cssi/SciSciGPT-SciSciNet huggingface dataset:
Collaborative Compressive Detection With Physical Layer Secrecy Constraints

This paper considers the problem of detecting a high dimensional signal based on compressed measurements with physical layer secrecy guarantees. First, we propose a collaborative compressive detection (CCD) framework to compensate for the performance loss due to compression with a single sensor. We characterize the tradeoff between dimensionality reduction achieved by a universal compressive sensing (CS)-based measurement scheme and the achievable performance of CCD analytically. Next, we consider a scenario where the network operates in the presence of an eavesdropper who wants to discover the state of the nature being monitored by the system. To keep the data secret from the eavesdropper, we propose to use cooperating trustworthy nodes that assist the fusion center (FC) by injecting artificial noise to deceive the eavesdropper. We also design the system by determining the optimal values of parameters which maximize the CS-based collaborative detection performance at the FC while ensuring perfect secrecy at the eavesdropper. Experiments are conducted on synthetic data sets for spectrum sensing in cognitive radio networks to verify theoretical findings.
Superlinearly convergent asynchronous distributed network newton method

The problem of minimizing a sum of local convex objective functions over a networked system captures many important applications and has received much attention in the distributed optimization field. Most of existing work focuses on development of fast distributed algorithms under the presence of a central clock. The only known algorithms with convergence guarantees for this problem in asynchronous setup could achieve either sublinear rate under totally asynchronous setting or linear rate under partially asynchronous setting (with bounded delay). In this work, we built upon existing literature to develop and analyze an asynchronous Newton based approach for solving a penalized version of the problem. We show that this algorithm converges almost surely with global linear rate and local superlinear rate in expectation. Numerical studies confirm superior performance against other existing asynchronous methods.
Modeling the impact of communication loss on the power grid under emergency control

We study the interaction between the power grid and the communication network used for its control. We design a centralized emergency control scheme under both full and partial communication support, to improve the performance of the power grid. We use our emergency control scheme to model the impact of communication loss on the grid. We show that unlike previous models used in the literature, the loss of communication does not necessarily lead to the failure of the correspondent power nodes; i.e. the "point-wise" failure model is not appropriate. In addition, we show that the impact of communication loss is a function of several parameters such as the size and structure of the power and communication failure, as well as the operating mode of power nodes disconnected from the communication network. Our model can be used to design the dependency between the power grid and the communication network used for its control, so as to maximize the benefit in terms of intelligent control, while minimizing the risks due to loss of communication.
Approximation algorithms for finding maximum induced expanders

We initiate the study of approximating the largest induced expander in a given graph G. Given a Δ-regular graph G with n vertices, the goal is to find the set with the largest induced expansion of size at least δ · n. We design a bi-criteria approximation algorithm for this problem; if the optimum has induced spectral expansion λ our algorithm returns a [EQUATION]-(spectral) expander of size at least δn (up to constants).Our proof introduces and employs a novel semidefinite programming relaxation for the largest induced expander problem. We expect to see further applications of our SDP relaxation in graph partitioning problems. In particular, because of the close connection to the small set expansion problem, one may be able to obtain new insights into the unique games problem.
NEUZZ: Efficient Fuzzing with Neural Program Smoothing

Fuzzing has become the de facto standard technique for finding software vulnerabilities. However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs. Most popular fuzzers use evolutionary guidance to generate inputs that can trigger different bugs. Such evolutionary algorithms, while fast and simple to implement, often get stuck in fruitless sequences of random mutations. Gradient-guided optimization presents a promising alternative to evolutionary guidance. Gradient-guided techniques have been shown to significantly outperform evolutionary algorithms at solving high-dimensional structured optimization problems in domains like machine learning by efficiently utilizing gradients or higher-order derivatives of the underlying function. However, gradient-guided approaches are not directly applicable to fuzzing as real-world program behaviors contain many discontinuities, plateaus, and ridges where the gradient-based methods often get stuck. We observe that this problem can be addressed by creating a smooth surrogate function approximating the target program’s discrete branching behavior. In this paper, we propose a novel program smoothing technique using surrogate neural network models that can incrementally learn smooth approximations of a complex, real-world program's branching behaviors. We further demonstrate that such neural network models can be used together with gradient-guided input generation schemes to significantly increase the efficiency of the fuzzing process. Our extensive evaluations demonstrate that NEUZZ significantly outperforms 10 state-of-the-art graybox fuzzers on 10 popular real-world programs both at finding new bugs and achieving higher edge coverage. NEUZZ found 31 previously unknown bugs (including two CVEs) that other fuzzers failed to find in 10 real-world programs and achieved 3X more edge coverage than all of the tested graybox fuzzers over 24 hour runs. Furthermore, NEUZZ also outperformed existing fuzzers on both LAVA-M and DARPA CGC bug datasets.
Grid Graphs and Lattice Surfaces

First, we apply Thurston's construction of pseudo-Anosov homeomorphisms to grid graphs and obtain translation surfaces whose Veech groups are commensurable to $(m,n,\infty)$ triangle groups. These surfaces were first discovered by Bouw and Moller, however our treatment of the surfaces differs. We construct these surfaces by gluing together polygons in two ways. We use these elementary descriptions to compute the Veech groups, resolve primitivity questions, and describe the surfaces algebraically. Second, we show that some $(m,n, \infty)$ triangle groups can not arise as Veech groups. This generalizes work of Hubert and Schmidt.
Do Software Languages Engineers Evaluate their Languages

Domain Specific Languages (DSLs) can contribute to increment productivity, while reducing the required maintenance and programming expertise. We hypothesize that Software Languages Engineering (SLE) developers consistently skip, or relax, Language Evaluation. Based on the experience of engineering other types of software products, we assume that this may potentially lead to the deployment of inadequate languages. The fact that the languages already deal with concepts from the problem domain, and not the solution domain, is not enough to validate several issues at stake, such as its expressiveness, usability, effectiveness, maintainability, or even the domain expert’s productivity while using them. We present a systematic review on articles published in top ranked venues, from 2001 to 2008, which report DSLs’ construction, to characterize the common practice. This work confirms our initial hypothesis and lays the ground for the discussion on how to include a systematic approach to DSL evaluation in the SLE process.
Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures

Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable, near-uniform tessellation of rotation space using 4D tetrahedra, leading to more efficient optimization compared to the common axis-angle tessellation. We provide objective function bounds for pruning given the proposed tessellation, and prove that BB converges to the optimum of the cost function along with providing its computational complexity. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to real-world conditions such as missing data and partial overlap.
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation

Language is dynamic, constantly evolving and adapting with respect to time, domain or topic. The adaptability of language is an active research area, where researchers discover social, cultural and domain-specific changes in language using distributional tools such as word embeddings. In this paper, we introduce the global anchor method for detecting corpus-level language shifts. We show both theoretically and empirically that the global anchor method is equivalent to the alignment method, a widely-used method for comparing word embeddings, in terms of detecting corpus-level language shifts. Despite their equivalence in terms of detection abilities, we demonstrate that the global anchor method is superior in terms of applicability as it can compare embeddings of different dimensionalities. Furthermore, the global anchor method has implementation and parallelization advantages. We show that the global anchor method reveals fine structures in the evolution of language and domain adaptation. When combined with the graph Laplacian technique, the global anchor method recovers the evolution trajectory and domain clustering of disparate text corpora.
THE KARDAR-PARISI-ZHANG EQUATION AND UNIVERSALITY CLASS

Brownian motion is a continuum scaling limit for a wide class of random processes, and there has been great success in developing a theory for its properties (such as distribution functions or regularity) and expanding the breadth of its universality class. Over the past 25 years a new universality class has emerged to describe a host of important physical and probabilistic models (including one-dimensional interface growth processes, interacting particle systems and polymers in random environments) which display characteristic, though unusual, scalings and new statistics. This class is called the Kardar–Parisi–Zhang (KPZ) universality class and underlying it is, again, a continuum object — a non-linear stochastic partial differential equation — known as the KPZ equation. The purpose of this survey is to explain the context for, as well as the content of a number of mathematical breakthroughs which have culminated in the derivation of the exact formula for the distribution function of the KPZ equation started with narrow wedge initial data. In particular we emphasize three topics: (1) The approximation of the KPZ equation through the weakly asymmetric simple exclusion process; (2) The derivation of the exact one-point distribution of the solution to the KPZ equation with narrow wedge initial data; (3) Connections with directed polymers in random media. As the purpose of this article is to survey and review, we make precise statements but provide only heuristic arguments with indications of the technical complexities necessary to make such arguments mathematically rigorous.
Sparse generalized principal component analysis for large-scale applications beyond Gaussianity

Principal Component Analysis (PCA) is a dimension reduction technique. It produces inconsistent estimators when the dimensionality is moderate to high, which is often the problem in modern large-scale applications where algorithm scalability and model interpretability are difficult to achieve, not to mention the prevalence of missing values. While existing sparse PCA methods alleviate inconsistency, they are constrained to the Gaussian assumption of classical PCA and fail to address algorithm scalability issues. We generalize sparse PCA to the broad exponential family distributions under high-dimensional setup, with built-in treatment for missing values. Meanwhile we propose a family of iterative sparse generalized PCA (SG-PCA) algorithms such that despite the non-convexity and non-smoothness of the optimization task, the loss function decreases in every iteration. In terms of ease and intuitive parameter tuning, our sparsity-inducing regularization is far superior to the popular Lasso. Furthermore, to promote overall scalability, accelerated gradient is integrated for fast convergence, while a progressive screening technique gradually squeezes out nuisance dimensions of a large-scale problem for feasible optimization. High-dimensional simulation and real data experiments demonstrate the efficiency and efficacy of SG-PCA.
Internal DLA in higher dimensions

Let $A(t)$ denote the cluster produced by internal diffusion limited aggregation (internal DLA) with $t$ particles in dimension $d \geq 3$. We show that $A(t)$ is approximately spherical, up to an $O(\sqrt{\log t})$ error.
Mixing in Reaction-Diffusion Systems: Large Phase Offsets

We consider Reaction-Diffusion systems on $${\mathbb{R}}$$ , and prove diffusive mixing of asymptotic states $${u_0(kx - \phi_{\pm}, k)}$$ , where u0 is a spectrally stable periodic wave. Our analysis is the first to treat arbitrarily large phase-offsets $${\phi_d = \phi_{+}- \phi_{-}}$$ , so long as this offset proceeds in a sufficiently regular manner. The offset $${\phi_d}$$ completely determines the size of the asymptotic profiles in any topology, placing our analysis in the large data setting. In addition, the present result is a global stability result, in the sense that the class of initial data considered is not near the asymptotic profile in any sense. We prove the global existence, decay, and asymptotic self-similarity of the associated wavenumber equation. We develop a functional analytic framework to handle the linearized operator around large Burgers profiles via the exact integrability of the underlying Burgers flow. This framework enables us to prove a crucial, new mean-zero coercivity estimate, which we then combine with a nonlinear energy method.
Existence, uniqueness and the strong Markov property of solutions to Kimura diffusions with singular drift

Motivated by applications to proving regularity of solutions to degenerate parabolic equations arising in population genetics, we study existence, uniqueness and the strong Markov property of weak solutions to a class of degenerate stochastic differential equations. The stochastic differential equations considered in our article admit solutions supported in the set [0,∞)×R, and they are degenerate in the sense that the diffusion matrix is not strictly elliptic, as the smallest eigenvalue converges to zero at a rate proportional to the distance to the boundary of the domain, and the drift coefficients are allowed to have power-type singularities in a neighborhood of the boundary of the domain. Under suitable regularity assumptions on the coefficients, we establish existence of weak solutions that satisfy the strong Markov property, and uniqueness in law in the class of Markov processes.
Resourceful Contextual Bandits

We study contextual bandits with ancillary constraints on resources, which are common in real-world applications such as choosing ads or dynamic pricing of items. We design the first algorithm for solving these problems that handles constrained resources other than time, and improves over a trivial reduction to the non-contextual case. We consider very general settings for both contextual bandits (arbitrary policy sets, e.g. Dudik et al. (UAI’11)) and bandits with resource constraints (bandits with knapsacks, Badanidiyuru et al. (FOCS’13)), and prove a regret guarantee with near-optimal statistical properties.
An Entity Resolution Approach to Isolate Instances of Human Trafficking Online

Human trafficking is a challenging law enforcement problem, and traces of victims of such activity manifest as ‘escort advertisements’ on various online forums. Given the large, heterogeneous and noisy structure of this data, building models to predict instances of trafficking is a convoluted task. In this paper we propose an entity resolution pipeline using a notion of proxy labels, in order to extract clusters from this data with prior history of human trafficking activity. We apply this pipeline to 5M records from backpage.com and report on the performance of this approach, challenges in terms of scalability, and some significant domain specific characteristics of our resolved entities.
Testing for the Gorenstein property

We answer a question of Celikbas, Dao, and Takahashi by establishing the following characterization of Gorenstein rings: a commutative noetherian local ring \((R,\mathfrak m)\) is Gorenstein if and only if it admits an integrally closed \(\mathfrak m\)-primary ideal of finite Gorenstein dimension. This is accomplished through a detailed study of certain test complexes. Along the way we construct such a test complex that detect finiteness of Gorenstein dimension, but not that of projective dimension.
On Solving a Generalized Chinese Remainder Theorem in the Presence of Remainder Errors

In estimating frequencies given that the signal waveforms are undersampled multiple times, Xia et al. proposed to use a generalized version of Chinese remainder Theorem (CRT), where the moduli are \(M_1, M_2, \ldots , M_k\) which are not necessarily pairwise coprime. If the errors of the corrupted remainders are within \(\tau = \displaystyle \max _{1\le i\le k} \min _{{\mathop {j\ne i}\limits ^{1\le j\le k}}} \frac{\gcd (M_i,M_j)}{4}\), their schemes can be used to construct an approximation of the solution to the generalized CRT with an error smaller than \(\tau \). Accurately finding the quotients is a critical ingredient in their approach. In this paper, we shall start with a faithful historical account of the generalized CRT. We then present two treatments of the problem of solving generalized CRT with erroneous remainders. The first treatment follows the route of Wang and Xia to find the quotients, but with a simplified process. The second treatment considers a simplified model of generalized CRT and takes a different approach by working on the corrupted remainders directly. This approach also reveals some useful information about the remainders by inspecting extreme values of the erroneous remainders modulo \(4\tau \). Both of our treatments produce efficient algorithms with essentially optimal performance. Finally, this paper constructs a counterexample to prove the sharpness of the error bound \(\tau \).
Learning SMaLL predictors

We introduce a new framework for learning in severely resource-constrained settings. Our technique delicately amalgamates the representational richness of multiple linear predictors with the sparsity of Boolean relaxations, and thereby yields classifiers that are compact, interpretable, and accurate. We provide a rigorous formalism of the learning problem, and establish fast convergence of the ensuing algorithm via relaxation to a minimax saddle point objective. We supplement the theoretical foundations of our work with an extensive empirical evaluation.
Balanced truncation model reduction of nonstationary systems interconnected over arbitrary graphs

Abstract This paper deals with the balanced truncation of discrete-time, linear time-varying, heterogeneous subsystems interconnected over finite arbitrary directed graphs. The information transfer between the subsystems is subject to a communication latency of one time-step. The method guarantees the preservation of the interconnection structure and further allows for its simplification. In addition to truncating temporal states associated with the subsystems, the method allows for the order reduction of spatial states associated with the interconnections between the subsystems and even the removal of whole interconnections. Upper bounds on the l 2 -induced norm of the resulting error system are derived. The method is illustrated through an example.
Differential equations and logarithmic intertwining operators for strongly graded vertex algebras

We derive certain systems of differential equations for matrix elements of products and iterates of logarithmic intertwining operators among strongly graded generalized modules for a strongly graded vertex algebra under a certain finiteness condition and a condition related to the horizontal gradings. Using these systems of differential equations, we verify the convergence and extension property needed in the logarithmic tensor category theory for such strongly graded generalized modules developed by Huang, Lepowsky and Zhang.
Visual causal feature learning

We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed from micro-variables. We prove the Causal Coarsening Theorem, which allows us to gain causal knowledge from observational data with minimal experimental effort. The theorem provides a connection to standard inference techniques in machine learning that identify features of an image that correlate with, but may not cause, the target behavior. Finally, we propose an active learning scheme to learn a manipulator function that performs optimal manipulations on the image to automatically identify the visual cause of a target behavior. We illustrate our inference and learning algorithms in experiments based on both synthetic and real data.
External rays and the real slice of the Mandelbrot set

This paper investigates the set of angles of the parameter rays which land on the real slice [-2,1/4] of the Mandelbrot set. We prove that this set has zero length but Hausdorff dimension 1. We obtain the corresponding results for tuned images of the real slice. Applications of these estimates in the study of critically non-recurrent real quadratics as well as bi-accessible points of quadratic Julia sets are given.
Normal Vector of a Random Hyperplane

Let v_1,...,v_{n-1} be n-1 independent vectors in R^n (or C^n). We study x, the unit normal vector of the hyperplane spanned by the v_i. Our main finding is that x resembles a random vector chosen uniformly from the unit sphere, under some randomness assumption on the v_i. Our result has applications in random matrix theory. Consider an n by n random matrix with iid entries. We first prove an exponential bound on the upper tail for the least singular value, improving the earlier linear bound by Rudelson and Vershynin. Next, we derive optimal delocalization for the eigenvectors corresponding to eigenvalues of small modulus.
A dynamic jamming game for real-time status updates

We study timely status updates of a real-time system in an adversarial setting. The system samples a physical process, and sends the samples from the source (e.g., a sensor) to the destination (e.g, a control center) through a channel. For real-time monitoring/control tasks, it is crucial for the system to update the status of the physical process “timely”. We measure the timeliness of status updates by the time elapsed since the latest update at the destination was generated at the source, and define the time elapsed as age of information, or age in short. To sabotage the system, an attacker aims to maximize the age by jamming the channel and hence causing delay in status updates. The system aims to minimize the age by judiciously choosing when to sample and send the updates. We model the ongoing repeated interaction between the attacker and the system as a dynamic game. In each stage game, the attacker chooses the jamming time according to the jamming time distribution, and the system responds by delaying the sampling according to the sampling policy. We prove that there exists a unique stationary equilibrium in the game, and provide a complete analytical characterization of the equilibrium. Our results shed lights on how the attacker sabotages the system and how the system should defend against the attacker.
Tunable thermal transport and thermal rectification in strained graphene nanoribbons

Using molecular dynamics (MD) simulations, we study thermal transport in graphene nanoribbons (GNR) subjected to uniform uniaxial and nonuniform strain fields. We predict significant thermal rectification (over $70%$) in a rectangular armchair GNR by applying a transverse force asymmetrically. The heat flux is larger from the less stressed region to the more stressed region. Furthermore, we develop a theoretical framework based on the nonequilibrium thermodynamics to discuss when thermal rectification under a stress gradient can occur. We conclude with a discussion of details relevant to experiment.
Market-based microgrid optimal scheduling

This paper presents an optimal scheduling model for a microgrid participating in the electricity distribution market in interaction with the Distribution Market Operator (DMO). The DMO is a concept proposed here, which administers the established electricity market in the distribution level, i.e., similar to the role of Independent System Operator (ISO) in the wholesale electricity market, sets electricity prices, determines the amounts of the power exchange between market participators, and interacts with the ISO. Considering a predetermined main grid power transfer to the microgrid, the microgrid scheduling problem will aim at balancing the power supply and demand while taking financial objectives into account. A stochastic programming method is employed to model prevailing uncertainties in the microgrid grid-connected and islanded operations. Numerical simulations exhibit the application and the effectiveness of the proposed market-based microgrid scheduling model.
Probabilistic models for agents' beliefs and decisions

Many applications of intelligent systems require reasoning about the mental states of agents in the domain. We may want to reason about an agent's beliefs, including beliefs about other agents; we may also want to reason about an agent's preferences, and how his beliefs and preferences relate to his behavior. We define a probabilistic epistemic logic (PEL) in which belief statements are given a formal semantics, and provide an algorithm for asserting and querying PEL formulas in Bayesian networks. We then show how to reason about an agent's behavior by modeling his decision process as an influence diagram and assuming that he behaves rationally. PEL can then be used for reasoning from an agent's observed actions to conclusions about other aspects of the domain, including unobserved domain variables and the agent's mental states.
Better Unrelated Machine Scheduling for Weighted Completion Time via Random Offsets from Non-uniform Distributions

In this paper we consider the classic scheduling problem of minimizing total weighted completion time on unrelated machines when jobs have release times, i.e, R|rij| Σj wjCj using the three-field notation. For this problem, a 2-approximation is known based on a novel convex programming (J. ACM 2001 by Skutella). It has been a long standing open problem if one can improve upon this 2-approximation (Open Problem 8 in J. of Sched. 1999 by Schuurman and Woeginger). We answer this question in the affirmative by giving a 1.8786-approximation. We achieve this via a surprisingly simple linear programming, but a novel rounding algorithm and analysis. A key ingredient of our algorithm is the use of random offsets sampled from non-uniform distributions. We also consider the preemptive version of the problem, i.e, R|rij, pmtn|ΣjwjCj. We again use the idea of sampling offsets from non-uniform distributions to give the first better than 2-approximation for this problem. This improvement also requires use of a configuration LP with variables for each job's complete schedules along with more careful analysis. For both non-preemptive and preemptive versions, we break the approximation barrier of 2 for the first time.
Shapes, fingerprints and rational lemniscates

It has been known for a long time that any smooth Jordan curve in the plane can be represented by its so-called fingerprint, an orientation preserving smooth diffeomorphism of the unit circle onto itself. In this paper, we give a new, simple proof of a theorem of Ebenfelt, Khavinson and Shapiro stating that the fingerprint of a polynomial lemniscate of degree n is given by the n-th root of a Blaschke product of degree n and that conversely, any smooth diffeomorphism induced by such a map is the fingerprint of a polynomial lemniscate of the same degree. The proof is easily generalized to the case of rational lemniscates, thus solving a problem raised by the previously mentioned authors.
Algorithmically complex residually finite groups

We construct the first examples of algorithmically complex finitely presented residually finite groups and the first examples of finitely presented residually finite groups with arbitrarily large (recursive) Dehn functions, and arbitrarily large depth functions. The groups are solvable of class 3.
Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration.

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure that they behave in ways aligned with the values of society, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations and reinforcement learning to learn to maximize environmental rewards. A contextual-bandit-based orchestrator then picks between the two policies: constraint-based and environment reward-based. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward-maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using Pac-Man and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.
On the equivalence of Playfair’s axiom to the parallel postulate

We show that the classical equivalence of Euclid’s parallel postulate and Playfair’s axiom collapses in the absence of triangle congruence. In particular, we construct a non-SAS geometry that models the Playfair axiom but not the parallel postulate.
OpenRoACH: A Durable Open-Source Hexapedal Platform with Onboard Robot Operating System (ROS)

OpenRoACH is a 15-cm 200-gram self-contained hexapedal robot with an onboard single-board computer. To our knowledge, it is the smallest legged robot with the capability of running the Robot Operating System (ROS) onboard. The robot is fully open sourced, uses accessible materials and off-the-shelf electronic components, can be fabricated with benchtop fast-prototyping machines such as a laser cutter and a 3D printer, and can be assembled by one person within two hours. Its sensory capacity has been tested with gyroscopes, accelerometers, Beacon sensors, color vision sensors, linescan sensors and cameras. It is low-cost within $150 including structure materials, motors, electronics, and a battery. The capabilities of OpenRoACH are demonstrated with multi-surface walking and running, 24-hour continuous walking burn-ins, carrying 200-gram dynamic payloads and 800-gram static payloads, and ROS control of steering based on camera feedback. Information and files related to mechanical design, fabrication, assembly, electronics, and control algorithms are all publicly available on https://wiki.eecs.berkeley.edu/biomimetics/Main/OpenRoACH.
Measurement reduction in variational quantum algorithms

Variational quantum algorithms are promising applications of noisy intermediate-scale quantum (NISQ) computers. These algorithms consist of a number of separate prepare-and-measure experiments that estimate terms in a Hamiltonian. The number of terms can become overwhelmingly large for problems at the scale of NISQ hardware that may soon be available. We use unitary partitioning (developed independently by Izmaylov et al. [J. Chem. Theory Comput. 16, 190 (2020)]) to define variational quantum eigensolver procedures in which additional unitary operations are appended to the ansatz preparation to reduce the number of terms. This approach may be scaled to use all coherent resources available after ansatz preparation. We also study the use of asymmetric qubitization to implement the additional coherent operations with lower circuit depth. Using this technique, we find a constant factor speedup for lattice and random Pauli Hamiltonians. For electronic structure Hamiltonians, we prove that linear term reduction with respect to the number of orbitals, which has been previously observed in numerical studies, is always achievable. For systems represented on 10--30 qubits, we find that there is a reduction in the number of terms by approximately an order of magnitude. Applied to the plane-wave dual-basis representation of fermionic Hamiltonians, however, unitary partitioning offers only a constant factor reduction. Finally, we show that noncontextual Hamiltonians may be reduced to effective commuting Hamiltonians using unitary partitioning.
Analysis of the relationship of happiness to economic achievement and other factors in US states

Overall emotional well-being, notably happiness, has been the subject of numerous studies in psychology, business and other disciplines. In this study, happiness, aka life satisfaction, is measured by people's own personal assessment of happiness, not measured by 'how happy people ought to be' based on 'well-being' measures such as income or community amenities. The top ten happiest states, in order, are Louisiana, Hawaii, Florida, Tennessee, Arizona, Mississippi, Montana, South Carolina, Alabama and Maine. The ten unhappiest states are Rhode Island, Massachusetts, Ohio, Illinois, California, Indiana, Michigan, New Jersey, Connecticut and New York. The findings indicate that happier states on average are significantly more religious and more politically conservative. Findings show that the happier states had a significantly lower per capita GDP; thus, money does not buy happiness. Businesses can facilitate happiness among employees, by supporting work-life balance of employees, being parent-friendly, being marriage-friendly and enabling employees to integrate their spiritual values in their job roles.
Children with Attention-Deficit/Hyperactivity Disorder Perform Differently on Pediatric Concussion Assessment.

Objective To compare Child Sport Concussion Assessment Tool Fifth Edition (Child SCAT5) performance between uninjured children with attention-deficit/hyperactivity disorder (ADHD) and precisely matched controls without ADHD. Study design A nested case-control study was conducted within a cohort of middle school athletes (age 11-12 years) who completed preseason testing. Students with ADHD were individually matched to students without ADHD based on age, sex, language spoken at home, number of prior concussions, sport, and school they attended. The final sample included 54 students (27 with ADHD and 27 controls), 38 (70.4%) boys and 16 (29.6%) girls (average age: 11.7 years, SD = 0.5). Results Children with ADHD reported more symptoms (M = 13.33, SD = 5.69, P Conclusions Children with ADHD endorsed more concussion-like symptoms and performed worse on balance testing during preseason Child SCAT5 assessment compared with matched controls without ADHD. These findings highlight the challenges of interpreting Child SCAT5 performance in children with ADHD following a concussion or suspected concussion and illustrate the value of administering the measure to children to document their pre-injury performance.
HARE: a Flexible Highlighting Annotator for Ranking and Exploration.

Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning. We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features. We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools. Our system is available online at https://github.com/OSU-slatelab/HARE.
Bond-order bond energy model for alloys

Abstract We introduce a novel way to parameterize alloy energies in the form of a bond-order bond energy model. There, a bond order function models the transition between competing phases and switches their respective bond energies on and off. For the case of the Ni-Cr-Mo alloys investigated here, which assume face- or body-centered cubic structures, we propose a sigmoidal switching function fitted to the c/a ratio of “Bain-like” cells. With that, the model does an excellent job in describing the DFT-calculated alloy energies. We also show that the average atomic charge density can vary considerably as a function of composition, which can significantly modify alloy bond energies from simple expectation. The fitted bond energies can among others be used to determine phase diagrams, where we find excellent agreement with previous assessments in the solid range for Ni-Cr and Cr-Mo phase diagrams once the necessary entropy terms are added. They also allow quantitative, composition-dependent calculation of chemical potentials, which we use to determine vacancy formation energies in binary NiCr alloys for configurations that have been energy-minimized with Monte Carlo simulations. We show that the resulting regime of negative formation energies is a sign for thermodynamic instability of the underlying crystal and lies in the two-phase concentration range in the phase diagram, resulting in a holistic picture that unites defect and phase stability through the fully quantitative link between stoichiometry and chemical potentials enabled by the proposed bond energy model.
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks

We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM , three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained.
Land Reform and Civil Conflict: Theory and Evidence from Peru

How does land reform impact civil conflict? This paper examines this question in the prominent case of Peru by leveraging original data on all land expropriations under military rule from 1969-1980 and event-level data from the Peruvian Truth and Reconciliation Commission on rural killings during Peru's internal conflict from 1980-2000. Using a regression discontinuity design that takes advantage of Peru’s regional approach to land reform through zones that did not entirely map onto major pre-existing administrative boundaries, I find that greater land reform dampened subsequent conflict. Districts in core areas of land reform zones that received intense land reform witnessed less conflict relative to comparable districts in adjacent peripheral areas where less land reform occurred. Further tests suggest that land reform mitigated conflict by facilitating counterinsurgency and intelligence gathering, building local organizational capacity later used to deter violence, undercutting the Marxist left, and increasing opportunity costs to supporting armed groups.
In-plane radial fluid flow characterization of fibrous materials

Several techmques employing fluid flow to characterize the structure of fibrous materials are presented. A radial flow cell is described in which a fibrous mat with a central hole is compressed bet...
Versorgung für Jugendliche mit CED

Die Manifestationen der chronisch-entzundlichen Darmerkrankungen (CED) bei Kindern unterscheiden sich in Bezug auf Ausdehnung und Schwere von denen der Erwachsenen. Die Versorgungsbedingungen sind davon abhangig, ob die Kinder bei padiatrischen oder internistischen Gastroenterologen betreut werden, wie eine niederlandische populationsbezogene Studie zeigt.
Optimal Convergence Rate of Hamiltonian Monte Carlo for Strongly Logconcave Distributions.

We study Hamiltonian Monte Carlo (HMC) for sampling from a strongly logconcave density proportional to $e^{-f}$ where $f:\mathbb{R}^d \to \mathbb{R}$ is $\mu$-strongly convex and $L$-smooth (the condition number is $\kappa = L/\mu$). We show that the relaxation time (inverse of the spectral gap) of ideal HMC is $O(\kappa)$, improving on the previous best bound of $O(\kappa^{1.5})$; we complement this with an example where the relaxation time is $\Omega(\kappa)$. When implemented using a nearly optimal ODE solver, HMC returns an $\varepsilon$-approximate point in $2$-Wasserstein distance using $\widetilde{O}((\kappa d)^{0.5} \varepsilon^{-1})$ gradient evaluations per step and $\widetilde{O}((\kappa d)^{1.5}\varepsilon^{-1})$ total time.
Rob-GAN: Generator, Discriminator, and Adversarial Attacker

We study two important concepts in adversarial deep learning---adversarial training and generative adversarial network (GAN). Adversarial training is the technique used to improve the robustness of discriminator by combining adversarial attacker and discriminator in the training phase. GAN is commonly used for image generation by jointly optimizing discriminator and generator. We show these two concepts are indeed closely related and can be used to strengthen each other---adding a generator to the adversarial training procedure can improve the robustness of discriminators, and adding an adversarial attack to GAN training can improve the convergence speed and lead to better generators. Combining these two insights, we develop a framework called Rob-GAN to jointly optimize generator and discriminator in the presence of adversarial attacks---the generator generates fake images to fool discriminator; the adversarial attacker perturbs real images to fool discriminator, and the discriminator wants to minimize loss under fake and adversarial images. Through this end-to-end training procedure, we are able to simultaneously improve the convergence speed of GAN training, the quality of synthetic images, and the robustness of discriminator under strong adversarial attacks. Experimental results demonstrate that the obtained classifier is more robust than the state-of-the-art adversarial training approach (Madry 2017), and the generator outperforms SN-GAN on ImageNet-143.
Mechanical effects of MitraClip on leaflet stress and myocardial strain in functional mitral regurgitation – A finite element modeling study

Purpose MitraClip is the sole percutaneous device approved for functional mitral regurgitation (MR; FMR) but MR recurs in over one third of patients. As device-induced mechanical effects are a potential cause for MR recurrence, we tested the hypothesis that MitraClip increases leaflet stress and procedure-related strain in sub-valvular left ventricular (LV) myocardium in FMR associated with coronary disease (FMR-CAD). Methods Simulations were performed using finite element models of the LV + mitral valve based on MRI of 5 sheep with FMR-CAD. Models were modified to have a 20% increase in LV volume (↑LV_VOLUME) and MitraClip was simulated with contracting beam elements (virtual sutures) placed between nodes in the center edge of the anterior (AL) and posterior (PL) mitral leaflets. Effects of MitraClip on leaflet stress in the peri-MitraClip region of AL and PL, septo-lateral annular diameter (SLAD), and procedure-related radial strain (Err) in the sub-valvular myocardium were calculated. Results MitraClip increased peri-MitraClip leaflet stress at end-diastole (ED) by 22.3±7.1 kPa (p<0.0001) in AL and 14.8±1.2 kPa (p<0.0001) in PL. MitraClip decreased SLAD by 6.1±2.2 mm (p<0.0001) and increased Err in the sub-valvular lateral LV myocardium at ED by 0.09±0.04 (p<0.0001)). Furthermore, MitraClip in ↑LV_VOLUME was associated with persistent effects at ED but also at end-systole where peri-MitraClip leaflet stress was increased in AL by 31.9±14.4 kPa (p = 0.0268) and in PL by 22.5±23.7 kPa (p = 0.0101). Conclusions MitraClip for FMR-CAD increases mitral leaflet stress and radial strain in LV sub-valvular myocardium. Mechanical effects of MitraClip are augmented by LV enlargement.
Stochastic activity networks

We shall review the history of “stochastic activity networks” (SANs) since their inception with the PERT model in 1959, and the various embellishments and contributions that have taken place since then in the subject of project planning and control in the presence of uncertainty. We shall also introduce the new concept of the “Alphorn of Uncertainty” (AoU), with examples of its application, and its use in the financial planning of a project.
‘Big Mac Affordability’ and real-income inequality across countries

Using the Big Mac Index and focusing on the deciles of the income distribution, we produce a descriptive study of real-income inequality from households in 29 countries and for period 2000 to 2013....
Civitas: A Secure Remote Voting System

Civitas is the first implementation of a coercion-resistant, universally verifiable, remote voting scheme. This paper describes the design of Civitas, details the cryptographic protocols used in its construction, and illustrates how language-enforced information-flow security policies yield assurance in the implementation. The performance of Civitas scales well in the number of voters and offers reasonable tradeoffs between time, cost, and security. These results suggest that secure electronic voting is achievable. The name of this system as presented at Dagstuhl was CIVS. In August 2007, the name was changed to Civitas. For more information, see the Civitas website at http://www.cs.cornell.edu/projects/civitas.
Large-aperture coatings for fusion-class laser systems.

Optical coatings for fusion-class laser systems pose unique challenges, given the large substrate sizes, the high intensities incident on the coatings, and the system-focusing requirements, necessitating a well-controlled optical wavefront. Significant advancements have taken place in the past 30 years to achieve the coating capabilities necessary to build laser systems such as the National Ignition Facility, Laser Megajoule, OMEGA EP, and OMEGA. This work summarizes the coating efforts and advancements to support such system construction and maintenance.