cssi/SciSciGPT-SciSciNet huggingface dataset:
A new phase-field approach to variational implicit solvation of charged molecules with the Coulomb-field approximation

We construct a new phase-field model for the solvation of charged molecules with a variational implicit solvent. Our phase-field free-energy functional includes the surface energy, solute-solvent van der Waals dispersion energy, and electrostatic interaction energy that is described by the Coulomb-field approximation, all coupled together self-consistently through a phase field. By introducing a new phase-field term in the description of the solute-solvent van der Waals and electrostatic interactions, we can keep the phase-field values closer to those describing the solute and solvent regions, respectively, making it more accurate in the free-energy estimate. We first prove that our phase-field functionals $\Gamma$-converge to the corresponding sharp-interface limit. We then develop and implement an efficient and stable numerical method to solve the resulting gradient-flow equation to obtain equilibrium conformations and their associated free energies of the underlying charged molecular system. Our numerical method combines a linear splitting scheme, spectral discretization, and exponential time differencing Runge-Kutta approximations. Applications to the solvation of single ions and a two-plate system demonstrate that our new phase-field implementation improves the previous ones by achieving the localization of the system forces near the solute-solvent interface and maintaining more robustly the desirable hyperbolic tangent profile for even larger interfacial width. This work provides a scheme to resolve the possible unphysical feature of negative values in the phase-field function found in the previous phase-field modeling (cf. H. Sun, et al. J. Chem. Phys., 2015) of charged molecules with the Poisson--Boltzmann equation for the electrostatic interaction.
Polynomial quadratic differentials on the complex plane and light-like polygons in the Einstein Universe

Abstract We construct geometrically a homeomorphism between the moduli space of polynomial quadratic differentials on the complex plane and light-like polygons in the 2-dimensional Einstein Universe. As an application, we find a class of minimal Lagrangian maps between ideal polygons in the hyperbolic plane.
New examples of three-dimensional dilational materials

Two-dimensional dilational materials, for which the only easy mode of deformation is dilation are reviewed and connections are drawn between models previously proposed in the literature. Some models, which appear to be dilational materials, but which in fact are not, are also discussed. Finally, four new examples of three-dimensional dilational materials are given.
Diffraction of an obliquely incident electromagnetic wave by an impedance right-angled concave wedge

Scattering of a plane electromagnetic wave by an anisotropic impedance right-angled concave wedge at skew incidence is analyzed. A closed-form solution is derived by reducing the problem to a symmetric order-2 vector Riemann-Hilbert problem (RHP) on the real axis. The problem of matrix factorization leads to a scalar RHP on a genus-3 Riemann surface. Its solution is derived by the Weierstrass integrals. Due to a special symmetry of the problem the associated Jacobi inversion problem is solved in terms of elliptic integrals, not a genus-3 Riemann \`e-function. The electric and magnetic field components are expressed through the Sommerfeld integrals, and the incident and reflected waves are recovered.
Identifiability of 3-class Jukes-Cantor mixtures

We prove identifiability of the tree parameters of the 3-class Jukes-Cantor mixture model. The proof uses ideas from algebraic statistics, in particular: finding phylogenetic invariants that separate the varieties associated to different triples of trees; computing dimensions of the resulting phylogenetic varieties; and using the disentangling number to reduce to trees with a small number of leaves. Symbolic computation also plays a key role in handling the many different cases and finding relevant phylogenetic invariants.
Dealing with Range Anxiety in Mean Estimation via Statistical Queries

We give algorithms for estimating the expectation of a given real-valued function $\phi:X\to {\bf R}$ on a sample drawn randomly from some unknown distribution $D$ over domain $X$, namely ${\bf E}_{{\bf x}\sim D}[\phi({\bf x})]$. Our algorithms work in two well-studied models of restricted access to data samples. The first one is the statistical query (SQ) model in which an algorithm has access to an SQ oracle for the input distribution $D$ over $X$ instead of i.i.d. samples from $D$. Given a query function $\phi:X \to [0,1]$, the oracle returns an estimate of ${\bf E}_{{\bf x}\sim D}[\phi({\bf x})]$ within some tolerance $\tau$. The second, is a model in which only a single bit is communicated from each sample. In both of these models the error obtained using a naive implementation would scale polynomially with the range of the random variable $\phi({\bf x})$ (which might even be infinite). In contrast, without restrictions on access to data the expected error scales with the standard deviation of $\phi({\bf x})$. Here we give a simple algorithm whose error scales linearly in standard deviation of $\phi({\bf x})$ and logarithmically with an upper bound on the second moment of $\phi({\bf x})$. As corollaries, we obtain algorithms for high dimensional mean estimation and stochastic convex optimization in these models that work in more general settings than previously known solutions.
Some Automatic Continuity Theorems for Operator Algebras and Centralizers of Pedersen’s Ideal

We prove automatic continuity theorems for “decomposable” or“local” linear transformations between certain natural subspaces of operator algebras. The transformations involved are not algebra homomorphisms but often are module homomorphisms. We show that all left (respectively quasi-) centralizers of the Pedersen ideal of a C*-algebra A are locally bounded if and only if A has no infinite dimensional elementary direct summand. It has previously been shown by Lazar and Taylor and Phillips that double centralizers of Pedersen’s ideal are always locally bounded.
Computational methods for stochastic control with metric interval temporal logic specifications

This paper studies an optimal control problem for continuous-time stochastic systems subject to objectives specified in a subclass of metric interval temporal logic specifications, a temporal logic with real-time constraints. We propose a computational method for synthesizing an optimal control policy that maximizes the probability of satisfying a specification based on a discrete approximation of the underlying stochastic system. First, we show that the original problem can be formulated as a stochastic optimal control problem in a state space augmented with finite memory and states of some clock variables. Second, we present a numerical method for computing an optimal policy with which the given specification is satisfied with the maximal probability in point-based semantics in the discrete approximation of the underlying system. We show that the policy obtained in the discrete approximation converges to the optimal one for satisfying the specification in the continuous or dense-time semantics as the discretization becomes finer in both state and time. Finally, we illustrate our approach with a robotic motion planning example.
Curvature Estimates for Four-Dimensional Gradient Steady Ricci Solitons

In this paper, we derive certain curvature estimates for 4-dimensional gradient steady Ricci solitons either with positive Ricci curvature or with scalar curvature decay $$\lim _{x\rightarrow \infty } R(x)=0$$.
On computational fluid dynamics models for sinonasal drug transport: Relevance of nozzle subtraction and nasal vestibular dilation.

Generating anatomically realistic 3-dimensional (3D) models of the human sinonasal cavity for numerical investigations of sprayed drug transport presents a host of methodological ambiguities. For example, subject-specific radiographic images used for 3D reconstructions typically exclude spray bottles. Subtracting a bottle contour from the 3D airspace and dilating the anterior nasal vestibule for nozzle placement augment the complexity of model building. So we explored the question: how essential are these steps to adequately simulate nasal airflow and identify the optimal delivery conditions for intranasal sprays? In particular, we focused on particle deposition patterns in the maxillary sinus, a critical target site for chronic rhinosinusitis. The models were reconstructed from postsurgery computed tomography scans for a 39-year-old Caucasian male, with chronic rhinosinusitis history. Inspiratory airflow patterns during resting breathing are reliably tracked through computational fluid dynamics-based steady-state laminar-viscous modeling, and such regimes portray relative lack of sensitivity to inlet perturbations. Consequently, we hypothesized that the posterior airflow transport and the particle deposition trends should not be radically affected by the nozzle subtraction and vestibular dilation. The study involved 1 base model and 2 derived models; the latter 2 with nozzle contours (2 different orientations) subtracted from the dilated anterior segment of the left vestibule. We analyzed spray transport in the left maxillary sinus for multiple release conditions. Similar release points, localized on an approximately 2 mm × 4.5 mm contour, facilitated improved maxillary deposition in all 3 test cases. This suggests functional redundancy of nozzle insertion in a 3D numerical model for identifying the optimal spray release locations.
Stochastic variance reduced optimization for nonconvex sparse learning

We propose a stochastic variance reduced optimization algorithm for solving a class of large-scale nonconvex optimization problems with cardinality constraints, and provide sufficient conditions under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. Numerical experiments demonstrate the efficiency of our method in terms of both parameter estimation and computational performance.
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions

Stochastic optimization algorithms have many desirable features for large-scale machine learning, and accordingly have been the focus of renewed and intensive study in the last several years (e.g., see the papers [2], [5], [14] and references therein). The empirical efficiency of these methods is backed with strong theoretical guarantees, providing sharp bounds on their convergence rates. These convergence rates are known to depend on the structure of the underlying objective function, with faster rates being possible for objective functions that are smooth and/or (strongly) convex, or optima that have desirable features such as sparsity. More precisely, for an objective function that is strongly convex, stochastic gradient descent enjoys a convergence rate ranging from O(1/T ), when features vectors are extremely sparse, to O(d/T ) when feature vectors are dense [10], [6].
Inapproximability of VC Dimension and Littlestone's Dimension

We study the complexity of computing the VC Dimension and Littlestone's Dimension. Given an explicit description of a finite universe and a concept class (a binary matrix whose $(x,C)$-th entry is $1$ iff element $x$ belongs to concept $C$), both can be computed exactly in quasi-polynomial time ($n^{O(\log n)}$). Assuming the randomized Exponential Time Hypothesis (ETH), we prove nearly matching lower bounds on the running time, that hold even for approximation algorithms.
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates

In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [11]. While previous constructions required Ω(n4) running time [11, 45], our sparsification routine can be implemented in almost-quadratic running time O(n2+e). The fundamental conceptual novelty of our work is the leveraging of a strong connection between sparsification and a regret minimization problem over density matrices. This connection was known to provide an interpretation of the randomized sparsifiers of Spielman and Srivastava [39] via the application of matrix multiplicative weight updates (MWU) [17, 43]. In this paper, we explain how matrix MWU naturally arises as an instance of the Follow-the-Regularized-Leader framework and generalize this approach to yield a larger class of updates. This new class allows us to accelerate the construction of linear-sized spectral sparsifiers, and give novel insights on the motivation behind Batson, Spielman and Srivastava [11].
Spectra of Kohn Laplacians on spheres

We study the spectrum of the Kohn Laplacian on the unit spheres in ℂn and revisit Folland’s classical eigenvalue computation. We also look at the growth rate of the eigenvalue counting function in this context. Finally, we consider the growth rate of the eigenvalues of the perturbed Kohn Laplacian on the Rossi sphere in ℂ2.
Degree Ramsey numbers for even cycles

Abstract Let H ⟶ s G denote that any s -coloring of E ( H ) contains a monochromatic G . The degree Ramsey number of a graph G , denoted by R Δ ( G , s ) , is min { Δ ( H ) : H ⟶ s G } . We consider degree Ramsey numbers where G is a fixed even cycle. Kinnersley, Milans, and West showed that R Δ ( C 2 k , s ) ≥ 2 s , and Kang and Perarnau showed that R Δ ( C 4 , s ) = Θ ( s 2 ) . Our main result is that R Δ ( C 6 , s ) = Θ ( s 3 ∕ 2 ) and R Δ ( C 10 , s ) = Θ ( s 5 ∕ 4 ) . Additionally, we substantially improve the lower bound for R Δ ( C 2 k , s ) for general k .
A local Ramsey theory for block sequences

We develop local forms of Ramsey-theoretic dichotomies for block sequences in infinite-dimensional vector spaces, analogous to Mathias' selective coideal form of Silver's theorem for analytic partitions of $[\mathbb{N}]^\infty$. Under large cardinals, these results are extended to partitions in $\mathbf{L}(\mathbb{R})$ and $\mathbf{L}(\mathbb{R})$-generic filters of block sequences are characterized. Variants of these results are also established for block sequences in Banach spaces and for projections in the Calkin algebra.
Computational modeling of spectral data fitting with nonlinear distortions

Substances such as chemical compounds and biological agents are invisible to human eyes, they are usually captured by sensing equipments with their spectral fingerprints. Although spectra of pure substances can be identified by visual inspection, the spectra of their mixtures take a variety of complicated forms. Given the knowledge of spectral references of the constituent substances, the task of data fitting is to solve their weights, which usually can be solved by a least squares. Complications occur if the basis functions (reference spectra) may not be used directly to best fit the data. In fact, random spectral distortions such as shifting, compression, and expansion have been observed in some spectra. In this paper, we formulate mathematical model for such distortions and build them into data fitting algorithms. If minimal knowledge of the distortions is available, a deterministic approach termed augmented least squares is developed by fitting the spectral references together with their derivatives to the mixtures. If the distribution of the distortions is known a prior, we propose to solve the problem with maximum likelihood estimators which incorporate the distortions into the variance matrix. The proposed methods are substantiated with numerical examples including data from Raman spectroscopy, nuclear magnetic resonance, and differential optical absorption spectroscopy and show satisfactory results.
Harmonic forms on principal bundles

We show a relationship between Chern-Simons 1- and 3-forms and harmonic forms on a principal bundle. Doing so requires one to consider an adiabatic limit. For the 3-form case, assume that G is simple and the corresponding Chern-Weil 4-form is exact. Then, the Chern-Simons 3-form on the princpal bundle G-bundle, minus a canonical term from the base, is harmonic in the adiabatic limit.
Scheme representation for first-order logic

In this work, we present a construction of \logical schemes," geometric entities which represent logical theories in much the same way that algebraic schemes represent rings. These also involve two components: a semantic spectral space and a syntactic structure sheaf. As in the algebraic case, we can recover a theory from its scheme representation (up to a conservative completion) and the structure sheaf is local in a certain logical sense. From these ane pieces we can build up a 2-category of logical schemes which share some of the nice properties of algebraic schemes. The \logical spectrum" of a
Communication-efficient Distributed Sparse Linear Discriminant Analysis

We propose a communication-efficient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size $N$ into $m$ machines, and estimates a local sparse LDA estimator on each machine using the data subset of size $N/m$. After the distributed estimation, our method aggregates the debiased local estimators from $m$ machines, and sparsifies the aggregated estimator. We show that the aggregated estimator attains the same statistical rate as the centralized estimation method, as long as the number of machines $m$ is chosen appropriately. Moreover, we prove that our method can attain the model selection consistency under a milder condition than the centralized method. Experiments on both synthetic and real datasets corroborate our theory.
Achieving budget-optimality with adaptive schemes in crowdsourcing

Adaptive schemes, where tasks are assigned based on the data collected thus far, are widely used in practical crowdsourcing systems to efficiently allocate the budget. However, existing theoretical analyses of crowdsourcing systems suggest that the gain of adaptive task assignments is minimal. To bridge this gap, we investigate this question under a strictly more general probabilistic model, which has been recently introduced to model practical crowdsourcing data sets. Under this generalized Dawid-Skene model, we characterize the fundamental trade-off between budget and accuracy, and introduce a novel adaptive scheme that matches this fundamental limit. We further quantify the gain of adaptivity, by comparing the trade-off with the one for non-adaptive schemes, and confirm that the gain is significant and can be made arbitrarily large depending on the distribution of the difficulty level of the tasks at hand.
Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures

We describe a general technique that yields the first Statistical Query lower bounds} fora range of fundamental high-dimensional learning problems involving Gaussian distributions. Our main results are for the problems of (1) learning Gaussian mixture models (GMMs), and (2) robust (agnostic) learning of a single unknown Gaussian distribution. For each of these problems, we show a super-polynomial gap} between the (information-theoretic)sample complexity and the computational complexity of any} Statistical Query algorithm for the problem. Statistical Query (SQ) algorithms are a class of algorithms that are only allowed to query expectations of functions of the distribution rather than directly access samples. This class of algorithms is quite broad: a wide range of known algorithmic techniques in machine learning are known to be implementable using SQs.Moreover, for the unsupervised learning problems studied in this paper, all known algorithms with non-trivial performance guarantees are SQ or are easily implementable using SQs. Our SQ lower bound for Problem (1)is qualitatively matched by known learning algorithms for GMMs. At a conceptual level, this result implies that &#x2013; as far as SQ algorithms are concerned &#x2013; the computational complexity of learning GMMs is inherently exponential in the dimension of the latent space} &#x2013; even though there is no such information-theoretic barrier. Our lower bound for Problem (2) implies that the accuracy of the robust learning algorithm in \cite{DiakonikolasKKLMS16} is essentially best possible among all polynomial-time SQ algorithms. On the positive side, we also give a new (SQ) learning algorithm for Problem (2) achievingthe information-theoretically optimal accuracy, up to a constant factor, whose running time essentially matches our lower bound. Our algorithm relies on a filtering technique generalizing \cite{DiakonikolasKKLMS16} that removes outliers based on higher-order tensors.Our SQ lower bounds are attained via a unified moment-matching technique that is useful in other contexts and may be of broader interest. Our technique yields nearly-tight lower bounds for a number of related unsupervised estimation problems. Specifically, for the problems of (3) robust covariance estimation in spectral norm, and (4) robust sparse mean estimation, we establish a quadratic statistical&#x2013;computational tradeoff} for SQ algorithms, matching known upper bounds. Finally, our technique can be used to obtain tight sample complexitylower bounds for high-dimensional testing} problems. Specifically, for the classical problem of robustly testing} an unknown mean (known covariance) Gaussian, our technique implies an information-theoretic sample lower bound that scales linearly} in the dimension. Our sample lower bound matches the sample complexity of the corresponding robust learning} problem and separates the sample complexity of robust testing from standard (non-robust) testing. This separation is surprising because such a gap does not exist for the corresponding learning problem.
Learning privately from multiparty data

Learning a classifier from private data collected by multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party's private data? We propose to transfer the 'knowledge' of the local classifier ensemble by first creating labeled data from auxiliary unlabeled data, and then train a global e-differentially private classifier. We show that majority voting is too sensitive and therefore propose a new risk weighted by class probabilities estimated from the ensemble. Relative to a non-private solution, our private solution has a generalization error bounded by O(e-2M-2) where M is the number of parties. This allows strong privacy without performance loss when M is large, such as in crowdsensing applications. We demonstrate the performance of our method with realistic tasks of activity recognition, network intrusion detection, and malicious URL detection.
Distinguished theta representations for certain covering groups

For Brylinski-Deligne covering groups of an arbitrary split reductive group, we consider theta representations attached to certain exceptional genuine characters. The goal of the paper is to determine when a theta representation has exactly a one-dimensional space of Whittaker functionals, in which case it is called distinguished. For this purpose, we first give effective lower and upper bounds for the dimension of Whittaker functionals for general theta representations. As a consequence, the dimension in many cases can be reduced to simple combinatorial computations, e.g., the Kazhdan-Patterson covering groups, or covering groups whose complex dual group is of adjoint type. In the second part of the paper, we consider covering groups of certain simply-connected groups and give necessary and sufficient condition for the theta representation to be distinguished. There are subtleties arising from the relation between the rank and the degree of the covering group. However, in each case we will determine the exceptional character such that its associated theta representation is distinguished.
Interference Power Bound Analysis of a Network of Wireless Robots

We consider a fundamental problem concerning the deployment of a wireless robotic network: to fulfill various end-to-end performance requirements, a “sufficient” number of robotic relays must be deployed to ensure that links are of acceptable quality. Prior work has not addressed how to find this number. We use the properties of Carrier Sense Multiple Access (CSMA) based wireless communication to derive an upper bound on the spacing between any transmitter-receiver pair, which directly translates to a lower bound on the number of robots to deploy. We focus on SINR-based performance requirements due to their wide applicability. Next, we show that the bound can be improved by exploiting the geometrical structure of a network, such as linearity in the case of flow-based robotic router networks. Furthermore, we also use the bound on robot count to formulate a lower bound on the number of orthogonal codes required for a high probability of interference free communication. We demonstrate and validate our proposed bounds through simulations.
The Weyl Law for the phase transition spectrum and density of limit interfaces

We prove a Weyl Law for the phase transition spectrum based on the techniques of Liokumovich–Marques–Neves. As an application we give phase transition adaptations of the proofs of the density and equidistribution of minimal hypersufaces for generic metrics by Irie–Marques–Neves and Marques–Neves–Song, respectively. We also prove the density of separating limit interfaces for generic metrics in dimension 3, based on the recent work of Chodosh–Mantoulidis, and for generic metrics on manifolds containing only separating minimal hypersurfaces, e.g. $${H_{n}(M,\mathbb{Z}_2) = 0}$$ , for $${4 \leq n + 1 \leq 7}$$ . These provide alternative proofs of Yau’s conjecture on the existence of infinitely many minimal hypersurfaces for generic metrics on each setting, using the Allen–Cahn approach.
Boundary-based Image Forgery Detection by Fast Shallow CNN

Image forgery detection is the task of detecting and localizing forged parts in tampered images. Previous works mostly focus on high resolution images using traces of resampling features, demosaicing features or sharpness of edges. However, a good detection method should also be applicable to low resolution images because compressed or resized images are common these days. To this end, we propose a Shallow Convolutional Neural Network (SCNN) capable of distinguishing the boundaries of forged regions from original edges in low resolution images. SCNN is designed to utilize the information of chroma and saturation. Based on SCNN, two approaches that are named Sliding Windows Detection (SWD) and Fast SCNN respectively, are developed to detect and localize image forgery region. Our model is evaluated on the CASIA 2.0 dataset. The results show that Fast SCNN performs well on low resolution images and achieves significant improvements over the state-of-the-art.
Family Seiberg–Witten invariants and wall crossing formulas

In this paper we set up the family Seiberg-Witten theory. It can be applied to the counting of nodal pseudo-holomorphic curves in a symplectic 4-manifold (especially a Kahler surface). A new feature in this theory is that the chamber structure plays a more prominent role. We derive some wall crossing formulas measuring how the family Seiberg-Witten invariants change from one chamber to another.
On Open-Universe Causal Reasoning.

We extend two kinds of causal models, structural equation models and simulation models, to infinite variable spaces. This enables a semantics for conditionals founded on a calculus of intervention, and axiomatization of causal reasoning for rich, expressive generative models -- including those in which a causal representation exists only implicitly -- in an open-universe setting. Further, we show that under suitable restrictions the two kinds of models are equivalent, perhaps surprisingly as their axiomatizations differ substantially in the general case. We give a series of complete axiomatizations in which the open-universe nature of the setting is seen to be essential.
The Entrepreneurial Commercialization of Science: Evidence from “Twin” Discoveries

When are scientific advances translated into commercial products via startup formation? Although prior literature has offered several categories of answers, the commercial potential of a scientific...
Quasi-Direct Drive for Low-Cost Compliant Robotic Manipulation

Robots must cost less and be force-controlled to enable widespread, safe deployment in unconstrained human environments. We propose Quasi-Direct Drive actuation as a capable paradigm for robotic force-controlled manipulation in human environments at low-cost. Our prototype - Blue - is a human scale 7 Degree of Freedom arm with 2kg payload. Blue can cost less than $5000. We show that Blue has dynamic properties that meet or exceed the needs of human operators: the robot has a nominal position-control bandwidth of 7.5Hz and repeatability within 4mm. We demonstrate a Virtual Reality based interface that can be used as a method for telepresence and collecting robot training demonstrations. Manufacturability, scaling, and potential use-cases for the Blue system are also addressed. Videos and additional information can be found online at berkeleyopenarms.github.io.
On Interface Conditions for Flows in Coupled Free-Porous Media

Many processes in nature (e.g., physical and biogeochemical processes in hyporheic zones, and arterial mass transport) occur near the interface of free-porous media. A firm understanding of these processes needs an accurate prescription of flow dynamics near the interface which (in turn) hinges on an appropriate description of interface conditions along the interface of free-porous media. Although the conditions for the flow dynamics at the interface of free-porous media have received considerable attention, many of these studies were empirical and lacked a firm theoretical underpinning. In this paper, we derive a complete and self-consistent set of conditions for flow dynamics at the interface of free-porous media. We first propose a principle of virtual power by incorporating the virtual power expended at the interface of free-porous media. Then by appealing to the calculus of variations, we obtain a complete set of interface conditions for flows in coupled free-porous media. A noteworthy feature of our approach is that the derived interface conditions apply to a wide variety of porous media models. We also show that the two most popular interface conditions—the Beavers–Joseph condition and the Beavers–Joseph–Saffman condition—are special cases of the approach presented in this paper. The proposed principle of virtual power also provides a minimum power theorem for a class of flows in coupled free-porous media, which has a similar mathematical structure as the ones enjoyed by flows in uncoupled free and porous media. The derived interface conditions are summarized along with a pictorial description of the problem, which pertains to the flow of an incompressible fluid in coupled free-porous media. $$\Psi $$ is the power expended density along the interface. $$\mathbf {v}_{\text {free}}$$ and $$\mathbf {v}_{\text {por}}$$ are the velocities in the free and porous regions, respectively. A superposed asterisk on a (vectorial) quantity denotes its tangential component along the interface. $$v_n$$ is the normal component of the velocity at the interface from the free region into the porous region. $$\mathbf {T}_{\text {free}}^{\text {extra}}$$ and $$\mathbf {T}_{\text {por}}^{\text {extra}}$$ , respectively, denote the extra Cauchy stresses in the free and porous regions. $$\mathbf {t}_{\text {free}}$$ and $$\mathbf {t}_{\text {por}}$$ , respectively, denote the tractions on the free and porous sides of the interface with outward normals $$\widehat{\mathbf {n}}_{\text {free}}$$ and $$\widehat{\mathbf {n}}_{\text {por}}$$ . A unit tangential vector along the interface is denoted by $$\widehat{\mathbf {s}}$$ .
Between-Ride Routing for Private Transportation Services

Spurred by the growth of transportation network companies and increasing data capabilities, vehicle routing and ride-matching algorithms can improve the efficiency of private transportation services. However, existing routing solutions do not address where drivers should travel after dropping off a passenger and before receiving the next passenger ride request, i.e., during the between-ride period. We address this problem by developing an efficient algorithm to find the optimal policy for drivers between rides in order to maximize driver profits. We model the road network as a graph, and we show that the between-ride routing problem is equivalent to a stochastic shortest path problem, an infinite dynamic program with no discounting. We prove under reasonable assumptions that an optimal routing policy exists that avoids cycles; policies of this type can be efficiently found. We present an iterative approach to find an optimal routing policy. Our approach can account for various factors, including the frequency of passenger ride requests at different locations, traffic conditions, and surge pricing. We demonstrate the effectiveness of the approach by implementing it on road network data from Boston and New York City.
Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segway

The Cassie bipedal robot designed by Agility Robotics is providing academics with a common platform for sharing and comparing algorithms for locomotion, perception, and navigation. This paper focuses on feedback control for standing and walking using the methods of virtual constraints and gait libraries. The designed controller was implemented six weeks after the robot arrived at the University of Michigan and allowed it to stand in place as well as walk over sidewalks, grass, snow, sand, and burning brush. The controller for standing also enables the robot to ride a Segway. Software supporting the work in this paper is available on GitHub.
ARIANNA: Measurement of cosmic rays with a radio neutrino detector in Antarctica

The ARIANNA detector aims to detect neutrinos with energies above 1016 eV by instrumenting 0.5 Teratons of ice with a surface array of a thousand independent radio detector stations in Antarctica. The Antarctic ice is transparent to the radio signals caused by the Askaryan effect which allows for a cost-effective instrumentation of large volumes. Several pilot stations are currently operating successfully at the Moore’s Bay site (Ross Ice Shelf) and at the South Pole. As the ARIANNA detector stations are positioned at the surface, the more abundant cosmic-ray air showers are also measured and serve as a direct way to prove the capabilities of the detector. We will present measured cosmic rays and will show how the incoming direction, polarization and electric field of the cosmicray pulse can be reconstructed from single detector stations comprising 4 upward and 4 downward facing LPDA antennas.
Locality and the uniqueness of quantum invariants

We introduce the notion of a “state function” for framed tangles in a disk. After choosing a finite set of states for each marked disk, a state function is a projection from the vector space spanne...
Dependence-aware, unbounded sound predictive race detection

Data races are a real problem for parallel software, yet hard to detect. Sound predictive analysis observes a program execution and detects data races that exist in some other, unobserved execution. However, existing predictive analyses miss races because they do not scale to full program executions or do not precisely incorporate data and control dependence. This paper introduces two novel, sound predictive approaches that incorporate data and control dependence and handle full program executions. An evaluation using real, large Java programs shows that these approaches detect more data races than the closest related approaches, thus advancing the state of the art in sound predictive race detection.
Public beliefs about the accuracy and importance of forensic evidence in the United States.

Abstract Recent advances in forensic science, especially the use of DNA technology, have revealed that faulty forensic analyses may have contributed to miscarriages of justice. In this study we build on recent research on the general public’s perceptions of the accuracy of 10 forensic science techniques and of each stage in the investigation process. We find that individuals in the United States hold a pessimistic view of the forensic science investigation process, believing that an error can occur about half of the time at each stage of the process. We find that respondents believe that forensics are far from perfect, with accuracy rates ranging from a low of 55% for voice analysis to a high of 83% for DNA analysis, with most techniques being considered between 65% and 75% accurate. Nevertheless, respondents still believe that forensic evidence is a key part of a criminal case, with nearly 30% of respondents believing that the absence of forensic evidence is sufficient for a prosecutor to drop the case and nearly 40% believing that the presence of forensic evidence – even if other forms of evidence suggest that the defendant is not guilty – is enough to convict the defendant.
Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy

Local Differential Privacy (LDP) is popularly used in practice for privacy-preserving data collection. Although existing LDP protocols offer high utility for large user populations (100,000 or more users), they perform poorly in scenarios with small user populations (such as those in the cybersecurity domain) and lack perturbation mechanisms that are effective for both ordinal and non-ordinal item sequences while protecting sequence length and content simultaneously. In this paper, we address the small user population problem by introducing the concept of Condensed Local Differential Privacy (CLDP) as a specialization of LDP, and develop a suite of CLDP protocols that offer desirable statistical utility while preserving privacy. Our protocols support different types of client data, ranging from ordinal data types in finite metric spaces (numeric malware infection statistics), to non-ordinal items (OS versions, transaction categories), and to sequences of ordinal and non-ordinal items. Extensive experiments are conducted on multiple datasets, including datasets that are an order of magnitude smaller than those used in existing approaches, which show that proposed CLDP protocols yield high utility. Furthermore, case studies with Symantec datasets demonstrate that our protocols accurately support key cybersecurity-focused tasks of detecting ransomware outbreaks, identifying targeted and vulnerable OSs, and inspecting suspicious activities on infected machines.
How Organizational Minorities Form and Use Social Ties: Evidence from Teachers in Majority-White and Majority-Black Schools

This article draws on 11 months of multisite ethnographic fieldwork and 103 interviews to investigate how teachers in school faculty of varying racial compositions form and use their social ties to...
Insulin Potentiates JAK/STAT Signaling to Broadly Inhibit Flavivirus Replication in Insect Vectors.

Summary The World Health Organization estimates that more than half of the world’s population is at risk for vector-borne diseases, including arboviruses. Because many arboviruses are mosquito borne, investigation of the insect immune response will help identify targets to reduce the spread of arboviruses. Here, we use a genetic screening approach to identify an insulin-like receptor as a component of the immune response to arboviral infection. We determine that vertebrate insulin reduces West Nile virus (WNV) replication in Drosophila melanogaster as well as WNV, Zika, and dengue virus titers in mosquito cells. Mechanistically, we show that insulin signaling activates the JAK/STAT, but not RNAi, pathway via ERK to control infection in Drosophila cells and Culex mosquitoes through an integrated immune response. Finally, we validate that insulin priming of adult female Culex mosquitoes through a blood meal reduces WNV infection, demonstrating an essential role for insulin signaling in insect antiviral responses to human pathogens.
Exact Local Supersymmetry Absence of Superpartners and Noncommutative Geometries

It is pointed out that if we allow for the possibility of a multilayered universe, it is possible to maintain exact supersymmetry and arrange, in principle, for the vanishing of the cosmological constant. Superpartners of a known particle will then be associated with other layers of such a universe. A concrete model realizing this scenario is exhibited in 2+1 dimensions, and it is suggested that it may be realizable in 3+1 dimensions. The connection between this non classical geometry and noncommutative geometries is disscussed.
Novel Edge and Density Metrics for Link Cohesion

We present a new metric of link cohesion for measuring the strength of edges in complex, highly connected graphs. Link cohesion accounts for local small hop connections and associated node degrees and can be used to support edge scoring and graph simplification. We also present a novel graph density measure to estimate the average cohesion across nodes. Link cohesion and the density measure are employed to demonstrate community detection through graph sparsification by maximizing graph density. Link cohesion is also shown to be loosely correlated with edge betweenness centrality.
Sluggish Cognitive Tempo in Adolescents with and without ADHD: Differentiation from Adolescent-Reported ADHD Inattention and Unique Associations with Internalizing Domains

A growing number of studies support the internal and external validity of youth self-reported sluggish cognitive tempo (SCT) symptoms. However, no study has examined SCT in adolescents without ADHD, examined whether adolescent self-reported SCT is distinct from adolescent self-reported ADHD inattention (ADHD-IN), or evaluated whether links between SCT and internalizing problems differ for adolescents with or without ADHD. The present study is the first to (1) determine the convergent and discriminant validity of self-reported SCT and ADHD-IN symptoms in both adolescents with and without ADHD, (2) test the invariance of SCT and ADHD-IN symptoms across ADHD and comparison groups, (3) examine SCT as uniquely related to a range of internalizing-relevant domains, and (4) evaluate if the association between SCT with internalizing correlates differs for adolescents with or without ADHD. Participants were adolescents (Mage = 13 years) with (n = 162) and without (n = 140) ADHD. Adolescents and parents completed measures of internalizing symptoms and emotion dysregulation; adolescents completed measures of rumination and suicidal ideation. Analyses indicated that 13 of the 15 SCT items demonstrated convergent and discriminant validity from ADHD-IN, and SCT and ADHD-IN demonstrated invariance across the ADHD and comparison groups and across sex. SCT, but not ADHD-IN, was uniquely associated with greater adolescent-reported internalizing symptoms and suicidal ideation. Both SCT and ADHD-IN were uniquely associated with adolescent-reported emotion dysregulation and parent-reported internalizing symptoms. Only ADHD-IN was uniquely associated with parent-reported emotion dysregulation. Findings support the differentiation of adolescent-reported SCT and ADHD-IN and demonstrate associations between SCT and increased internalizing problems in adolescents with and without ADHD.
Revealed Preference Analysis of School Choice Models

Preferences for schools are important determinants of equitable access to high-quality education, effects of expanded choice on school improvement, and school choice mechanism design. Standard meth...
UK Biobank Whole-Exome Sequence Binary Phenome Analysis with Robust Region-Based Rare-Variant Test.

In biobank data analysis, most binary phenotypes have unbalanced case-control ratios, and this can cause inflation of type I error rates. Recently, a saddle point approximation (SPA) based single-variant test has been developed to provide an accurate and scalable method to test for associations of such phenotypes. For gene- or region-based multiple-variant tests, a few methods exist that can adjust for unbalanced case-control ratios; however, these methods are either less accurate when case-control ratios are extremely unbalanced or not scalable for large data analyses. To address these problems, we propose SKAT- and SKAT-O- type region-based tests; in these tests, the single-variant score statistic is calibrated based on SPA and efficient resampling (ER). Through simulation studies, we show that the proposed method provides well-calibrated p values. In contrast, when the case-control ratio is 1:99, the unadjusted approach has greatly inflated type I error rates (90 times that of exome-wide sequencing α = 2.5 × 10−6). Additionally, the proposed method has similar computation time to the unadjusted approaches and is scalable for large sample data. In our application, the UK Biobank whole-exome sequence data analysis of 45,596 unrelated European samples and 791 PheCode phenotypes identified 10 rare-variant associations with p value
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data. Leaning on recent theoretical insights, we demonstrate how an insurance claims database of 60 million members, a collection of 20 million clinical notes, and 1.7 million full text biomedical journal articles can be combined to embed concepts into a common space, resulting in the largest ever set of embeddings for 108,477 medical concepts. To evaluate our approach, we present a new benchmark methodology based on statistical power specifically designed to test embeddings of medical concepts. Our approach, called cui2vec, attains state-of-the-art performance relative to previous methods in most instances. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings.
The formation and evolution of wide-orbit stellar multiples in magnetized clouds

Stars rarely form in isolation. Nearly half of the stars in the Milky Way have a companion, and this fraction increases in star-forming regions. However, why some dense cores and filaments form bound pairs while others form single stars remains unclear. We present a set of three-dimensional, gravo-magnetohydrodynamic simulations of turbulent star-forming clouds, aimed at understanding the formation and evolution of multiple-star systems formed through large scale (>~$10^3$ AU) turbulent fragmentation. We investigate three global magnetic field strengths, with global mass-to-flux ratios of $\mu_\phi$=2, 8, and 32. The initial separations of protostars in multiples depends on the global magnetic field strength, with stronger magnetic fields (e.g., $\mu_\phi$=2) suppressing fragmentation on smaller scales. The overall multiplicity fraction (MF) is between 0.4-0.6 for our strong and intermediate magnetic field strengths, which is in agreement with observations. The weak field case has a lower fraction. The MF is relatively constant throughout the simulations, even though stellar densities increase as collapse continues. While the MF rarely exceeds 60% in all three simulations, over 80% of all protostars are part of a binary system at some point. We additionally find that the distribution of binary spin mis-alignment angles is consistent with a randomized distribution. In all three simulations, several binaries originate with wide separations and dynamically evolve to <~ $10^2$ AU separations. We show that a simple model of mass accretion and dynamical friction with the gas can explain this orbital evolution.