cssi/SciSciGPT-SciSciNet huggingface dataset:
High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis

In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. In this article, we introduce a surprisingly simple, interpretable, and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides both corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. The merit of the procedure is illustrated through real data analysis and simulation studies.
Equivalence of the Traditional and Non-Standard Definitions of Concepts from Real Analysis

ACL2(r) is a variant of ACL2 that supports the irrational real and complex numbers. Its logical foundation is based on internal set theory (IST), an axiomatic formalization of non-standard analysis (NSA). Familiar ideas from analysis, such as continuity, differentiability, and integrability, are defined quite differently in NSA-some would argue the NSA definitions are more intuitive. In previous work, we have adopted the NSA definitions in ACL2(r), and simply taken as granted that these are equivalent to the traditional analysis notions, e.g., to the familiar epsilon-delta definitions. However, we argue in this paper that there are circumstances when the more traditional definitions are advantageous in the setting of ACL2(r), precisely because the traditional notions are classical, so they are unencumbered by IST limitations on inference rules such as induction or the use of pseudo-lambda terms in functional instantiation. To address this concern, we describe a formal proof in ACL2(r) of the equivalence of the traditional and non-standards definitions of these notions.
The Formal Shift Operator on the Yangian Double

Let $\mathfrak{g}$ be a symmetrizable Kac-Moody algebra with associated Yangian $Y_\hbar\mathfrak{g}$ and Yangian double $\mathrm{D}Y_\hbar\mathfrak{g}$. An elementary result of fundamental importance to the theory of Yangians is that, for each $c\in \mathbb{C}$, there is an automorphism $\tau_c$ of $Y_\hbar\mathfrak{g}$ corresponding to the translation $t\mapsto t+c$ of the complex plane. Replacing $c$ by a formal parameter $z$ yields the so-called formal shift homomorphism $\tau_z$ from $Y_\hbar\mathfrak{g}$ to the polynomial algebra $Y_\hbar\mathfrak{g}[z]$. We prove that $\tau_z$ uniquely extends to an algebra homomorphism $\Phi_z$ from the Yangian double $\mathrm{D}Y_\hbar\mathfrak{g}$ into the $\hbar$-adic closure of the algebra of Laurent series in $z^{-1}$ with coefficients in the Yangian $Y_\hbar\mathfrak{g}$. This induces, via evaluation at any point $c\in \mathbb{C}^\times$, a homomorphism from $\mathrm{D}Y_\hbar\mathfrak{g}$ into the completion of the Yangian with respect to its grading. We show that each such homomorphism gives rise to an isomorphism between completions of $\mathrm{D}Y_\hbar\mathfrak{g}$ and $Y_\hbar\mathfrak{g}$ and, as a corollary, we find that the Yangian $Y_\hbar\mathfrak{g}$ can be realized as a degeneration of the Yangian double $\mathrm{D}Y_\hbar\mathfrak{g}$. Using these results, we obtain a Poincare-Birkhoff-Witt theorem for $\mathrm{D}Y_\hbar\mathfrak{g}$ applicable when $\mathfrak{g}$ is of finite type or of simply-laced affine type.
Uncertainty and the social organization of economic activity

The last two chapters of Frank H. Knight’s Risk, Uncertainty and Profit are seldom cited and frequently ignored. But in those chapters, Knight moves from entrepreneurial judgment and organizational strategies to meet the challenges uncertainty presents at the firm level to the wider question of how uncertainty affects the social organization of economic activity. Ignoring the concluding chapters of Knight’s book means we treat his book as if it stood alone, unconnected to his other early career work on free enterprise and social economic organization. The purpose of the paper is to lay out the perspective on uncertainty and the social organization of economic activity that he outlined in the final two chapters of his first major work within the context of his other contemporary writings.
Nonblocking Persistent Software Transactional Memory

Newly emerging nonvolatile alternatives to DRAM raise the possibility that applications might compute directly on long-lived data, rather than serializing them to and from a file system or database. To ensure crash consistency, such data must, like a file system or database, provide failure-atomic transactional semantics. Several persistent software transactional memory (STM) systems have been devised to provide these semantics, but only one—the OneFile system of Ramalhete et al.—is nonblocking. Nonblocking progress is desirable to avoid both performance anomalies due to process preemption or failures and deadlock due to priority inversion. Unfortunately, OneFile achieves nonblocking progress at the cost of 2 × space overhead, sacrificing much of the cost and density benefit of nonvolatile memory relative to DRAM. OneFile also requires extensive and intrusive changes to data declarations, and works only on a machine with double-width compare-and-swap (CAS) or load-linked/store-conditional (LL/SC) instructions. To address these limitations, we introduce QSTM, a nonblocking persistent STM that requires neither the modification of target data structures nor the availability of a wide CAS instruction. We describe our system, give arguments for safety and liveness, and compare performance to that of the Mnemosyne and OneFile persistent STM systems. We argue that modest performance costs (within a factor of 2 of OneFile in almost all cases) are easily justified by dramatically lower space overhead and higher programmer convenience.
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks

When equipped with efficient optimization algorithms, the over-parameterized neural networks have demonstrated high level of performance even though the loss function is non-convex and non-smooth. While many works have been focusing on understanding the loss dynamics by training neural networks with the gradient descent (GD), in this work, we consider a broad class of optimization algorithms that are commonly used in practice. For example, we show from a dynamical system perspective that the Heavy Ball (HB) method can converge to global minimum on mean squared error (MSE) at a linear rate (similar to GD); however, the Nesterov accelerated gradient descent (NAG) may only converges to global minimum sublinearly. Our results rely on the connection between neural tangent kernel (NTK) and finite over-parameterized neural networks with ReLU activation, which leads to analyzing the limiting ordinary differential equations (ODE) for optimization algorithms. We show that, optimizing the non-convex loss over the weights corresponds to optimizing some strongly convex loss over the prediction error. As a consequence, we can leverage the classical convex optimization theory to understand the convergence behavior of neural networks. We believe our approach can also be extended to other optimization algorithms and network architectures.
Research challenges in designing differentially private text generation mechanisms

Accurately learning from user data while ensuring quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. Recent literature has demonstrated the applicability of a generalized form of Differential Privacy to provide guarantees over text queries. Such mechanisms add privacy preserving noise to vectorial representations of text in high dimension and return a text based projection of the noisy vectors. However, these mechanisms are sub-optimal in their trade-off between privacy and utility. In this proposal paper, we describe some challenges in balancing this trade-off. At a high level, we provide two proposals: (1) a framework called LAC which defers some of the noise to a privacy amplification step and (2), an additional suite of three different techniques for calibrating the noise based on the local region around a word. Our objective in this paper is not to evaluate a single solution but to further the conversation on these challenges and chart pathways for building better mechanisms.
Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction

Inverse problems spanning four or more dimensions such as space, time andother independent parameters have become increasingly important. State-of-the-art 4D reconstruction methods use model based iterative reconstruction (MBIR), but depend critically on the quality of the prior modeling. Recently, plug-and-play (PnP) methods have been shown to be an effective way to incorporate advanced prior models using state-of-the-art denoising algorithms. However, state-of-the-art denoisers such as BM4D and deep convolutional neural networks (CNNs) are primarily available for 2D or 3D images and extending them to higher dimensions is difficult due to algorithmic complexity and the increased difficulty of effective training. In this paper, we present multi-slice fusion , a novel algorithm for 4D reconstruction, based on the fusion of multiple low-dimensional denoisers. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play, as a framework for integrating the multiple lower-dimensional models. We apply our method to 4D cone-beam X-ray CT reconstruction for non destructive evaluation (NDE) of samples that are dynamically moving during acquisition. We implement multi-slice fusion on distributed, heterogeneous clusters in order to reconstruct large 4D volumes in reasonable time and demonstrate the inherent parallelizable nature of the algorithm. We present simulated and real experimental results on sparse-view and limited-angle CT data to demonstrate that multi-slice fusion can substantially improve the quality of reconstructions relative to traditional methods, while also being practical to implement and train.
Local and non-local dependency learning and emergence of rule-like representations in speech data by deep convolutional generative adversarial networks

Abstract This paper argues that training Generative Adversarial Networks (GANs) on local and non-local dependencies in speech data offers insights into how deep neural networks discretize continuous data and how symbolic-like rule-based morphophonological processes emerge in a deep convolutional architecture. Acquisition of speech has recently been modeled as a dependency between latent space and data generated by GANs in Begus (2020b), who models learning of a simple local allophonic distribution. We extend this approach to test learning of local and non-local phonological processes that include approximations of morphological processes. We further parallel outputs of the model to results of a behavioral experiment where human subjects are trained on the data used for training the GAN network. Four main conclusions emerge: (i) the networks provide useful information for computational models of speech acquisition even if trained on a comparatively small dataset of an artificial grammar learning experiment; (ii) local processes are easier to learn than non-local processes, which matches both behavioral data in human subjects and typology in the world’s languages. This paper also proposes (iii) how we can actively observe the network’s progress in learning and explore the effect of training steps on learning representations by keeping latent space constant across different training steps. Finally, this paper shows that (iv) the network learns to encode the presence of a prefix with a single latent variable; by interpolating this variable, we can actively observe the operation of a non-local phonological process. The proposed technique for retrieving learning representations has general implications for our understanding of how GANs discretize continuous speech data and suggests that rule-like generalizations in the training data are represented as an interaction between variables in the network’s latent space.
Private Regulation and Foreign Conduct

Current U.S. policy on safety regulation for imported food is based largely on ex post measures. Several reform proposals seek to strengthen the ex ante component of this regulatory program. These proposals rely on one or more of three basic strategies: direct extraterritorial regulation; delegation of regulatory authority to private entities; and delegation of regulatory authority to foreign government agencies. This paper explores the ability of each strategy to respond to several principal-agent problems relevant to imported-food safety: the regulatory license problem; interest group capture; and the reality of bribery and threats in many food-exporting countries. Through the lens of these principal-agent problems, delegation to a foreign government agency is the least attractive of the three strategies. The choice between the remaining two strategies is an empirical question on which little data is currently available. To the extent governments wish to experiment with delegation to private entities, they should do so in a limited manner, focusing on specific product categories or regions. The success or failure of these programs could then inform decisions on whether to rely more broadly on delegation to private entities as a regulatory strategy.
Spinal Cord Injury Reduces Serum Levels of Fibroblast Growth Factor-21 and Impairs Its Signaling Pathways in Liver and Adipose Tissue in Mice.

Spinal cord injury (SCI) results in dysregulation of carbohydrate and lipid metabolism; the underlying cellular and physiological mechanisms remain unclear. Fibroblast growth factor 21 (FGF21) is a circulating protein primarily secreted by the liver that lowers blood glucose levels, corrects abnormal lipid profiles, and mitigates non-alcoholic fatty liver disease. FGF21 acts via activating FGF receptor 1 and s-klotho in adipose tissue and stimulating release of adiponectin from adipose tissue which in turn signals in the liver and skeletal muscle. We examined FGF21/adiponectin signaling after spinal cord transection in mice fed a high fat diet (HFD) or a standard mouse chow. Tissues were collected at 84 days after spinal cord transection or a sham SCI surgery. SCI reduced serum FGF21 levels and hepatic FGF21 expression, as well as β-klotho and FGF receptor-1 (FGFR1) mRNA expression in adipose tissue. SCI also reduced serum levels and adipose tissue mRNA expression of adiponectin and leptin, two major adipokines. In addition, SCI suppressed hepatic type 2 adiponectin receptor (AdipoR2) mRNA expression and PPARα activation in the liver. Post-SCI mice fed a HFD had further suppression of serum FGF21 levels and hepatic FGF21 expression. Elevated serum free fatty acid (FFA) levels after HFD feeding were observed in post-SCI mice but not in sham-mice, suggesting defective FFA uptake after SCI. Moreover, after SCI several genes that are implicated in insulin's action had reduced expression in tissues of interest. These findings suggest that downregulated FGF21/adiponectin signaling and impaired responsiveness of adipose tissues to FGF21 may, at least in part, contribute to the overall picture of metabolic dysfunction after SCI.
Cell surface integrin α5ß1 clustering negatively regulates receptor tyrosine kinase signaling in colorectal cancer cells via glycogen synthase kinase 3.

As a key process within the tissue microenvironment, integrin signaling can influence cell functional responses to growth factor stimuli. We show here that clustering of integrin α5s1 at the plasma membrane of colorectal cancer-derived epithelial cells modulates their ability to respond to stimulation by receptor tyrosine kinase (RTK)-activating growth factors EGF, NRG and HGF, through GSK3-mediated suppression of Akt pathway. We observed that integrin α5s1 is lost from the membrane of poorly organized human colorectal tumors and that treatment with the integrin-clustering antibody P4G11 is sufficient to induce polarity in a mouse tumor xenograft model. While adding RTK growth factors (EGF, NRG and HGF) to polarized colorectal cancer cells induced invasion and loss of monolayer formation in 2D and 3D, this pathological behavior could be blocked by P4G11. Phosphorylation of ErbB family members as well as MET following EGF, NRG and HGF treatment was diminished in cells pretreated with P4G11. Focusing on EGFR, we found that blockade of integrin α5s1 increased EGFR phosphorylation. Since activity of multiple downstream kinase pathways were altered by these various treatments, we employed computational machine learning techniques to ascertain the most important effects. Partial least-squares discriminant analysis identified GSK3 as a major regulator of EGFR pathway activities influenced by integrin α5s1. Moreover, we used partial correlation analysis to examine signaling pathway crosstalk downstream of EGF stimulation and found that integrin α5s1 acts as a negative regulator of the AKT signaling cascade downstream of EGFR, with GSK3 acting as a key mediator. We experimentally validated these computational inferences by confirming that blockade of GSK3 activity is sufficient to induce loss of polarity and increase of oncogenic signaling in the colonic epithelial cells.
A new measure of multiple jobholding in the U.S. economy

Abstract We create a measure of multiple jobholding from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics data. This new series shows that 7.8 percent of persons in the U.S. are multiple jobholders, this percentage is pro-cyclical, and has been trending upward during the past several decades. The data also show that earnings from secondary jobs are, on average, 27.8 percent of a multiple jobholder's total quarterly earnings. Multiple jobholding occurs at all levels of earnings, with both higher- and lower-earnings multiple jobholders earning more than 25 percent of their total earnings from multiple jobs. In a regression analysis that controls for age, gender, and industry, multiple jobholders earn more at all jobs than do non-multiple jobholders. These new statistics tell us that multiple jobholding is more important in the U.S. economy than we knew.
Non-conservative effects on spinning black holes from world-line effective field theory

We generalize the worldline EFT formalism developed in [4–9] to calculate the non-conservative tidal effects on spinning black holes in a long wavelength approximation that is valid to all orders in the magnitude of the spin. We present results for the rate of change of mass and angular momentum in a background field and find agreement with previous calculations obtained by different techniques. We also present new results for both the non-conservative equations of motion and power loss/gain for a binary inspiral, which start at 5PN and 2.5PN order respectively and manifest the Penrose process.
Decoupling of asymmetric dark matter during an early matter dominated era

In models of Asymmetric Dark Matter (ADM) the relic density is set by a particle asymmetry in an analogous manner to the baryons. Here we explore the scenario in which ADM decouples from the Standard Model thermal bath during an early period of matter domination. We first present a model independent analysis for a generic ADM candidate with s-wave annihilation cross section with fairly general assumptions regarding the origin of the early matter dominated period. We contrast our results to those from conventional ADM models which assume radiation domination during decoupling. Subsequently, we examine an explicit example of this scenario in the context of an elegant SO(10) implementation of ADM in which the matter dominated era is due to a long lived heavy right-handed neutrino. In the concluding remarks we discuss the prospects for superheavy ADM in this setting.
The Salted Kalman Filter: Kalman filtering on hybrid dynamical systems

Abstract Many state estimation and control algorithms require knowledge of how probability distributions propagate through dynamical systems. However, despite hybrid dynamical systems becoming increasingly important in many fields, there has been little work on utilizing the knowledge of how probability distributions map through hybrid transitions. Here, we make use of a propagation law that employs the saltation matrix (a first-order update to the sensitivity equation) to create the Salted Kalman Filter (SKF), a natural extension of the Kalman Filter and Extended Kalman Filter to hybrid dynamical systems. Away from hybrid events, the SKF is a standard Kalman filter. When a hybrid event occurs, the saltation matrix plays an analogous role as that of the system dynamics, subsequently inducing a discrete modification to both the prediction and update steps. The SKF outperforms a naive variational update – the Jacobian of the reset map – by having a reduced mean squared error in state estimation, especially immediately after a hybrid transition event. Compared against a hybrid particle filter, the particle filter outperforms the SKF in mean squared error only when a large number of particles are used, likely due to a more accurate accounting of the split distribution near a hybrid transition.
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network

Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D scan volumes. We believe conditional cGAN to be a tractable approach to generate 3D CT volumes, even though the problem of generating full resolution deep fakes is presently impractical due to GPU memory limitations. We present results for autoencoder, denoising, and depixelating tasks which are trained and tested on two novel COVID19 CT datasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from 12.53 - 46.46 dB, and range from 0.89 to 1.
Hyperbolic 24-Cell 4-Manifolds With One Cusp

In this article, we describe all the hyperbolic 24-cell 4-manifolds with exactly one cusp. There are four of these manifolds up to isometry. These manifolds are the first examples of one-cusped hyp...
Shiftry: RNN Inference in 2KB of RAM

Traditionally, IoT devices send collected sensor data to an intelligent cloud where machine learning (ML) inference happens. However, this course is rapidly changing and there is a recent trend to run ML on the edge IoT devices themselves. An intelligent edge is attractive because it saves network round trip (efficiency) and keeps user data at the source (privacy). However, the IoT devices are much more resource constrained than the cloud, which makes running ML on them challenging. Specifically, consider Arduino Uno, a commonly used board, that has 2KB of RAM and 32KB of read-only Flash memory. Although recent breakthroughs in ML have created novel recurrent neural network (RNN) models that provide good accuracy with KB-sized models, deploying them on tiny devices with such hard memory requirements has remained elusive. We provide, Shiftry, an automatic compiler from high-level floating-point ML models to fixed-point C-programs with 8-bit and 16-bit integers, which have significantly lower memory requirements. For this conversion, Shiftry uses a data-driven float-to-fixed procedure and a RAM management mechanism. These techniques enable us to provide first empirical evaluation of RNNs running on tiny edge devices. On simpler ML models that prior work could handle, Shiftry-generated code has lower latency and higher accuracy.
Decomposing changes in establishment-level emissions with entry and exit

This paper decomposes pollution releases by U.S. manufacturing establishments to show the relative importance of four establishment-level channels: entry, exit, reallocation between survivors, and within-establishment adjustment of emissions intensity. Using a panel of establishment-level output and pollution emissions to air and water for U.S. manufacturers, we decompose changes in pollution emissions into the three channels typically presented in the literature: changes in scale (output), composition (industry market share), and industry-level technique (emissions intensity). We then decompose changes due to industry-level emissions intensity into four establishment level channels for three criteria air pollutants and water pollution. For volatile organic compound emissions, nearly two-thirds of the reduction in sector-level emissions intensity is due to within-establishment reductions in emissions intensity. The other third is driven by reallocation to cleaner establishments. Though the magnitudes differ, results are broadly similar for particulate matter and sulfur dioxide. Onsite releases of effluents to water exhibit a similar pattern, though the relative importance of reallocation is greater. Additionally we find that within-establishment reductions in water emissions are associated with increased transfers to offsite publicly owned treatment facilities. The heterogeneous contributions across channels suggests that the cleanup in the U.S. manufacturing sector likely has multiple sources.
Prosecutors, court communities, and policy change: The impact of internal DOJ reforms on federal prosecutorial practices*

The current study examines how key internal U.S. Department of Justice (DOJ) policy changes have been translated into front-line prosecutorial practices. Extending courts-as-communities scholarship and research on policy implementation practices, we use U.S. Sentencing Commission data from 2004 to 2019 to model outcomes for several measures of prose- cutorial discretion in federal drug trafficking cases, including the use of mandatory minimum charges and prosecutor-endorsed departures, to test the impact of the policy changes on case processing outcomes. We contrast prosecutorial measures with measures that are more impervious to discretionary manipulation, such as criminal history, and those that represent judicial and blended discretion, including judicial departures and final sentence lengths. We find a significant effect of the policy reforms on how prosecutorial tools are used across DOJ policy periods, and we find variation across districts as a function of contextual conditions, consistent with the court communities literature. We also find that a powerful driver of changes in pros- ecutorial practices during our most recent period is the confirmation of individual Trump-appointed U.S. Attorneys at the district level, suggesting an important theoretical place for midlevel actors in policy translation and implementation.
Do Investors Care Who Did the Audit? Evidence from Form AP

In early 2017, the PCAOB mandated the disclosure of audit participants, including the lead audit partner and other audit firms participating in the audit (“component auditors”). In this study, we examine whether investors use these disclosures in a way that influences their investment decisions, a primary goal of the PCAOB. Using trading volume, absolute abnormal returns, and bid-ask spreads, we find little evidence of a significant investor response following the disclosure of partner identity or component auditor participation in the first three years of the requirement. We also examine instances where these disclosures are most likely to be informative (e.g., partners associated with restatements or component auditors with PCAOB deficiencies) and find no significant investor response. Taken together, we find little evidence that capital markets respond to partner and component auditor identity in the United States.
A Hybrid Scan Gibbs Sampler for Bayesian Models with Latent Variables

Gibbs sampling is a widely popular Markov chain Monte Carlo algorithm that can be used to analyze intractable posterior distributions associated with Bayesian hierarchical models. There are two standard versions of the Gibbs sampler: The systematic scan (SS) version, where all variables are updated at each iteration, and the random scan (RS) version, where a single, randomly selected variable is updated at each iteration. The literature comparing the theoretical properties of SS and RS Gibbs samplers is reviewed, and an alternative hybrid scan Gibbs sampler is introduced, which is particularly well suited to Bayesian models with latent variables. The word “hybrid” reflects the fact that the scan used within this algorithm has both systematic and random elements. Indeed, at each iteration, one updates the entire set of latent variables, along with a randomly chosen block of the remaining variables. The hybrid scan (HS) Gibbs sampler has important advantages over the two standard scan Gibbs samplers. First, the HS algorithm is often easier to analyze from a theoretical standpoint. In particular, it can be much easier to establish the geometric ergodicity of a HS Gibbs Markov chain than to do the same for the corresponding SS and RS versions. Second, the sandwich methodology developed in (Ann. Statist. 36 (2008) 532–554), which is also reviewed, can be applied to the HS Gibbs algorithm (but not to the standard scan Gibbs samplers). It is shown that, under weak regularity conditions, adding sandwich steps to the HS Gibbs sampler always results in a theoretically superior algorithm. Three specific Bayesian hierarchical models of varying complexity are used to illustrate the results. One is a simple location-scale model for data from the Student’s t distribution, which is used as a pedagogical tool. The other two are sophisticated, yet practical Bayesian regression models.
Recovery-based a Posteriori Error Analysis for Plate Bending Problems

We present two new recovery-based a posteriori error estimates for the Hellan–Herrmann–Johnson method in Kirchhoff–Love plate theory. The first error estimator uses a postprocessed deflection and controls the $$L^2$$ moment error and the discrete $$H^2$$ deflection error. The second one controls the $$L^2\times H^1$$ total error and utilizes superconvergent postprocessed moment field and deflection. The effectiveness of the theoretical results is numerically validated in several experiments.
Gallai–Ramsey numbers for graphs with chromatic number three

Abstract Given a graph H and an integer k ≥ 1 , the Gallai–Ramsey number G R k ( H ) is defined to be the minimum integer n such that every k -edge coloring of the complete graph K n contains either a rainbow (all different colored) triangle or a monochromatic copy of H . In this paper, we study Gallai–Ramsey numbers for graphs with chromatic number three such as K m for m ≥ 2 , where K m is a kipas with m + 1 vertices obtained from the join of K 1 and P m , and a class of graphs with five vertices, denoted by ℋ . We first study the general lower bound of such graphs and propose a conjecture for the exact value of G R k ( K m ) . Then we give a unified proof to determine the Gallai–Ramsey numbers for many graphs in ℋ and obtain the exact value of G R k ( K 4 ) for k ≥ 1 . Our outcomes not only indicate that the conjecture on G R k ( K m ) is true for m ≤ 4 , but also imply several results on G R k ( H ) for some H ∈ ℋ which are proved individually in different papers.
IoTAthena: Unveiling IoT Device Activities from Network Traffic

The recent spate of cyber attacks towards Internet of Things (IoT) devices in smart homes calls for effective techniques to understand, characterize, and unveil IoT device activities. In this paper, we present a new system, named IoTAthena, to unveil IoT device activities from raw network traffic consisting of timestamped IP packets. IoTAthena characterizes each IoT device activity using an activity signature consisting of an ordered sequence of IP packets with inter-packet time intervals. IoTAthena has two novel polynomial time algorithms, sigMatch and actExtract. For any given signature, sigMatch can capture all matches of the signature in the raw network traffic. Using sigMatch as a subfunction, actExtract can accurately unveil the sequence of various IoT device activities from the raw network traffic. Using the network traffic of heterogeneous IoT devices collected at the router of a real-world smart home testbed and a public IoT dataset, we demonstrate that IoTAthena is able to characterize and generate activity signatures of IoT device activities and accurately unveil the sequence of IoT device activities from raw network traffic.
A Localization and Navigation Method for an In-Pipe Robot in Water Distribution System Through Wireless Control Towards Long-Distance Inspection

In this paper, we propose an operation procedure for our previously developed in-pipe robotic system that is used for water quality monitoring in water distribution systems (WDS). The proposed operation procedure synchronizes a developed wireless communication system that is suitable for harsh environments of soil, water, and rock with a multi-phase control algorithm. The new “wireless control algorithm” facilitates “smart navigation” and “near real-time wireless data transmission” during operation for our in-pipe robot in WDS. The smart navigation enables the robot to pass through different configurations of the pipeline with long inspection capability with a battery in which is mounted on the robot. To this end, we have divided the operation procedure into five steps that assign a specific motion control phase and wireless communication task to the robot. We describe each step and the algorithm associated with that step in this paper. The proposed robotic system defines the configuration type in each pipeline with the pre-programmed pipeline map that is given to the robot before the operation and the wireless communication system. The wireless communication system includes some relay nodes that perform bi-directional communication in the operation procedure. The developed wireless robotic system along with operation procedure facilitates localization and navigation for the robot toward long-distance inspection in WDS.
Self-enforcing peace agreements that preserve the status quo

Abstract On the basis of a single-period, guns-versus-butter, complete-information model in which two agents dispute control over an insecure portion of their combined output, we study the choice between a peace agreement that maintains the status quo without arming (or unarmed peace) and open conflict (or war) that is possibly destructive. With a focus on outcomes that are immune to both unilateral deviations and coalitional deviations, we find that, depending on war's destructive effects, the degree of output security and the initial distribution of resources, peace can, but need not necessarily, emerge in equilibrium. We also find that, ex ante resource transfers without commitments can improve the prospects for peace, but only when the configuration of parameters describing the degree of output security and the degree of war's destruction ensures the possibility of peace without such transfers at least for some sufficiently even initial resource distributions.
Dimensional reduction and (Anti) de Sitter bounds

Dimensional reduction has proven to be a surprisingly powerful tool for delineating the boundary between the string landscape and the swampland. Bounds from the Weak Gravity Conjecture and the Repulsive Force Conjecture, for instance, are exactly preserved under dimensional reduction. Motivated by its success in these cases, we apply a similar dimensional reduction analysis to bounds on the gradient of the scalar field potential V and the mass scale m of a tower of light particles in terms of the cosmological constant Λ, which ideally may pin down ambiguous O(1) constants appearing in the de Sitter Conjecture and the (Anti) de Sitter Distance Conjecture, respectively. We find that this analysis distinguishes the bounds $$ \left|\nabla V\right|/V\ge \sqrt{4/\left(d-2\right)} $$ , m ≲ |Λ|1/2, and m ≲ |Λ|1/d in d-dimensional Planck units. The first of these bounds is equivalent to the strong energy condition in Einstein-dilaton gravity and precludes accelerated expansion of the universe. It is almost certainly violated in our universe, though it may apply in asymptotic limits of scalar field space. The second bound cannot be satisfied in our universe, though it is saturated in supersymmetric AdS vacua with well-understood uplifts to 10d/11d supergravity. The third bound likely has a limited range of validity in quantum gravity as well, so it may or may not apply to our universe. However, if it does apply, it suggests a possible relation between the cosmological constant and the neutrino mass, which (by the see-saw mechanism) may further provide a relation between the cosmological constant problem and the hierarchy problem. We also work out the conditions for eternal inflation in general spacetime dimensions, and we comment on the behavior of these conditions under dimensional reduction.
Elamipretide (SS-31) treatment attenuates age-associated post-translational modifications of heart proteins

It has been demonstrated that elamipretide (SS-31) rescues age-related functional deficits in the heart but the full set of mechanisms behind this have yet to be determined. We investigated the hypothesis that elamipretide influences post-translational modifications to heart proteins. The S-glutathionylation and phosphorylation proteomes of mouse hearts were analyzed using shotgun proteomics to assess the effects of aging on these post-translational modifications and the ability of the mitochondria-targeted drug elamipretide to reverse age-related changes. Aging led to an increase in oxidation of protein thiols demonstrated by increased S-glutathionylation of cysteine residues on proteins from Old (24 months old at the start of the study) mouse hearts compared to Young (5–6 months old). This shift in the oxidation state of the proteome was almost completely reversed by 8 weeks of treatment with elamipretide. Many of the significant changes that occurred were in proteins involved in mitochondrial or cardiac function. We also found changes in the mouse heart phosphoproteome that were associated with age, some of which were partially restored with elamipretide treatment. Parallel reaction monitoring of a subset of phosphorylation sites revealed that the unmodified peptide reporting for Myot S231 increased with age, but not its phosphorylated form and that both phosphorylated and unphosphorylated forms of the peptide covering cMyBP-C S307 increased, but that elamipretide treatment did not affect these changes. These results suggest that changes to thiol redox state and phosphorylation status are two ways in which age may affect mouse heart function, which can be restored by treatment with elamipretide.
Do U.S. firms disguise acquisitions to avoid taxes

We present evidence that tax laws distort acquisition decisions for many of the largest U.S. firms. We find that tax “sophistication”, proxied by the use of Double Irish subsidiary structures, is associated with between a 7.4% and 15.1% increase in domestic acquisitions. These results are concentrated in deals less likely to trigger regulatory scrutiny, and these deals are associated with decreases in offshore earnings using difference-in-difference tests. This is consistent with sophisticated firms using complicated transactions to invest pre-tax offshore profits domestically without repatriation taxes. We control for several sources of endogeneity that potentially affect other foreign tax minimization proxies.
exp(ASPc): Explaining ASP Programs with Choice Atoms and Constraint Rules.

We present an enhancement of exp(ASP), a system that generates explanation graphs for a literal l - an atom a or its default negation ~a - given an answer set A of a normal logic program P, which explain why l is true (or false) given A and P. The new system, exp(ASPc), differs from exp(ASP) in that it supports choice rules and utilizes constraint rules to provide explanation graphs that include information about choices and constraints.
QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices

Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future. Such services are poised to reinvent the process of navigation and tracking of people and assets in a variety of indoor and subterranean environments. The growing ownership of computationally capable smartphones has laid the foundations of portable fingerprinting-based indoor localization through deep learning. However, as the demand for accurate localization increases, the computational complexity of the associated deep learning models increases as well. We present an approach for reducing the computational requirements of a deep learning-based indoor localization framework while maintaining localization accuracy targets. Our proposed methodology is deployed and validated across multiple smartphones and is shown to deliver up to 42% reduction in prediction latency and 45% reduction in prediction energy as compared to the best-known baseline deep learning-based indoor localization model.
Broadband Imaging to Study the Spectral Distribution of Meteor Radio Afterglows

We present observations of 86 meteor radio afterglows (MRAs) using the new broadband imager at the Long Wavelength Array Sevilleta (LWA-SV) station. The MRAs were detected using the all-sky images with a bandwidth up to 20 MHz. We fit the spectra with both a power law and a log-normal function. When fit with a power law, the spectra varied from flat to steep and the derived spectral index distribution from the fit peaked at -1.73. When fit with a log-normal function, the spectra exhibits turnovers at frequencies between 30-40 MHz, and appear to be a better functional fit to the spectra. We compared the spectral parameters from the two fitting methods with the physical properties of MRAs. We observe a weak correlation between the log-normal turnover frequency and the altitude of MRAs. The spectral indices from the power law fit do not show any strong correlations with the physical properties of MRAs. However, the full width half maximum (FWHM) duration of MRAs is correlated with the local time, incidence angle, luminosity and optically derived kinetic energy of parent meteoroid. Also, the average luminosity of MRAs seems to be correlated with the kinetic energy of parent meteoroid and the altitude at which they occur.
Perturbed factor analysis: Accounting for group differences in exposure profiles

In this article we investigate group differences in phthalate exposure profiles using NHANES data. Phthalates are a family of industrial chemicals used in plastics and as solvents. There is increasing evidence of adverse health effects of exposure to phthalates on reproduction and neurodevelopment and concern about racial disparities in exposure. We would like to identify a single set of low-dimensional factors summarizing exposure to different chemicals, while allowing differences across groups. Improving on current multigroup additive factor models, we propose a class of Perturbed Factor Analysis (PFA) models that assume a common factor structure after perturbing the data via multiplication by a group-specific matrix. Bayesian inference algorithms are defined using a matrix normal hierarchical model for the perturbation matrices. The resulting model is just as flexible as current approaches in allowing arbitrarily large differences across groups but has substantial advantages that we illustrate in simulation studies. Applying PFA to NHANES data, we learn common factors summarizing exposures to phthalates, while showing clear differences across groups.
Resonances as Viscosity Limits for Black Box Perturbations

We show that the complex absorbing potential (CAP) method for computing scattering resonances applies to an abstractly defined class of black box perturbations of the Laplacian in $${{\mathbb {R}}}^n$$ which can be analytically extended from $${{\mathbb {R}}}^n$$ to a conic neighborhood in $${{\mathbb {C}}}^n$$ near infinity. The black box setting allows a unifying treatment of diverse problems ranging from obstacle scattering to scattering on finite volume surfaces.
Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy.

In temporal lobe epilepsy, the ability of the dentate gyrus to limit excitatory cortical input to the hippocampus breaks down, leading to seizures. The dentate gyrus is also thought to help discriminate between similar memories by performing pattern separation, but whether epilepsy leads to a breakdown in this neural computation, and thus to mnemonic discrimination impairments, remains unknown. Here we show that temporal lobe epilepsy is characterized by behavioral deficits in mnemonic discrimination tasks, in both humans (females and males) and mice (C57Bl6 males, systemic low-dose kainate model). Using a recently developed assay in brain slices of the same epileptic mice, we reveal a decreased ability of the dentate gyrus to perform certain forms of pattern separation. This is because of a subset of granule cells with abnormal bursting that can develop independently of early EEG abnormalities. Overall, our results linking physiology, computation, and cognition in the same mice advance our understanding of episodic memory mechanisms and their dysfunction in epilepsy. SIGNIFICANCE STATEMENT People with temporal lobe epilepsy (TLE) often have learning and memory impairments, sometimes occurring earlier than the first seizure, but those symptoms and their biological underpinnings are poorly understood. We focused on the dentate gyrus, a brain region that is critical to avoid confusion between similar memories and is anatomically disorganized in TLE. We show that both humans and mice with TLE experience confusion between similar situations. This impairment coincides with a failure of the dentate gyrus to disambiguate similar input signals because of pathologic bursting in a subset of neurons. Our work bridges seizure-oriented and memory-oriented views of the dentate gyrus function, suggests a mechanism for cognitive symptoms in TLE, and supports a long-standing hypothesis of episodic memory theories.
Robinson–Schensted correspondence for unit interval orders

The Stanley–Stembridge conjecture associates a symmetric function to each natural unit interval order $$\mathcal {P}$$ . In this paper, we define relations a la Knuth on the symmetric group for each $$\mathcal {P}$$ and conjecture that the associated $$\mathcal {P}$$ -Knuth equivalence classes are Schur-positive, refining theorems of Gasharov, Brosnan-Chow, Guay-Paquet, and Shareshian-Wachs. The resulting equivalence graphs fit into the framework of D graphs studied by Assaf. Furthermore, we conjecture that the Schur expansion is given by column-readings of $$\mathcal {P}$$ -tableaux that occur in the equivalence class. We prove these conjectures for $$\mathcal {P}$$ avoiding two specific suborders by introducing $$\mathcal {P}$$ -analog of Robinson–Schensted insertion, giving an answer to a long standing question of Chow.