretation framework identifies indirect calls that uniquely characterize the underlying exploit behavior of a malware family. In our second case study, the interpretation framework extracts insightful information such as cryptography-related APIs when applied on images created from API existence, but generate ambiguous interpretation on images created from API sequences and frequencies. Our findings indicate that current image-based interpretation techniques are promising for explaining vision-based malware classification. We continue to develop image-based interpretation schemes specifically for security applications.
NPR-1 Modulates Plasticity in C. elegans Stress-Induced Sleep.

Summary Sleep is beneficial yet antagonistic to critical functions such as foraging and escape, and we aim to understand how these competing drives are functionally integrated. C. elegans, which lives in reduced oxygen environments, engages in developmentally timed sleep (DTS) during larval stage transitions and engages in stress-induced sleep (SIS) during recovery from damaging conditions. Although DTS and SIS use distinct mechanisms to coordinate multiple sleep-associated behaviors, we show that movement quiescence in these sleep states is similarly integrated with the competing drive to avoid oxygen. Furthermore, by manipulating oxygen to deprive animals of SIS, we observe sleep rebound in a wild C. elegans isolate, indicating that sleep debt accrues during oxygen-induced SIS deprivation. Our work suggests that multiple sleep states adopt a common, highly plastic effector of movement quiescence that is suppressed by aversive stimuli and responsive to homeostatic sleep pressure, providing a limited window of opportunity for escape.
Boundedness and continuity of the time derivative in the parabolic Signorini problem

We prove the boundedness of the time derivative in the parabolic Signorini problem, as well as establish its Holder continuity at regular free boundary points.
Quantity doesn't buy quality syntax with neural language models

Recurrent neural networks can learn to predict upcoming words remarkably well on average; in syntactically complex contexts, however, they often assign unexpectedly high probabilities to ungrammatical words. We investigate to what extent these shortcomings can be mitigated by increasing the size of the network and the corpus on which it is trained. We find that gains from increasing network size are minimal beyond a certain point. Likewise, expanding the training corpus yields diminishing returns; we estimate that the training corpus would need to be unrealistically large for the models to match human performance. A comparison to GPT and BERT, Transformer-based models trained on billions of words, reveals that these models perform even more poorly than our LSTMs in some constructions. Our results make the case for more data efficient architectures.
Yersiniabactin-Producing Adherent/Invasive Escherichia coli Promotes Inflammation-Associated Fibrosis in Gnotobiotic Il10-/- Mice.

ABSTRACT Fibrosis is a significant complication of intestinal disorders associated with microbial dysbiosis and pathobiont expansion, notably Crohn’s disease (CD). Mechanisms that favor fibrosis are not well understood, and therapeutic strategies are limited. Here we demonstrate that colitis-susceptible Il10-deficient mice develop inflammation-associated fibrosis when monoassociated with adherent/invasive Escherichia coli (AIEC) that harbors the yersiniabactin (Ybt) pathogenicity island. Inactivation of Ybt siderophore production in AIEC nearly abrogated fibrosis development in inflamed mice. In contrast, inactivation of Ybt import through its cognate receptor FyuA enhanced fibrosis severity. This corresponded with increased colonic expression of profibrogenic genes prior to the development of histological disease, therefore suggesting causality. fyuA-deficient AIEC also exhibited greater localization within subepithelial tissues and fibrotic lesions that was dependent on Ybt biosynthesis and corresponded with increased fibroblast activation in vitro. Together, these findings suggest that Ybt establishes a profibrotic environment in the host in the absence of binding to its cognate receptor and indicate a direct link between intestinal AIEC and the induction of inflammation-associated fibrosis.
Fast Generalized DFTs for all Finite Groups

For any finite group G, we give an arithmetic algorithm to compute generalized Discrete Fourier Transforms (DFTs) with respect to G, using O(|G|^ω/2+e) operations, for any e > 0. Here, ω is the exponent of matrix multiplication.
Controlling Heterogeneity and Increasing Titer from Riboswitch-Regulated Bacillus subtilis Spores for Time-Delayed Protein Expression Applications

Sporulated cells have potential as time-delayed expression chassis of proteins for applications such as "on-demand" biologics production, whole cell biosensors, or oral vaccines. However, the desired attributes of high expression rates and low product variances are difficult to maintain from germinated spores. In this work, we study the effect of an integrating vs theta-replicating plasmid in a wild-type Bacillus subtilis and two PolY mutants. The cells were engineered to produce a fluorescent reporter protein (RFP) under the control of a riboswitch activated by theophylline. This allowed for greater sensitivity to point mutations. The fluorescence and cell-growth curves were fit with a custom kinetic model, and a peak kinetic rate (LKPmax) was extracted for each clonal population (n = 30 for all cell, vector, and growth combinations). Plasmid-based expression yields higher (8.7×) expression rates because of an increased copy number of the expression cassette (10× over integrated). The variance of LKPmax values increased 2.1× after sporulation for the wild-type strain. This increase in variance from sporulation is very similar to what is observed with UV exposure. This effect can be partially mitigated by the use of PolY knockouts observed in suspended cell growths and adherent biofilms.
Divide and Conquer: A Deep CASA Approach to Talker-Independent Monaural Speaker Separation

We address talker-independent monaural speaker separation from the perspectives of deep learning and computational auditory scene analysis (CASA). Specifically, we decompose the multi-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network. In the second stage, the frame-level separated spectra are sequentially grouped to different speakers by a clustering network. The proposed deep CASA approach optimizes frame-level separation and speaker tracking in turn, and produces excellent results for both objectives. Experimental results on the benchmark WSJ0-2mix database show that the new approach achieves the state-of-the-art results with a modest model size.
Leave‐Out Estimation of Variance Components

We propose leave-out estimators of quadratic forms designed for the study of linear models with unrestricted heteroscedasticity. Applications include analysis of variance and tests of linear restrictions in models with many regressors. An approximation algorithm is provided that enables accurate computation of the estimator in very large datasets. We study the large sample properties of our estimator allowing the number of regressors to grow in proportion to the number of observations. Consistency is established in a variety of settings where plug-in methods and estimators predicated on homoscedasticity exhibit first-order biases. For quadratic forms of increasing rank, the limiting distribution can be represented by a linear combination of normal and non-central χ2 random variables, with normality ensuing under strong identification. Standard error estimators are proposed that enable tests of linear restrictions and the construction of uniformly valid confidence intervals for quadratic forms of interest. We find in Italian social security records that leave-out estimates of a variance decomposition in a two-way fixed effects model of wage determination yield substantially different conclusions regarding the relative contribution of workers, firms, and worker-firm sorting to wage inequality than conventional methods. Monte Carlo exercises corroborate the accuracy of our asymptotic approximations, with clear evidence of non-normality emerging when worker mobility between blocks of firms is limited.
A trajectory mechanics approach for the study of wave propagation in an anisotropic elastic medium

Author(s): Vasco, DW; Nihei, KT | Abstract: We derive equations describing the path and traveltime of a coherent elastic wave propagating in an anisotropic medium, generalizing expressions from conventional high-frequency asymptotic ray theory. The methodology is valid across a broad range of frequencies and allows for subwavelength variations in the material properties of the medium. The primary difference from current ray methods is the retention of a term that is neglected in the derivation of the eikonal equation. The additional term contains spatial derivatives of the properties of the medium and of the amplitude field, and its presence couples the equations governing the evolution of the amplitude and phase along the trajectory. The magnitude of this term provides a measure of the validity of expressions based upon high-frequency asymptotic methods, such as the eikonal equation, when modelling wave propagation dominated by a band of frequencies. In calculations involving a layer with gradational boundaries, we find that asymptotic estimates do deviate from those of our frequency-dependent approach when the width of the layer boundaries become sufficiently narrow. For example, for a layer with boundaries that vary over tens of meters, the term neglected by a high-frequency asymptotic approximation is significant for frequencies around 10 Hz. The visible differences in the paths of the rays that traverse the layer substantiate this conclusion. For a velocity model derived from an observed well log, the majority of the trajectories calculated using the extended approach, accounting for the frequency-dependence of the rays, are noticeably different from those produced by the eikonal equation. A suite of paths from a source to a specified receiver, calculated for a range of frequencies between 10 and 100 Hz, define a region of sensitivity to velocity variations and may be used for an augmented form of tomographic imaging.
Geniculate neuralgia successfully treated with microvascular decompression

Abstract Background First described by John Nottingham in 1857, geniculate neuralgia remains a rare condition associated with vascular compression of the nervus intermedius by the anterior inferior cerebellar artery (AICA), which results in paroxysmal unilateral periauricular pain. Furthermore, limited and controversial treatment options for symptom relief exist given the rarity of the condition and limited cases reported in the literature. Case description This is a case of a 37-year-old one-pack-per-day smoker with diabetes mellitus who presented to our clinic for evaluation of episodic lancinating pain localizing to the right periauricular region. The patients symptoms were attempted to be managed medically, however, remained refractory to medical management for a period greater than one year. The patient’s exam demonstrated a trigger point slightly anterior and inferior to the right tragus, and the pain was reproducible when touched or tapped. The patient was otherwise neurologically intact. Magnetic resonance imaging (MRI) was performed and demonstrated a loop of the AICA in contact with the root entry zone of the facial nerve. This patient was offered an elective microvascular decompression (MVD) for treatment of geniculate neuralgia. Conclusions Surgical microvascular decompression is a safe and effective treatment option for patients suffering from neuralgia refractory to medical therapy. Furthermore, our case report demonstrates that MVD is an effective treatment option for patients suffering from geniculate neuralgia with imaging evidence of AICA compression of the nervus intermedius that is refractory to medical management.
GoTcha: an interactive debugger for GoT-based distributed systems

Debugging distributed systems is hard. Most of the techniques that have been developed for debugging such systems use either extensive model checking, or postmortem analysis of logs and traces. Interactive debugging is typically a tool that is only effective in single threaded and single process applications, and is rarely applied to distributed systems. While the live observation of state changes using interactive debuggers is effective, it comes with a host of problems in distributed scenarios. In this paper, we discuss the requirements an interactive debugger for distributed systems should meet, the role the underlying distributed model plays in facilitating the debugger, and the implementation of our interactive debugger: GoTcha. GoTcha is a browser based interactive debugger for distributed systems built on the Global Object Tracker (GoT) programming model. We show how the GoT model facilitates the debugger, and the features that the debugger can offer. We also demonstrate a typical debugging workflow.
Dynamic Testing of A/D Converters Using the Multiple Coherence Function

The multiple coherence function is utilized to dynamically test an arbitrary number of A/D converters that all sample the same signal. Based on the multiple-channel coherent removal method that has been popularized by Bendat and Piersol, the residual spectrum for each A/D converter under test is determined by removing the portion of the signal that can linearly be predicted by the remaining channels. Following accepted practice, the multiple coherence function is calculated from cross-spectral densities by using the Welch periodogram method. The acquisition channel of each A/D converter is modeled using an equivalent noise source model. The residual spectrum of any particular channel, which is expressed in terms of the equivalent noise sources, is determined to be the converter noise of the particular channel plus the parallel combination of the converter noise of the remaining channels. The resulting set of equations can iteratively be solved, yielding the converter noise of every channel under test. Broadband test signals are recommended so that close-to-unity coherence is achieved throughout the frequency band. When sine waves are used, the requirement for a sine wave of high spectral purity is eliminated, similar to the requirement for sidelobe suppression that uses data windowing or coherent sampling (acquiring an integer number of oscillations). Since the method confirms both synchronization and resolution, it is well suited to simultaneously test sampled multiple-channel data acquisition cards. Testing results with white noise and sine-wave signals that use an eight-channel 24-bit data acquisition card, which is synchronized with a four-channel, 16-bit data acquisition card, are presented and compared to the signal-to-noise-and-distortion ratio (SINAD) testing procedure. This procedure estimates converter noise by removing all harmonic content that can linearly be predicted by the remaining channels. With sine waves of sufficient amplitude, the removed harmonic content includes harmonic distortion that was created in the A/D converter process. For broadband test signals and small-amplitude sine waves, harmonic distortion that is created in the A/D converter process is negligible compared to the converter noise.
The Kernel Spatial Scan Statistic

Kulldorff's (1997) seminal paper on spatial scan statistics (SSS) has led to many methods considering different regions of interest, different statistical models, and different approximations while also having numerous applications in epidemiology, environmental monitoring, and homeland security. SSS provides a way to rigorously test for the existence of an anomaly and provide statistical guarantees as to how "anomalous" that anomaly is. However, these methods rely on defining specific regions where the spatial information a point contributes is limited to binary 0 or 1, of either inside or outside the region, while in reality anomalies will tend to follow smooth distributions with decaying density further from an epicenter. In this work, we propose a method that addresses this shortcoming through a continuous scan statistic that generalizes SSS by allowing the point contribution to be defined by a kernel. We provide extensive experimental and theoretical results that shows our methods can be computed efficiently while providing high statistical power for detecting anomalous regions.
Selection on mutators is not frequency-dependent

The evolutionary fate of mutator mutations - genetic variants that raise the genome-wide mutation rate - in asexual populations is often described as being frequency (or number) dependent. Mutators can invade a population by hitchhiking with a sweeping beneficial mutation, but motivated by earlier experiments results, it has been repeatedly suggested that mutators must be sufficiently frequent to produce such a driver mutation before non-mutators do. Here, we use stochastic, agent-based simulations to show that neither the strength nor the sign of selection on mutators depend on their initial frequency, and while the overall probability of hitchhiking increases predictably with frequency, the per-capita probability of fixation remains unchanged.
Sharp Guarantees for Solving Random Equations with One-Bit Information

We study the performance of a wide class of convex optimization-based estimators for recovering a signal from corrupted one-bit measurements in high-dimensions. Our general result predicts sharply the performance of such estimators in the linear asymptotic regime when the measurement vectors have entries iid Gaussian. This includes, as a special case, the previously studied least-squares estimator and various novel results for other popular estimators such as least-absolute deviations, hinge-loss and logistic-loss. Importantly, the sharp nature of our results allows for accurate comparisons between these different estimators. Numerical simulations corroborate our theoretical findings and suggest they are accurate even for relatively small problem dimensions.
Teaching for Transfer to First-Year L2 Writers

Previous studies have identified that genres and genre knowledge are not only pivotal for the development of writing expertise but also for facilitating writing-related transfer. However, little research concerns issues of teaching genres for writing transfer to first-year English as a second language (L2) writers at universities in the US. This article attempts to develop a genre-based pedagogic framework for L2 transfer teaching, aiming to help first-year L2 students address linguistic, rhetorical, and genre-bound challenges they confront and improve their writing expertise, as well as develop their ability of writing transfer across disciplines. The goals of this article are dual: (a) to address an existing gap in the literature and research on transfer, and (b) provide academic writing instructions for teaching first-year L2 writers at universities in the United States.
Tourism In Belize: Ensuring Sustained Growth

Belize’s tourism sector has witnessed impressive growth in recent years with overnight tourist arrivals registering double digit annual growth rates since 2016. To guide the development of the tourism sector from 2012 to 2030, the government endorsed a National Sustainable Tourism Master Plan in 2011, setting various initiatives and targets for the immediate and medium terms. Using a panel regression analysis on twelve Caribbean countries, this paper finds that accelerating structural reforms, fortifying governance frameworks, reducing crime, and mitigating the impact of natural disasters will help sustain tourism growth in Belize and contribute to economic well-being. This is in addition to tackling infrastructure bottlenecks and mitigating concerns relating to the “shared economy”.
Min-max theory for networks of constant geodesic curvature

Abstract We prove that on a closed surface, for any c > 0 , our min-max theory for prescribing mean curvature produces a solution given by a curve of constant geodesic curvature c which is almost embedded, except for finitely many points, at which the solution is a stationary junction with integer density. Moreover, each smooth segment has multiplicity one. The key is a classification of blowups which is new even for c = 0 .
Fundamental Limits of Approximate Gradient Coding

In the distributed graident coding problem, it has been established that, to exactly recover the gradient under s slow machines, the mmimum computation load (number of stored data partitions) of each worker is at least linear ($s+1$), which incurs a large overhead when s is large~\citetandon2017gradient. In this paper, we focus on approximate gradient coding that aims to recover the gradient with bounded error e. Theoretically, our main contributions are three-fold: (i) we analyze the structure of optimal gradient codes, and derive the information-theoretical lower bound of minimum computation load: $O(log(n)/log(n/s))$ for e = 0$ and $d\geq O(log(1/e)/log(n/s))$ for e>0$, where d is the computation load, and e is the error in the gradient computation; (ii) we design two approximate gradient coding schemes that exactly match such lower bounds based on random edge removal process; (iii) we implement our schemes and demonstrate the advantage of the approaches over the current fastest gradient coding strategies. The proposed schemes provide order-wise improvement over the state of the art in terms of computation load, and are also optimal in terms of both computation load and latency.
Enduring and Sex-specific Changes in Hippocampal Gene Expression after a Subchronic Immune Challenge.

Abstract Major illnesses, including heart attack and sepsis, can cause cognitive impairments, depression, and progressive memory decline that persist long after recovery from the original illness. In rodent models of sepsis or subchronic immune challenge, memory deficits also persist for weeks or months, even in the absence of ongoing neuroimmune activation. This raises the question of what mechanisms in the brain mediate such persistent changes in neural function. Here, we used RNA-sequencing as a large-scale, unbiased approach to identify changes in hippocampal gene expression long after a subchronic immune challenge previously established to cause persistent memory impairments in both males and females. We observed enduring dysregulation of gene expression three months after the end of a subchronic immune challenge. Surprisingly, there were striking sex differences in both the magnitude of changes and the specific genes and pathways altered, where males showed persistent changes in both immune- and plasticity-related genes three months after immune challenge, whereas females showed few such changes. In contrast, females showed striking differential gene expression in response to a subsequent immune challenge. Thus, immune activation has enduring and sex-specific consequences for hippocampal gene expression and the transcriptional response to subsequent stimuli. Together with findings of long-lasting memory impairments after immune challenge, these data suggest that illnesses can cause enduring vulnerability to, cognitive decline, affective disorders, and memory impairments via dysregulation of transcriptional processes in the brain.
Was Keynesian economics ever dead? If so, has it been resurrected?

This article reflects on rising interest in Keynesian macroeconomics in the aftermath of the Great Recession. I identify aspects of Keynesian thinking that never were completely banished from the mainstream as well as threads of Keynesian macroeconomics that have become more influential since the crisis. However, the way most mainstream analysis continues to invoke the zero lower bound for short-term interest rates and the concept of the 'naturalâ€™ rate of interest implies that any Keynesian resurrection outside of heterodoxy remains incomplete. The future may bring broader recognition of how demand leads economic growth and of ways in which the demand side leads the supply side beyond the typical textbook 'short run.â€™
BBN and the Primordial Abundances

The relic abundances of the light elements synthesized during the first few minutes of the evolution of the Universe provide unique probes of cosmology and the building blocks for stellar and galactic chemical evolution, while also enabling constraints on the baryon (nucleon) density and on models of particle physics beyond the standard model. Recent WMAP analyses of the CBR temperature fluctuation spectrum, combined with other, relevant, observational data, has yielded very tight constraints on the baryon density, permitting a detailed, quantitative confrontation of the predictions of Big Bang Nucleosynthesis with the post-BBN abundances inferred from observational data. The current status of this comparison is presented, with an emphasis on the challenges to astronomy, astrophysics, particle physics, and cosmology it identifies.
Stochastic Growth in a Small World

We considered the Edwards-Wilkinson model on a small-world network. We studied the finite-size behavior of the surface width by performing exact numerical diagonalization for the underlying coupling matrix. We found that the spectrum exhibits a gap or a pseudo-gap, which is responsible for a finite width in the thermodynamic limit for an arbitrarily weak but nonzero magnitude of the random interactions.
Totally Asynchronous Distributed Quadratic Programming with Independent Stepsizes and Regularizations

Quadratic programs arise in robotics, communications, smart grids, and many other applications. As these problems grow in size, finding solutions becomes much more computationally demanding, and new algorithms are needed to efficiently solve them. Targeting large-scale problems, we develop a multi-agent quadratic programming framework in which each agent updates only a small number of the total decision variables in a problem. Agents communicate their updated values to each other, though we do not impose any restrictions on the timing with which they do so, nor on the delays in these transmissions. Furthermore, we allow weak parametric coupling among agents, in the sense that they are free to independently choose their stepsizes, subject to mild restrictions. We show that these stepsize restrictions depend upon a problem's condition number. We further provide the means for agents to independently regularize the problem they solve, thereby improving condition numbers and, as we will show, convergence properties, while preserving agents' independence in selecting parameters. Simulation results are provided to demonstrate the success of this framework on a practical quadratic program.
The Role of Strategic Load Participants in Two-Stage Settlement Electricity Markets

Two-stage electricity market clearing is designed to maintain market efficiency under ideal conditions, e.g., perfect forecast and nonstrategic generation. This work demonstrates that the individual strategic behavior of inelastic load participants in a two-stage settlement electricity market can deteriorate efficiency. Our analysis further implies that virtual bidding can play a role in alleviating this loss of efficiency by mitigating the market power of strategic load participants. We use real-world market data from New York ISO to validate our theory.
A Communication-Efficient Algorithm for Exponentially Fast Non-Bayesian Learning in Networks

We introduce a simple time-triggered protocol to achieve communication-efficient non-Bayesian learning over a network. Specifically, we consider a scenario where a group of agents interact over a graph with the aim of discerning the true state of the world that generates their joint observation profiles. To address this problem, we propose a novel distributed learning rule wherein agents aggregate neighboring beliefs based on a min-protocol, and the inter-communication intervals grow geometrically at a rate a ≥ 1. Despite such sparse communication, we show that each agent is still able to rule out every false hypothesis exponentially fast with probability 1, as long as a is finite. For the special case when communication occurs at every time-step, i.e., when a = 1, we prove that the asymptotic learning rates resulting from our algorithm are network-structure independent, and a strict improvement over existing rates. In contrast, when a>1, our analysis reveals that the asymptotic learning rates vary across agents, and exhibit a non-trivial dependence on the network topology and the relative entropies of the agents’ likelihood models. This motivates us to consider the problem of allocating signal structures to agents to maximize appropriate performance metrics. In certain special cases, we show that the eccentricity centrality and the decay centrality of the underlying graph help identify optimal allocations; for more general cases, we bound the deviation from the optimal allocation as a function of the parameter a, and the diameter of the graph.
ShapeVis: High-dimensional Data Visualization at Scale

We present ShapeVis, a scalable visualization technique for point cloud data inspired from topological data analysis. Our method captures the underlying geometric and topological structure of the data in a compressed graphical representation. Much success has been reported by the data visualization technique Mapper, that discreetly approximates the Reeb graph of a filter function on the data. However, when using standard dimensionality reduction algorithms as the filter function, Mapper suffers from considerable computational cost. This makes it difficult to scale to high-dimensional data. Our proposed technique relies on finding a subset of points called landmarks along the data manifold to construct a weighted witness-graph over it. This graph captures the structural characteristics of the point cloud, and its weights are determined using a Finite Markov Chain. We further compress this graph by applying induced maps from standard community detection algorithms. Using techniques borrowed from manifold tearing, we prune and reinstate edges in the induced graph based on their modularity to summarize the shape of data. We empirically demonstrate how our technique captures the structural characteristics of real and synthetic data sets. Further, we compare our approach with Mapper using various filter functions like t-SNE, UMAP, LargeVis and show that our algorithm scales to millions of data points while preserving the quality of data visualization.
A Prospect Theoretic Approach for Trust Management in IoT Networks Under Manipulation Attacks

As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous in our daily lives, it necessitates the capability to measure the trustworthiness of the aggregate data from such systems to make fair decisions. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application. In addition, there exist varying levels of uncertainty associated with an evidence upon which a trust model is built. Hence, the data integrity scoring mechanisms require some provisions to adapt to different risk attitudes and uncertainties. In this article, we propose a prospect theoretic framework for data integrity scoring that quantifies the trustworthiness of the collected data from IoT devices in the presence of adversaries who try to manipulate the data. In our proposed method, we consider an imperfect anomaly monitoring mechanism that tracks the transmitted data from each device and classifies the outcome (trustworthiness of data) as not compromised, compromised, or undecided. These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters. These parameters are then used for calculating a utility value via prospect theory to evaluate the reliability of the aggregate data at an IoT hub. In addition, to take into account different risk attitudes, we propose two types of fusion rule at IoT hub—optimistic and conservative. Furthermore, we put forward asymmetric weighted moving average scheme to measure the trustworthiness of aggregate data in presence of On-Off attacks. The proposed framework is validated using extensive simulation experiments for both uniform and On-Off attacks. We show how trust scores vary under a variety of system factors like attack magnitude and inaccurate detection. In addition, we measure the trustworthiness of the aggregate data using the well-known expected utility theory and compare the results with that obtained by prospect theory. The simulation results reveal that prospect theory quantifies trustworthiness of the aggregate data better than expected utility theory.
Nanoparticle-based targeted delivery of pentagalloyl glucose reverses elastase-induced abdominal aortic aneurysm and restores aorta to the healthy state in mice.

Aim Abdominal aortic aneurysms (AAA) is a life-threatening weakening and expansion of the abdominal aorta due to inflammatory cell infiltration and gradual degeneration of extracellular matrix (ECM). There are no pharmacological therapies to treat AAA. We tested the hypothesis that nanoparticle (NP) therapy that targets degraded elastin and delivers anti-inflammatory, anti-oxidative, and ECM stabilizing agent, pentagalloyl glucose (PGG) will reverse advance stage aneurysm in an elastase-induced mouse model of AAA. Method and results Porcine pancreatic elastase (PPE) was applied periadventitially to the infrarenal aorta in mice and AAA was allowed to develop for 14 days. Nanoparticles loaded with PGG (EL-PGG-NPs) were then delivered via IV route at 14-day and 21-day (10 mg/kg of body weight). A control group of mice received no therapy. The targeting of NPs to the AAA site was confirmed with fluorescent dye marked NPs and gold NPs. Animals were sacrificed at 28-d. We found that targeted PGG therapy reversed the AAA by decreasing matrix metalloproteinases MMP-9 and MMP-2, and the infiltration of macrophages in the medial layer. The increase in diameter of the aorta was reversed to healthy controls. Moreover, PGG treatment restored degraded elastic lamina and increased the circumferential strain of aneurysmal aorta to the healthy levels. Conclusion Our results support that site-specific delivery of PGG with targeted nanoparticles can be used to treat already developed AAA. Such therapy can reverse inflammatory markers and restore arterial homeostasis.
APC Controls Wnt-Induced β-catenin Destruction Complex Recruitment in Human Colonocytes

Wnt/β-catenin signaling is essential for intestinal homeostasis and is aberrantly activated in most colorectal cancers (CRC) through mutation of the tumor suppressor Adenomatous Polyposis Coli (APC). APC is an essential component of a cytoplasmic protein complex that targets β-catenin for destruction. Following Wnt ligand presentation, this complex is inhibited. However, a role for APC in this inhibition has not been shown. Here, we utilized Wnt3a-beads to locally activate Wnt co-receptors. In response, the endogenous β-catenin destruction complex reoriented toward the local Wnt cue in CRC cells with full-length APC, but not if APC was truncated or depleted. Non-transformed human colon epithelial cells displayed similar Wnt-induced destruction complex localization which appeared to be dependent on APC and less so on Axin. Our results expand the current model of Wnt/β-catenin signaling such that in response to Wnt, the β-catenin destruction complex: (1) maintains composition and binding to β-catenin, (2) moves toward the plasma membrane, and (3) requires full-length APC for this relocalization.
Implicit upwind methods for the compressible Navier-Stokes equations

A class of implicit upwind differencing methods for the compressible Navier-Stokes equations is described and applied. The methods are based on the use of local eigenvalues or wave speeds to control spatial differencing of inviscid terms and are aimed at increasing the level of accuracy and stability achievable in computation. Techniques for accelerating the rate of convergence to a steady state solution are also used. Applications to inviscid and viscous transonic flows are discussed and compared with other methods and experimental measurements. It is shown that accurate and efficient transonic airfoil calculations can be made on the Cray-l computer in less than 2 min.
Competing with complementors: An empirical look at Amazon.com

Research Summary: Platform owners sometimes enter complementors' product spaces and compete against them. Using data from Amazon.com to study Amazon's entry pattern into third‐party sellers' product spaces, we find that Amazon is more likely to target successful product spaces. We also find that Amazon is less likely to enter product spaces that require greater seller efforts to grow, suggesting that complementors' platform‐specific investments influence platform owners' entry decisions. While Amazon's entry discourages affected third‐party sellers from subsequently pursuing growth on the platform, it increases product demand and reduces shipping costs for consumers. We consider the implications of these findings for complementors in platform‐based markets. Managerial Summary: Platform owners can exert considerable influence over their complementors' welfare. Many complementors with successful products are pushed out of markets because platform owners enter their product spaces and compete directly with them. To mitigate such risks, complementors could build their businesses by aggregating nonblockbuster products or focusing on products requiring significant platform‐specific investments to grow. They should also develop capabilities in new product discovery so that they could continually bring innovative products to their platforms.
Confessions of an Internet Monopolist: Demand Estimation for a Versioned Information Good

We investigate profit- maximizing versioning plans for an information goods monopolist. The analysis employs data obtained from a web-based field experiment in which potential buyers were offered information goods in varied price-quality configurations. Maximum simulated likelihood (MSL) methods are used to estimate parameters describing the distribution of utility function parameters across potential buyers of the good. The resulting estimates are used to examine the impact of versioning on seller profits and market efficiency.
AN INTEGRATED MODEL OF DOWNTOWN PARKING AND TRAFFIC CONGESTION

Abstract This paper is the first to look at cruising for parking from an economic perspective. We present a downtown parking model that integrates traffic congestion and saturated on-street parking; the stock of cars cruising for parking adds to traffic congestion. Two major results emerge from the model, one of which is robust. The robust one is that, whether or not the amount of on-street parking is optimal, it is efficient to raise the on-street parking fee to the point where cruising for parking is eliminated without parking becoming unsaturated. The other is that, if the parking fee is fixed at a sub-optimal level, it is second-best optimal to increase the amount of curbside allocated to parking until cruising for parking is eliminated without parking becoming unsaturated
Improving Style Transfer with Calibrated Metrics

Style transfer produces a transferred image which is a rendering of a content image in the manner of a style image. We seek to understand how to improve style transfer.To do so requires quantitative evaluation procedures, but current evaluation is qualitative, mostly involving user studies. We describe a novel quantitative evaluation procedure. Our procedure relies on two statistics: the Effectiveness (E) statistic measures the extent that a given style has been transferred to the target, and the Coherence (C) statistic measures the extent to which the original image’s content is preserved. Our statistics are calibrated to human preference: targets with larger values of E and C will reliably be preferred by human subjects in comparisons of style and content, respectively.We use these statistics to investigate relative performance of a number of Neural Style Transfer (NST) methods, revealing a number of intriguing properties. Admissible methods lie on a Pareto frontier (i.e. improving E reduces C, or vice versa). Three methods are admissible: Universal style transfer produces very good C but weak E; modifying the optimization used for Gatys’ loss produces a method with strong E and strong C; and a modified cross-layer method has slightly better E at strong cost in C. While the histogram loss improves the E statistics of Gatys’ method, it does not make the method admissible. Surprisingly, style weights have relatively little effect in improving EC scores, and most variability in transfer is explained by the style itself (meaning experimenters can be misguided by selecting styles). Our GitHub Link is available.1
Lagrange interpolation over division rings

For a division ring F, the polynomials f∈F[z] can be evaluated “on the left” and “on the right” giving rise to left and right Lagrange interpolation problems. The problems containing interpolation ...
Well-being and education of urology residents during the COVID-19 pandemic: Results of an American National Survey.

Background The rapid spread of COVID-19 has placed tremendous strain on the American healthcare system. Few prior studies have evaluated the well-being of or changes to training for American resident physicians during the COVID-19 pandemic. We aim to study predictors of trainee well-being and changes to clinical practice using an anonymous survey of American urology residents. Methods An anonymous, voluntary, 47-question survey was sent to all ACGME-accredited urology programmes in the United States. We executed a cross-sectional analysis evaluating risk factors of perception of anxiety and depression both at work and home and educational outcomes. Multiple linear regressions models were used to estimate beta coefficients and 95% confidence intervals. Results Among ~1800 urology residents in the USA, 356 (20%) responded. Among these respondents, 24 had missing data leaving a sample size of 332. Important risk factors of mental health outcomes included perception of access to PPE, local COVID-19 severity and perception of susceptible household members. Risk factors for declination of redeployment included current redeployment, having children and concerns regarding ability to reach case minimums. Risk factors for concern of achieving operative autonomy included cancellation of elective cases and higher level of training. Conclusions Several potential actions, which could be taken by urology residency programme directors and hospital administration, may optimise urology resident well-being, morale, and education. These include advocating for adequate access to PPE, providing support at both the residency programme and institutional levels, instituting telehealth education programmes, and fostering a sense of shared responsibility of COVID-19 patients.
Singularities of Complex-Valued Solutions to Linear Parabolic Equations

We construct examples of blowup from smooth data for complex-valued solutions to linear uniformly parabolic equations in dimension $n \geq 2$, which are exactly as irregular as parabolic energy estimates allow.