cssi/SciSciGPT-SciSciNet huggingface dataset:
Inferential Privacy Guarantees for Differentially Private Mechanisms.

The following is a summary of the paper "Inferential Privacy Guarantees for Differentially Private Mechanisms", presented at the Eighth Innovations in Theoretical Computer Science Conference in January 2017. The full version of the paper can be found on arXiv at the URL https://arxiv.org/abs/1603.01508.
Temporal reasoning about uncertain worlds

We present a program that manages a database of temporally scoped beliefs. The basic functionality of the system includes maintaining a network of constraints among time points, supporting a variety of fetches, mediating the application of causal rules, monitoring intervals of time for the addition of new facts, and managing data dependencies that keep the database consistent. At this level the system operates independent of any measure of belief or belief calculus. We provide an example of how an application program might use this functionality to implement a belief calculus.
An infinite torus braid yields a categorified Jones&#8211;Wenzl projector

A sequence of Temperley-Lieb algebra elements corresponding to torus braids with growing twisting numbers converges to the Jones-Wenzl projector. We show that a sequence of categorification complexes of these braids also has a limit which may serve as a categorification of the Jones-Wenzl projector.
Thermal conductivity minimum: a new water anomaly

We investigate the thermal conductivity of liquid water using computer simulations of the TIP5P model of water. Our simulations show that, in addition to the maximum at high temperatures at constant pressure that it exhibits in experiments, the thermal conductivity also displays a minimum at low temperatures. We find that the temperature of minimum thermal conductivity in supercooled liquid water coincides with the temperature of maximum specific heat. We discuss our results in the context of structural changes in liquid water at low temperatures.
Some operator and trace function convexity theorems

We consider trace functions (A,B)↦Tr[(A^(q/2)B^pA^(q/2))^s] where A and B are positive n×n matrices and ask when these functions are convex or concave. We also consider operator convexity/concavity of A^(q/2)B^pA^(q/2) and convexity/concavity of the closely related trace functional Tr[A^(q/2)B^pA^(q/2)C^r]. The concavity questions are completely resolved, thereby settling cases left open by Hiai; the convexity questions are settled in many cases. As a consequence, the Audenaert–Datta Renyi entropy conjectures are proved for some cases.
Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features

Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder–Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu_4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that the added features improve the quality of the generated questions.
An isomorphism theorem for Alexander biquandles

We show that two Alexander biquandles M and M′ are isomorphic if and only if there is an isomorphism of ℤ[s±1, t±1]-modules h : (1 - st)M → (1 - st)M′ and a bijection g : Os(A) → Os(A′) between the s-orbits of sets of coset representatives of M/(1 - st)M and M′/(1 - st)M′ respectively satisfying certain compatibility conditions.
Provable Methods for Training Neural Networks with Sparse Connectivity

Author(s): Sedghi, Hanie; Anandkumar, Anima | Editor(s): Bengio, Yoshua; LeCun, Yann | Abstract: We provide novel guaranteed approaches for training feedforward neural networks with sparse connectivity. We leverage on the techniques developed previously for learning linear networks and show that they can also be effectively adopted to learn non-linear networks. We operate on the moments involving label and the score function of the input, and show that their factorization provably yields the weight matrix of the first layer of a deep network under mild conditions. In practice, the output of our method can be employed as effective initializers for gradient descent.
Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach

Patterns of event propagation in online social networks provide novel insights on the modeling and analysis of information dissemination over networks and physical systems. This paper studies the importance of follower links for event propagation on Twitter. Three recent event propagation traces are collected with the Twitter user language field being used to identify the Network of Networks (NoN) structure embedded in the Twitter follower networks. We first formulate event propagation on Twitter as an iterative state equation, and then propose an effective score function on follower links accounting for the containment of event propagation via link removals. Furthermore, we find that utilizing the NoN model can successfully identify influential follower links such that their removals lead to a remarkable reduction in event propagation on Twitter follower networks. Experimental results find that the between-network follower links, though only account for a small portion of the total follower links, are crucial to event propagation on Twitter.
DocFace: Matching ID Document Photos to Selfies*

Numerous activities in our daily life, including purchases, travels and access to services, require us to verify who we are by showing ID documents containing face images, such as passports and driver licenses. An automatic system for matching ID document photos to live face images in real time with high accuracy would speed up the verification process and reduce the burden on human operators. In this paper, we propose a new method, DocFace, for ID document photo matching using the transfer learning technique. We propose to use a pair of sibling networks to learn domain specific parameters from heterogeneous face pairs. Cross validation testing on an ID-Selfie dataset shows that while the best CNN-based general face matcher only achieves a TAR=61.14% at FAR=0.1% on the problem, the DocFace improves the TAR to 92.77%. Experimental results also indicate that given sufficiently large training data, a viable system for automatic ID document photo matching can be developed and deployed.
Tightening MRF relaxations with planar subproblems

We describe a new technique for computing lower-bounds on the minimum energy configuration of a planar Markov Random Field (MRF). Our method successively adds large numbers of constraints and enforces consistency over binary projections of the original problem state space. These constraints are represented in terms of subproblems in a dual-decomposition framework that is optimized using subgradient techniques. The complete set of constraints we consider enforces cycle consistency over the original graph. In practice we find that the method converges quickly on most problems with the addition of a few subproblems and outperforms existing methods for some interesting classes of hard potentials.
Remote effects spatial process models for modeling teleconnections

While most spatial data can be modeled with the assumption that distant points are uncorrelated, some problems require dependence at both far and short distances. We introduce a model to directly incorporate dependence in phenomena that influence a distant response. Spatial climate problems often have such modeling needs as data are influenced by local factors in addition to remote phenomena, known as teleconnections. Teleconnections arise from complex interactions between the atmosphere and ocean, of which the El Nino–Southern Oscillation teleconnection is a well‐known example. Our model extends the standard geostatistical modeling framework to account for effects of covariates observed on a spatially remote domain. We frame our model as an extension of spatially varying coefficient models. Connections to existing methods are highlighted, and further modeling needs are addressed by additionally drawing on spatial basis functions and predictive processes. Notably, our approach allows users to model teleconnected data without prespecifying teleconnection indices, which other methods often require. We adopt a hierarchical Bayesian framework to conduct inference and make predictions. The method is demonstrated by predicting precipitation in Colorado while accounting for local factors and teleconnection effects with Pacific Ocean sea surface temperatures. We show how the proposed model improves upon standard methods for estimating teleconnection effects and discuss its utility for climate applications.
Smooth q-Gram, and Its Applications to Detection of Overlaps among Long, Error-Prone Sequencing Reads

We propose smooth q-gram, the first variant of q-gram that captures q-gram pair within a small edit distance. We apply smooth q-gram to the problem of detecting overlapping pairs of error-prone reads produced by single molecule real time sequencing (SMRT), which is the first and most critical step of the de novo fragment assembly of SMRT reads. We have implemented and tested our algorithm on a set of real world benchmarks. Our empirical results demonstrated the significant superiority of our algorithm over the existing q-gram based algorithms in accuracy.
Link groups and the A-B slice problem

The A i B slice problem is a reformulation of the topological 4idimensional surgery conjecture in terms of decompositions of the 4iball and link homotopy. We show that link groups, a recently developed invariant of 4imanifolds, provide an obstruction for the class of model decompositions, introduced by M. Freedman and X.-S. Lin. This unifles and extends the previously known partial obstructions in the AiB slice program. As a consequence, link groups satisfy Alexander duality when restricted to the class of model decompositions, but not for general submanifolds of the 4iball.
Approximating Dense Max 2-CSPs

In this paper, we present a polynomial-time algorithm that approximates sufficiently high-value Max 2-CSPs on sufficiently dense graphs to within O(N^epsilon) approximation ratio for any constant epsilon > 0. Using this algorithm, we also achieve similar results for free games, projection games on sufficiently dense random graphs, and the Densest k-Subgraph problem with sufficiently dense optimal solution. Note, however, that algorithms with similar guarantees to the last algorithm were in fact discovered prior to our work by Feige et al. and Suzuki and Tokuyama. In addition, our idea for the above algorithms yields the following by-product: a quasi-polynomial time approximation scheme (QPTAS) for satisfiable dense Max 2-CSPs with better running time than the known algorithms.
The sequence of fractional parts of roots

, where θ is a positive real number, ⌊·⌋ and {·} are the floor and fractional part functions, respectively. Nathanson proved, among other properties of Mθ, that if log θ is rational, then for all but finitely many positive integers n, Mθ(n) = ⌊n/ log θ − 1/2⌋. We extend this by showing that, without condition on θ, all but a zero-density set of integers n satisfy Mθ(n) = ⌊n/ log θ − 1/2⌋. Using a metric result of Schmidt, we show that almost all θ have asymptotically (log θ log x)/12 exceptional n ≤ x. Using continued fractions, we produce uncountably many θ that have only finitely many exceptional n, and also give uncountably many explicit θ that have infinitely many exceptional n.
Adversarial Regression with Multiple Learners

Despite the considerable success enjoyed by machine learning techniques in practice, numerous studies demonstrated that many approaches are vulnerable to attacks. An important class of such attacks involves adversaries changing features at test time to cause incorrect predictions. Previous investigations of this problem pit a single learner against an adversary. However, in many situations an adversary's decision is aimed at a collection of learners, rather than specifically targeted at each independently. We study the problem of adversarial linear regression with multiple learners. We approximate the resulting game by exhibiting an upper bound on learner loss functions, and show that the resulting game has a unique symmetric equilibrium. We present an algorithm for computing this equilibrium, and show through extensive experiments that equilibrium models are significantly more robust than conventional regularized linear regression.
Evaluation of Mother-Daughter Architectures for Asteroid Belt Exploration

This paper examines the effectiveness of an asteroid exploration architecture comprised of multiple nanosatellite sized spacecraft deployed from a single mother ship into a heliocentric orbit in the main asteroid belt where the mothership is ideally located in region of high density. Basic mission requirements associated with a Mother-Daughter architecture are established utilizing a relatively large number (10-20) daughter spacecraft distributed from a mothership within the asteroid belt for the purpose of executing sample and return missions. A number of trade analyses are performed to establish system performance to changes in initial orbit, delta-V capability and maximum small spacecraft flight time. The balance between the initial delta-V burn and asteroid velocity matching are also examined, with a goal of minimizing the amount of fuel needed in the small spacecraft. Preliminary requirements for the system are established using these results, and a conceptual design is presented for comparison to other asteroid exploration techniques. Preliminary results indicate that the aforementioned concept of a mothership with small spacecraft is viable and should be considered as an alternative approach to first order surveying of the asteroid belt.
A tutorial on distributed (non-Bayesian) learning: Problem, algorithms and results

We overview some results on distributed learning with focus on a family of recently proposed algorithms known as non-Bayesian social learning. We consider different approaches to the distributed learning problem and its algorithmic solutions for the case of finitely many hypotheses. The original centralized problem is discussed at first, and then followed by a generalization to the distributed setting. The results on convergence and convergence rate are presented for both asymptotic and finite time regimes. Various extensions are discussed such as those dealing with directed time-varying networks, Nesterov's acceleration technique and a continuum sets of hypothesis.
The lymphoid-associated interleukin 7 receptor (IL7R) regulates tissue-resident macrophage development

The discovery of a fetal origin for tissue-resident macrophages (trMacs) has inspired an intense search for the mechanisms underlying their development. Here, we performed in vivo lineage tracing of cells with an expression history of IL7Rα, a marker exclusively associated with the lymphoid lineage in adult hematopoiesis. Surprisingly, we found that Il7r-Cre labeled fetal-derived, adult trMacs. Labeling was almost complete in some tissues and partial in others. The putative progenitors of trMacs, yolk sac (YS) erythromyeloid progenitors, did not express IL7R, and YS hematopoiesis was unperturbed in IL7R-deficient mice. In contrast, tracking of IL7Rα message levels, surface expression, and Il7r-Cre-mediated labeling across fetal development revealed dynamic regulation of Il7r mRNA expression and rapid upregulation of IL7Rα surface protein upon transition from monocyte to macrophage within fetal tissues. Fetal monocyte differentiation in vitro produced IL7R+ macrophages, supporting a direct progenitor-progeny relationship. Additionally, blockade of IL7R function during late gestation specifically impaired the establishment of fetal-derived trMacs in vivo These data provide evidence for a distinct function of IL7Rα in fetal myelopoiesis and identify IL7R as a novel regulator of trMac development.
Improved and simplified inapproximability for k-means

Abstract The k-means problem consists of finding k centers in R d that minimize the sum of the squared distances of all points in an input set P from R d to their closest respective center. Awasthi et al. recently showed that there exists a constant e ′ > 0 such that it is NP-hard to approximate the k-means objective within a factor of 1 + e ′ . We establish that 1 + e ′ is at least 1.0013.
Invariant varieties for polynomial dynamical systems

We study algebraic dynamical systems (and, more generally, s -varieties) F:A n C ?A n C given by coordinatewise univariate polynomials by refining an old theorem of Ritt on compositional identities amongst polynomials. More precisely, we find a nearly canonical way to write a polynomial as a composition of �clusters� from which one may easily read off possible compositional identities. Our main result is an explicit description of the (weakly) skew-invariant varieties, that is, for a fixed field automorphism s:C?C those algebraic varieties X?A n C for which F(X)?X s . As a special case, we show that if f(x)?C[x] is a polynomial of degree at least two that is not conjugate to a monomial, Chebyshev polynomial or a negative Chebyshev polynomial, and X?A 2 C is an irreducible curve that is invariant under the action of (x,y)?(f(x),f(y)) and projects dominantly in both directions, then X must be the graph of a polynomial that commutes with f under composition. As consequences, we deduce a variant of a conjecture of Zhang on the existence of rational points with Zariski dense forward orbits and a strong form of the dynamical Manin-Mumford conjecture for liftings of the Frobenius. We also show that in models of ACFA 0 , a disintegrated set defined by s(x)=f(x) for a polynomial f has Morley rank one and is usually strongly minimal, that model theoretic algebraic closure is a locally finite closure operator on the nonalgebraic points of this set unless the skew-conjugacy class of f is defined over a fixed field of a power of s , and that nonorthogonality between two such sets is definable in families if the skew-conjugacy class of f is defined over a fixed field of a power of s
Fusion Subcategories of Representation Categories of Twisted Quantum Doubles of Finite Groups

We describe all fusion subcategories of the representation category of a twisted quantum double , where G is a finite group and is a 3-cocycle on G. In view of the fact that every group-theoretical braided fusion category can be embedded into some , this gives a complete description of all group-theoretical braided fusion categories. We describe the lattice and give formulas for some invariants of the fusion subcategories of . We also give a characterization of group-theoretical braided fusion categories as equivariantizations of pointed categories.
Transformation Autoregressive Networks

The fundamental task of general density estimation $p(x)$ has been of keen interest to machine learning. In this work, we attempt to systematically characterize methods for density estimation. Broadly speaking, most of the existing methods can be categorized into either using: \textit{a}) autoregressive models to estimate the conditional factors of the chain rule, $p(x_{i}\, |\, x_{i-1}, \ldots)$; or \textit{b}) non-linear transformations of variables of a simple base distribution. Based on the study of the characteristics of these categories, we propose multiple novel methods for each category. For example we proposed RNN based transformations to model non-Markovian dependencies. Further, through a comprehensive study over both real world and synthetic data, we show for that jointly leveraging transformations of variables and autoregressive conditional models, results in a considerable improvement in performance. We illustrate the use of our models in outlier detection and image modeling. Finally we introduce a novel data driven framework for learning a family of distributions.
Determinacy of adversarial Gowers games

We prove a game theoretic dichotomy for Gδσ sets of block sequences in vector spaces that extends, on the one hand, the block Ramsey theorem of W. T. Gowers proved for analytic sets of block sequences and, on the other hand, M. Davis’ proof of Σ3 determinacy.
Making inferences about racial disparities in police violence.

A recent PNAS study, Johnson et al. (1), investigates the role of race in fatal police shootings. Unlike previous studies which focused on victim race alone, the paper features original data about the race of officers who use deadly force and offers a rare accounting of other shooting attributes that contextualize fatal encounters. Johnson et al. (1) discuss possible “discrimination by White officers” (ref. 1, p. 15877), but conclude racial diversity in police agencies brings limited benefits—a claim cited by major news outlets and in US Congressional testimony, inflaming an already contentious policy debate. Despite the value of this much-needed research, its approach is mathematically incapable of supporting its central claims. In this letter, we clarify the gap between what Johnson et al.’s study asserts and what it actually estimates, as well as the implications of that difference for policymaking and future scholarship on race and policing. Johnson et al.'s study asks “the degree to which Black civilians are more likely to be fatally shot than White civilians” (ref. 1, p. 15877) and prominently asserts “White officers are not more likely to shoot minority civilians than non-White officers” (ref. 1 … [↵][1]2To whom correspondence may be addressed. Email: jmummolo{at}princeton.edu. [1]: #xref-corresp-1-1
Deep Local Trajectory Replanning and Control for Robot Navigation

We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the behavior of the robot through attention mechanisms such that it moves towards the goal, avoids obstacles, and respects the space of nearby pedestrians. Both the structure of the proposed deep models and the use of attention mechanisms make the system’s execution interpretable. Our simulation experiments suggest that the proposed architecture outperforms baselines that try to map global plan information and sensor data directly to velocity commands. In comparison to a hand-designed traditional navigation system, the proposed approach showed more consistent performance.
Bridging the data charging gap in the cellular edge

The 4G/5G cellular edge promises low-latency experiences anywhere, anytime. However, data charging gaps can arise between the cellular operators and edge application vendors, and cause over-/under-billing. We find that such gap can come from data loss, selfish charging, or both. It can be amplified in the edge, due to its low-latency requirements. We devise TLC, a Trusted, Loss-tolerant Charging scheme for the cellular edge. In its core, TLC enables loss-selfishness cancellation to bridge the gap, and constructs publicly verifiable, cryptographic proof-of-charging for mutual trust. We implement TLC with commodity edge nodes, OpenEPC and small cells. Our experiments in various edge scenarios validate TLC's viability of reducing the gap with marginal latency and other overhead.
Topological bands and localized vibration modes in quasiperiodic beams

We investigate a family of quasiperiodic continuous elastic beams, the topological properties of their vibrational spectra, and their relation to the existence of localized modes. We specifically consider beams featuring arrays of ground springs at locations determined by projecting from a circle onto an underlying periodic system. A family of periodic and quasiperiodic structures is obtained by smoothly varying a parameter defining such projection. Numerical simulations show the existence of vibration modes that first localize at a boundary, and then migrate into the bulk as the projection parameter is varied. Explicit expressions predicting the change in the density of states of the bulk define topological invariants that quantify the number of modes spanning a gap of a finite structure. We further demonstrate how modulating the phase of the ground springs distribution causes the topological states to undergo an edge-to-edge transition. The considered configurations and topological studies provide a framework for inducing localized modes in continuous elastic structural components through globally spanning, deterministic perturbations of periodic patterns defined by the considered projection operations.
The rise of domestic capital markets for corporate financing: Lessons from East Asia

Abstract Firms from emerging economies have significantly increased the amount of bond financing, particularly after the Global Financial Crisis and when compared to other financing instruments. The literature links this growth to a rise in international bond issuances as the supply of capital by foreign investors expanded. This paper explores the role of domestic markets by comparing domestic and international bond issuances by East Asian firms, which are the most active bond issuers in emerging economies. The paper shows that an expansion in domestic bond issuances, in domestic currency, by more and smaller firms was the main component behind the overall growth in bond financing. This expansion was accompanied by a decline in bond yields, higher firm leverage, and higher cash accumulation following issuances after 2008. The evidence is consistent with a higher supply of funds by domestic investors being an important driver behind the rise in domestic bond financing.
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pairs of images and their associated labels. While simple to implement, it has shown to be a surprisingly effective method of data augmentation for image classification; DNNs trained with mixup show noticeable gains in classification performance on a number of image classification benchmarks. In this work, we discuss a hitherto untouched aspect of mixup training -- the calibration and predictive uncertainty of models trained with mixup. We find that DNNs trained with mixup are significantly better calibrated -- i.e the predicted softmax scores are much better indicators of the actual likelihood of a correct prediction -- than DNNs trained in the regular fashion. We conduct experiments on a number of image classification architectures and datasets -- including large-scale datasets like ImageNet -- and find this to be the case. Additionally, we find that merely mixing features does not result in the same calibration benefit and that the label smoothing in mixup training plays a significant role in improving calibration. Finally, we also observe that mixup-trained DNNs are less prone to over-confident predictions on out-of-distribution and random-noise data. We conclude that the typical overconfidence seen in neural networks, even on in-distribution data is likely a consequence of training with hard labels, suggesting that mixup training be employed for classification tasks where predictive uncertainty is a significant concern.
PyBioNetFit and the Biological Property Specification Language.

Summary In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric

Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, for the more diverse downstream uses of risk scores and their non-binary nature. To better account for this, in this paper, we investigate the fairness of predictive risk scores from the point of view of a bipartite ranking task, where one seeks to rank positive examples higher than negative ones. We introduce the xAUC disparity as a metric to assess the disparate impact of risk scores and define it as the difference in the probabilities of ranking a random positive example from one protected group above a negative one from another group and vice versa. We provide a decomposition of bipartite ranking loss into components that involve the discrepancy and components that involve pure predictive ability within each group. We use xAUC analysis to audit predictive risk scores for recidivism prediction, income prediction, and cardiac arrest prediction, where it describes disparities that are not evident from simply comparing within-group predictive performance.
A Closed-Form Analytical Solution for Optimal Coordination of Connected and Automated Vehicles

In earlier work, a decentralized optimal control framework was established for coordinating online connected and automated vehicles (CAVs) in merging roadways, urban intersections, speed reduction zones, and roundabouts. The dynamics of each vehicle were represented by a double integrator and the Hamiltonian analysis was applied to derive an analytical solution that minimizes the $L^{2}$ -norm of the control input. However, the analytical solution did not consider the rear-end collision avoidance constraint. In this paper, we derive a complete, closed-form analytical solution that includes the rear-end safety constraint in addition to the state and control constraints. We augment the double integrator model that represents a vehicle with an additional state corresponding to the distance from its preceding vehicle. Thus, the rear-end collision avoidance constraint is included as a state constraint. The effectiveness of the solution is illustrated through simulation.
Cultural tightness-looseness: Its nature and missiological applications:

The focus of much missionary work concerns sharing the gospel with others so that they may put their faith in Jesus Christ. However, members of some cultures are much more resistant to this than ar...
Trimming Graphs Using Clausal Proof Optimization

We present a method to gradually compute a smaller and smaller unsatisfiable core of a propositional formula by minimizing proofs of unsatisfiability. The goal is to compute a minimal unsatisfiable core that is relatively small compared to other minimal unsatisfiable cores of the same formula. We try to achieve this goal by postponing deletion of arbitrary clauses from the formula as long as possible—in contrast to existing minimal unsatisfiable core algorithms. We applied this method to reduce the smallest known unit-distance graph with chromatic number 5 from 553 vertices and \(2\,720\) edges to 529 vertices and \(2\,670\) edges.
Reduced order H/sub /spl infin// compensator design for an inverted pendulum control problem

A new method for dealing with one of the nonstandard cases of reduced order H/sub /spl infin// design is fully developed for a singular control problem. A reduced order H/sub /spl infin// controller is designed for an inverted pendulum on a cart where the output measurement is disturbance free. The results are compared to a full order compensator.
The Focusing Energy-Critical Nonlinear Wave Equation With Random Initial Data

We consider the focusing energy-critical quintic nonlinear wave equation in three dimensional Euclidean space. It is known that this equation admits a one-parameter family of radial stationary solutions, called solitons, which can be viewed as a curve in $ \dot H^s_x(\mathbb{R}^3) \times H^{s-1}_x(\mathbb{R}^3)$, for any $s > 1/2$. By randomizing radial initial data in $ \dot H^s_x(\mathbb{R}^3) \times H^{s-1}_x(\mathbb{R}^3)$ for $s > 5/6$, which also satisfy a certain weighted Sobolev condition, we produce with high probability a family of radial perturbations of the soliton which give rise to global forward-in-time solutions of the focusing nonlinear wave equation that scatter after subtracting a dynamically modulated soliton. Our proof relies on a new randomization procedure using distorted Fourier projections associated to the linearized operator around a fixed soliton. To our knowledge, this is the first long-time random data existence result for a focusing wave or dispersive equation on Euclidean space outside the small data regime.
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

Autonomous vehicles are expected to navigate in complex traffic scenarios with multiple surrounding vehicles. The correlations between road users vary over time, the degree of which, in theory, could be infinitely large, thus posing a great challenge in modeling and predicting the driving environment. In this paper, we propose a method to model multi-vehicle interactions using a stochastic vector field model and apply non-parametric Bayesian learning to extract the underlying motion patterns from a large quantity of naturalistic traffic data. We then use this model to reproduce the high-dimensional driving scenarios in a finitely tractable form. We use a Gaussian process to model multi-vehicle motion, and a Dirichlet process to assign each observation to a specific scenario. We verify the effectiveness of the proposed method on highway and intersection datasets from the NGSIM project, in which complex multi-vehicle interactions are prevalent. The results show that the proposed method can capture motion patterns from both settings, without imposing heroic prior, and hence demonstrate the potential application for a wide array of traffic situations. The proposed modeling method could enable simulation platforms and other testing methods designed for autonomous vehicle evaluation, to easily model and generate traffic scenarios emulating large scale driving data.
A Preliminary Study on a Physical Model Oriented Learning Algorithm With Application to UAVs

This paper provides a preliminary study for an efficient learning algorithm by reasoning the error from first principle physics to generate learning signals in near real time. Motivated by iterative learning control (ILC), this learning algorithm is applied to the feedforward control loop of the unmanned aerial vehicles (UAVs), enabling the learning from errors made by other UAVs with different dynamics or flying in different scenarios. This learning framework improves the data utilization efficiency and learning reliability via analytically incorporating the physical model mapping, and enhances the flexibility of the model-based methodology with equipping it with the self-learning capability. Numerical studies are performed to validate the proposed learning algorithm.
Social Support and Stress-Related Acculturative Experiences of an English-Speaking Afro-Caribbean Female Student in U.S. Higher Education

This two-year qualitative single critical case study research investigated the stress-related adjustment experiences and academic progression of a female English-speaking Afro-Caribbean collegian in an American postsecondary institution through the lens of the “triple bind” phenomenon and the stress buffer hypothesis. Student development theory and research on college student outcomes have largely focused on Black students’ experiences and achievement outcomes through a homogeneous African American cultural lens. Minimal existing research has shown differences in the lived experiences and achievement outcomes between Afro-Caribbean students and domestic African American students in U.S. postsecondary education.
Restricting seniority as a factor in public school district layoffs: Analyzing the impact of state legislation on graduation rates

Abstract Following the Great Recession, employment in the U.S. local education sector fell by about 364,000. I analyze whether state legislation that prohibits or limits the use of seniority in layoff decisions has an impact on public high school graduation rates. I find that over a ten-year time span, all else held constant, such legislation on average increases the yearly growth of district graduation rates by about 0.3 percentage points. This is economically significant, as the average yearly increase in the national graduation rate from 2010–11 to 2015–16 was 1 percentage point. When states prohibit or limit using seniority to determine a layoff order, districts must utilize other considerations such as teacher quality. In states with this legislation, teachers remaining following layoffs may be more effective than when states use seniority to determine the layoff order.
Tracking Human Pose During Robot-Assisted Dressing using Single-Axis Capacitive Proximity Sensing

Dressing is a fundamental task of everyday living and robots offer an opportunity to assist people with motor impairments. While several robotic systems have explored robot-assisted dressing, few have considered how a robot can manage errors in human pose estimation, or adapt to human motion in real time during dressing assistance. In addition, estimating pose changes due to human motion can be challenging with vision-based techniques since dressing is often intended to visually occlude the body with clothing. We present a method to track a person's pose in real time using capacitive proximity sensing. This sensing approach gives direct estimates of distance with low latency, has a high signal-to-noise ratio, and has low computational requirements. Using our method, a robot can adjust for errors in the estimated pose of a person and physically follow the contours and movements of the person while providing dressing assistance. As part of an evaluation of our method, the robot successfully pulled the sleeve of a hospital gown and a cardigan onto the right arms of 10 human participants, despite arm motions and large errors in the initially estimated pose of the person's arm. We also show that a capacitive sensor is unaffected by visual occlusion of the body and can sense a person's body through cotton clothing.
Machine Learning Reinforced Crystal Plasticity Modeling Under Experimental Uncertainty

This Paper addresses a two-step computational approach to building a robust modeling environment for the titanium–aluminum alloy, Ti-7Al, a candidate aerospace material owing to superior mechanical...
Many-player games of optimal consumption and investment under relative performance criteria

We study a portfolio optimization problem for competitive agents with CRRA utilities and a common finite time horizon. The utility of an agent depends not only on her absolute wealth and consumption but also on her relative wealth and consumption when compared to the averages among the other agents. We derive a closed form solution for the n-player game and the corresponding mean field game. This solution is unique in the class of equilibria with constant investment and continuous time-dependent consumption, both independent of the wealth of the agent. Compared to the classical Merton problem with one agent, the competitive model exhibits a wide range of highly nonlinear and non-monotone dependence on the agents’ risk tolerance and competitiveness parameters. Counter-intuitively, competitive agents with high risk tolerance may behave like non-competitive agents with low risk tolerance.
A theorem on discrete, torsion free subgroups of Isom $H^n$

Let H n be the hyperbolic n-space with n⩾ 2. Suppose that Γ <Isom H n is a discrete, torsion free subgroup and a is a point in the domain of discontinuity Ω (Γ). Let p be the projection map from H n to the quotient manifold M=H n /Γ. In this paper we prove that there exists an open neighborhood U of a in H n ∪ Ω (Γ) such that p is an isometry on U∩ H n .
Can machine learning find extraordinary materials

Abstract One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training data set. To investigate whether this is indeed the case, this work takes advantage of density functional theory calculated properties (bulk modulus, shear modulus, thermal conductivity, thermal expansion, band gap, and Debye temperature) to investigate whether machine learning is truly capable of predicting materials with properties that extend beyond previously seen values. We refer to these materials as extraordinary, meaning they represent the top 1% of values in the available data set. Interestingly, we show that even when machine learning is trained on a fraction of the bottom 99% we can consistently identify 3 4 of the highest performing compositions for all considered properties with a precision that is typically above 0.5. We explore model performance as the extrapolation distance is increased in various ways including, introduction of a gap, removal of certain elements, and removal of certain structure types. Moreover, we investigate a few different modeling choices and demonstrate how a classification approach can identify an equivalent amount of extraordinary compounds but with significantly fewer false positives than a regression approach. Finally, we discuss cautions and potential limitations in implementing such an approach to discover new record-breaking materials.
Methods for broad-scale plant phenology assessments using citizen scientists’ photographs

Premise Citizen science platforms for sharing photographed digital vouchers, such as iNaturalist, are a promising source of phenology data, but methods and best practices for use have not been developed. Here we introduce methods using Yucca flowering phenology as a case study, because drivers of Yucca phenology are not well understood despite the need to synchronize flowering with obligate pollinators. There is also evidence of recent anomalous winter flowering events, but with unknown spatiotemporal extents. Methods We collaboratively developed a rigorous, consensus-based approach for annotating and sharing whole plant and flower presence data from iNaturalist and applied it to Yucca records. We compared spatiotemporal flowering coverage from our annotations with other broad-scale monitoring networks (e.g., the National Phenology Network) in order to determine the unique value of photograph-based citizen science resources. Results Annotations from iNaturalist were uniquely able to delineate extents of unusual flowering events in Yucca. These events, which occurred in two different regions of the Desert Southwest, did not appear to disrupt the typical-period flowering. Discussion Our work demonstrates that best practice approaches to scoring iNaturalist records provide fine-scale delimitation of phenological events. This approach can be applied to other plant groups to better understand how phenology responds to changing climate.
Targeting viperin to the mitochondrion inhibits the thiolase activity of the trifunctional enzyme complex.

Understanding the mechanisms by which viruses evade host cell immune defenses is important for developing improved antiviral therapies. In an unusual twist, human cytomegalovirus co-opts the antiviral radical SAM enzyme viperin (virus-inhibitory protein, endoplasmic reticulum–associated, interferon-inducible) to enhance viral infectivity. This process involves translocation of viperin to the mitochondrion, where it binds the β-subunit (HADHB) of the mitochondrial trifunctional enzyme complex that catalyzes thiolysis of β-ketoacyl–CoA esters as part of fatty acid β-oxidation. Here we investigated how the interaction between these two enzymes alters their activities and affects cellular ATP levels. Experiments with purified enzymes indicated that viperin inhibits the thiolase activity of HADHB, but, unexpectedly, HADHB activates viperin, leading to synthesis of the antiviral nucleotide 3′-deoxy-3′,4′-didehydro-CTP. Measurements of enzyme activities in lysates prepared from transfected HEK293T cells expressing these enzymes mirrored the findings obtained with purified enzymes. Thus, localizing viperin to mitochondria decreased thiolase activity, and coexpression of HADHB significantly increased viperin activity. Furthermore, targeting viperin to mitochondria also increased the rate at which HADHB is retrotranslocated out of mitochondria and degraded, providing an additional mechanism by which viperin reduces HADHB activity. Targeting viperin to mitochondria decreased cellular ATP levels by more than 50%, consistent with the enzyme disrupting fatty acid catabolism. These results provide biochemical insight into the mechanism by which human cytomegalovirus subverts viperin; they also provide a biochemical rationale for viperin's recently discovered role in regulating thermogenesis in adipose tissues.
STRIPStream: Integrating Symbolic Planners and Blackbox Samplers.

Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, collision, and motion constraints involving robot configurations, object transforms, and robot trajectories. These constraints typically require specialized procedures to sample satisfying values. We extend the STRIPS planning language to support a generic, declarative specification for these procedures while treating their implementation as blackboxes. We provide several domain-independent algorithms that reduce STRIPStream problems to a sequence of finite-domain STRIPS planning problems. Additionally, we describe cost-sensitive planning within this framework. Finally, we evaluate our algorithms on three robotic task and motion planning domains.
Macroprudential Regulation Versus Mopping Up After the Crash

This paper compares ex-ante policy measures (such as macroprudential restrictions on leverage) and ex-post policy interventions (such as bailouts) to respond to systemic risk, i.e. to the danger that an economy may experience …nancial feedback loops in which falling asset prices, declining net worth and tightening …nancial constraints reinforce each other. Ex-post policy measures are better targeted, since they are taken only once a crisis has materialized, but they aggravate the over-investment problem ex ante and introduce a time consistency problem. Ex-ante policy measures are more blunt, but they can both mitigate the pecuniary externalities that arise during crises and resolve the time consistency problem in ex-post policies. We …nd that except in limit cases, it is optimal to respond to systemic risk by using a mix of both ex-ante measures and ex-post interventions, and characterize the optimal policy mix. Furthermore, limiting bailouts to the revenue accumulated in a bailout fund reduces welfare.