cssi/SciSciGPT-SciSciNet huggingface dataset:
Toward the Verification of a Simple Hypervisor

Virtualization promises significant benefits in security, efficiency, dependability, and cost. Achieving these benefits depends upon the reliability of the underlying virtual machine monitors (hypervisors). This paper describes an ongoing project to develop and verify MinVisor, a simple but functional Type-I x86 hypervisor, proving protection properties at the assembly level using ACL2. Originally based on an existing research hypervisor, MinVisor provides protection of its own memory from a malicious guest. Our long-term goal is to fully verify MinVisor, providing a vehicle to investigate the modeling and verification of hypervisors at the implementation level, and also a basis for further systems research. Functional segments of the MinVisor C code base are translated into Y86 assembly, and verified with respect to the Y86 model. The inductive assertions (also known as "compositional cutpoints") methodology is used to prove the correctness of the code. The proof of the code that sets up the nested page tables is described. We compare this project to related efforts in systems code verification and outline some useful steps forward.
Interpretable Categorization of Heterogeneous Time Series Data

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness

Understanding the singular value spectrum of an n x n matrix A is a fundamental task in countless numerical computation and data analysis applications. In matrix multiplication time, it is possible to perform a full SVD of A and directly compute the singular values \sigma_1,...,\sigma_n. However, little is known about algorithms that break this runtime barrier. Using tools from stochastic trace estimation, polynomial approximation, and fast linear system solvers, we show how to efficiently isolate different ranges of A's spectrum and approximate the number of singular values in these ranges. We thus effectively compute an approximate histogram of the spectrum, which can stand in for the true singular values in many applications. We use our histogram primitive to give the first algorithms for approximating a wide class of symmetric matrix norms and spectral sums faster than the best known runtime for matrix multiplication. For example, we show how to obtain a (1 + \epsilon) approximation to the Schatten 1-norm (i.e. the nuclear or trace norm) in just ~ O((nnz(A)n^{1/3} + n^2)\epsilon^{-3}) time for A with uniform row sparsity or \tilde O(n^{2.18} \epsilon^{-3}) time for dense matrices. The runtime scales smoothly for general Schatten-p norms, notably becoming \tilde O (p nnz(A) \epsilon^{-3}) for any real p >= 2. At the same time, we show that the complexity of spectrum approximation is inherently tied to fast matrix multiplication in the small \epsilon regime. We use fine-grained complexity to give conditional lower bounds for spectrum approximation, showing that achieving milder \epsilon dependencies in our algorithms would imply triangle detection algorithms for general graphs running in faster than state of the art matrix multiplication time. This further implies, through a reduction of (Williams & William, 2010), that highly accurate spectrum approximation algorithms running in subcubic time can be used to give subcubic time matrix multiplication. As an application of our bounds, we show that precisely computing all effective resistances in a graph in less than matrix multiplication time is likely difficult, barring a major algorithmic breakthrough.
Localization of electrical flows

We show that in any graph, the average length of a flow path in an electrical flow between the endpoints of a random edge is log2 n). This is a consequence of a more general result which shows that the spectral norm of the entrywise absolute value of the transfer impedance matrix of a graph is O(log2 n). This result implies a simple oblivious routing scheme based on electrical flows in the case of transitive graphs.
Poisson cohomology of two Fano threefolds

Abstract We study the variety of Poisson structures and compute Poisson cohomology for two families of Fano threefolds – smooth cubic threefolds and the del Pezzo quintic threefold. Along the way we reobtain by a different method earlier results of Loray, Pereira and Touzet in the special case we are considering.
The spacetime of a shift endomorphism

The automorphism group of a one dimensional shift space over a finite alphabet exhibits different types of behavior: for a large class with positive entropy, it contains a rich collection of subgroups, while for many shifts of zero entropy, there are strong constraints on the automorphism group. We view this from a different perspective, considering a single automorphism (and sometimes endomorphism) and studying the naturally associated two dimensional shift system. In particular, we describe the relation between nonexpansive subspaces in this two dimensional system and dynamical properties of an automorphism of the shift.
The Effective Cone of Moduli Spaces of Sheaves on a Smooth Quadric Surface

In this paper, we provide an approach to computing the effective cone of moduli spaces of sheaves on a smooth quadric surface. We find Brill-Noether divisors spanning extremal rays of the effective cone using resolutions of the general elements of the moduli space which are found using the machinery of exceptional bundles. We use this approach to provide many examples of extremal rays in these effective cones. In particular, we completely compute the effective cone of the first fifteen Hilbert schemes of points on a smooth quadric surface.
More reduced obstruction theories

This short note first develops a general formalism for globally removing a factor from an obstruction theory. This formalism is then applied to give a construction of a reduced obstruction theory on the moduli of maps from a curve to a surface satisfying a technical condition. This condition was recently identified in work of Kool and Thomas.
Real-time Robust Principal Components' Pursuit

In the recent work of Candes et al, the problem of recovering low rank matrix corrupted by i.i.d. sparse outliers is studied and a very elegant solution, principal component pursuit, is proposed. It is motivated as a tool for video surveillance applications with the background image sequence forming the low rank part and the moving objects/persons/abnormalities forming the sparse part. Each image frame is treated as a column vector of the data matrix made up of a low rank matrix and a sparse corruption matrix. Principal component pursuit solves the problem under the assumptions that the singular vectors of the low rank matrix are spread out and the sparsity pattern of the sparse matrix is uniformly random. However, in practice, usually the sparsity pattern and the signal values of the sparse part (moving persons/objects) change in a correlated fashion over time, for e.g., the object moves slowly and/or with roughly constant velocity. This will often result in a low rank sparse matrix. For video surveillance applications, it would be much more useful to have a real-time solution. In this work, we study the online version of the above problem and propose a solution that automatically handles correlated sparse outliers. In fact we also discuss how we can potentially use the correlation to our advantage in future work. The key idea of this work is as follows. Given an initial estimate of the principal directions of the low rank part, we causally keep estimating the sparse part at each time by solving a noisy compressive sensing type problem. The principal directions of the low rank part are updated every-so-often. In between two update times, if new Principal Components' directions appear, the “noise” seen by the Compressive Sensing step may increase. This problem is solved, in part, by utilizing the time correlation model of the low rank part. We call the proposed solution “Real-time Robust Principal Components' Pursuit”. It still requires the singular vectors of the low rank part to be spread out, but it does not require i.i.d.-ness of either the sparse part or the low rank part.
Efficient parallel verification of Galois field multipliers

Galois field (GF) arithmetic is used to implement critical arithmetic components in communication and security-related hardware, and verification of such components is of prime importance. Current techniques for formally verifying such components are based on computer algebra methods that proved successful in verification of integer arithmetic circuits. However, these methods are sequential in nature and do not offer any parallelism. This paper presents an algebraic functional verification technique of gate-level GF(2m) multipliers, in which verification is performed in bit-parallel fashion. The method is based on extracting a unique polynomial in Galois field of each output bit independently. We demonstrate that this method is able to verify an n-bit GF multiplier in n threads. Experiments performed on pre- and post-synthesized Mastrovito and Montgomery multipliers show high efficiency up to 571 bits.
A Generalization of the Hamilton–Waterloo Problem on Complete Equipartite Graphs

The Hamilton–Waterloo problem asks for which s and r the complete graph Kn can be decomposed into s copies of a given 2-factor F1 and r copies of a given 2-factor F2 (and one copy of a 1-factor if n is even). In this paper, we generalize the problem to complete equipartite graphs K(n:m) and show that K(xyzw:m) can be decomposed into s copies of a 2-factor consisting of cycles of length xzm; and r copies of a 2-factor consisting of cycles of length yzm, whenever m is odd, s,r≠1, gcd(x,z)=gcd(y,z)=1, and xyz≠0(mod4). We also give some more general constructions where the cycles in a given two factor may have different lengths. We use these constructions to find solutions to the Hamilton–Waterloo problem for complete graphs.
Optimal causal inference: Estimating stored information and approximating causal architecture.

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding—a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system’s causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.
Scalable Bayesian rule lists

We present an algorithm for building probabilistic rule lists that is two orders of magnitude faster than previous work. Rule list algorithms are competitors for decision tree algorithms. They are associative classifiers, in that they are built from pre-mined association rules. They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree. Instead of using greedy splitting and pruning like decision tree algorithms, we aim to fully optimize over rule lists, striking a practical balance between accuracy, interpretability, and computational speed. The algorithm presented here uses a mixture of theoretical bounds (tight enough to have practical implications as a screening or bounding procedure), computational reuse, and highly tuned language libraries to achieve computational efficiency. Currently, for many practical problems, this method achieves better accuracy and sparsity than decision trees, with practical running times. The predictions in each leaf are probabilistic.
Thermodynamic Bethe Ansatz and dilogarithm identities I.

We consider the (A_n,A_1) Y-system arising in the Thermodynamic Bethe Ansatz. We prove the periodicity of solutions of this Y-system conjectured by Al.B. Zamolodchikov, and the dilogarithm identities conjectured by F. Gliozzi and R. Tateo.
Quadratic Forms and Semiclassical Eigenfunction Hypothesis for Flat Tori

Let Q(X) be any integral primitive positive definite quadratic form in k variables, where $${k\geq4}$$ , and discriminant D. For any integer n, we give an upper bound on the number of integral solutions of Q(X) = n in terms of n, k, and D. As a corollary, we prove a conjecture of Lester and Rudnick on the small scale equidistribution of almost all functions belonging to any orthonormal basis of a given eigenspace of the Laplacian on the flat torus $${\mathbb{T}^d}$$ for $${d\geq 5}$$ . This conjecture is motivated by the work of Berry [2,3] on the semiclassical eigenfunction hypothesis.
ON UNIFORM f -VECTORS OF CUTSETS IN THE TRUNCATED BOOLEAN LATTICE

and let be the collection of all subsets of [n] ordered by inclusion. is a cutset if it meets every maximal chain in , and the width of is the minimum number of chains in a chain decomposition of . Fix . What is the smallest value of such that there exists a cutset that consists only of subsets of sizes between m and l, and such that it contains exactly k subsets of size i for each ? The answer, which we denote by , gives a lower estimate for the width of a cutset between levels m and l in . After using the Kruskal–Katona Theorem to give a general characterization of cutsets in terms of the number and sizes of their elements, we find lower and upper bounds (as well as some exact values) for .
On the elastic anatomy of heterogeneous fractures in rock

Abstract This study examines the feasibility of the full-field ultrasonic characterization of fractures in rock. To this end, a slab-like specimen of granite is subjected to in-plane, O(10 4 Hz) excitation while monitoring the induced 2D wavefield by a Scanning Laser Doppler Vibrometer (SLDV) with sub-centimeter spatial resolution. Upon suitable filtering and interpolation, the observed wavefield is verified to conform with the plane-stress approximation and used to: (i) compute the maps of elastic modulus in the specimen (before and after fracturing) via a rudimentary application of the principle of elastography; (ii) reconstruct the fracture geometry ; (iii) expose the fracture's primal (traction-displacement jump) contact behavior, and (iv) identify its profiles of shear and normal specific stiffness. Through the use of full-field ultrasonic data, the approach provides an unobscured, high-resolution insight into the fracture's contact behavior, foreshadowing in-depth laboratory exploration of interdependencies between the fracture geometry, aperture, interphase properties, and its seismic characteristics.
Distortion of embeddings of a torsion-free finitely generated nilpotent group into a unitriangular group

In this paper, we study distortion of various well-known embeddings of finitely generated torsion-free nilpotent groups G into unitriangular groups UTn(ℤ). In particular, we show that there is no u...
Rational Curves on Del Pezzo Manifolds

We exploit an elementary specialization technique to study some properties of rational curves on index $n-1$ Fano $n$-folds. We prove a simple formula for counting rational curves passing through a suitable number of points in the case $n=3$. The arguments have immediate deformation theoretic consequences which translate to properties of the normal bundles of such curves.
On the intersection ring of graph manifolds

We calculate the intersection ring of three-dimensional graph manifolds with rational coefficients and give an algebraic characterization of these rings when the manifold's underlying graph is a tree. We are able to use this characterization to show that the intersection ring obstructs arbitrary three-manifolds from being homology cobordant to certain graph manifolds.
Demonstration of a Multiresolution Schema Mapping System.

Enterprise databases usually contain large and complex schemas. Authoring complete schema mapping queries in this case requires deep knowledge about the source and target schemas and is thereby very challenging to programmers. Sample-driven schema mapping allows the user to describe the schema mapping using data records. However, real data records are still harder to specify than other useful insights about the desired schema mapping the user might have. In this project, we develop a schema mapping system, PRISM, that enables multiresolution schema mapping. The end user is not limited to providing high-resolution constraints like exact data records but may also provide constraints of various resolutions, like incomplete data records, value ranges, and data types. This new interaction paradigm gives the user more flexibility in describing the desired schema mapping. This demonstration showcases how to use PRISM for schema mapping in a real database.
No positive cone in a free product is regular

We show that there exists no left order on the free product of two nontrivial, finitely generated, left-orderable groups such that the corresponding positive cone is represented by a regular language. Since there are orders on free groups of rank at least two with positive cone languages that are context-free (in fact, 1-counter languages), our result provides a bound on the language complexity of positive cones in free products that is the best possible within the Chomsky hierarchy. It also provides a strengthening of a result by Cristobal Rivas which states that the positive cone in a free product of nontrivial, finitely generated, left-orderable groups cannot be finitely generated as a semigroup. As another illustration of our method, we show that the language of all geodesics (with respect to the natural generating set) that represent positive elements in a graph product of groups defined by a graph of diameter at least 3 cannot be regular.
The Guided Team-Partitioning Problem: Definition, Complexity, and Algorithm.

A long line of literature has focused on the problem of selecting a team of individuals from a large pool of candidates, such that certain constraints are respected, and a given objective function is maximized. Even though extant research has successfully considered diverse families of objective functions and constraints, one of the most common limitations is the focus on the single-team paradigm. Despite its well-documented applications in multiple domains, this paradigm is not appropriate when the team-builder needs to partition the entire population into multiple teams. Team-partitioning tasks are very common in an educational setting, in which the teacher has to partition the students in her class into teams for collaborative projects. The task also emerges in the context of organizations, when managers need to partition the workforce into teams with specific properties to tackle relevant projects. In this work, we extend the team formation literature by introducing the Guided Team-Partitioning (GTP) problem, which asks for the partitioning of a population into teams such that the centroid of each team is as close as possible to a given target vector. As we describe in detail in our work, this formulation allows the team-builder to control the composition of the produced teams and has natural applications in practical settings. Algorithms for the GTP need to simultaneously consider the composition of multiple non-overlapping teams that compete for the same population of candidates. This makes the problem considerably more challenging than formulations that focus on the optimization of a single team. In fact, we prove that GTP is NP-hard to solve and even to approximate. The complexity of the problem motivates us to consider efficient algorithmic heuristics, which we evaluate via experiments on both real and synthetic datasets.
A Note on Generic Clifford Algebras of Binary Cubic Forms

We study the representation theoretic results of the binary cubic generic Clifford algebra $\mathcal C$ , which is an Artin-Schelter regular algebra of global dimension five. In particular, we show that $\mathcal C$ is a PI algebra of PI degree three and compute its point variety and discriminant ideals. As a consequence, we give a necessary and sufficient condition on a binary cubic form f for the associated Clifford algebra $\mathcal C_{f}$ to be an Azumaya algebra.
Profits and Violence in Illegal Markets: Evidence from Venezuela:

Some theories predict that profits facilitate peace in illegal markets, while others predict that profits fuel violence. I provide empirical evidence from drug trafficking in Venezuela. Using origi...
Visualizing image content to explain novel image discovery

The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends. We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the data set or, potentially, the discovery of previously unknown phenomena. To aid in interpreting and diagnosing the novel aspect of these selected observations, we recommend the use of novelty detection methods that generate explanations. In the context of large image data sets, these explanations should highlight what aspect of a given image is new (color, shape, texture, content) in a human-comprehensible form. We propose DEMUD-VIS, the first method for providing visual explanations of novel image content by employing a convolutional neural network (CNN) to extract image features, a method that uses reconstruction error to detect novel content, and an up-convolutional network to convert CNN feature representations back into image space. We demonstrate this approach on diverse images from ImageNet, freshwater streams, and the surface of Mars. Finally, we evaluate the utility of the visual explanations with a user study.
Comparing the effectiveness of market-based and choice-based environmental policy

Abstract This paper examines the welfare effects of market-based (permits, taxes) and choice-based (voluntary emission certifications, eco-labels) environmental policy. My analysis shows that choice-based eco-labels can be a welfare-improving policy. However, these welfare gains occur through “green” consumer surplus as opposed to (social benefiting) emission reductions while emission taxes and permits increase welfare by causing “dirtier” or less efficient firms to leave the market. Although greater environmental conscientiousness makes eco-labeling an effective tool to improve overall welfare, eco-labels are not as effective at reducing environmental damage relative to traditional market-based emission taxes or permits. Using research on consumer preferences and previously implemented environmental policies, the effectiveness of traditional emission taxes and eco-labeling programs are estimated. The results suggest that consumers participation in the eco-labeled product market benefits producers, but overall, eco-labeling programs are an inefficient policy tool relative to traditional market-based emission taxes or permits.
Persistence of neuronal representations through time and damage in the hippocampus

How do neurons encode long-term memories? Bilateral imaging of neuronal activity in the mouse hippocampus reveals that, from one day to the next, ~40% of neurons change their responsiveness to cues, but thereafter only 1% of cells change per day. Despite these changes, neuronal responses are resilient to a lack of exposure to a previously completed task or to hippocampus lesions. Unlike individual neurons, the responses of which change after a few days, groups of neurons with inter- and intrahemispheric synchronous activity show stable responses for several weeks. The likelihood that a neuron maintains its responsiveness across days is proportional to the number of neurons with which its activity is synchronous. Information stored in individual neurons is relatively labile, but it can be reliably stored in networks of synchronously active neurons.
A coupled autoencoder approach for multi-modal analysis of cell types

Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexity of brain circuits can be reduced, and effectively studied by means of interactions between cell types. While clustering of neuron populations based on a particular data modality can be used to define cell types, such definitions are often inconsistent across different characterization modalities. We pose this issue of cross-modal alignment as an optimization problem and develop an approach based on coupled training of autoencoders as a framework for such analyses. We apply this framework to a Patch-seq dataset consisting of transcriptomic and electrophysiological profiles for the same set of neurons to study consistency of representations across modalities, and evaluate cross-modal data prediction ability. We explore the problem where only a subset of neurons is characterized with more than one modality, and demonstrate that representations learned by coupled autoencoders can be used to identify types sampled only by a single modality.
Multi-Criteria Dimensionality Reduction with Applications to Fairness

Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average reconstruction error. In this paper, we introduce the multi-criteria dimensionality reduction problem where we are given multiple objectives that need to be optimized simultaneously. As an application, our model captures several fairness criteria for dimensionality reduction such as the Fair-PCA problem introduced by Samadi et al. [NeurIPS18] and the Nash Social Welfare (NSW) problem. In the Fair-PCA problem, the input data is divided into k groups, and the goal is to find a single d-dimensional representation for all groups for which the maximum reconstruction error of any one group is minimized. In NSW the goal is to maximize the product of the individual variances of the groups achieved by the common low-dimensinal space. Our main result is an exact polynomial-time algorithm for the two-criteria dimensionality reduction problem when the two criteria are increasing concave functions. As an application of this result, we obtain a polynomial time algorithm for Fair-PCA for k=2 groups, resolving an open problem of Samadi et al.[NeurIPS18], and a polynomial time algorithm for NSW objective for k=2 groups. We also give approximation algorithms for k>2. Our technical contribution in the above results is to prove new low-rank properties of extreme point solutions to semi-definite programs. We conclude with the results of several experiments indicating improved performance and generalized application of our algorithm on real-world datasets.
Universality and individuality in neural dynamics across large populations of recurrent networks

Many recent studies have employed task-based modeling with recurrent neural networks (RNNs) to infer the computational function of different brain regions. These models are often assessed by quantitatively comparing the low-dimensional neural dynamics of the model and the brain, for example using canonical correlation analysis (CCA). However, the nature of the detailed neurobiological inferences one can draw from such efforts remains elusive. For example, to what extent does training neural networks to solve simple tasks, prevalent in neuroscientific studies, uniquely determine the low-dimensional dynamics independent of neural architectures? Or alternatively, are the learned dynamics highly sensitive to different neural architectures? Knowing the answer to these questions has strong implications on whether and how to use task-based RNN modeling to understand brain dynamics. To address these foundational questions, we study populations of thousands of networks of commonly used RNN architectures trained to solve neuroscientifically motivated tasks and characterize their low-dimensional dynamics via CCA and nonlinear dynamical systems analysis. We find the geometry of the dynamics can be highly sensitive to different network architectures, and further find striking dissociations between geometric similarity as measured by CCA and network function, yielding a cautionary tale. Moreover, we find that while the geometry of neural dynamics can vary greatly across architectures, the underlying computational scaffold: the topological structure of fixed points, transitions between them, limit cycles, and linearized dynamics, often appears {\it universal} across all architectures. Overall, this analysis of universality and individuality across large populations of RNNs provides a much needed foundation for interpreting quantitative measures of dynamical similarity between RNN and brain dynamics.
Complexity and mission computability of adaptive computing systems

There is a subset of computational problems that existing algorithms may not complete due to a lack of adequate computational resources on tactical edge computing platforms. Although this subset is...
Insight into the dynamics of APOBEC3G protein in complexes with DNA assessed by high speed AFM

APOBEC3G (A3G) is a single-stranded DNA (ssDNA) binding protein that restricts the HIV virus by deamination of dC to dU during reverse transcription of the viral genome. A3G has two zinc-binding domains: the N-terminal domain (NTD), which efficiently binds ssDNA, and the C-terminal catalytic domain (CTD), which supports deaminase activity of A3G. Until now, structural information on A3G has been lacking, preventing elucidation of the molecular mechanisms underlying its interaction with ssDNA and deaminase activity. We have recently built a computational model for the full-length A3G monomer and validated its structure using data obtained by time-lapse High-Speed Atomic Force Microscopy (HS AFM). Here time-lapse HS AFM is applied to directly visualize the structure and dynamics of A3G in complexes with ssDNA. Our results demonstrate a highly dynamic structure of A3G, where two domains of the protein fluctuate between compact globular and extended dumbbell structures. Quantitative analysis of our data revealed a substantial increase in the number of A3G dumbbell structures in the presence of the DNA substrate, suggesting that the interaction of A3G with the ssDNA substrate stabilizes this dumbbell structure. Based on these data, we proposed a model explaining the interaction of globular and dumbbell structures of A3G with ssDNA and suggested a possible role of the dumbbell structure in A3G function.
Do Human Rationales Improve Machine Explanations

Work on “learning with rationales” shows that humans providing explanations to a machine learning system can improve the system’s predictive accuracy. However, this work has not been connected to work in “explainable AI” which concerns machines explaining their reasoning to humans. In this work, we show that learning with rationales can also improve the quality of the machine’s explanations as evaluated by human judges. Specifically, we present experiments showing that, for CNN-based text classification, explanations generated using “supervised attention” are judged superior to explanations generated using normal unsupervised attention.
Deciphering epigenomic code for cell differentiation using deep learning

Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiation. To fill this gap, we employed two types of deep convolutional neural networks (CNNs) constructed for each of differentially related cell types and for each of histone marks measured in the cells, to learn the sequence determinants of various histone modification patterns in each cell type. We applied our models to four differentially related human CD4+ T cell types and six histone marks measured in each cell type. The cell models can accurately predict the histone marks in each cell type, while the mark models can also accurately predict the cell types based on a single mark. Sequence motifs learned by both the cell or mark models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Both the unique histone mark patterns in each cell type and the different patterns of the same histone mark in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of sharing motifs learned in the different cell models reflects the lineage relationships of the cells, while the level of sharing motifs learned in the different histone mark models reflects their functional relationships. These models can also enable the prediction of the importance of learned motifs and their interactions in determining specific histone mark patterns in the cell types. Sequence determinants of various histone modification patterns in different cell types can be revealed by comparative analysis of motifs learned in the CNN models for multiple cell types and histone marks. The learned motifs are interpretable and may provide insights into the underlying molecular mechanisms of establishing the unique epigenomes in different cell types. Thus, our results support the hypothesis that DNA sequences ultimately determine the unique epigenomes of different cell types through their interactions with transcriptional factors, epigenome remodeling system and extracellular cues during cell differentiation.
Tailorable elasticity of cantilever using spatio-angular functionally graded biomimetic scales

Cantilevered beams are of immense importance as structural and sensorial members for a number of applications. Endowing tailorable elasticity can have wide ranging engineering ramification. Such tailorability could be possible using some type of spatial gradation in the beam’s material or cross section. However, these often require extensive additive and subtractive material processing or specialized casts. Herein, we demonstrate an alternative bio-inspired mechanical pathway, which is based on exploiting the nonlinearity that would arise from a functionally graded (FG) distribution of biomimetic scales on the surface using an analytical approach. This functional gradation is geometrically sourced and could arise from either spatial or angular gradation of scales. We analyze such FG cantilever beams under different loading conditions including point loading at the free end, uniform traction, linearly distributed traction, and concentrated moment loading at the free end. In comparison with uniformly distributed scales for all cases of the loading addressed, we find significant differences in bending stiffness for both spatial and angular gradations. Spatial and angular functional gradations share some universality but also sharp contrasts in their effect on the underlying beam. We also quantify the landscape of spatio-angular tailorability on stiffness gains. We compare our models with select experiments for validation. This highlights that a combination of both types of gradation in the structure can be used to alter stiffness and therefore offer a pathway to tailor the elasticity of a cantilever beam relatively easily. These results demonstrate an architected framework for designing and optimizing scale-covered FG beams.
Automated Surgical Activity Recognition with One Labeled Sequence

Prior work has demonstrated the feasibility of automated activity recognition in robot-assisted surgery from motion data. However, these efforts have assumed the availability of a large number of densely-annotated sequences, which must be provided manually by experts. This process is tedious, expensive, and error-prone. In this paper, we present the first analysis under the assumption of scarce annotations, where as little as one annotated sequence is available for training. We demonstrate feasibility of automated recognition in this challenging setting, and we show that learning representations in an unsupervised fashion, before the recognition phase, leads to significant gains in performance. In addition, our paper poses a new challenge to the community: how much further can we push performance in this important yet relatively unexplored regime?
Astronomy methods : a physical approach to astronomical observations

1. Astronomy through the centuries 2. Electromagnetic radiation 3. Co-ordinate systems and charts 4. Gravity, celestial motions, and time 5. Telescopes 6. Detectors and statistics 7. Multiple telescope interferometry 8. Point-like and extended sources 9. Properties and distances of celestial objects 10. Absorption and scattering of photons 11. Spectra of electromagnetic radiation 12. Astronomy beyond photons.
Big Bidirectional Insertion Representations for Documents

The Insertion Transformer is well suited for long form text generation due to its parallel generation capabilities, requiring O(log2 n) generation steps to generate n tokens. However, modeling long sequences is difficult, as there is more ambiguity captured in the attention mechanism. This work proposes the Big Bidirectional Insertion Representations for Documents (Big BIRD), an insertion-based model for document-level translation tasks. We scale up the insertion-based models to long form documents. Our key contribution is introducing sentence alignment via sentence-positional embeddings between the source and target document. We show an improvement of +4.3 BLEU on the WMT’19 English->German document-level translation task compared with the Insertion Transformer baseline.
Know thyself financially: How financial self-awareness can benefit consumers and financial advisors

We consider the relationship between an individual's financial self‐awareness, defined as detailed knowledge about one's current financial assets, liabilities, and spending patterns, and financial outcomes. The results of three studies show that a higher level of financial self‐awareness is associated with positive financial decisions and satisfaction among individuals. This effect is mediated by perceived efficacy about handling personal finances. We also find that financial literacy strengthens the association between financial self‐awareness and investing and saving, but does not affect the association between financial self‐awareness and either financial satisfaction or spending decisions. Increasing the financial self‐awareness of clients offers a useful method for financial advisors and policy makers to encourage prudent financial decisions. Full Text Available Here: https://doi.org/10.1002/cfp2.1069
Intravenous Ondansetron in Pregnancy and Risk of Congenital Malformations

This pharmacoepidemiology study uses Medicaid data to estimate associations between first-trimester use of intravenous ondansetron and risk of cardiac malformations and oral cleft in children of exposed mothers.
Power aware attitude computation during rapid rotational motion

A technique for increasing the accuracy of attitude update calculations when data sample rates are low is described. The technique is based on the use of a fixed-point coordinate rotation digital computer (CORDIC) function. In addition to improved accuracy, power consumption is reduced at lower data rates. The technique is also useful for maintaining computational accuracy during high angular rate inputs. The result of an experiment employing microelectromechanical system (MEMS) gyroscopes and comparing the CORDIC-based method with Taylor series approximations is described. In the experiment, a second-order Taylor's series approximation resulted in computed errors in the Euler angle of greater than 5/spl deg/ and 15/spl deg/ at respective sample rates of 60 and 30 samples/s, while the CORDIC-based method was accurate to within 2 arcseconds. Power consumption estimates are provided for several input data rates for CORDIC-based field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementations.
Developing Listening and Oral Expression Skills: Pillars of Influential Oral Communication:

Influence is a critical competency for success in the workplace. Past research has shown that two oral communication skills (listening and oral expression) are associated with interpersonal influen...
A Framework for Monitoring Human Physiological Response during Human Robot Collaborative Task

In this paper, a framework for monitoring human physiological response during Human-Robot Collaborative (HRC) task is presented. The framework highlights the importance of generation of event markers related to both human and robot, and also synchronization of data collected. This framework enables continuous data collection during an HRC task when changing robot movements as a form of stimuli to invoke a human physiological response. It also presents two case studies based on this framework and a data visualization tool for representation and easy analysis of the collected data during an HRC experiment.
A Machine Learning Approach to Detection of Geomagnetically Induced Currents in Power Grids

Geomagnetically induced currents (GICs) in power grids are mainly caused by geomagnetic disturbances especially during solar storms. Such currents can potentially cause negative impacts on power grid equipment and even damage the power transformers resulting in a significant risk of blackouts. Therefore, monitoring GICs in power systems and developing solutions to mitigate their impacts before rising to a certain threatening level is urgently in need. Monitoring GICs is, however, quite a challenge and costly, as they usually appear in forms of dc components in the high voltage transmission lines, which are barely accessible through transformers. By examining the measured currents from the current transformers, this article proposes a framework to detect GICs in power transmission systems through a hybrid time-frequency analysis combined with machine learning technology. Simulated results verify that the proposed approach can promisingly estimate GICs in power systems during a variety of grid operating conditions.
Generalized regular k-point grid generation on the fly

Abstract In the DFT community, it is common practice to use regular k -point grids (Monkhorst-Pack, MP) for Brillioun zone integration. Recently Wisesa et al. (2016) and Morgan et al. (2018) demonstrated that generalized regular (GR) grids offer an advantage over traditional MP grids. The difference is simple but effective. At the same k -point density, GR grids have greater symmetry and 60% fewer irreducible k-points. GR grids have not been widely adopted because one must search through a large number of candidate grids; in many cases, a brute force search could take hours. This work describes an algorithm that can quickly search over GR grids for those that have the most uniform distribution of points and the best symmetry reduction. The grids are ∼ 60% more efficient, on average, than MP grids and can now be generated on the fly in seconds.
Unique Two-Dimensional Multiple Phase Transition of Single-Anchored Aromatic Carboxylic Acids at Electrified Interfaces

We report a unique feature in an adsorption and molecular assembly discovered by classic electrochemical cyclic voltammetry (CV) and electrochemical scanning tunneling microscopy (EC-STM) technique...
Not All Groups Are LEF Groups, or Can You Know If a Group Is Infinite?

This is an introduction to the class of groups that are locally embeddable into finite groups.
The information content of Sarbanes-Oxley in predicting security breaches

Abstract How effective is compliance with the Sarbanes-Oxley Act (SOX) in identifying and eliminating firm threats from information systems breaches? We investigated publicly reported security breaches of internal controls in corporate systems to determine whether SOX assessments are information bearing with respect to breaches which can lead to materially significant losses and misstatements. The results of our tests varied significantly between breach types. SOX Section 404 adverse decisions on effectiveness of controls occurred in 100% of credit card data breaches and around 33% of insider breaches. SOX 404 audits provided a contrarian “effective” control decision on 88% of situations where there was a control breach concerning a portable device. This suggests that employees are subverting particularly strict internal controls by using portable devices that can be carried outside the physical boundaries of the firm. We found that management and SOX 404 auditors do not general agree on the underlying internal control situation at any time. Instead the SOX 404 auditors were likely to discover material weaknesses and “educate” management and internal audit teams about the importance of these control weaknesses. SOX attestations were poor at identifying control weaknesses from unintended disclosures, physical losses, hacking and malware, stationary devices, and situations where the cause of the breach was unknown. Hazard and occupancy structural models were constructed to extrapolate to a larger population. Results showed that both SOX 302 and 404 audits provided information germane to the frequency of breaches, with SOX 404 being three times as informative as section 302 reports. The hazard model found an expected 2.88% reduction in breaches when SOX 302 controls were effective. Management's “material weakness’ attestations provided no information in this structural model, whereas there would be around a 1% increase in breach occurrence when there exist significant deficiencies. SOX 404 attestations were the most informative; a negative SOX 404 attestation is projected to increase the frequency of breaches by around 8.5%. We concluded that the strength of internal controls attested in SOX reports is likely to be a significant factor in the occurrence of a security breach in a specific period.