cssi/SciSciGPT-SciSciNet huggingface dataset:
Greater Metacognition and Lower Fear of Negative Evaluation: Potential Factors Contributing to Improved Stigma Resistance among Individuals Diagnosed with Schizophrenia.

BACKGROUND Stigma resistance, one's ability to block the internalization of stigma, appears to be a key domain of recovery. However, the conditions in which one is most likely to resist stigma have not been identified, and models of stigma resistance have yet to incorporate one's ability to consider the mind of others. The present study investigated the impact of the interaction between metacognition, or one's ability to form an integrated representation of oneself, others, and the world, and fear of negative evaluation on one's ability to resist stigma. METHODS Narratives of encounters with stigma shared by 41 persons with schizophrenia or schizoaffective disorders were first coded for spontaneous expressions of fear of negative evaluation from others. Two-step cluster analyses were then conducted in order to test the hypothesis that metacognition and fearing negative evaluation from others are important, interacting pathways which contribute to resisting stigma. RESULTS Those with high (n = 11; 26.8%), intermediate (n = 9; 22.0%), and low metacognition (n = 21; 51.2%) significantly differed on stigma resistance (F = 9.49, p<0.001) and the high metacognition group was most likely to resist stigma. Those with high and low metacognition did not express fear of negative evaluation, while those with intermediate metacognition did express fear of negative evaluation.
Optimal transport natural gradient for statistical manifolds with continuous sample space

We study the Wasserstein natural gradient in parametric statistical models with continuous sample spaces. Our approach is to pull back the $$L^2$$ -Wasserstein metric tensor in the probability density space to a parameter space, equipping the latter with a positive definite metric tensor, under which it becomes a Riemannian manifold, named the Wasserstein statistical manifold. In general, it is not a totally geodesic sub-manifold of the density space, and therefore its geodesics will differ from the Wasserstein geodesics, except for the well-known Gaussian distribution case, a fact which can also be validated under our framework. We use the sub-manifold geometry to derive a gradient flow and natural gradient descent method in the parameter space. When parametrized densities lie in $$\mathbb {R}$$ , the induced metric tensor establishes an explicit formula. In optimization problems, we observe that the natural gradient descent outperforms the standard gradient descent when the Wasserstein distance is the objective function. In such a case, we prove that the resulting algorithm behaves similarly to the Newton method in the asymptotic regime. The proof calculates the exact Hessian formula for the Wasserstein distance, which further motivates another preconditioner for the optimization process. To the end, we present examples to illustrate the effectiveness of the natural gradient in several parametric statistical models, including the Gaussian measure, Gaussian mixture, Gamma distribution, and Laplace distribution.
Law, Structural Racism, and the COVID-19 Pandemic

brRacial and ethnic minorities have always been the most impacted by pandemics because of: disparities in exposure to the virus;disparities in susceptibility
Towards a Predictive Patent Analytics and Evaluation Platform

The importance of patents is well recognised across many regions of the world. Many patent mining systems have been proposed, but with limited predictive capabilities. In this demo, we showcase how predictive algorithms leveraging the state-of-the-art machine learning and deep learning techniques can be used to improve understanding of patents for inventors, patent evaluators, and business analysts alike. Our demo video is available at http://ibm.biz/ecml2019-demo-patent-analytics.
TBT: Targeted Neural Network Attack With Bit Trojan

Security of modern Deep Neural Networks (DNNs) is under severe scrutiny as the deployment of these models become widespread in many intelligence-based applications. Most recently, DNNs are attacked through Trojan which can effectively infect the model during the training phase and get activated only through specific input patterns (i.e, trigger) during inference. In this work, for the first time, we propose a novel Targeted Bit Trojan(TBT) method, which can insert a targeted neural Trojan into a DNN through bit-flip attack. Our algorithm efficiently generates a trigger specifically designed to locate certain vulnerable bits of DNN weights stored in main memory (i.e., DRAM). The objective is that once the attacker flips these vulnerable bits, the network still operates with normal inference accuracy with benign input. However, when the attacker activates the trigger by embedding it with any input, the network is forced to classify all inputs to a certain target class. We demonstrate that flipping only several vulnerable bits identified by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan-infected model. We perform extensive experiments of CIFAR-10, SVHN and ImageNet datasets on both VGG-16 and Resnet-18 architectures. Our proposed TBT could classify 92 of test images to a target class with as little as 84 bit-flips out of 88 million weight bits on Resnet-18 for CIFAR10 dataset.
RecQ DNA Helicase Rqh1 Promotes Rad3 ATR Kinase Signaling in the DNA Replication Checkpoint Pathway of Fission Yeast

Rad3 is the orthologue of ATR and the sensor kinase of the DNA replication checkpoint in Schizosaccharomyces pombe Under replication stress, it initiates checkpoint signaling at the forks necessary for maintaining genome stability and cell survival. To better understand the checkpoint initiation process, we have carried out a genetic screen in fission yeast by random mutation of the genome, looking for mutants defective in response to the replication stress induced by hydroxyurea. In addition to the previously reported mutant with a C-to-Y change at position 307 encoded by tel2 (tel2-C307Y mutant) (Y.-J. Xu, S. Khan, A. C. Didier, M. Wozniak, et al., Mol Cell Biol 39:e00175-19, 2019, https://doi.org/10.1128/MCB.00175-19), this screen has identified six mutations in rqh1 encoding a RecQ DNA helicase. Surprisingly, these rqh1 mutations, except for a start codon mutation, are all in the helicase domain, indicating that the helicase activity of Rqh1 plays an important role in the replication checkpoint. In support of this notion, integration of two helicase-inactive mutations or deletion of rqh1 generated a similar Rad3 signaling defect, and heterologous expression of human RECQ1, BLM, and RECQ4 restored the Rad3 signaling and partially rescued a rqh1 helicase mutant. Therefore, the replication checkpoint function of Rqh1 is highly conserved, and mutations in the helicase domain of these human enzymes may cause the checkpoint defect and contribute to the cancer predisposition syndromes.
Mechanism for embedded in-plane self-assembled nanowire formation

We report a growth mechanism that produces in-plane $[1\overline{1}0]$ oriented ErSb nanowires formed during codeposition of $\mathrm{E}{\mathrm{r}}_{0.3}\mathrm{G}{\mathrm{a}}_{0.7}\mathrm{Sb}$ via molecular beam epitaxy (MBE). Nanowires are characterized by in situ scanning tunneling microscopy (STM), as well as ex situ transmission electron microscopy (TEM) and electron channeling contrast imaging. We show that complexes of macrosteps with step heights on the order of 7 nm form during nanowire growth. The macrosteps are shown to be part of the in-plane nanowire growth process and are directly responsible for the observed stratified distribution of in-plane nanowires. TEM indicates that initial growth results in out-of-plane nanowires transitioning to in-plane nanowires after a critical film thickness. A surface energy model is put forward that shows the critical thickness is due to minimization of the $\mathrm{GaSb}{110}$ surfaces formed during out-of-plane nanowire growth. Kinetics of the transition are discussed with respect to observed features in STM, along with the material parameters needed to achieve in-plane nanowire growth.
The binary pulsar - Post-Newtonian timing effects

Certain post-Newtonian effects may be observable in the arrival times of pulses from the binary pulsar PSR 1913+16. Such effects include the gravitational propagation delay and the post-Newtonian corrections to the elliptical binary orbit. Fitting a model of these effects as they are predicted by general relativity to the pulse arrival times permits the estimation of more parameters than are necessary to determine the orbit and masses of the system. This redundancy provides an important check of the assumption that the observed periastron precession rate is entirely due to the effect of general relativity on compact masses.
8 Boolean Atoms Spanning the 256-Dimensional Entanglement-Probability Three-Set Algebra of the Two-Qutrit Hiesmayr-Loffler Magic Simplex of Bell States

We obtain formulas (bot. p. 12)--including $\frac{2}{121}$ and $\frac{4 \left(242 \sqrt{3} \pi -1311\right)}{9801}$--for the eight atoms (Fig. 11), summing to 1, which span a 256-dimensional three-set (P, S, PPT) entanglement-probability boolean algebra for the two-qutrit Hiesmayr-Loffler states. PPT denotes positive partial transpose, while P and S provide the Li-Qiao necessary and}sufficient conditions for entanglement. The constraints ensuring entanglement are $s> \frac{16}{9} \approx 1.7777$ and $p> \frac{2^{27}}{3^{18} \cdot 7^{15} \cdot13} \approx 5.61324 \cdot 10^{-15}$. Here, $s$ is the square of the sum (Ky Fan norm) of the eight singular values of the $8 \times 8$ correlation matrix in the Bloch representation, and $p$, the square of the product of the singular values. In the two-ququart Hiesmayr-Loffler case, one constraint is $s>\frac{9}{4} \approx 2.25$, while $\frac{3^{24}}{2^{134}} \approx 1.2968528306 \cdot 10^{-29}$ is an upper bound on the appropriate $p$ value, with an entanglement probability $\approx 0.607698$. The $S$ constraints, in both cases, prove equivalent to the well-known CCNR/realignment criteria. Further, we detect and verify--using software of A. Mandilara--pseudo-one-copy undistillable (POCU) negative partial transposed two-qutrit states distributed over the surface of the separable states. Additionally, we study the best separable approximation problem within this two-qutrit setting, and obtain explicit decompositions of separable states into the sum of eleven product states. Numerous quantities of interest--including the eight atoms--were, first, estimated using a quasirandom procedure.
Preperiodic points for quadratic polynomials over cyclotomic quadratic fields

Given a number field $K$ and a polynomial $f(z) \in K[z]$ of degree at least 2, one can construct a finite directed graph $G(f,K)$ whose vertices are the $K$-rational preperiodic points for $f$, with an edge $\alpha \to \beta$ if and only if $f(\alpha) = \beta$. Restricting to quadratic polynomials, the dynamical uniform boundedness conjecture of Morton and Silverman suggests that for a given number field $K$, there should only be finitely many isomorphism classes of directed graphs that arise in this way. Poonen has given a conjecturally complete classification of all such directed graphs over $\mathbb{Q}$, while recent work of the author, Faber, and Krumm has provided a detailed study of this question for all quadratic extensions of $\mathbb{Q}$. In this article, we give a conjecturally complete classification like Poonen's, but over the cyclotomic quadratic fields $\mathbb{Q}(\sqrt{-1})$ and $\mathbb{Q}(\sqrt{-3})$. The main tools we use are dynamical modular curves and results concerning quadratic points on curves.
Are homeownership patterns stable enough to tap home equity

Abstract As retirees live longer, spend more on medical care, and get less income replaced by Social Security, many may need to consider tapping their home equity to be comfortable in retirement. The most direct way to access home equity is downsizing, but few choose this option because they generally prefer to stay in their house. The alternative is withdrawing equity through a reverse mortgage or a property tax deferral, but few households use these options either. While this low take-up has been well documented, a more fundamental question is the extent to which homeowners exhibit sufficient stability in their housing patterns to make them well-suited to tap their equity. For example, if homeowners do tap their equity and then end up moving, they have to pay back the loan with interest, which could leave them with inadequate resources at a vulnerable time in their life. This paper assesses how likely households are to move as they age to see if borrowing against one’s home is a reasonable financial strategy. The analysis uses the Health and Retirement Study (HRS) to analyze three cohorts: the HRS cohort (ages 50–54 in 1992), the AHEAD cohort (ages 70–74 in 1993), and a synthetic cohort covering the whole lifespan from age 50 to death. The analysis identifies typical housing trajectories in retirement and explores how often, and for whom, tapping home equity would be a viable strategy. The paper found that: • Seventy percent of households have very stable homeownership patterns, even over several decades. They either stay in the home they own in their 50 s (53 percent) or purchase a new home around retirement and stay for the rest of their life (17 percent). • The 30 percent of households that do move consist of two distinct subgroups. Frequent movers (14 percent) appear to face labor market challenges. Late movers (16 percent) look like a slightly more affluent version of the households that never move, but then face a health shock that forces them out of the home that they owned into a rental unit or a long-term services and supports facility. • Overall, the findings largely support the narrative from prior research that most people want to age in place and move only in response to a shock. The policy implications of the findings are: • Most homeowners experience enough residential stability to tap home equity through reverse mortgages or property tax deferrals. • Retirees might be more likely to tap their home equity if they felt that they had adequate public or private insurance protection against the risk of needing long-term services and supports.
Coxeter group actions and limits of hypergeometric series

In this paper, we use combinatorial group theory and a limiting process to connect various types of hypergeometric series, and of relations among such series. We begin with a set S of 56 distinct translates of a certain function M, which takes the form of a Barnes integral, and is expressible as a sum of two very-well-poised $$_9F_8$$ hypergeometric series of unit argument. We consider a known, transitive action of the Coxeter group $$W(E_7)$$ on this set. We show that, by removing from $$W(E_7)$$ a particular generator, we obtain a subgroup that is isomorphic to $$W(D_6)$$ , and that acts intransitively on S, partitioning it into three orbits, of sizes 32, 12, and 12, respectively. Taking certain limits of the M functions in the first orbit yields a set of 32 J functions, each of which is a sum of two Saalschutzian $$_4F_3$$ hypergeometric series of unit argument. The original action of $$W(D_6)$$ on the M functions in this orbit is then seen to correspond to a known action of this group on this set of J functions. In a similar way, the image of each of the size-12 orbits, under a similar limiting process, is a set of 12 L functions that have been investigated in earlier works. In fact, these two image sets are the same. The limiting process is seen to preserve distance, except on pairs consisting of one M function from each size-12 orbit. Finally, each known three-term relation among the J and L functions is seen to be obtainable as a limit of a known three-term relation among the M functions.
Column-Randomized Linear Programs: Performance Guarantees and Applications

We propose a randomized method for solving linear programs with a large number of columns but a relatively small number of constraints. Since enumerating all the columns is usually unrealistic, such linear programs are commonly solved by column generation, which is often still computationally challenging due to the intractability of the subproblem in many applications. Instead of iteratively introducing one column at a time as in column generation, our proposed method involves sampling a collection of columns according to a user-specified randomization scheme and solving the linear program consisting of the sampled columns. While similar methods for solving large-scale linear programs by sampling columns (or, equivalently, sampling constraints in the dual) have been proposed in the literature, in this paper we derive an upper bound on the optimality gap that holds with high probability and converges with rate $1/\sqrt{K}$, where $K$ is the number of sampled columns, to the value of a linear program related to the sampling distribution. To the best of our knowledge, this is the first paper addressing the convergence of the optimality gap for sampling columns/constraints in generic linear programs without additional assumptions on the problem structure and sampling distribution. We further apply the proposed method to various applications, such as linear programs with totally unimodular constraints, Markov decision processes, covering problems and packing problems, and derive problem-specific performance guarantees. We also generalize the method to the case that the sampled columns may not be statistically independent. Finally, we numerically demonstrate the effectiveness of the proposed method in the cutting-stock problem and in nonparametric choice model estimation.
Starbursts, Dark Matter and Dwarf Galaxy Evolution

Optical and H i imaging of both dwarf irregular (dI) and Blue Compact Dwarf (BCD) galaxies reveal important clues on how dwarf galaxies evolve and their star formation is regulated. Both usually show evidence for stellar and gaseous disks. However, their total mass is dominated by dark matter. Gas rich dwarfs form with a range of disk structural properties. These have been arbitrarily separated into two classes on the basis of central surface brightness. Dwarfs with μ 0(B)≲22 mag arcsec−2 are usually classified as BCDs, while those fainter than limit are usually classified as dIs. Both classes experience bursts of star formation, but with an absolute intensity correlated with the disk surface brightness. Even in BCDs the bursts typically represent only a modest ≲ 1 mag enhancement to the B luminosity of the disk. While starbursts are observed to power significant galactic winds, the fractional ISM loss remains modest. Dark matter halos play an important role in determining dwarf galaxy morphology by setting the equilibrium surface brightness of the disk.
C-DOC: Co-State Desensitized Optimal Control

In this paper, co-states are used to develop a framework that desensitizes the optimal cost. A general formulation for an optimal control problem with fixed final time is considered. The proposed scheme involves elevating the parameters of interest into states, and further augmenting the co-state equations of the optimal control problem to the dynamical model. A running cost that penalizes the co-states of the targeted parameters is then added to the original cost function. The solution obtained by minimizing the augmented cost yields a control which reduces the dispersion of the original cost with respect to parametric variations. The relationship between co-states and the cost-to-go function, for any given control law, is established substantiating the approach.
Domestic price dollarization in emerging economies

Abstract This paper studies the dollarization of prices in retail markets of emerging economies. We develop a model of the firm’s optimal currency choice in retail markets in inflationary economies. We derive theoretical predictions regarding the optimality of dollar pricing, and test them using data from the largest e-trade platform in Latin America. Across countries, price dollarization is positively correlated with asset dollarization and inflation, and negatively correlated with exchange rate volatility. At the micro level, larger sellers are more likely to price in dollars, and more tradeable goods are more likely to be posted in dollars. We then show that the currency of prices determines the short-run reaction of both prices and quantities to a nominal exchange rate shock.
DIY ecology class: Transitioning field activities to an online format

The COVID-19 pandemic has forced the transition of many traditional face-to-face classes into an online format with little time to prepare best practice guidelines. In this article, we share a case study of how we adapted a group field activity into an individual laboratory assignment that can be completed during shelter-in-place restrictions. We conclude with ideas for future applications while staying mindful of the ways in which this pandemic has highlighted the inequities of the classroom, especially at community colleges.
Natural Resources and Natural Law Part II: The Public Trust Doctrine

Natural Resources and Natural Law Part I: Prior Appropriation analyzed claims by some western ranchers, grounded in natural law, that they have property rights in grazing resources on federal public lands through prior appropriation. Those advocates asserted their position in part through civil disobedience, sometimes including armed standoffs with federal officials. They also asserted that their duty to obey theistic natural law overrode any duty to obey the Nation’s positive law. Similar claims that individual religious beliefs override positive law have been made recently regarding a range of other controversial issues, such as same-sex marriage, public insurance for birth control, and the right to bear arms. Prior appropriation doctrine is consistent with secular natural law theory. Existing positive law, however, accepts prior appropriation for western water rights but rejects its application to grazing rights on federal public lands, for reasons consistent with secular natural law. Natural law doctrine allows citizens advocate for change but requires them to respect the positive law of the societies in which they live. Separation of church and state also bars natural law claims based on religious doctrine unless those principles are also adopted in secular positive law. This sequel addresses claims from the opposite side of the political-environmental spectrum that natural law provides one justification for the public trust doctrine, and that courts should enforce an atmospheric public trust to redress catastrophic global climate change. Although some religious groups have embraced environmental agendas supported by religious doctrine, public trust claims are secular in origin. Just as natural law provides support for prior appropriation, it supports the idea that some resources, such as water, wildlife, and air, should be held in common rather than made available for private ownership. From this perspective, the two doctrines merge into a single issue of resource allocation. Which resources are best made available for appropriation as private property, and which are best left in common? Natural law theory helps to explain the liberty and welfare goals that inform those choices. Positive law embraces the public trust doctrine with respect to some natural resources and does not preclude its applicability to the atmosphere or other common resources.
Pilot CEOs and trade credit

By using a pilot license as a proxy for the sensation-seeking personality trait, we examine the relation between sensation-seeking CEOs and trade credit. With a sample of pilot CEOs and non-pilot C...
Lossless Compression of Deep Neural Networks.

Deep neural networks have been successful in many predictive modeling tasks, such as image and language recognition, where large neural networks are often used to obtain good accuracy. Consequently, it is challenging to deploy these networks under limited computational resources, such as in mobile devices. In this work, we introduce an algorithm that removes units and layers of a neural network while not changing the output that is produced, which thus implies a lossless compression. This algorithm, which we denote as LEO (Lossless Expressiveness Optimization), relies on Mixed-Integer Linear Programming (MILP) to identify Rectified Linear Units (ReLUs) with linear behavior over the input domain. By using \(\ell _1\) regularization to induce such behavior, we can benefit from training over a larger architecture than we would later use in the environment where the trained neural network is deployed.
Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning

Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generalizes quickly (with few samples). We demonstrate interactions between physical properties of this device and optimal implementation of neuroscience-inspired plasticity learning rules, and highlight performance on a suite of tasks. Our energy analysis confirms the value of the approach, as the learning budget stays below 20μJ even for large tasks used typically in machine learning.
Variance lower bound on fluorescence microscopy image denoising.

The signal to noise ratio of high-speed fluorescence microscopy is heavily influenced by photon counting noise and sensor noise due to the expected low photon budget. Denoising algorithms are developed to decrease these noise fluctuations in microscopy data by incorporating additional knowledge or assumptions about imaging systems or biological specimens. One question arises: whether there exists a theoretical precision limit for the performance of a microscopy denoising algorithm. In this paper, combining Cramer-Rao Lower Bound with constraints and the low-pass-filter property of microscope systems, we develop a method to calculate a theoretical variance lower bound of microscopy image denoising. We show that this lower bound is influenced by photon count, readout noise, detection wavelength, effective pixel size and the numerical aperture of the microscope system. We demonstrate our development by comparing multiple state-of-the-art denoising algorithms to this bound. This method establishes a framework to generate theoretical performance limit, under a specific prior knowledge, or assumption, as a reference benchmark for microscopy denoising algorithms.
Automated Intracranial Artery Labeling Using a Graph Neural Network and Hierarchical Refinement

Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ART-LABEL.
Partitions into distinct parts with bounded largest part

We prove an asymptotic formula for the number of partitions of n into distinct parts where the largest part is at most $$t\sqrt{n}$$ for fixed $$t \in {\mathbb {R}}$$ . Our method follows a probabilistic approach of Romik, who gave a simpler proof of Szekeres’ asymptotic formula for distinct parts partitions when instead the number of parts is bounded by $$t\sqrt{n}$$ . Although equivalent to a circle method/saddle-point method calculation, the probabilistic approach motivates some of the more technical steps and even predicts the shape of the asymptotic formula, to some degree.
Solar coronal and photospheric abundances from solar energetic particle measurements

We find that the ionic charge-to-mass ratio (Q/M) is the principal organizing parameter for the fractionation of solar energetic particles (SEPs) by acceleration and propagation processes and for flare-to-flare variability. Unfractionated coronal abundances for 20 elements with 6< or =Z< or =30 are derived by applying a single-parameter Q/M-dependent correction to the average SEP abundances, and unfractionated photospheric abundances are obtained by applying an additional correction based on first ionization potential.
The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry

Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection.
How Much Can Potential Jurors Tell Us About Liability for Medical Artificial Intelligence

See the associated article on page [17][1]. Artificial intelligence (AI) is rapidly entering medical practice, whether for risk prediction, diagnosis, or treatment recommendation. But a persistent question keeps arising: What happens when things go wrong? When patients are injured, and AI was
Localized transient jamming in discontinuous shear thickening

We report direct measurements of spatially resolved surface stresses over the entire surface of a dense suspension during discontinuous shear thickening (DST) using boundary stress microscopy (BSM) in a parallel-plate rheometer. We find that large fluctuations in the bulk rheological response at the onset of DST are the result of localized transitions to a state with very high stress, consistent with a fully jammed solid that makes direct contact with the shearing boundaries. This jammed solidlike phase (SLP) is rapidly fractured, producing two separate SLPs that propagate in opposite directions. By comparing the speed of propagation of the SLPs with the motion of the confining plates, we deduce that one remains in contact with the bottom boundary and the other remains in contact with the top. These regions grow, bifurcate, and eventually interact and decay in a complex manner that depends on the measurement conditions (constant shear rate vs constant stress). In a constant applied stress mode, BSM directly reveals dramatic stress fluctuations that are completely missed in the standard bulk rheology.
Phases of Granular Matter

An understanding of homogeneous nucleation of crystalline structure from a disordered medium such as a liquid remains an important unsolved problem in condensed matter physics. Guided by the results from a number of experiments on granular and colloidal systems in the past two decades, including in particular observations of homogeneous nucleation in colloidal and granular systems, we suggest an alternative to the statistical mechanics approach to static granular matter initiated by Edwards and Oakeshott in 1989.
Power corrections to TMD factorization for Z-boson production

A typical factorization formula for production of a particle with a small transverse momentum in hadron-hadron collisions is given by a convolution of two TMD parton densities with cross section of production of the final particle by the two partons. For practical applications at a given transverse momentum, though, one should estimate at what momenta the power corrections to the TMD factorization formula become essential. In this paper we calculate the first power corrections to TMD factorization formula for Z-boson production and Drell-Yan process in high-energy hadron-hadron collisions. At the leading order in Nc power corrections are expressed in terms of leading power TMDs by QCD equations of motion.
Quantum hydrodynamics of vorticity

Inhomogeneities of collective degrees of freedom can be associated with topological conservation laws, yielding unconventional transport phenomena in solid state. While this is usually engendered by semiclassical field configurations in low-energy treatments, the authors show that a fully quantum regime of such topological hydrodynamics is also possible. They point out an underlying bulk-edge correspondence and exploit particle-vortex duality in an illustrative example of vortex superfluidity.
A Propagative Model of Simultaneous Impact: Existence, Uniqueness, and Design Consequences

This paper presents existence and uniqueness results for a propagative model of simultaneous impacts that is guaranteed to conserve energy and momentum in the case of elastic impacts with extensions to perfectly plastic and inelastic impacts. A corresponding time-stepping algorithm that guarantees conservation of continuous energy and discrete momentum is developed, also with extensions to plastic and inelastic impacts. The model is illustrated in simulation using billiard balls and a two-dimensional legged robot as examples; the latter is optimized over geometry and gait parameters to achieve unique simultaneous impacts.
The Dissociation Catastrophe in Fluctuating-Charge Models and its Implications for the Concept of Atomic Electronegativity

We have recently developed the QTPIE (charge transfer with polarization current equilibration) fluctuating-charge model, a new model with correct dissociation behavior for nonequilibrium geometries. The correct asymptotics originally came at the price of representing the solution in terms of charge-transfer variables instead of atomic charges. However, we have found an exact reformulation of fluctuating-charge models in terms of atomic charges again, which is made possible by the symmetries of classical electrostatics. We show how this leads to the distinction between two types of atomic electronegativities in our model. While one is a intrinsic property of individual atoms, the other takes into account the local electrical surroundings. This distinction could resolve some confusion surrounding the concept of electronegativity as to whether it is an intrinsic property of elements, or otherwise. We also use the QTPIE model to create a three-site water model and discuss simple applications.
THERMAL TRANSPORT MEASUREMENT TECHNIQUES FOR NANOWIRES AND NANOTUBES

Recent advances in the synthesis of inorganic and organic nanowires and nanotubes have provided both components for various functional devices and platforms for the study of low- dimensional transport phenomena. However, tremendous challenges have remained not only for the integration of these building blocks into functional devices, but also in the characterization of the fundamental transport properties in these nanoscale model systems. In particular, thermal and thermoelectric transport measurements can be considerably more complicated than electron transport measurements, especially for individual nanostructures. During the past decade, a number of experimental methods for measuring the thermal and thermoelectric properties of individual nanowires and nanotubes have been devised to address these challenges, some of which are reviewed and analyzed in this chapter. Although the Seebeck coefficient and electrical conductivity can also be obtained from some of the measurement methods, this chapter is focused on measurement techniques of the thermal conductivity and thermal diffusivity of nanowires and nanotubes. It is suggested that the limitations in the current experimental capability will provide abundant opportunities for innovative approaches to probing fundamental thermal and thermoelectric transport phenomena in individual nanostructures.
Efficient Resource Matching in Heterogeneous Grid Using Resource Vector

In this paper, a method for efficient scheduling to obtain optimum job throughput in a distributed campus grid environment is presented; Traditional job schedulers determine job scheduling using user and job resource attributes. User attributes are related to current usage, historical usage, user priority and project access. Job resource attributes mainly comprise of soft requirements (compilers, libraries) and hard requirements like memory, storage and interconnect. A job scheduler dispatches jobs to a resource if a job’s hard and soft requirements are met by a resource. In current scenario during execution of a job, if a resource becomes unavailable, schedulers are presented with limited options, namely re-queuing job or migrating job to a different resource. Both options are expensive in terms of data and compute time. These situations can be avoided, if the often ignored factor, availability time of a resource in a grid environment is considered. We propose resource rank approach, in which jobs are dispatched to a resource which has the highest rank among all resources that match the job’s requirement. The results show that our approach can increase throughput of many serial / monolithic jobs.
On the classification of Heegaard splittings

The long-standing classification problem in the theory of Heegaard splittings of 3-manifolds is to exhibit for each closed 3-manifold a complete list, without duplication, of all its irreducible Heegaard surfaces, up to isotopy. We solve this problem for non-Haken hyperbolic 3-manifolds.
NEAR-INFRARED PHOTOMETRIC SURVEY OF HERBIG Ae/Be CANDIDATE STARS

We report near-infrared photometric measurements of 35 Herbig Ae/Be candidate stars obtained with direct imaging and aperture photometry. Observations were made through the broadband J, H, and K' filters, with each source imaged in at least one of the wavebands. We achieved subarcsecond angular resolution for all observations, providing us with the opportunity to search for close binary candidates and extended structure. The imaging revealed five newly identified binary candidates and one previously resolved T Tauri binary among the target sources with separations of 25. Separate photometry is provided for each of the binary candidate stars. We detect one extended source that has been identified as a protoplanetary nebula. Comparing our magnitudes to past measurements yields significant differences for some sources, possibly indicating photometric variability. H-band finding charts for all of our sources are provided to aid follow-up high-resolution imaging.
Spin canting and orbital order in spinel vanadate thin films

We report on the epitaxial film growth and characterization of ${\mathrm{CoV}}_{2}{\mathrm{O}}_{4}$, a near-itinerant spinel vanadate, grown on (001) ${\mathrm{SrTiO}}_{3}$. The symmetry lowering of the unit cell from cubic in the bulk to orthorhombic in the films results in dramatic differences in the magnetic anisotropy compared to bulk, as determined from structural and magnetic characterization. Bulk cubic ${\mathrm{CoV}}_{2}{\mathrm{O}}_{4}$ has been found to defy predictions by showing orbital degeneracy seemingly lasting to very low temperatures, with only small anomalies in magnetization and neutron experiments signaling a possible spin/orbital glass transition at $T=90$ K. In epitaxial thin films presented in this paper, structurally tuning the ${\mathrm{CoV}}_{2}{\mathrm{O}}_{4}$ away from cubic symmetry leads to a completely different low temperature noncollinear ground state. Via magnetization and neutron scattering measurements we show that the 90-K transition is associated with a major spin reorientation away from the ferrimagnetic easy axis [001] to the [110] direction. Furthermore, the V spins cant away from this direction with extracted perpendicular moments providing evidence of a larger canting angle compared to bulk. This result indicates that compressive strain pushes the system deeper into the insulating state, i.e., away from the localized-itinerant crossover regime.
Droplet states in quantum XXZ spin systems on general graphs

We study XXZ spin systems on general graphs. In particular, we describe the formation of droplet states near the bottom of the spectrum in the Ising phase of the model, where the Z-term dominates the XX-term. As key tools, we use particle number conservation of XXZ systems and symmetric products of graphs with their associated adjacency matrices and Laplacians. Of particular interest to us are strips and multi-dimensional Euclidean lattices, for which we discuss the existence of spectral gaps above the droplet regime. We also prove a Combes-Thomas bound which shows that the eigenstates in the droplet regime are exponentially small perturbations of strict (classical) droplets.We study XXZ spin systems on general graphs. In particular, we describe the formation of droplet states near the bottom of the spectrum in the Ising phase of the model, where the Z-term dominates the XX-term. As key tools, we use particle number conservation of XXZ systems and symmetric products of graphs with their associated adjacency matrices and Laplacians. Of particular interest to us are strips and multi-dimensional Euclidean lattices, for which we discuss the existence of spectral gaps above the droplet regime. We also prove a Combes-Thomas bound which shows that the eigenstates in the droplet regime are exponentially small perturbations of strict (classical) droplets.
Symmetry and Rigidity of Minimal Surfaces with Plateau-like Singularities

By employing the method of moving planes in a novel way we extend some classical symmetry and rigidity results for smooth minimal surfaces to surfaces that have singularities of the sort typically observed in soap films.
Multiwavelength Observations of GX 339-4 in 1996. III. Keck Spectroscopy

As part of our multiwavelength campaign of observations of GX 339-4 in 1996, we present our Keck spectroscopy performed on May 12 UT. At this time, neither the All-Sky Monitor (ASM) on the Rossi X-Ray Timing Explorer (RXTE) nor the Burst and Transient Source Experiment (BATSE) on the Compton Gamma-Ray Observatory (CGRO) detected the source. The optical emission was still dominated by the accretion disk, with V ≈ 17 mag. The dominant emission line is Hα, and for the first time we are able to resolve a double-peaked profile. The peak separation Δv = 370 ± 40 km s-1. Double-peaked Hα emission lines have been seen in the quiescent optical counterparts of many black hole X-ray novae. However, we find that the peak separation is significantly smaller in GX 339-4, implying that the optical emission comes from a larger radius than in the novae. The Hα emission line may be more akin to the one in Cyg X-1, where it is very difficult to determine whether the line is intrinsically double peaked, because absorption and emission lines from the companion star dominate.
Malicious User Experience Design Research for Cybersecurity

This paper explores the factors and theory behind the user-centered research necessary to create a successful game-like prototype, and user experience, for malicious users in a cybersecurity context. We explore what is known about successful addictive design in the fields of video games and gambling to understand the allure of breaking into a system, and the joy of thwarting the security to reach a goal or a reward of data. Based on the malicious user research, game user research, and using the GameFlow framework, we propose a novel malicious user experience design approach.
Polarization spectroscopy of atomic erbium in a hollow cathode lamp

In this work we perform polarization spectroscopy of erbium atoms in a hollow cathode lamp. We review the theory behind Doppler-free polarization spectroscopy, theoretically model the expected erbium polarization spectra, and compare the numerically calculated spectra to our experimental data. We further analyze the dependence of the measured spectra on the hollow cathode lamp current and the peak intensities of our pump and probe lasers to determine conditions. Applications include wavelength stabilization of diode laser radiation to the 400.91-nm erbium transition.
Quantum Transport in Graphene Nanoribbons with Realistic Edges

Due to their unique electrical properties, graphene nanoribbons (GNRs) show great promise as the building blocks of novel electronic devices. However, these properties are strongly dependent on the geometry of the edges of the graphene devices. Thus far, only zigzag and armchair edges have been extensively studied. However, several other self-passivating edge reconstructions are possible, and were experimentally observed. Here we utilize the nonequilibrium Green’s function (NEGF) technique in conjunction with tight binding methods to model quantum transport through armchair, zigzag, and several other self-passivated edge reconstructions. In addition, we consider the experimentally relevant cases of mixed edges, where random combinations of possible terminations exist on a given GNR boundary. We find that transport through GNRs with self-passivating edge reconstructions is governed by the sublattice structure of the edges, in a manner similar to their parent zigzag or armchair configurations. Furthermore, ...