cssi/SciSciGPT-SciSciNet huggingface dataset:
Linguistic Reflexes of Well-Being and Happiness in Echo

Different theories posit different sources for feelings of well-being and happiness. Appraisal theory grounds our emotional responses in our goals and desires and their fulfillment, or lack of fulfillment. Self-Determination theory posits that the basis for well-being rests on our assess- ments of our competence, autonomy and social connection. And surveys that mea- sure happiness empirically note that peo- ple require their basic needs to be met for food and shelter, but beyond that tend to be happiest when socializing, eating or hav- ing sex. We analyze a corpus of private micro-blogs from a well-being application called ECHO, where users label each writ- ten post about daily events with a happi- ness score between 1 and 9. Our goal is to ground the linguistic descriptions of events that users experience in theories of well-being and happiness, and then exam- ine the extent to which different theoretical accounts can explain the variance in the happiness scores. We show that recurrent event types, such as OBLIGATION and IN- COMPETENCE, which affect people’s feel- ings of well-being are not captured in cur- rent lexical or semantic resources.
Estimating animal densities and home range in regions with irregular boundaries and holes: A lattice-based alternative to the kernel density estimator

Density estimates based on point processes are often restrained to regions with irregular boundaries or holes. We propose a density estimator, the lattice-based density estimator, which produces reasonable density estimates under these circumstances. The estimation process starts with overlaying the region with nodes, linking these together in a lattice and then computing the density of random walks of length k on the lattice. We use an approximation to the unbiased crossvalidation criterion to find the optimal walk length k. The technique is illustrated using walleye (Sander vitreus) radiotelemetry relocations in Lake Monroe, Indiana. We also use simulation to compare the technique to the traditional kernel density estimate in the situation where there are no significant boundary effects.
Large-Scale QA-SRL Parsing.

We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6% and span-level accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.
Underactuated Hand Design Using Mechanically Realizable Manifolds

Hand synergies, or joint coordination patterns, have become an effective tool for achieving versatile robotic grasping with simple hands or planning algorithms. Here we propose a method to determine the hand synergies such that they can be physically implemented in an underactuated fashion. Given a kinematic hand model and a set of desired grasps, our algorithm optimizes a Mechanically Realizable Manifold designed to be achievable by a physical underactuation mechanism, enabling the resulting hand to achieve the desired grasps with few actuators. Furthermore, in contrast to existing methods for determining synergies which are only concerned with hand posture, our method explicitly optimizes the stability of the target grasps. We implement this method in the design of a three-finger single-actuator hand as an example, and evaluate its effectiveness numerically and experimentally.
A note on orderability and Dehn filling

We improve upon a recent result of Culler and Dunfield on orderability of certain Dehn fillings by removing a difficult condition they required.
Average Zsigmondy sets, dynamical Galois groups, and the Kodaira-Spencer map

Let $K$ be a global function field and let $\phi\in K[x]$. For all wandering basepoints $b\in K$, we show that there is a bound on the size of the elements of the dynamical Zsigmondy set $\mathcal{Z}(\phi,b)$ that depends only on $\phi$, the poles of the $b$, and $K$. Moreover, when we order $b\in\mathcal{O}_{K,S}$ by height, we show that $\mathcal{Z}(\phi,b)$ is empty on average. As an application, we prove that the inverse limit of the Galois groups of iterates of $\phi(x)=x^d+f$ is a finite index subgroup of an iterated wreath product of cyclic groups. Finally, we establish similar results on Zsigmondy sets when $K$ is the field of rational numbers or $K$ is a quadratic imaginary field subject to an added stipulation: either zero has finite orbit under iteration of $\phi$ or the Vojta conjecture for algebraic points on curves holds.
Alignment strength and correlation for graphs

Abstract When two graphs have a correlated Bernoulli distribution, we prove that the alignment strength of their natural bijection strongly converges to a novel measure of graph correlation ϱT that neatly combines intergraph with intragraph distribution parameters. Within broad families of the random graph parameter settings, we illustrate that exact graph matching runtime and also matchability are both functions of ϱT, with thresholding behavior starkly illustrated in matchability.
On the Capacity of Information Processing Systems

We propose and analyze a family of information processing systems, where a finite set of experts or servers are employed to extract information about a stream of incoming jobs. Each job is associated with a hidden label drawn from some prior distribution. An inspection by an expert produces a noisy outcome that depends both on the job’s hidden label and the type of the expert and occupies the expert for a finite time duration. A decision-maker’s task is to dynamically assign inspections so that the resulting outcomes can be used to accurately recover the labels of all jobs, while keeping the system stable. Among our chief motivations are applications in crowd sourcing, diagnostics, and experiment designs, where one wishes to efficiently learn the nature of a large number of items, using a finite pool of computational resources or human agents.We focus on the capacity of such an information processing system. Given a level of accuracy guarantee, we ask how many experts are needed in order to stabilize the ...
Reducing subspaces of de Branges-Rovnyak spaces

Abstract For b ∈ H 1 ∞ , the closed unit ball of H ∞ , the de Branges-Rovnyak space H ( b ) is a Hilbert space contractively contained in the Hardy space H 2 that is invariant by the backward shift operator S ⁎ . We consider the reducing subspaces of the operator S ⁎ 2 | H ( b ) . When b is an inner function, S ⁎ 2 | H ( b ) is a truncated Toeplitz operator and its reducibility was characterized by Douglas and Foias using model theory. We use another approach to extend their result to the case where b is extreme. We prove that if b is extreme but not inner, then S ⁎ 2 | H ( b ) is reducible if and only if b is even or odd, and describe the structure of reducing subspaces.
Escher squares and lattice links

Abstract We give a shorter and simpler proof of the result of [2] , which gives a necessary and sufficient condition for a lattice diagram to be the projection of a lattice link.
On total flexibility of local structures of Finsler tori without conjugate points

We show that given a point on a Finsler surface, one can always find a neighborhood of the point and isometrically embed this neighborhood into a Finsler torus without conjugate points.
Topology optimization for transient response of structures subjected to dynamic loads

This paper presents a topology optimization framework for structural problems subjected to transient loading. The mechanical model assumes a linear elastic isotropic material, infinitesimal strains, and a dynamic response. The optimization problem is solved using the gradient-based optimizer Method of Moving Asymptotes (MMA) with time-dependent sensitivities provided via the adjoint method. The stiffness of materials is interpolated using the Solid Isotropic Material with Penalization (SIMP) method and the Heaviside Projection Method (HPM) is used to stabilize the problem numerically and improve the manufacturability of the topology-optimized designs. Both static and dynamic optimization examples are considered here. The resulting optimized designs demonstrate the ability of topology optimization to tailor the transient response of structures.
Tailoring architecture centric design method with rapid prototyping

Many engineering processes exist in the industry, text books and international standards. However, in practice rarely any of the processes are followed consistently and literally. It is observed across industries the processes are altered based on the requirements of the projects. Two features commonly lacking from many engineering processes are, 1) the formal capacity to rapidly develop prototypes in the rudimentary stage of the project, 2) transitioning of requirements into architectural designs, when and how to evaluate designs and how to use the throw away prototypes throughout the system lifecycle. Prototypes are useful for eliciting requirements, generating customer feedback and identifying, examining or mitigating risks in a project where the product concept is at a cutting edge or not fully perceived. Apart from the work that the product is intended to do, systemic properties like availability, performance and modifiability matter as much as functionality. Architects must even these concerns with the method they select to promote these systemic properties and at the same time equip the stakeholders with the desired functionality. Architectural design and prototyping is one of the key ways to build the right product embedded with the desired systemic properties. Once the product is built it can be difficult to retrofit the system with the desired attributes. This paper customizes the architecture centric development method with rapid prototyping to achieve the above-mentioned goals and reducing the number of iterations across the stages of ACDM.
Sensitivity Analysis Without Assumptions

Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having an unmeasured confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one unmeasured confounder. Without imposing any assumptions on the unmeasured confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.
Causal Inference with Noisy and Missing Covariates via Matrix Factorization

Valid causal inference in observational studies often requires controlling for confounders. However, in practice measurements of confounders may be noisy, and can lead to biased estimates of causal effects. We show that we can reduce bias induced by measurement noise using a large number of noisy measurements of the underlying confounders. We propose the use of matrix factorization to infer the confounders from noisy covariates. This flexible and principled framework adapts to missing values, accommodates a wide variety of data types, and can enhance a wide variety of causal inference methods. We bound the error for the induced average treatment effect estimator and show it is consistent in a linear regression setting, using Exponential Family Matrix Completion preprocessing. We demonstrate the effectiveness of the proposed procedure in numerical experiments with both synthetic data and real clinical data.
Convergence rate estimates for consensus over random graphs

Multi-agent coordination algorithms with randomized interactions have seen use in a variety of settings in the multi-agent systems literature. In some cases, these algorithms can be random by design, as in a gossip-like algorithm, and in other cases they are random due to external factors, as in the case of intermittent communications. Targeting both of these scenarios, we present novel convergence rate estimates for consensus problems solved over random graphs. Established results provide asymptotic convergence in this setting, and we provide estimates of the rate of convergence in two forms. First, we estimate decreases in a quadratic Lyapunov function over time to bound how quickly the agents' disagreement decays, and second we bound the probability of being at least a given distance from the point of agreement. Simulation results are provided to support the theoretical developments made.
Calabi flow on toric varieties with bounded Sobolev constant, I

Let $(X, P)$ be a toric variety. In this note, we show that the $C^0$-norm of the Calabi flow $\varphi(t)$ on $X$ is uniformly bounded in $[0, T)$ if the Sobolev constant of $\varphi(t)$ is uniformly bounded in $[0, T)$. We also show that if $(X, P)$ is uniform $K$-stable, then the modified Calabi flow converges exponentially fast to an extremal K\"ahler metric if the Ricci curvature and the Sobolev constant are uniformly bounded. At last, we discuss an extension of our results to a quasi-proper K\"ahler manifold.
Series of Broad Resonances in Atomic Three-Body Systems

We re-examine the series of resonances found earlier in atomic three-body systems by solving the Faddeev-Merkuriev integral equations. These resonances are rather broad and line up at each threshold with gradually increasing gaps. This lining up takes place in the same way for all thresholds and is irrespective of the spatial symmetry. We relate these resonances to the Gailitis mechanism, which is a consequence of the polarization potential.
Learning geometric concepts with nasty noise

We study the efficient learnability of geometric concept classes — specifically, low-degree polynomial threshold functions (PTFs) and intersections of halfspaces — when a fraction of the training data is adversarially corrupted. We give the first polynomial-time PAC learning algorithms for these concept classes with dimension-independent error guarantees in the presence of nasty noise under the Gaussian distribution. In the nasty noise model, an omniscient adversary can arbitrarily corrupt a small fraction of both the unlabeled data points and their labels. This model generalizes well-studied noise models, including the malicious noise model and the agnostic (adversarial label noise) model. Prior to our work, the only concept class for which efficient malicious learning algorithms were known was the class of origin-centered halfspaces. At the core of our results is an efficient algorithm to approximate the low-degree Chow-parameters of any bounded function in the presence of nasty noise. Our robust approximation algorithm for the Chow parameters provides near-optimal error guarantees for a range of distribution families satisfying mild concentration bounds and moment conditions. At the technical level, this algorithm employs an iterative “spectral” technique for outlier detection and removal inspired by recent work in robust unsupervised learning, which makes essential use of low-degree multivariate polynomials. Our robust learning algorithm for low-degree PTFs provides dimension-independent error guarantees for a class of tame distributions, including Gaussians and, more generally, any logconcave distribution with (approximately) known low-degree moments. For LTFs under the Gaussian distribution, using a refinement of the localization technique, we give a polynomial-time algorithm that achieves a near-optimal error of O(є), where є is the noise rate. Our robust learning algorithm for intersections of halfspaces proceeds by projecting down to an appropriate low-dimensional subspace. Its correctness makes essential use of a novel robust inverse independence lemma that is of independent interest.
Towards Practical Oblivious RAM

We take an important step forward in making Oblivious RAM (O-RAM) practical. We pro- pose an O-RAM construction achieving an amortized overhead of 20 ∼ 35X (for an O-RAM roughly 1 terabyte in size), about 63 times faster than the best existing scheme. On the theoretic front, we propose a fundamentally novel technique for constructing Oblivious RAMs: specifi- cally, we partition a bigger O-RAM into smaller O-RAMs, and employ a background eviction technique to obliviously evict blocks from the client-side cache into a randomly assigned server- side partition. This novel technique is the key to achieving the gains in practical performance.
Inertial spin alignment in a circular magnetic nanotube

Abstract In cobalt nanotubes with a curling magnetization, the orbital motion of the conduction electrons interacts with their spin. As the spin goes around the nanotube it cannot follow the magnetization, since with the Fermi velocity it moves too fast. Instead, we predict that the spin precesses about an axis that is almost parallel to the axis of the nanotube and that rotates with the angular velocity of the electron. Therefore, the (absolute) value of the magnetic energy of the spin | μ ⋅ B | is strongly reduced. The physics of the ferromagnet is considerably modified.
The Diversity Multiplexing Tradeoff for Interference Networks.

The diversity-multiplexing tradeoff (DMT) for interference networks, such as the interference channel, the X channel, the Z interference channel and the Z channel, is analyzed. In particular, we investigate the impact of rate-splitting and channel knowledge at the transmitters. We also use the DMT of the Z channel and the Z interference channel to distill insights into the "loud neighbor" problem for femto-cell networks.
Accelerated Method for Stochastic Composition Optimization With Nonsmooth Regularization

Stochastic composition optimization draws much attention recently and has been successful in many emerging applications of machine learning, statistical analysis, and reinforcement learning. In this paper, we focus on the composition problem with nonsmooth regularization penalty. Previous works either have slow convergence rate, or do not provide complete convergence analysis for the general problem. In this paper, we tackle these two issues by proposing a new stochastic composition optimization method for composition problem with nonsmooth regularization penalty. In our method, we apply variance reduction technique to accelerate the speed of convergence. To the best of our knowledge, our method admits the fastest convergence rate for stochastic composition optimization: for strongly convex composition problem, our algorithm is proved to admit linear convergence; for general composition problem, our algorithm significantly improves the state-of-the-art convergence rate from O(T–1/2) to O((n1+n2)2/3T-1). Finally, we apply our proposed algorithm to portfolio management and policy evaluation in reinforcement learning. Experimental results verify our theoretical analysis.
Deep Component Analysis via Alternating Direction Neural Networks

Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications. On the other hand, shallow representation learning with component analysis is associated with rich intuition and theory, but smaller capacity often limits its usefulness. To bridge this gap, we introduce Deep Component Analysis (DeepCA), an expressive multilayer model formulation that enforces hierarchical structure through constraints on latent variables in each layer. For inference, we propose a differentiable optimization algorithm implemented using recurrent Alternating Direction Neural Networks (ADNNs) that enable parameter learning using standard backpropagation. By interpreting feed-forward networks as single-iteration approximations of inference in our model, we provide both a novel perspective for understanding them and a practical technique for constraining predictions with prior knowledge. Experimentally, we demonstrate performance improvements on a variety of tasks, including single-image depth prediction with sparse output constraints.
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularization framework. SSDKL combines the hierarchical representation learning of neural networks with the probabilistic modeling capabilities of Gaussian processes. By leveraging unlabeled data, we show improvements on a diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression.
Experimental Design for Cost-Aware Learning of Causal Graphs

We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove that we can achieve a constant factor approximation to this problem with a greedy algorithm. We then constrain the sparsity of each intervention. We develop an algorithm that returns an intervention design that is nearly optimal in terms of size for sparse graphs with sparse interventions and we discuss how to use it when there are costs on the vertices.
Approximate Knowledge Compilation by Online Collapsed Importance Sampling

We introduce collapsed compilation, a novel approximate inference algorithm for discrete probabilistic graphical models. It is a collapsed sampling algorithm that incrementally selects which variable to sample next based on the partial compila- tion obtained so far. This online collapsing, together with knowledge compilation inference on the remaining variables, naturally exploits local structure and context- specific independence in the distribution. These properties are used implicitly in exact inference, but are difficult to harness for approximate inference. More- over, by having a partially compiled circuit available during sampling, collapsed compilation has access to a highly effective proposal distribution for importance sampling. Our experimental evaluation shows that collapsed compilation performs well on standard benchmarks. In particular, when the amount of exact inference is equally limited, collapsed compilation is competitive with the state of the art, and outperforms it on several benchmarks.
Compact actions whose orbit equivalence relations are not profinite

Abstract Let Γ ↷ ( X , μ ) be a measure preserving action of a countable group Γ on a standard probability space ( X , μ ) . We prove that if the action Γ ↷ X is not profinite and satisfies a certain spectral gap condition, then there does not exist a countable-to-one Borel homomorphism from its orbit equivalence relation to the orbit equivalence relation of any modular action (i.e., an inverse limit of actions on countable sets). As a consequence, we show that if Γ is a countable dense subgroup of a compact non-profinite group G such that the left translation action Γ ↷ G has spectral gap, then Γ ↷ G is antimodular and not orbit equivalent to any, not necessarily free, profinite action. This provides the first such examples of compact actions, partially answering a question of Kechris and answering a question of Tsankov.
Adaptively Secure MPC with Sublinear Communication Complexity

A central challenge in the study of MPC is to balance between security guarantees, hardness assumptions, and resources required for the protocol. In this work, we study the cost of tolerating adaptive corruptions in MPC protocols under various corruption thresholds. In the strongest setting, we consider adaptive corruptions of an arbitrary number of parties (potentially all) and achieve the following results: A two-round secure function evaluation (SFE) protocol in the CRS model, assuming LWE and indistinguishability obfuscation (iO). The communication, the CRS size, and the online-computation are sublinear in the size of the function. The iO assumption can be replaced by secure erasures. Previous results required either the communication or the CRS size to be polynomial in the function size. Under the same assumptions, we construct a “Bob-optimized” 2PC (where Alice talks first, Bob second, and Alice learns the output). That is, the communication complexity and total computation of Bob are sublinear in the function size and in Alice’s input size. We prove impossibility of “Alice-optimized” protocols. Assuming LWE, we bootstrap adaptively secure NIZK arguments to achieve proof size sublinear in the circuit size of the NP-relation.
A robust multi-batch L-BFGS method for machine learning*

ABSTRACTThis paper describes an implementation of the L-BFGS method designed to deal with two adversarial situations. The first occurs in distributed computing environments where some of the comput...
Developing an Adaptive Strategy for Connected Eco-Driving under Uncertain Traffic Condition

The eco-approach and departure (EAD) application for signalized intersections has been proved to be environmentally efficient in a Connected and Automated Vehicles (CAVs) system. The traffic and signal phase and timing (SPaT) information transmitted from the roadside equipment unit, vehicle equipped sensors (e.g. radars) and other connected vehicles are the main inputs to the existing algorithms. However, due to the limitation of the communication and sensing range, it is too late to start eco-driving until preceding traffic is fully detected. Instead, the historical data, such as queue length distribution may be applied to developing a robust speed profile that enables eco-driving to start in an early stage. In this paper, a two-phase iterative approach is developed with the use of historical queue distribution. A graph-based model is created with nodes representing states of the host vehicle and traffic condition, and directed edges with weight representing expected energy consumption between two connected states. The shortest path is calculated that minimizes the total energy consumption for vehicles approaching a pre-timed signalized intersection. Numerical simulations have shown that the proposed method is robust and adaptive to varying traffic and queue conditions, and could achieve around 9% energy savings compared to other baseline methods.
BMP7 functions predominantly as a heterodimer with BMP2 or BMP4 during mammalian embryogenesis.

BMP7/BMP2 or BMP7/BMP4 heterodimers are more active than homodimers in vitro, but it is not known whether these heterodimers signal in vivo. To test this, we generated knock in mice carrying a mutation (Bmp7R-GFlag) that prevents proteolytic activation of the dimerized BMP7 precursor protein. This mutation eliminates the function of BMP7 homodimers and all other BMPs that normally heterodimerize with BMP7. While Bmp7 null homozygotes are live born, Bmp7R-GFlag homozygotes are embryonic lethal and have broadly reduced BMP activity. Furthermore, compound heterozygotes carrying the Bmp7R-G allele together with a null allele of Bmp2 or Bmp4 die during embryogenesis with defects in ventral body wall closure and/or the heart. Co-immunoprecipitation assays confirm that endogenous BMP4/7 heterodimers exist. Thus, BMP7 functions predominantly as a heterodimer with BMP2 or BMP4 during mammalian development, which may explain why mutations in either Bmp4 or Bmp7 lead to a similar spectrum of congenital defects in humans.
Active-testing surveillance for multiple target detection with composite hypotheses

We consider a multiple target detection problem in which there are N target sites, or cells, which can be energized independently, while the total energy available is finite. In the specific model used here, the data are conditionally Poisson, with the mean being the product of the transmitted energy and the gamma distributed reflectivity. A Bayesian sequential analysis procedure for target detection is proposed and strategies for partitioning the available energy at each iteration are compared.
Non-Reciprocal Wave Transmission in a Bilinear Spring-Mass System

Abstract Significant amplitude-independent and passive non-reciprocal wave motion can be achieved in a one-dimensional (1D) discrete chain of masses and springs with bilinear elastic stiffness. Some fundamental asymmetric spatial modulations of the bilinear spring stiffness are first examined for their non-reciprocal properties. These are combined as building blocks into more complex configurations with the objective of maximizing non-reciprocal wave behavior. The non-reciprocal property is demonstrated by the significant difference between the transmitted pulse displacement amplitudes and energies for incidence from opposite directions. Extreme non-reciprocity is realized when almost-zero transmission is achieved for the propagation from one direction with a noticeable transmitted pulse for incidence from the other. These models provide the basis for a class of simple 1D non-reciprocal designs and can serve as the building blocks for more complex and higher dimensional non-reciprocal wave systems.
Signal Selection for Estimation and Identification in Networks of Dynamic Systems: A Graphical Model Approach

Network systems have become a ubiquitous modeling tool in many areas of science where nodes in a graph represent distributed processes and edges between nodes represent a form of dynamic coupling. When a network topology is already known (or partially known), two associated goals are, to derive estimators for nodes of the network, which cannot be directly observed or are impractical to measure; and to quantitatively identify the dynamic relations between nodes. We address both problems in the challenging scenario where only some outputs of the network are being measured and the inputs are not accessible. The approach makes use of the notion of $d$ -separation for the graph associated with the network. In the considered class of networks, it is shown that the proposed technique can determine or guide the choice of optimal sparse estimators. This article also derives identification methods that are applicable to cases where loops are present providing a different perspective on the problem of closed-loop identification. The notion of $d$ -separation is a central concept in the area of probabilistic graphical models, thus an additional contribution is to create connections between control theory and machine learning techniques.
Optimizing Information Freshness in Wireless Networks Under General Interference Constraints

Age of information (AoI) is a recently proposed metric for measuring information freshness. AoI measures the time that elapsed since the last received update was generated. We consider the problem of minimizing average and peak AoI in a wireless networks, consisting of a set of source-destination links, under general interference constraints. When fresh information is always available for transmission, we show that a stationary scheduling policy is peak age optimal. We also prove that this policy achieves average age that is within a factor of two of the optimal average age. In the case where fresh information is not always available, and packet/information generation rate has to be controlled along with scheduling links for transmission, we prove an important separation principle : the optimal scheduling policy can be designed assuming fresh information, and independently, the packet generation rate control can be done by ignoring interference. Peak and average AoI for discrete time G/Ber/1 queue is analyzed for the first time, which may be of independent interest.
ACTT: Automotive CAN Tokenization and Translation

Modern vehicles contain scores of Electrical Control Units (ECUs) that broadcast messages over a Controller Area Network (CAN). Vehicle manufacturers rely on security through obscurity by concealing their unique mapping of CAN messages to vehicle functions which differs for each make, model, year, and even trim. This poses a major obstacle for after-market modifications notably performance tuning and in-vehicle network security measures. We present ACTT: Automotive CAN Tokenization and Translation, a novel, vehicle-agnostic, algorithm that leverages available diagnostic information to parse CAN data into meaningful messages, simultaneously cutting binary messages into tokens, and learning the translation to map these contiguous bits to the value of the vehicle function communicated.
PTracer: a linux kernel patch trace bot

We present PTracer, a Linux kernel patch trace bot based on an improved PatchNet. PTracer continuously monitors new patches in the git repository of the mainline Linux kernel, filters out unconcerned ones, classifies the rest as bug-fixing or non bug-fixing patches, and reports bug-fixing patches to the kernel experts of commercial operating systems. We use the patches in February 2019 of the mainline Linux kernel to perform the test. As a result, PTracer recommended 151 patches to CGEL kernel experts out of 5,142, and 102 of which were accepted. PTracer has been successfully applied to a commercial operating system and has the advantages of improving software quality and saving labor cost.
Quasimetric Embeddings and Their Applications

We study generalizations of classical metric embedding results to the case of quasimetric spaces; that is, spaces that do not necessarily satisfy symmetry. Quasimetric spaces arise naturally from the shortest-path distances on directed graphs. Perhaps surprisingly, very little is known about low-distortion embeddings for quasimetric spaces.Random embeddings into ultrametric spaces are arguably one of the most successful geometric tools in the context of algorithm design. We extend this to the quasimetric case as follows. We show that any n-point quasimetric space supported on a graph of treewidth t admits a random embedding into quasiultrametric spaces with distortion $$O(t \log ^2 n)$$ , where quasiultrametrics are a natural generalization of ultrametrics. This result allows us to obtain $$t\log ^{O(1)} n$$ -approximation algorithms for the Directed Non-Bipartite Sparsest Cut and the Directed Multicut problems on n-vertex graphs of treewidth t, with running time polynomial in both n and t. The above results are obtained by considering a generalization of random partitions to the quasimetric case, which we refer to as random quasipartitions. Using this definition and a construction of Chuzhoy and Khanna (JACM 56(2):6, 2009) we derive a polynomial lower bound on the distortion of random embeddings of general quasimetric spaces into quasiultrametric spaces. Finally, we establish a lower bound for embedding the shortest-path quasimetric of a graph G into graphs that exclude G as a minor. This lower bound is used to show that several embedding results from the metric case do not have natural analogues in the quasimetric setting.
Orbit Characterization, Stabilization and Composition on 3D Underactuated Bipedal Walking via Hybrid Passive Linear Inverted Pendulum Model

A Hybrid passive Linear Inverted Pendulum (H-LIP) model is proposed for characterizing, stabilizing and composing periodic orbits for 3D underactuated bipedal walking. Specifically, Period-l (P1) and Period -2 (P2) orbits are geometrically characterized in the state space of the H-LIP. Stepping controllers are designed for global stabilization of the orbits. Valid ranges of the gains and their optimality are derived. The optimal stepping controller is used to create and stabilize the walking of bipedal robots. An actuated Spring-loaded Inverted Pendulum (aSLIP) model and the underactuated robot Cassie are used for illustration. Both the aSLIP walking with PI or P2 orbits and the Cassie walking with all 3D compositions of the PI and P2 orbits can be smoothly generated and stabilized from a stepping-in-place motion. This approach provides a perspective and a methodology towards continuous gait generation and stabilization for 3D underactuated walking robots.
Synchronization of coupled Kuramoto oscillators under resource constraints

A fundamental understanding of synchronized behavior in multiagent systems can be acquired by studying analytically tractable Kuramoto models. However, such models typically diverge from many real systems whose dynamics evolve under nonnegligible resource constraints. Here we construct a system of coupled Kuramoto oscillators that consume or produce resources as a function of their oscillation frequency. At high coupling, we observe strongly synchronized dynamics, whereas at low coupling, we observe independent oscillator dynamics as expected from standard Kuramoto models. For intermediate coupling, which typically induces a partially synchronized state, we empirically observe that (and theoretically explain why) the system can exist in either: (i) a state in which the order parameter oscillates in time, or (ii) a state in which multiple synchronization states are simultaneously stable. Whether (i) or (ii) occurs depends upon whether the oscillators consume or produce resources, respectively. Relevant for systems as varied as coupled neurons and social groups, our paper lays important groundwork for future efforts to develop quantitative predictions of synchronized dynamics for systems embedded in environments marked by sparse resources.
Evaluation of Dose-Fractionated Polymyxin B on Acute Kidney Injury Using a Translational In Vivo Rat Model.

We investigated dose-fractionated polymyxin B (PB) on acute kidney injury (AKI). PB at 12 mg of drug/kg of body weight per day (once, twice, and thrice daily) was administered in rats over 72 h. The thrice-daily group demonstrated the highest KIM-1 increase (P = 0.018) versus that of the controls (P = 0.99) and histopathological damage (P = 0.013). A three-compartment model best described the data (bias, 0.129 mg/liter; imprecision, 0.729 mg2/liter2; R 2, 0.652,). Area under the concentration-time curve at 24 h (AUC24) values were similar (P = 0.87). The thrice-daily dosing scheme resulted in the most PB-associated AKI in a rat model.
Effects of Human vs. Automatic Feedback on Students' Understanding of AI Concepts and Programming Style

The use of automatic grading tools has become nearly ubiquitous in large undergraduate programming courses, and recent work has focused on improving the quality of automatically generated feedback. However, there is a relative lack of data directly comparing student outcomes when receiving computer-generated feedback and human-written feedback. This paper addresses this gap by splitting one 90-student class into two feedback groups and analyzing differences in the two cohorts' performance. The class is an intro to AI with programming HW assignments. One group of students received detailed computer-generated feedback on their programming assignments describing which parts of the algorithms' logic was missing; the other group additionally received human-written feedback describing how their programs' syntax relates to issues with their logic, and qualitative (style) recommendations for improving their code. Results on quizzes and exam questions suggest that human feedback helps students obtain a better conceptual understanding, but analyses found no difference between the groups' ability to collaborate on the final project. The course grade distribution revealed that students who received human-written feedback performed better overall; this effect was the most pronounced in the middle two quartiles of each group. These results suggest that feedback about the syntax-logic relation may be a primary mechanism by which human feedback improves student outcomes.
5G or Wi-Fi for HA/DR in the 60 GHz Band?

This paper studies the feasibility of Wi-Fi and 5G technologies in the unlicensed 60 GHz band for humanitarian assistance and disaster relief (HA/DR) operations. Building a wireless communications system in the 60 GHz band can benefit HA/DR activities for two reasons: (i) no license is needed; (ii) a wide bandwidth is available. Our simulation results show that both of Wi-Fi and 5G can achieve data rates exceeding the requirements for most of the HA/DR missions, which proves the feasibility of the two wireless technologies for operation of high-data-rate HA/DR activities such as real-time video streaming.
Fuchs' problem for 2-groups

Abstract Nearly 60 years ago, Laszlo Fuchs posed the problem of determining which groups can be realized as the group of units of a commutative ring. To date, the question remains open, although significant progress has been made. Along this line, one could also ask the more general question as to which finite groups can be realized as the group of units of a finite ring. In this paper, we consider the question of which 2-groups are realizable as unit groups of finite rings, a necessary step toward determining which nilpotent groups are realizable. We prove that all 2-groups of exponent 4 are realizable in characteristic 2. Moreover, while some groups of exponent greater than 4 are realizable as unit groups of rings, we establish general constraints on the exponent of a realizable 2-group. These constraints are used to describe examples of 2-groups that cannot be the group of units of a finite ring.
Survey of public assay data: opportunities and challenges to understanding antimicrobial resistance

Learning methods allow researchers to make predictions, draw inferences, and automate generation of mathematical models. These models are crucial to solving real world problems including, for example, antimicrobial resistance (AR). Machine learning (ML) methods depend upon ground truth data to achieve specificity and sensitivity. Since AR assay data is limited, it is of paramount importance to understand the distribution of ground truth data, the analyses it is suited for, and any limitations that bias downstream methods. In this paper, we report an analysis of bacterial biochemical assay data associated with whole genome sequencing (WGS) and discuss important implications when making use of assay data in combination with genetic features as training data for ML models.
Limiting distribution of eigenvalues in the large sieve matrix

The large sieve inequality is equivalent to the bound λ_1 ⩽ N+Q^2−1 for the largest eigenvalue λ_1 of the N by N matrix A⋆A, naturally associated to the positive definite quadratic form arising in the inequality. For arithmetic applications the most interesting range is N≍Q^2. Based on his numerical data Ramare conjectured that when N ∼ αQ^2 as Q → ∞ for some finite positive constant α, the limiting distribution of the eigenvalues of A⋆A, scaled by 1/N, exists and is non-degenerate. In this paper we prove this conjecture by establishing the convergence of all moments of the eigenvalues of A⋆A as Q → ∞. Previously only the second moment was known, due to Ramare. Furthermore, we obtain an explicit description of the moments of the limiting distribution, and establish that they vary continuously with α. Some of the main ingredients in our proof include the large-sieve inequality and results on n-correlations of Farey fractions.
DOE-Based Extraction of CMP, Active and Via Fill Impact on Capacitances

Chemical-mechanical polishing (CMP), active and via fills have become indispensable aspects of semiconductor manufacturing. CMP fills are used to reduce metal thickness variations due to chemical-mechanical polishing. Via fills are used to improve neighboring via printability and reliability of low-k and ultra low-k dielectrics. Active region fills are used for STI CMP uniformity and stress optimization. Although modern parasitic extraction tools accurately handle grounded fills and regular interconnects, such tools use only rough approximations to assess the capacitance impact of floating fills, such as assuming that floating fills are grounded or that each fill is merged with neighboring ones. To reduce such inaccuracies, we provide a design of experiments (DOE) which complements what is possible with existing extraction tools. Through the proposed DOE set, a design or mask house can generate normalized fill tables to correct for the inaccuracies of existing extraction tools when floating fills are present. Golden interconnect capacitance values can be updated using these normalized fill tables. Our proposed DOE enables extensive analyses of fill impacts on coupling capacitances. We show through 3-D field solver simulations that the assumptions used in extractors result in significant inaccuracies. We present analyses of fill impacts for an example technology and also provide analyses using the normalized fill tables to be used in the extraction flow for three different standard fill algorithms. We also extend our analyses and methodology to via fills and active region fills, which have more recently been introduced into semiconductor design-manufacturing methodologies and for which sufficient understanding is still lacking.
Agreement, Modeling of Categorical†

This entry describes models that can be used to describe the agreement between observers in ways that are more satisfactory than using simple single measures such as kappa. These models may take chance agreement and ordinal association into account, and the effects of covariates may also be incorporated. Modeling techniques include the conditional logistic regression model and the loglinear model when more than two categories are involved. More complex models are required if there are more than two raters. Keywords: marginal homogeneity; covariates; multiple raters; logistic regression; loglinear model
Zeta-equivalent digraphs: Simultaneous cospectrality

Abstract We introduce a zeta function of digraphs that determines, and is determined by, the spectra of all linear combinations of the adjacency matrix, its transpose, the out-degree matrix, and the in-degree matrix. In particular, zeta-equivalence of graphs encompasses simultaneous cospectrality with respect to the adjacency, the Laplacian, the signless Laplacian, and the normalized Laplacian matrix. In addition, we express zeta-equivalence in terms of Markov chains and in terms of invasions where each edge is replaced by a fixed digraph. We finish with a method for constructing zeta-equivalent digraphs.
Zero spacings of paraorthogonal polynomials on the unit circle

Abstract We prove some new results about the spacing between neighboring zeros of paraorthogonal polynomials on the unit circle. Our methods also provide new proofs of some existing results. The main tool we will use is a formula for the phase of the appropriate Blaschke product at points on the unit circle.