cssi/SciSciGPT-SciSciNet huggingface dataset:
One-dimensional stable rings

A commutative ring R is stable provided every ideal of R containing a nonzerodivisor is projective as a module over its ring of endomorphisms. The class of stable rings includes the one-dimensional local Cohen–Macaulay rings of multiplicity at most 2, as well as certain rings of higher multiplicity, necessarily analytically ramified. The former are important in the study of modules over Gorenstein rings, while the latter arise in a natural way from generic formal fibers and derivations. We characterize one-dimensional stable local rings in several ways. The characterizations involve the integral closure R‾ of R and the completion of R in a relevant ideal-adic topology. For example, we show: If R is a reduced stable ring, then there are exactly two possibilities for R: (1) R is a Bass ring, that is, R is a reduced Noetherian local ring such that R‾ is finitely generated over R and every ideal of R is generated by two elements; or (2) R is a bad stable domain, that is, R is a one-dimensional stable local domain such that R‾ is not a finitely generated R-module.
Nonlinear stability of periodic traveling wave solutions of systems of viscous conservation laws in the generic case

Abstract Extending previous results of Oh–Zumbrun and Johnson–Zumbrun, we show that spectral stability implies linearized and nonlinear stability of spatially periodic traveling wave solutions of viscous systems of conservation laws for systems of generic type, removing a restrictive assumption that wave speed be constant to first order along the manifold of nearby periodic solutions. Key to our analysis is a nonlinear cancellation estimate observed by Johnson and Zumbrun, along with a detailed understanding of the Whitham averaged system. The latter motivates a careful analysis of the Bloch perturbation expansion near zero frequency and suggests factoring out an appropriate translational modulation of the underlying wave, allowing us to derive the sharpened low-frequency estimates needed to close the nonlinear iteration arguments.
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation

We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the signal reconstruction mean squared error (MSE). To that end, we formulate the task at hand as the minimization of MSE subject to binary constraints, and approximate the resulting NP-hard problem via semidefinite programming (SDP) relaxation. Moreover, we provide an alternative formulation based on maximizing a monotone weak submodular function and propose a randomized-greedy algorithm to find a sub-optimal subset. We then derive a worst-case performance guarantee on the MSE returned by the randomized greedy algorithm for general non-stationary graph signals. The efficacy of the proposed methods is illustrated through numerical simulations on synthetic and realworld graphs. Notably, the randomized greedy algorithm yields an order-of-magnitude speedup over state-of-the-art greedy sampling schemes, while incurring only a marginal MSE performance loss.
A note on two-sided ideals in locally C*-algebras

Copyright c � 2013 Alexander A. Katz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Gradient estimates for Stokes systems in domains

We study the stationary Stokes system with Dini mean oscillation coefficients in a domain having $C^{1,\rm{Dini}}$ boundary. We prove that if $(u, p)$ is a weak solution of the system with zero Dirichlet boundary condition, then $(Du,p)$ is continuous up to the boundary. We also prove a weak type-$(1,1)$ estimate for $(Du, p)$.
Precise Recovery of Latent Vectors from Generative Adversarial Networks

Generative adversarial networks (GANs) transform latent vectors into visually plausible images. It is generally thought that the original GAN formulation gives no out-of-the-box method to reverse the mapping, projecting images back into latent space. We introduce a simple, gradient-based technique called stochastic clipping. In experiments, for images generated by the GAN, we precisely recover their latent vector pre-images 100% of the time. Additional experiments demonstrate that this method is robust to noise. Finally, we show that even for unseen images, our method appears to recover unique encodings.
Gelfand-Kirillov dimension of cosemisimple Hopf algebras

In this note, we compute the Gelfand-Kirillov dimension of cosemisimple Hopf algebras that arise as deformations of a linearly reductive algebraic group. Our work lies in a purely algebraic setting and generalizes results of Goodearl-Zhang (2007), of Banica-Vergnioux (2009), and of D'Andrea-Pinzari-Rossi (2017).
A classical Perron method for existence of smooth solutions to boundary value and obstacle problems for degenerate-elliptic operators via holomorphic maps

Abstract We prove existence of solutions to boundary value problems and obstacle problems for degenerate-elliptic, linear, second-order partial differential operators with partial Dirichlet boundary conditions using a new version of the Perron method. The elliptic operators considered have a degeneracy along a portion of the domain boundary which is similar to the degeneracy of a model linear operator identified by Daskalopoulos and Hamilton [9] in their study of the porous medium equation or the degeneracy of the Heston operator [21] in mathematical finance. Existence of a solution to the partial Dirichlet problem on a half-ball, where the operator becomes degenerate on the flat boundary and a Dirichlet condition is only imposed on the spherical boundary, provides the key additional ingredient required for our Perron method. Surprisingly, proving existence of a solution to this partial Dirichlet problem with “mixed” boundary conditions on a half-ball is more challenging than one might expect. Due to the difficulty in developing a global Schauder estimate and due to compatibility conditions arising where the “degenerate” and “non-degenerate boundaries” touch, one cannot directly apply the continuity or approximate solution methods. However, in dimension two, there is a holomorphic map from the half-disk onto the infinite strip in the complex plane and one can extend this definition to higher dimensions to give a diffeomorphism from the half-ball onto the infinite “slab”. The solution to the partial Dirichlet problem on the half-ball can thus be converted to a partial Dirichlet problem on the slab, albeit for an operator which now has exponentially growing coefficients. The required Schauder regularity theory and existence of a solution to the partial Dirichlet problem on the slab can nevertheless be obtained using previous work of the author and C. Pop [16] . Our Perron method relies on weak and strong maximum principles for degenerate-elliptic operators, concepts of continuous subsolutions and supersolutions for boundary value and obstacle problems for degenerate-elliptic operators, and maximum and comparison principle estimates previously developed by the author [13] .
Recognizing Material Properties from Images

Humans implicitly rely on the properties of materials to guide our interactions. Grasping smooth materials, for example, requires more care than rough ones. We may even visually infer non-visual properties (e.g., softness is a physical material property). We refer to visually-recognizable material properties as visual material attributes. Recognizing these attributes in images can provide valuable information for scene understanding and material recognition. Unlike typical object and scene attributes, however, visual material attributes are local (i.e., “fuzziness” does not have a shape). Given full supervision, we may accurately recognize such attributes from purely local information (small image patches). Obtaining consistent full supervision at scale, however, is challenging. To solve this problem, we probe the human visual perception of materials. By asking simple yes/no questions comparing pairs of image patches, we obtain the weak supervision required to build a set of classifiers for attributes that, while unnamed, function similarly to the attributes with which we describe materials. Furthermore, we integrate this method in the end-to-end learning of a CNN that simultaneously recognizes materials and their visual attributes. Experiments show that visual material attributes serve as both a useful representation for known material categories and as a basis for transfer learning.
Feeding kinematics and morphology of the alligator gar (Atractosteus spatula, Lacépède, 1803).

Living gars are a small clade of seven species that occupy an important position on the actinopterygian phylogenetic tree as members of Holostei, sister-group to teleosts, and exhibit many plesiomorphic traits used to interpret and reconstruct early osteichthyan feeding mechanisms. Previous studies of gar feeding kinematics have focused on the ram-based, lateral-snapping mode of prey capture found in the narrow-snouted Lepisosteus genus, whereas this study focuses on a member of the broad-snouted Atractosteus sister-genus, the alligator gar (Atractosteus spatula, Lacepede, 1803). High-speed videography reveals that the feeding system of alligator gars is capable of rapid expansion from anterior to posterior, timed in a way to generate suction, counteract the effects of a bow-wave during ram-feeding, and direct a unidirectional flow of water through the feeding system. Reconstructed contrast-enhanced μCT-based cranial anatomy and three-dimensional modeling of linkage mechanics show that a lateral-sliding palatoquadrate, flexible intrasuspensorial joint, pivoting interhyal, and retractable pectoral girdle increase the range of motion and expansive capabilities of the alligator gar feeding mechanism. Reconstructions of muscular anatomy, inferences from in vivo kinematics, and in situ manipulations show that input from the hyoid constrictors and hypaxials play an important role in decoupling and modulating the dual roles of the sternohyoideus during feeding: hyoid retraction (jaw opening) and hyoid rotation (pharyngeal expansion). The alligator gar possesses an intricate feeding mechanism, capable of precise control with plesiomorphic muscles that represent one of the many ways the ancestral osteichthyan feeding mechanism has been modified for prey capture.
Cooperative Self‑Assembly of Pyridine‑2,6‑Diimine‑Linked Macrocycles into Mechanically Robust Nanotubes

Nanotubes assembled from macrocyclic precursors offer a unique combination of low dimensionality, structural rigidity, and distinct interior and exterior microenvironments. Usually the weak stacking energies of macrocycles limit the length and mechanical strength of the resultant nanotubes. Imine-linked macrocycles were recently found to assemble into high-aspect ratio (>103 ), lyotropic nanotubes in the presence of excess acid. Yet these harsh conditions are incompatible with many functional groups and processing methods, and lower acid loadings instead catalyze macrocycle degradation. Here we report pyridine-2,6-diimine-linked macrocycles that assemble into high-aspect ratio nanotubes in the presence of less than 1 equiv of CF3 CO2 H per macrocycle. Analysis by gel permeation chromatography and fluorescence spectroscopy revealed a cooperative self-assembly mechanism. The low acid concentrations needed to induce assembly enabled nanofibers to be obtained by touch-spinning, which exhibit higher Young's moduli (1.33 GPa) than many synthetic polymers and biological filaments. These findings represent a breakthrough in the design of inverse chromonic liquid crystals, as assembly under such mild conditions will enable the design of structurally diverse and mechanically robust nanotubes from synthetically accessible macrocycles.
Nonstandard existence proofs for reaction diffusion equations

We give an existence proof for distribution solutions to a scalar reaction diffusion equation, with the aim of illustrating both the differences and the common ingredients of the nonstandard and standard approaches. In particular, our proof shows how the operation of taking the standard part of a nonstandard real number can replace several different compactness theorems, such as Ascoli’s theorem and the Banach–Alaoglu theorem on weak∗-compactness of the unit ball in the dual of a Banach space.
Asymmetric Message Franking: Content Moderation for Metadata-Private End-to-End Encryption

Content moderation is crucial for stopping abusive and harassing messages in online platforms. Existing moderation mechanisms, such as message franking, require platform providers to be able to associate user identifiers to encrypted messages. These mechanisms fail in metadata-private messaging systems, such as Signal, where users can hide their identities from platform providers. The key technical challenge preventing moderation is achieving cryptographic accountability while preserving deniability.
Percolation models of pathogen spillover

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.
KSOS—Development methodology for a secure operating system

The goal of the Department of Defense Kemelized Secure Operating System (KSOS) project is to design, implement and prove a secure operating system. Specifically, it is desired that KSOS be designed and proven to enforce a security model, derived from the security practices of the Department of Defense, referred to as "multilevel security."
An Analysis of Malware Trends in Enterprise Networks.

We present an empirical and large-scale analysis of malware samples captured from two different enterprises from 2017 to early 2018. Particularly, we perform threat vector, social-engineering, vulnerability and time-series analysis on our dataset. Unlike existing malware studies, our analysis is specifically focused on the recent enterprise malware samples. First of all, based on our analysis on the combined datasets of two enterprises, our results confirm the general consensus that AV-only solutions are not enough for real-time defenses in enterprise settings because on average 40% of the malware samples, when first appeared, are not detected by most AVs on VirusTotal or not uploaded to VT at all (i.e., never seen in the wild yet). Moreover, our analysis also shows that enterprise users transfer documents more than executables and other types of files. Therefore, attackers embed malicious codes into documents to download and install the actual malicious payload instead of sending malicious payload directly or using vulnerability exploits. Moreover, we also found that financial matters (e.g., purchase orders and invoices) are still the most common subject seen in Business Email Compromise (BEC) scams that aim to trick employees. Finally, based on our analysis on the timestamps of captured malware samples, we found that 93% of the malware samples were delivered on weekdays. Our further analysis also showed that while the malware samples that require user interaction such as macro-based malware samples have been captured during the working hours of the employees, the massive malware attacks are triggered during the off-times of the employees to be able to silently spread over the networks.
Time-invariant restrictions of volatility functionals: Efficient estimation and specification tests

Abstract This paper studies the estimation and inference problems of time-invariant restrictions on certain known functions of the stochastic volatility process. We first develop a more efficient GMM estimator and derive the efficiency bound under such restrictions. Then we construct an integrated Hausman-type test by summing up the squared differences between this more efficient estimator and the unrestricted estimator computed at different time points. Although less efficient under the null, the latter estimator is consistent under both the null and the alternative. The efficient GMM estimator can also be used to update an existing Bierens-type test and simplify the calculation of the asymptotic variance. Since the quadratic function puts more weight on large values, the Hausman-type test can have superior power than the Bierens-type test, which is based on a linear function of the differences. The simulation study shows that except for very small local window sizes, the Hausman-type test has good size and superior power. We finally apply these tests to studying the constant beta hypothesis using empirical data and find substantial evidence against this hypothesis.
Emotion Mining in Social Media Data

Abstract Emotions are known to influence the perception of human beings along with their memory, thinking and imagination. Human perception is important in today’s world in a wide range of factors including but not limited to business, education, art, and music. Microblogging and Social networking sites like Twitter, Facebook are challenging sources of information that allow people to share their feelings and thoughts on a daily basis. In this paper we propose an approach to automatically detect emotions on Twitter messages that explores characteristics of the tweets and the writer’s emotion using Support Vector Machine LibLinear model and achieve 98% accuracy. Emotion mining gained attraction in the field of computer science due to the vast variety of systems that can be developed and promising applications like remote health care system, customer care services, smart phones that react based on users’s emotion, vehicles that sense emotion of the driver. These emotions help understand the current state of user. In order to perform suitable actions or provide suggestions on how user’s can enhance their feeling for a better healthy life-style we use actionable recommendations. In this work we extract action rules with respect to the user emotions that help provide suggestions for user’s.
An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM

It holds great implications for practical applications to enable centimeter-accuracy positioning for mobile and wearable sensor systems. In this paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM algorithm, termed Schmidt-EKF VI-SLAM (SEVIS), which optimally fuses IMU measurements and monocular images in a tightly-coupled manner to provide 3D motion tracking with bounded error. In particular, we adapt the Schmidt Kalman filter formulation to selectively include informative features in the state vector while treating them as nuisance parameters (or Schmidt states) once they become matured. This change in modeling allows for significant computational savings by no longer needing to constantly update the Schmidt states (or their covariance), while still allowing the EKF to correctly account for their cross-correlations with the active states. As a result, we achieve linear computational complexity in terms of map size, instead of quadratic as in the standard SLAM systems. In order to fully exploit the map information to bound navigation drifts, we advocate efficient keyframe-aided 2D-to-2D feature matching to find reliable correspondences between current 2D visual measurements and 3D map features. The proposed SEVIS is extensively validated in both simulations and experiments.
Study of a CMOS I/O protection circuit using circuit-level simulation

In this work, circuit-level simulation is conducted for a typical two-stage protection circuit. We demonstrate that the circuit failure mechanism is correctly predicted by simulation. Furthermore, the current protection levels estimated by simulation are in good agreement with the experiments; therefore, circuit-level simulation can be used to predict the HBM-ESD protection level. In addition, the failure site is explained by simulation.
Why Does a Visual Question Have Different Answers

Visual question answering is the task of returning the answer to a question about an image. A challenge is that different people often provide different answers to the same visual question. To our knowledge, this is the first work that aims to understand why. We propose a taxonomy of nine plausible reasons, and create two labelled datasets consisting of ~45,000 visual questions indicating which reasons led to answer differences. We then propose a novel problem of predicting directly from a visual question which reasons will cause answer differences as well as a novel algorithm for this purpose. Experiments demonstrate the advantage of our approach over several related baselines on two diverse datasets. We publicly share the datasets and code at https://vizwiz.org.
Maximum-entropy meshfree method for compressible and near-incompressible elasticity

Abstract Numerical integration errors and volumetric locking in the near-incompressible limit are two outstanding issues in Galerkin-based meshfree computations. In this paper, we present a modified Gaussian integration scheme on background cells for meshfree methods that alleviates errors in numerical integration and ensures patch test satisfaction to machine precision. Secondly, a locking-free small-strain elasticity formulation for meshfree methods is proposed, which draws on developments in assumed strain methods and nodal integration techniques. In this study, maximum-entropy basis functions are used; however, the generality of our approach permits the use of any meshfree approximation. Various benchmark problems in two-dimensional compressible and near-incompressible small strain elasticity are presented to demonstrate the accuracy and optimal convergence in the energy norm of the maximum-entropy meshfree formulation.
Experimental data for solid–liquid flows at intermediate and high Stokes numbers

Experimental data for turbulent solid–liquid flow in a vertical pipe were collected for glass beads with diameters from 0.5 mm to 5 mm, at concentrations up to 2 % v/v, and Reynolds numbers from 200 000 to 350 000. In addition, data for crushed glass, steel shot and two sizes of stainless-steel cylinders were also collected. The experiments span from the intermediate to the inertia-dominated regimes, and the results include direct measurements for the pressure drops, the solids concentration and the three velocity components for each of the phases using laser Doppler velocimetry and phase Doppler anemometry. In addition, the results include the Reynolds stresses, the granular temperature, the kinetic energy and calculations for the turbulence modulation. The results show augmentation of turbulence for all the conditions studied. The velocity fluctuations for the solid and the liquid are reduced with increasing Reynolds numbers at all conditions. The Reynolds number dictates the behaviour of the relative velocity with concentration: for the Reynolds number of 350 000, the relative velocity increases with increasing concentrations, which can be explained by a decrease in the solid shear and an increase in the solid-phase pressure with rising concentration. In contrast, for the Reynolds number of 200 000, the relative velocity decreases with increasing concentrations, which can be attributed to an increase in drag force at higher concentration. The unique dataset presented begins to close the gap in knowledge for two-phase flow experimentation at concentrations above 0.7 % v/v and Reynolds numbers above 30 000.
On the tensor structure of modules for compact orbifold vertex operator algebras

Suppose $$V^G$$ is the fixed-point vertex operator subalgebra of a compact group G acting on a simple abelian intertwining algebra V. We show that if all irreducible $$V^G$$ -modules contained in V live in some braided tensor category of $$V^G$$ -modules, then they generate a tensor subcategory equivalent to the category $${{\,\mathrm{Rep}\,}}G$$ of finite-dimensional representations of G, with associativity and braiding isomorphisms modified by the abelian 3-cocycle defining the abelian intertwining algebra structure on V. Additionally, we show that if the fusion rules for the irreducible $$V^G$$ -modules contained in V agree with the dimensions of spaces of intertwiners among G-modules, then the irreducibles contained in V already generate a braided tensor category of $$V^G$$ -modules. These results do not require rigidity on any tensor category of $$V^G$$ -modules and thus apply to many examples where braided tensor category structure is known to exist but rigidity is not known; for example they apply when $$V^G$$ is $$C_2$$ -cofinite but not necessarily rational. When $$V^G$$ is both $$C_2$$ -cofinite and rational and V is a vertex operator algebra, we use the equivalence between $$\mathrm {Rep}\,G$$ and the corresponding subcategory of $$V^G$$ -modules to show that V is also rational. As another application, we show that a certain category of modules for the Virasoro algebra at central charge 1 admits a braided tensor category structure equivalent to $${{\,\mathrm{Rep}\,}}SU(2)$$ , up to modification by an abelian 3-cocycle.
Lateral Stability of Frames by Energy Method

Method of analyzing elastic stability of rigid frame structures subject to sidesway; energy method is applied to assumed frame deformation compatible with its restraints; assumed deformations are chosen so that compatibility may be achieved by one standard moment distribution; formulas are given and presented in graph form; two examples are compared to results obtained by more rigorous methods.
Optimal certification policy, entry, and investment in the presence of public signals

We explore the optimal disclosure policy of a certification intermediary in an environment where (i) the seller's decision on entry and investment in product quality are endogenous and (ii) the buyers observe an additional public signal on quality. The intermediary mutes the seller's entry incentives but enhances investment incentives following entry, and the optimal policy maximizes rent extraction from the seller in the face of this trade-off. We identify conditions under which full, partial or no disclosure can be optimal. The intermediary's report becomes noisier as the public signal gets more precise, but if the public signal becomes too precise, the intermediary resorts to full disclosure. In the presence of an intermediary, a more precise public signal may also lead to lower social welfare.
Identifying Acute Low Back Pain Episodes in Primary Care Practice From Clinical Notes: Observational Study.

Background: Acute and chronic low back pain (LBP) are different conditions with different treatments. However, they are coded in electronic health records with the same International Classification of Diseases, 10th revision (ICD-10) code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents an efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options. Objective: The objective of this study was to evaluate the feasibility of automatically distinguishing acute LBP episodes by analyzing free-text clinical notes. Methods: We used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as acute LBP and 2973 were generally associated with LBP via the recorded ICD-10 code. We compared different supervised and unsupervised strategies for automated identification: keyword search, topic modeling, logistic regression with bag of n-grams and manual features, and deep learning (a convolutional neural network-based architecture [ConvNet]). We trained the supervised models using either manual annotations or ICD-10 codes as positive labels. Results: ConvNet trained using manual annotations obtained the best results with an area under the receiver operating characteristic curve of 0.98 and an F score of 0.70. ConvNet’s results were also robust to reduction of the number of manually annotated documents. In the absence of manual annotations, topic models performed better than methods trained using ICD-10 codes, which were unsatisfactory for identifying LBP acuity. Conclusions: This study uses clinical notes to delineate a potential path toward systematic learning of therapeutic strategies, billing guidelines, and management options for acute LBP at the point of care.
Vandermondes in superspace

Superspace of rank $n$ is a $\mathbb{Q}$-algebra with $n$ commuting generators $x_1, \dots, x_n$ and $n$ anticommuting generators $\theta_1, \dots, \theta_n$. We present an extension of the Vandermonde determinant to superspace which depends on a sequence $\mathbf{a} = (a_1, \dots, a_r)$ of nonnegative integers of length $r \leq n$. We use superspace Vandermondes to construct graded representations of the symmetric group. This construction recovers hook-shaped Tanisaki quotients, the coinvariant ring for the Delta Conjecture constructed by Haglund, Rhoades, and Shimozono, and a superspace quotient related to positroids and Chern plethysm constructed by Billey, Rhoades, and Tewari. We define a notion of partial differentiation with respect to anticommuting variables to construct doubly graded modules from superspace Vandermondes. These doubly graded modules carry a natural ring structure which satisfies a 2-dimensional version of Poincare duality. The application of polarization operators gives rise to other bigraded modules which give a conjectural module for the symmetric function $\Delta'_{e_{k-1}} e_n$ appearing in the Delta Conjecture of Haglund, Remmel, and Wilson.
IoT Notary: Sensor Data Attestation in Smart Environment

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced — IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals' privacy or service integrity. To address such concerns, we propose IoT Notary, a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable ‘proof-of-integrity,’ based on which a verifier can attest that captured sensor data adheres to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at UCI to provide various real-time location-based services in the campus. IoT Notary imposes nominal overheads for verification, thereby users can verify their data of one day in less than two seconds.
Differentially Private Meta-Learning

Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, with personalization, and reinforcement learning. However, parameter-transfer algorithms often require sharing models that have been trained on the samples from specific tasks, thus leaving the task-owners susceptible to breaches of privacy. We conduct the first formal study of privacy in this setting and formalize the notion of task-global differential privacy as a practical relaxation of more commonly studied threat models. We then propose a new differentially private algorithm for gradient-based parameter transfer that not only satisfies this privacy requirement but also retains provable transfer learning guarantees in convex settings. Empirically, we apply our analysis to the problems of federated learning with personalization and few-shot classification, showing that allowing the relaxation to task-global privacy from the more commonly studied notion of local privacy leads to dramatically increased performance in recurrent neural language modeling and image classification.
Towards on-site detection of cadmium in human urine

Abstract Cadmium (Cd) is a non-essential toxic heavy metal. Human exposure to Cd occurs from multiple sources, including diet, tobacco smoke, fossil fuel combustion, and various contaminated sources. This work reports a rapid method for Cd2+ detection in simulated urine samples containing glucose (GLC) and human urine using the electrochemical technique of square wave anodic stripping voltammetry (ASV). Electrochemical techniques for onsite analysis and detection are preferred for their quick response time, application simplicity, inexpensive instrumentation, and potential portability. This approach is a step forward towards Cd2+ detection in biological fluids, despite their composition complexity due to possible interference of its constituents. Application of a simple, well controlled, and uniform carbon nanotube (CNT) thin film generated through spinnable CNT arrays enabled us to increase the surface area of traditional glassy carbon electrodes and made possible the detection of nanomolar concentration of Cd ions in urine samples. This voltammetric technique led to 1.9 nM limit of detection (LOD) in simulated urine, and 5.85 nM (female) and 324 nM (male) LOD in human urine. The developed method would facilitate high throughput screening of human urine samples for assessing Cd exposure in future studies.
Principal Component Analysis in the Local Differential Privacy Model

Abstract In this paper, we study the Principal Component Analysis (PCA) problem under the (distributed) non-interactive local differential privacy model. For the low dimensional case (i.e., p ≪ n ), we show the optimal rate of Θ ( k p n ϵ 2 ) (omitting the eigenvalue terms) for the private minimax risk of the k-dimensional PCA using the squared subspace distance as the measurement, where n is the sample size and ϵ is the privacy parameter. For the high dimensional (i.e., p ≫ n ) row sparse case, we first give a lower bound of Ω ( k s log ⁡ p n ϵ 2 ) on the private minimax risk, where s is the underlying sparsity parameter. Then we provide an efficient algorithm to achieve the upper bound of O ( s 2 log ⁡ p n ϵ 2 ) . Experiments on both synthetic and real world datasets confirm our theoretical guarantees.
The Three Dimensional Viscous Camassa–Holm Equations, and Their Relation to the Navier–Stokes Equations and Turbulence Theory

We show here the global, in time, regularity of the three dimensional viscous Camassa–Holm (Navier–Stokes-alpha) (NS-α) equations. We also provide estimates, in terms of the physical parameters of the equations, for the Hausdorff and fractal dimensions of their global attractor. In analogy with the Kolmogorov theory of turbulence, we define a small spatial scale, l ∈ , as the scale at which the balance occurs in the mean rates of nonlinear transport of energy and viscous dissipation of energy. Furthermore, we show that the number of degrees of freedom in the long-time behavior of the solutions to these equations is bounded from above by (L/l ∈ )3, where L is a typical large spatial scale (e.g., the size of the domain). This estimate suggests that the Landau–Lifshitz classical theory of turbulence is suitable for interpreting the solutions of the NS-α equations. Hence, one may consider these equations as a closure model for the Reynolds averaged Navier–Stokes equations (NSE). We study this approach, further, in other related papers. Finally, we discuss the relation of the NS-α model to the NSE by proving a convergence theorem, that as the length scale α 1 tends to zero a subsequence of solutions of the NS-α equations converges to a weak solution of the three dimensional NSE.
AlignFlow: Cycle consistent learning from multiple domains via normalizing flows

Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain. Variants of this problem have been studied in many contexts, such as cross-domain translation and domain adaptation. We propose AlignFlow, a generative modeling framework that models each domain via a normalizing flow. The use of normalizing flows allows for a) flexibility in specifying learning objectives via adversarial training, maximum likelihood estimation, or a hybrid of the two methods; and b) learning and exact inference of a shared representation in the latent space of the generative model. We derive a uniform set of conditions under which AlignFlow is marginally-consistent for the different learning objectives. Furthermore, we show that AlignFlow guarantees exact cycle consistency in mapping datapoints from a source domain to target and back to the source domain. Empirically, AlignFlow outperforms relevant baselines on image-to-image translation and unsupervised domain adaptation and can be used to simultaneously interpolate across the various domains using the learned representation.
Reconciling the disagreement between observed and simulated temperature responses to deforestation

Land use changes have great potential to influence temperature extremes. However, contradictory summer daytime temperature responses to deforestation are reported between observations and climate models. Here we present a pertinent comparison between multiple satellite-based datasets and climate model deforestation experiments. Observationally-based methods rely on a space-for-time assumption, which compares neighboring locations with contrasting land covers as a proxy for land use changes over time without considering possible atmospheric feedbacks. Offline land simulations or subgrid-level analyses agree with observed warming effects only when the space-for-time assumption is replicated. However, deforestation-related cloud and radiation effects manifest in coupled climate simulations and observations at larger scales, which show that a reduction of hot extremes with deforestation – as simulated in a number of CMIP5 models – is possible. Our study provides a design and analysis methodology for land use change studies and highlights the importance of including land-atmosphere coupling, which can alter deforestation-induced temperature changes. Models show a cooler surface temperature response to deforestation than observations which has been attributed to uncertainties in the models. A comparison of satellite observations and model experiments shows that the disagreement is due to the role of atmospheric feedbacks, which are not well captured in the observational space-for-time approach.
Improved Grid Optimization and Fitting in Least Squares Tensor Hypercontraction.

A new method for generating fitting grids for least-squares tensor hypercontraction (LS-THC) is presented. This method draws inspiration from the related interpolative separable density fitting (IS...
Analytical study of an isolated coaxial rotor system with lift offset

Abstract An analytical study, which is based on calculations from a rotorcraft comprehensive analysis of a notional coaxial rotor system in isolation, is presented to improve understanding of the aeromechanics of lift-offset coaxial rotors. The calculations include a trim analysis that enforces a fixed value for coaxial system lift in order to isolate the effect of lift offset. The quantities examined here are the blade pitch controls, coaxial system performance, blade airloads, blade structural loads, and coaxial system hub loads. Operating conditions examined form airspeed sweeps for four different values of lift offset. The maximum airspeed that the rotor may achieve trim is increased by increasing lift offset. For a given airspeed, increasing lift offset generally increases the coaxial system lift-to-effective-drag ratio as a result of a decrease in shaft power (despite an increase in drag power). Increasing lift offset decreases half peak-to-peak values of pitch link force, torsion moment, and in many cases, chord bending moment. While increasing lift offset increases half peak-to-peak flap bending moment for sufficiently low airspeeds, it may decrease this quantity for higher airspeeds. Lastly, increasing lift offset generally decreases the fixed-frame, vibratory, system hub longitudinal force and pitch moment.
Modeling Uncertainty in Large Natural Resource Allocation Problems

The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. This study introduces a novel numerical method to solve large-scale dynamic stochastic natural resource allocation problems that cannot be addressed by conventional methods. The method is illustrated with an application focusing on the allocation of global land resource use under stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields.
Tearing down disciplinary barriers

Profesor Hannes Alfven's life-long battle against scientific narrow-mindedness and parochial approaches to the solution of scientific problems is well known and deeply appreciated by this author. In this article the new interdisciplinary trends in science are critically examined and the psychological impacts of crumbling disciplinary barriers on the participating scientists are analyzed. Several examples of interdisciplinary research programs are discussed and some thoughts on the structural reform of scientific organizations, agencies, and universities needed to face these trends are given.
Approximation of the Zero-Index Transmission Eigenvalues with a Conductive Boundary and Parameter Estimation

In this paper, we present a spectral-Galerkin method to approximate the zero-index transmission eigenvalues with a conductive boundary condition. This is a new eigenvalue problem derived from the scalar inverse scattering problem for an isotropic media with a conductive boundary condition. In our analysis, we will consider the equivalent fourth-order eigenvalue problem where we establish the convergence when the approximation space is the span of finitely many Dirichlet eigenfunctions for the Laplacian. We establish the convergence rate of the spectral approximation by appealing to Weyl’s law. Numerical examples for computing the eigenvalues and eigenfunctions for the unit disk and unit square are presented. Lastly, we provide a method for estimating the refractive index assuming the conductivity parameter is either sufficiently large or small but otherwise unknown.
Q-learning for POMDP: An application to learning locomotion gaits

This paper presents a Q-learning framework for learning optimal locomotion gaits in robotic systems modeled as coupled rigid bodies. Inspired by prevalence of periodic gaits in bio-locomotion, an open loop periodic input is assumed to (say) affect a nominal gait. The learning problem is to learn a new (modified) gait by using only partial noisy measurements of the state. The objective of learning is to maximize a given reward modeled as an objective function in optimal control settings. The proposed control architecture has three main components: (i) Phase modeling of dynamics by a single phase variable; (ii) A coupled oscillator feedback particle filter to represent the posterior distribution of the phase conditioned in the sensory measurements; and (iii) A Q-learning algorithm to learn the approximate optimal control law. The architecture is illustrated with the aid of a planar two-body system. The performance of the learning is demonstrated in a simulation environment.
Ultrastructural Details of Mammalian Chromosome Architecture

Over the past decade, 3C-related methods have provided remarkable insights into chromosome folding in vivo. To overcome the limited resolution of prior studies, we extend a recently developed Hi-C variant, Micro-C, to map chromosome architecture at nucleosome resolution in human ESCs and fibroblasts. Micro-C robustly captures known features of chromosome folding including compartment organization, topologically associating domains, and interactions between CTCF binding sites. In addition, Micro-C provides a detailed map of nucleosome positions and localizes contact domain boundaries with nucleosomal precision. Compared to Hi-C, Micro-C exhibits an order of magnitude greater dynamic range, allowing the identification of ∼20,000 additional loops in each cell type. Many newly identified peaks are localized along extrusion stripes and form transitive grids, consistent with their anchors being pause sites impeding cohesin-dependent loop extrusion. Our analyses comprise the highest-resolution maps of chromosome folding in human cells to date, providing a valuable resource for studies of chromosome organization.
Aversion of the invasive Asian longhorned tick to the white‐footed mouse, the dominant reservoir of tick‐borne pathogens in the U.S.A.

The Asian longhorned tick (Haemaphysalis longicornis) was reported for the first time in the U.S.A. in 2017 and has now spread across 12 states. The potential of this invasive tick vector to transmit pathogens will be determined through its association to hosts, such as the white-footed mouse (Peromyscus leucopus), which is the primary reservoir for the causative agent of Lyme disease (Borrelia burgdorferi) and other zoonotic pathogens. Larval H. longicornis were placed on P. leucopus; 65% of the larvae (n = 40) moved off the host within a short period of time, and none engorged. By contrast, larval blacklegged ticks (Ixodes scapularis) did not move from where they were placed in the ear of the mouse. A laboratory behavioural assay was then conducted to assess the interaction of H. longicornis with the hair of potential mammalian host species in the U.S.A. H. longicornis larvae were significantly less likely to enter the hair zone of P. leucopus and humans compared to the hair of domestic cats, domestic dogs and white-tailed deer. This study identifies a tick-host interaction behaviour, which can be quantified in a laboratory assay to predict tick-host associations and provides insights into how ticks select a host.