cssi/SciSciGPT-SciSciNet huggingface dataset:
Multi-modal synthesis of regular expressions

In this paper, we propose a multi-modal synthesis technique for automatically constructing regular expressions (regexes) from a combination of examples and natural language. Using multiple modalities is useful in this context because natural language alone is often highly ambiguous, whereas examples in isolation are often not sufficient for conveying user intent. Our proposed technique first parses the English description into a so-called hierarchical sketch that guides our programming-by-example (PBE) engine. Since the hierarchical sketch captures crucial hints, the PBE engine can leverage this information to both prioritize the search as well as make useful deductions for pruning the search space. We have implemented the proposed technique in a tool called Regel and evaluate it on over three hundred regexes. Our evaluation shows that Regel achieves 80 % accuracy whereas the NLP-only and PBE-only baselines achieve 43 % and 26 % respectively. We also compare our proposed PBE engine against an adaptation of AlphaRegex, a state-of-the-art regex synthesis tool, and show that our proposed PBE engine is an order of magnitude faster, even if we adapt the search algorithm of AlphaRegex to leverage the sketch. Finally, we conduct a user study involving 20 participants and show that users are twice as likely to successfully come up with the desired regex using Regel compared to without it.
STARC: Structured Annotations for Reading Comprehension

We present STARC (Structured Annotations for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions. Our framework introduces a principled structure for the answer choices and ties them to textual span annotations. The framework is implemented in OneStopQA, a new high-quality dataset for evaluation and analysis of reading comprehension in English. We use this dataset to demonstrate that STARC can be leveraged for a key new application for the development of SAT-like reading comprehension materials: automatic annotation quality probing via span ablation experiments. We further show that it enables in-depth analyses and comparisons between machine and human reading comprehension behavior, including error distributions and guessing ability. Our experiments also reveal that the standard multiple choice dataset in NLP, RACE, is limited in its ability to measure reading comprehension. 47% of its questions can be guessed by machines without accessing the passage, and 18% are unanimously judged by humans as not having a unique correct answer. OneStopQA provides an alternative test set for reading comprehension which alleviates these shortcomings and has a substantially higher human ceiling performance.
XtremeDistil: Multi-stage Distillation for Massive Multilingual Models

Deep and large pre-trained language models are the state-of-the-art for various natural language processing tasks. However, the huge size of these models could be a deterrent to using them in practice. Some recent works use knowledge distillation to compress these huge models into shallow ones. In this work we study knowledge distillation with a focus on multilingual Named Entity Recognition (NER). In particular, we study several distillation strategies and propose a stage-wise optimization scheme leveraging teacher internal representations, that is agnostic of teacher architecture, and show that it outperforms strategies employed in prior works. Additionally, we investigate the role of several factors like the amount of unlabeled data, annotation resources, model architecture and inference latency to name a few. We show that our approach leads to massive compression of teacher models like mBERT by upto 35x in terms of parameters and 51x in terms of latency for batch inference while retaining 95% of its F1-score for NER over 41 languages.
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models.

We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on $n$ i.i.d. samples are known to have lower accuracy than the classical $n^{- \frac{1}{2}}$ error. We investigate whether the Expectation-Maximization (EM) algorithm also converges slowly for these models. We provide a rigorous characterization of EM for fitting a weakly identifiable Gaussian mixture in a univariate setting where we prove that the EM algorithm converges in order $n^{\frac{3}{4}}$ steps and returns estimates that are at a Euclidean distance of order ${ n^{- \frac{1}{8}}}$ and ${ n^{-\frac{1} {4}}}$ from the true location and scale parameter respectively. Establishing the slow rates in the univariate setting requires a novel localization argument with two stages, with each stage involving an epoch-based argument applied to a different surrogate EM operator at the population level. We demonstrate several multivariate ($d \geq 2$) examples that exhibit the same slow rates as the univariate case. We also prove slow statistical rates in higher dimensions in a special case, when the fitted covariance is constrained to be a multiple of the identity.
An Approach to Maximize Torque Density in a Brushless Doubly Fed Reluctance Machine

Brushless doubly fed reluctance machines (BDFRM) are an attractive option for megawatt-scale turbo-electric propulsion systems due to use of a partially rated power converter, reduced maintenance, and absence of permanent magnets. Conventional design approach of a BDFRM uses equal electrical loadings on both stators and an initial phase offset $\delta$ of $\pi /2$ between the two stator currents. However, this approach leads to poor torque density because the machine saturates even before the stator reaches the current density limit. This article proposes an approach to maximize the torque density by finding appropriate electrical excitations on both stator windings for chosen machine dimensions as well as for specific flux density, current density, and grid voltage limits. A single-objective optimization problem is formulated. The optimized solution establishes that an initial phase offset $\delta$ of $2\pi /3$ produces 75% more torque as compared to the conventional $\delta = \pi /2$ design. The new design allows a higher stator currents injection without saturating the machine. The results are verified through extensive simulations using a finite element model. Operating with the optimized design also leads to higher machine efficiency and better power factor on the secondary stator, thus reducing the converter rating.
Contracting Beyond the Market

Despite growing engagements between firms and nonmarket stakeholders—such as local communities and nongovernmental organizations —research has yet to examine the emergence of formal contracts betwe...
Inhibition of MLK3 decreases proliferation and increases antiproliferative activity of epidermal growth factor receptor (EGFR) inhibitor in pancreatic cancer cell lines

2339 Rationale: Pancreatic cancer (PC) is associated with advanced presentation and poor survival. Currently approved therapies, including EGFR inhibitors, have minimal effect on patient survival. PCs have a high incidence of activated Ras, which may bypass inhibition of EGFR, resulting in resistance to EGFR inhibitors. Mixed lineage kinases (MLKs) are members of a large family of mitogen-activated protein kinase (MAPK) kinase kinases (MAPKKKs) that activate MAPK pathways, involving JNK, ERK 1/2 and p38 cell signaling pathways.1 MLK3 is required for proliferation of tumor cells with oncogenic kras mutations.2 The role of MLK3 in the pathophysiology and resistance to therapy of PC is not well known. Methods: The requirement of MLK3 for cell proliferation and survival of PC cell lines PANC 1 (gift of Dr. J. McCormick, Michigan State University) and MiaPaCa2 (ATCC), both of which have activating ras mutations, was investigated using RNA interference (siRNA) and MLK3 inhibitor K252a to inhibit the expression and activity of MLK3. Cells were also exposed to EGFR inhibitor Compound 56 at various concentrations. Proliferation was assessed with MTT after 48-72 h exposure. The effect of MLK3 inhibition on relevant cell signaling pathways was assessed by immunoblotting with activation-specific phosphoantibodies directed against MAPKs and the prosurvival kinase Akt. Results: MLK3 expression was significantly higher in PC cell lines than in a normal pancreatic epithelial cell line (gift of Dr. M. Tsao, Princess Margaret Hosp, Toronto). siRNA decreased MLK3 expression in both PC cell lines. K252a also significantly decreased MLK3 expression. Inhibition of MLK3 by siRNA or K252a was associated with decreased cell proliferation in both PC cell lines, with concurrent decrease in pERK and pAkt in PANC-1 cells and decreased pERK and pJNK in MiaPaCa2 cells. Concomitant exposure to MLK3 inhibitor and Compound 56 further inhibited cell proliferation and increased caspase 3 activity, compared to either agent alone. These results suggest that MLK3 plays an important role in survival and proliferation in PC cells with kras mutations and inhibition of MLK3 may enhance the effects of EGFR inhibitors in PC.
The future of financial fraud

Abstract Is financial fraud becoming a bigger or smaller problem over time? Current empirical approaches to this question generate mixed inferences. As an alternative, I use two theoretical constructs that isolate several factors that motivate fraud, and use them to consider the impact of technological and wealth changes over time. Some changes, such as an increase in anonymity in some financial transactions, facilitate new fraud innovations and increase the possibility of fraud. The COVID-19 pandemic and resulting economic shutdown has fostered major disruptions in relative demands and organizational capital that also increase the likelihood of fraud over the next few years. Viewed over a longer time scale, however, the majority of technological and wealth changes seem likely to increase the use and effectiveness of reputational capital, third-party enforcement, and ethical motivations as fraud deterrents. I predict that, on net, these changes will drive a long-term decrease in the incidence of fraud.
Asymptotic solutions of inhomogeneous differential equations having a turning point

Asymptotic solutions are derived for inhomogeneous differential equations having a large real or complex parameter and a simple turning point. They involve Scorer functions and three slowly varying analytic coefficient functions. The asymptotic approximations are uniformly valid for unbounded complex values of the argument, and are applied to inhomogeneous Airy equations having polynomial and exponential forcing terms. Error bounds are available for all approximations, including new simple ones for the well-known asymptotic expansions of Scorer functions of large complex argument.
Irreducibility of the Fermi surface for planar periodic graph operators

We prove that the Fermi surface of a connected doubly periodic self-adjoint discrete graph operator is irreducible at all but finitely many energies provided that the graph (1) can be drawn in the plane without crossing edges, (2) has positive coupling coefficients, (3) has two vertices per period. If “positive” is relaxed to “complex,” the only cases of reducible Fermi surface occur for the graph of the tetrakis square tiling, and these can be explicitly parameterized when the coupling coefficients are real. The irreducibility result applies to weighted graph Laplacians with positive weights
Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD): Characterization of Ligand Binding Thermodynamics and Kinetics

Calculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method, ligand Gaussian accelerated molecular dynamics (LiGaMD), which selectively boosts the ligand nonbonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD_Dual simulations. The calculated guest binding free energies agreed excellently with experimental data with <1.0 kcal/mol errors. Compared with converged microsecond-time scale conventional molecular dynamics simulations, the sampling errors of LiGaMD_Dual simulations were also <1.0 kcal/mol. Accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers' rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a powerful enhanced sampling approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.
Waveform-Domain NOMA: The Future of Multiple Access

This paper introduces a novel non-orthogonal multiple access (NOMA) concept named waveform-domain NOMA, which proposes the coexistence of different waveforms in the same resource element (RE). Regarding the demands of each user equipments (UEs), appropriate waveforms are assigned intelligently and decoded at the receiver side properly. Since the performance gain of power-domain NOMA compared to multi-user multiple-input-multiple-output (MU-MIMO) degrades in the case of successive interference cancellation (SIC) and channel estimation errors, NOMA is not considered as a work-item in 3GPP anymore. The novel waveform-domain NOMA concept provides a remedy for the problems of the power-domain NOMA scheme as well as flexibility in terms of reliability, latency, and energy efficiency. To mitigate SIC errors, low-density parity-check (LDPC) codes aided soft interference cancellation technique is used. As numerically evaluated, the proposed waveform-domain NOMA scheme outperforms the conventional power-domain NOMA in the power-balanced scenario.
An estimator of the Opportunity for Selection that is independent of mean fitness.

Variation among individuals in number of offspring (fitness, k) sets an upper limit to the evolutionary response to selection. This constraint is quantified by Crow's Opportunity for Selection (I), which is the variance in relative fitness (I = σ2k /(uk )2 ). Crow's I has been widely used but remains controversial because it depends on mean offspring number in a sample ( k¯ ). Here, I used a generalized Wright-Fisher model that allows for unequal probabilities of producing offspring to evaluate behavior of Crow's I and related indices under a wide range of sampling scenarios. Analytical and numerical results are congruent and show that rescaling the sample variance (s2k ) to its expected value at a fixed k¯2 removes dependence of I on mean offspring number, but the result still depends on choice of k¯2 . A new index is introduced, ΔI = I - E(Idrift ) = I - 1/ k¯ , which makes I independent of sample k¯ without the need for variance rescaling. ΔI has a straightforward interpretation as the component of variance in relative fitness that exceeds that expected under a null model of random reproductive success. ΔI can be used to directly compare estimates of the Opportunity for Selection for samples from different studies, different sexes, and different life stages.
Innovate versus imitate: Theory and experimental evidence

Abstract We model and experimentally evaluate the trade-off between innovation and imitation commonly faced by firms. Innovation involves searching for a high payoff opportunity, but paying a cost in order to do so. Imitation involves avoiding that search cost and copying the most successful payoff opportunity uncovered thus far. We formulate a novel model of sequential innovation versus imitation decisions made by a group of n regret minimizing agents. We analyze the consequences of complete versus incomplete information about the distribution of payoffs from innovation on agents’ decisions. We then study these predictions in a laboratory experiment where we find evidence in support of our theoretical predictions.
Dynamics of Vesicles Driven Into Closed Constrictions by Molecular Motors

We study the dynamics of a model of membrane vesicle transport into dendritic spines, which are bulbous intracellular compartments in neurons driven by molecular motors. We reduce the lubrication model proposed in Fai et al. (Phys Rev Fluids 2:113601, 2017) to a fast-slow system, yielding an analytically and numerically tractable equation equivalent to the original model in the overdamped limit. The model's key parameters include: (1) the ratio of motors that prefer to push toward the head of the dendritic spine to the motors that prefer to push in the opposite direction, and (2) the viscous drag exerted on the vesicle by the spine constriction. We perform a numerical bifurcation analysis in these parameters and find that steady-state vesicle velocities appear and disappear through several saddle-node bifurcations. This process allows us to identify the region of parameter space in which multiple stable velocities exist. We show by direct calculations that there can only be unidirectional motion for sufficiently close vesicle-to-spine diameter ratios. Our analysis predicts the critical vesicle-to-spine diameter ratio, at which there is a transition from unidirectional to bidirectional motion, consistent with experimental observations of vesicle trajectories in the literature.
Bubbling Phenomenon for Hermitian Yang–Mills Connections

We construct local examples of singular Hermitian Yang–Mills connections over |$B_1\subset \mathbb{C}^3$| with uniformly bounded |$L^2$|-energy, but the number of essential singular points can be arbitrarily large.
ESG Investing: Conceptual Issues

Using criteria based on environmental, social, and governance (ESG) considerations has become an increasingly important aspect of investment decision making, particularly for high-profile institutional investors. As of 2019, sustainable assets under management were estimated to be $30 trillion worldwide. The claim here is that the enthusiasm for ESG investing has been exaggerated for three reasons. First, it is not clear what constitutes an ESG investment in the context of a complex, integrated economy. Second, the impact on investment performance of a preference for ESG investments has not been sufficiently recognized outside academic circles. Finally, many leading practitioners have stated that the importance of ESG considerations implies that the corporate objective of maximizing shareholder value, which lies at the core of much of finance theory, is outdated and needs to be replaced by a more comprehensive stakeholder model. The conclusion is that both the benefits of the traditional model and the dangers of a broader stakeholder model have not be adequately appreciated. TOPICS:ESG investing, portfolio construction, wealth management Key Findings • Investors need to recognize that a lower cost of capital for firms with high ESG scores means lower expected returns for investors. • Returns realized as the market comes to recognize highly rated ESG companies are not indicative of long-run expected returns. • ESG policies, such as a carbon tax, need to be set by elected officials, not corporate executives who have neither the proper training nor the proper incentives.
Data Privacy Considerations for Telehealth Consumers amid COVID-19

The COVID-19 emergency poses particularly high infection risks in a clinical setting, where patients and health care providers are placed in the same room. Due to these risks, patients are encouraged to avoid clinics and instead use Telemedicine for safer consultations and diagnoses. In March, the Office for Civil Rights (OCR) at the U.S. Department for Health and Human Services (HHS) issued a notice titled Notification of Enforcement Discretion for Telehealth Remote Communications During the COVID-19 Nationwide Public Health Emergency (the 'Notification'). The Notification relaxes the enforcement of privacy and security safeguards established by the Health Insurance Portability and Accountability Act (HIPAA) until further notice, in order to facilitate the transition to telehealth services for the broader purpose of promoting public health during the pandemic. Specifically, covered healthcare providers can use telehealth to provide all services that, in their professional judgment, they believe can be provided through telehealth. If providers make good faith efforts to provide the most timely and accessible care possible, they will not be subject to penalties for breaching the HIPAA Privacy, Security, and Breach Notification Rules. This paper examines the implications of the Notification on patients' health information privacy. It recommends that patients should undertake a careful reading of provider privacy policies to make sure their protected health information (PHI) is not at risk before switching to telehealth consultation. Acknowledging the limitations of patient self-protection from bad privacy practices when in need for medical treatment during pandemic, the paper proposes that consumers' data privacy should be protected through one of two alternative regulatory interventions: the FTC's authority under §5, or HIPAA's business associates agreements.
Robust Stability of Multicomponent Membranes: The Role of Glycolipids

We present the multicomponent functionalized free energies that characterize the low-energy packings of amphiphilic molecules within a membrane through a correspondence to connecting orbits within a reduced dynamical system. To each connecting orbit we associate a manifold of low energy membrane-type configurations parameterized by a large class of admissible interfaces. The normal coercivity of the manifolds is established through criteria depending solely on the structure of the associated connecting orbit. We present a class of examples that arise naturally from geometric singular perturbation techniques, focusing on a model that characterizes the stabilizing role of cholesterol-like glycolipids within phospholipid membranes.
Towards Understanding End-of-trip Instructions in a Taxi Ride Scenario

We introduce a dataset containing human-authored descriptions of target locations in an "end-of-trip in a taxi ride" scenario. We describe our data collection method and a novel annotation scheme that supports understanding of such descriptions of target locations. Our dataset contains target location descriptions for both synthetic and real-world images as well as visual annotations (ground truth labels, dimensions of vehicles and objects, coordinates of the target location,distance and direction of the target location from vehicles and objects) that can be used in various visual and language tasks. We also perform a pilot experiment on how the corpus could be applied to visual reference resolution in this domain.
Water on Mars: Insights from apatite in regolith breccia Northwest Africa 7034.

Abstract Determining the source of planetary water from the hydrogen isotope compositions of crustal samples is complicated by the overprinting of isotopically diverse source material by geologic and atmospheric processes. As Mars has no plate tectonics, crustal material, which may have isotopically exchanged with the martian atmosphere, is not recycled into the mantle keeping the water reservoirs in the mantle and atmosphere mostly isolated, buffered by the crust. As the only known martian samples that are regolith breccias with a composition representative of the average crust of Mars, Northwest Africa (NWA) 7034 and its paired stones provide an important opportunity to investigate the water content and hydrogen isotope composition of the martian crust. In particular, apatites in distinct clasts as well as the brecciated matrix of NWA 7034 record a complex history including magmatic and impact processes, and exchange with crustal fluids.
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics

We propose a framework for safe reinforcement learning that can handle stochastic nonlinear dynamical systems. We focus on the setting where the nominal dynamics are known, and are subject to additive stochastic disturbances with known distribution. Our goal is to ensure the safety of a control policy trained using reinforcement learning, e.g., in a simulated environment. We build on the idea of model predictive shielding (MPS), where a backup controller is used to override the learned policy as needed to ensure safety. The key challenge is how to compute a backup policy in the context of stochastic dynamics. We propose to use a tube-based robust nonlinear model predictive controller (NMPC) as the backup controller. We estimate the tubes using sampled trajectories, leveraging ideas from statistical learning theory to obtain high-probability guarantees. We empirically demonstrate that our approach can ensure safety in stochastic systems, including cart-pole and a non-holonomic particle with random obstacles.
Modeling strength and failure variability due to porosity in additively manufactured metals

Abstract To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quantification methodology based on pseudo marginal likelihood and embedded variability techniques. We account for both the porosity resolvable in computed tomography scans of the initial material and the sub-threshold distribution of voids through a physically motivated model. Calibration of the model indicates that the sub-threshold population of defects dominates the yield and failure response. The technique also allows us to quantify the distribution of material parameters connected to microstructural variability created by the manufacturing process, and, thereby, make assessments of material quality and process control.
Newton Polygons of Hecke Operators

In this computational paper we verify a truncated version of the Buzzard–Calegari conjecture on the Newton polygon of the Hecke operator $$T_2$$ for all large enough weights. We first develop a formula for computing p-adic valuations of exponential sums, which we then implement to compute 2-adic valuations of traces of Hecke operators acting on spaces of cusp forms. Finally, we verify that if Newton polygon of the Buzzard–Calegari polynomial has a vertex at $$n\le 15$$ , then it agrees with the Newton polygon of $$T_2$$ up to n.
Connected Vehicle-Based Traffic Signal Coordination

Abstract This study presents a connected vehicles (CVs)-based traffic signal optimization framework for a coordinated arterial corridor. The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program (MINLP). The optimal phase durations and offsets are solved together by minimizing fuel consumption and travel time considering an individual vehicle’s trajectories. Due to the complexity of the model, we decompose the problem into two levels: an intersection level to optimize phase durations using dynamic programming (DP), and a corridor level to optimize the offsets of all intersections. In order to solve the two-level model, a prediction-based solution technique is developed. The proposed models are tested using traffic simulation under various scenarios. Compared with the traditional actuated signal timing and coordination plan, the signal timing plans generated by solving the MINLP and the two-level model can reasonably improve the signal control performance. When considering varies vehicle types under high demand levels, the proposed two-level model reduced the total system cost by 3.8% comparing to baseline actuated plan. MINLP reduced the system cost by 5.9%. It also suggested that coordination scheme was beneficial to corridors with relatively high demand levels. For intersections with major and minor street, coordination conducted for major street had little impacts on the vehicles at the minor street.
HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks.

Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to transform recorded speech to sound as though it had been recorded in a studio. We use an end-to-end feed-forward WaveNet architecture, trained with multi-scale adversarial discriminators in both the time domain and the time-frequency domain. It relies on the deep feature matching losses of the discriminators to improve the perceptual quality of enhanced speech. The proposed model generalizes well to new speakers, new speech content, and new environments. It significantly outperforms state-of-the-art baseline methods in both objective and subjective experiments.
Gauntlet: Finding bugs in compilers for programmable packet processing

Programmable packet-processing devices such as programmable switches and network interface cards are becoming mainstream. These devices are configured in a domain-specific language such as P4, using a compiler to translate packet-processing programs into instructions for different targets. As networks with programmable devices become widespread, it is critical that these compilers be dependable. This paper considers the problem of finding bugs in compilers for packet processing in the context of P4-16. We introduce domain-specific techniques to induce both abnormal termination of the compiler (crash bugs) and miscompilation (semantic bugs). We apply these techniques to (1) the open-source P4 compiler (P4C) infrastructure, which serves as a common base for different P4 back ends; (2) the P4 back end for the P4 reference software switch; and (3) the P4 back end for the Barefoot Tofino switch. Across the 3 platforms, over 8 months of bug finding, our tool Gauntlet detected 96 new and distinct bugs (62 crash and 34 semantic), which we confirmed with the respective compiler developers. 54 have been fixed (31 crash and 23 semantic); the remaining have been assigned to a developer. Our bug-finding efforts also led to 6 P4 specification changes. We have open sourced Gauntlet at this http URL and it now runs within P4C's continuous integration pipeline.
External Validity: From Do-Calculus to Transportability Across Populations

The generalizability of empirical findings to new environments, settings or populations, often called �external validity,� is essential in most scientific explorations. This paper treats a particular problem of generalizability, called �transportability,� defined as a license to transfer causal effects learned in experimental studies to a new population, in which only observational studies can be conducted. We introduce a formal representation called �selection diagrams� for expressing knowledge about differences and commonalities between populations of interest and, using this representation, we reduce questions of transportability to symbolic derivations in the docalculus. This reduction yields graph-based procedures for deciding, prior to observing any data, whether causal effects in the target population can be inferred from experimental findings in the study population. When the answer is affirmative, the procedures identify what experimental and observational findings need be obtained from the two populations, and how they can be combined to ensure bias-free transport.
Topological cathodes: Controlling the space charge limit of electron emission using metamaterials

Tailoring the optical properties of a photocathode surface through nanostructure engineering can nearly eliminate the space-charge limit due to image charges.
TSP With Locational Uncertainty: The Adversarial Model.

In this paper we study a natural special case of the Traveling Salesman Problem (TSP) with point-locational-uncertainty which we will call the adversarial TSP problem (ATSP). Given a metric space (X, d) and a set of subsets R = {R_1, R_2, ... , R_n} : R_i subseteq X, the goal is to devise an ordering of the regions, sigma_R, that the tour will visit such that when a single point is chosen from each region, the induced tour over those points in the ordering prescribed by sigma_R is as short as possible. Unlike the classical locational-uncertainty-TSP problem, which focuses on minimizing the expected length of such a tour when the point within each region is chosen according to some probability distribution, here, we focus on the adversarial model in which once the choice of sigma_R is announced, an adversary selects a point from each region in order to make the resulting tour as long as possible. In other words, we consider an offline problem in which the goal is to determine an ordering of the regions R that is optimal with respect to the ``worst'' point possible within each region being chosen by an adversary, who knows the chosen ordering. We give a 3-approximation when R is a set of arbitrary regions/sets of points in a metric space. We show how geometry leads to improved constant factor approximations when regions are parallel line segments of the same lengths, and a polynomial-time approximation scheme (PTAS) for the important special case in which R is a set of disjoint unit disks in the plane.
A family of transversely nonsimple knots

Transverse knots play an important role in contact topology, but surprisingly little is known about their classification even in the simplest setting, R with the standard contact structure. Any transverse knot has an underlying topological knot type, and it also carries at least one other piece of data, the self-linking number. A topological knot type in R is transversely simple if transverse knots of that underlying type are completely classified by their self-linking number; otherwise, it is transversely nonsimple. Various knots are known to be transversely simple, including the unknot by Eliashberg [6], torus knots by Etnyre [8], and the figure eight knot by Etnyre and Honda [10].
Software for multiplier ideals

We describe a new software package for computing multiplier ideals in certain cases, including monomial ideals, monomial curves, generic de- terminantal ideals, and hyperplane arrangements. In these cases we take advan- tage of combinatorial formulas for multiplier ideals given by results of Howald, Thompson, and Johnson. The package uses the package Normaliz. It is available as a library for Macaulay2.
Spiral Structure in the Gas Disk of TW Hya

We report the detection of spiral substructure in both the gas velocity and temperature structure of the disk around TW~Hya, suggestive of planet-disk interactions with an unseen planet. Perturbations from Keplerian rotation tracing out a spiral pattern are observed in the SE of the disk, while significant azimuthal perturbations in the gas temperature are seen in the outer disk, outside 90~au, extending the full azimuth of the disk. The deviation in velocity is either $\Delta v_{\phi} \, / \, v_{\rm kep} \sim 0.1$ or $\Delta v_{z} \, / \, v_{\rm kep} \sim 0.01$ depending on whether the perturbation is in the rotational or vertical direction, while radial perturbations can be ruled out. Deviations in the gas temperature are $\pm 4$ K about the azimuthally averaged profile, equivalent to deviations of $\Delta T_{\rm gas} \, / \, T_{\rm gas} \sim 0.05$. Assuming all three structures can be described by an Archimedean spiral, measurements of the pitch angles of both velocity and temperature spirals show a radially decreasing trend for all three, ranging from 9$^{\circ}$ at 70 au, dropping to 3$^{\circ}$ at 200 au. Such low pitch-angled spirals are not readily explained through the wake of an embedded planet in the location of previously reported at 94 au, but rather require a launching mechanism which results in much more tightly wound spirals. Molecular emission tracing distinct heights in the disk is required to accurately distinguish between spiral launching mechanisms.
Ground-based spectroscopy of the exoplanet XO-2B using a systematic wavelength calibration

Exoplanets orbiting close to their host star are expected to support a large ionosphere, which extends to larger pressures than witnessed in our Solar System. These ionospheres can be investigated with ground-based transit observations of the optical signatures of alkali metals, which are the source of the ions. Here, we present new observations and analyses of optical transit spectra that cover the Na doublet in the atmosphere of the exoplanet XO-2 b. To assess the consistency of our results, observations were obtained from two separate platforms: Gemini/GMOS and Mayall/KOSMOS. To mitigate the systematic errors, we chose XO-2, because it has a binary companion of the same brightness and stellar type, which provides an ideal reference star to model Earth's atmospheric effects. We find that interpretation of the data is highly sensitive to time-varying translations along the detector, which change according to wavelength and differ between the target and reference star. It was necessary to employ a time-dependent cross-correlation to align our wavelength bins and correct for atmospheric differential refraction. This approach allows us to resolve the wings of the Na line across 5 wavelength bins at a resolution of $\sim$1.6nm and limit the abundance of Na. We obtain consistent results from each telescope with a Na amplitude of 521$\pm$161 ppm and 403$\pm$186 ppm for GMOS and KOSMOS respectively. The results are analyzed with a radiative transfer model that includes the effects of ionization. The data are consistent with a clear atmosphere between $\sim$1--100 mbar which establish a lower limit on Na at 0.4$^{+2}_{-0.3}$ ppm ([Na/H]=-0.64$^{+0.78}_{-0.6}$), consistent with solar. However, we can not rule out the presence of clouds at $\sim$10 mbar which allow for higher Na abundances which would be consistent with stellar metallicity measured for the host star ([Na/H]=0.485$\pm$0.043).
Automated fixing of access policy implementation in Industrial Networked Systems

Access control (AC) is the core of every architectural solution for information security. Indeed, no effective protection scheme can abstract from the careful design of access control policies, and infrastructures underlying modern Industrial Networked Systems (INSs) are not exceptions from this point of view. This paper presents a comprehensive framework for INS access control. The proposed approach enables the description of both positive and negative AC policies, by applying the Role Based Access Control (RBAC) paradigm to typical INS implementations, while taking into account different levels of abstraction. Suitable techniques are adopted to check whether or not policies are correctly implemented in the system (verification). When conflicts are detected, possible (re)assignments of credentials to the system users are automatically computed, that can be adopted to correct anomalies (conflict resolution).
A Generalized Voter Model on Complex Networks

We study a generalization of the voter model on complex networks, focusing on the scaling of mean exit time. Previous work has defined the voter model in terms of an initially chosen node and a randomly chosen neighbor, which makes it difficult to disentangle the effects of the stochastic process itself relative to the network structure. We introduce a process with two steps, one that selects a pair of interacting nodes and one that determines the direction of interaction as a function of the degrees of the two nodes and a parameter α which sets the likelihood of the higher degree node giving its state to the other node. Traditional voter model behaviors can be recovered within the model, as well as the invasion process. We find that on a complete bipartite network, the voter model is the fastest process. On a random network with power law degree distribution, we observe two regimes. For modest values of α, exit time is dominated by diffusive drift of the system state, but as the high-degree nodes become more influential, the exit time becomes dominated by frustration effects dependent on the exact topology of the network.
The hemolymph of Biomphalaria snail vectors of schistosomiasis supports a diverse microbiome

The microbiome - the microorganism community that is found on or within an organism's body - is increasingly recognized to shape many aspects of its host biology and is a key determinant of health and disease. Microbiomes modulate the capacity of insect disease vectors (mosquitoes, tsetse flies, sandflies) to transmit parasites and disease. We investigate the diversity and abundance of microorganisms within the hemolymph (i.e. blood) of Biomphalaria snails, the intermediate host for Schistosoma mansoni, using Illumina MiSeq sequencing of the bacterial 16S V4 rDNA. We sampled hemolymph from five snails from six different laboratory populations of B. glabrata and one population of B. alexandrina. We observed 279.84 ± 0.79 amplicon sequence variants per snail. There were significant differences in microbiome composition at the level of individual snails, snail populations and species. Snail microbiomes were dominated by Proteobacteria and Bacteroidetes while water microbiomes from snail tank were dominated by Actinobacteria. We investigated the absolute bacterial load using qPCR: hemolymph samples contained 2784 ± 339 bacteria/μl. We speculate that the microbiome may represent a critical, but unexplored intermediary in the snail-schistosome interaction as hemolymph is in very close contact with the parasite at each step of its development.
Khovanov homology and causality in spacetimes

We observe that Khovanov homology detects causality in (2 + 1)-dimensional globally hyperbolic spacetimes whose Cauchy surface is homeomorphic to R2.
Characterization of photomultiplier tubes in a novel operation mode for Secondary Emission Ionization Calorimetry

Hamamatsu single anode R7761 and multi-anode R5900-00-M16 Photomultiplier Tubes have been characterized for use in a Secondary Emission (SE) Ionization Calorimetry study. SE Ionization Calorimetry is a novel technique to measure electromagnetic shower particles in extreme radiation environments. The different operation modes used in these tests were developed by modifying the conventional PMT bias circuit. These modifications were simple changes to the arrangement of the voltage dividers of the baseboard circuits. The PMTs with modified bases, referred to as operating in SE mode, are used as an SE detector module in an SE calorimeter prototype, and placed between absorber materials (Fe, Cu, Pb, W, etc.). Here, the technical design of different operation modes, as well as the characterization measurements of both SE modes and the conventional PMT mode are reported.
DeepWalking: Enabling Smartphone-Based Walking Speed Estimation Using Deep Learning

Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions? We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learning- based walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root- mean-squared-error (RMSE) of estimated walking speed is 0.16m&#x002F;s which is comparable to the results obtained by state-of- the-art approaches based on a number of body- worn sensors (i.e., RMSE of 0.11m&#x002F;s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.
High-Confidence Attack Detection via Wasserstein-Metric Computations

This letter considers a sensor attack and fault detection problem for linear cyber-physical systems, which are subject to system noise that can obey an unknown light-tailed distribution. We propose a new threshold-based detection mechanism that employs the Wasserstein metric, and which guarantees system performance with high confidence with a finite number of measurements. The proposed detector may generate false alarms with a rate $\Delta $ in normal operation, where $\Delta $ can be tuned to be arbitrarily small by means of a benchmark distribution . Thus, the proposed detector is sensitive to sensor attacks and faults which have a statistical behavior that is different from that of the system noise. We quantify the impact of stealthy attacks on open-loop stable systems—which perturb the system operation while producing false alarms consistent with the natural system noise—via a probabilistic reachable set. Tractable implementation is enabled via a linear optimization to compute the detection measure and a semidefinite program to bound the reachable set.
QCD Reggeon field theory for every day: Pomeron loops included

We derive the evolution equation for hadronic scattering amplitude at high energy. Our derivation includes the nonlinear effects of finite partonic density in the hadronic wave function as well as the effect of multiple scatterings for scattering on dense hadronic target. It thus includes Pomeron loops. It is based on the evolution of the hadronic wave function derived in \cite{foam}. The kernel of the evolution equation defines the second quantized Hamiltonian of the QCD Reggeon Field Theory, HRFT beyond the limits considered so far. The two previously known limits of the evolution: dilute target (JIMWLK limit) and dilute projectile (KLWMIJ limit) are recovered directly from our final result. The Hamiltonian HRFT is applicable for the evolution of scattering amplitude for arbitrarily dense hadronic projectiles/targets - from "dipole-dipole" to "nucleus-nucleus" scattering processes.
Subfactors of Index less than 5, Part 1: The Principal Graph Odometer

In this series of papers we show that there are exactly ten subfactors, other than A ∞ subfactors, of index between 4 and 5. Previously this classification was known up to index $${3+\sqrt{3}}$$ . In the first paper we give an analogue of Haagerup’s initial classification of subfactors of index less than $${3+\sqrt{3}}$$ , showing that any subfactor of index less than 5 must appear in one of a large list of families. These families will be considered separately in the three subsequent papers in this series.
Probing Maxwell’s Demon with a Nanoscale Thermometer

A precise definition for a quantum electron thermometer is given, as an electron reservoir coupled locally (e.g., by tunneling) to a sample, and brought into electrical and thermal equilibrium with it. A realistic model of a scanning thermal microscope with atomic resolution is then developed, including the effect of thermal coupling of the probe to the ambient environment. We show that the temperatures of individual atomic orbitals or bonds in a conjugated molecule with a temperature gradient across it exhibit quantum oscillations, whose origin can be traced to a realization of Maxwell’s demon at the single-molecule level. These oscillations may be understood in terms of the rules of covalence describing bonding in π-electron systems. Fourier’s law of heat conduction is recovered as the resolution of the temperature probe is reduced, indicating that the macroscopic law emerges as a consequence of coarse graining.