cssi/SciSciGPT-SciSciNet huggingface dataset:
Stratigraphic aspects of the Upper Jurassic to Lower Cretaceous of Saudi Arabia

The Upper Jurassic to Lower Cretaceous, Oxfordian to Valanginian, lithostratigraphic succession of Saudi Arabia is divided into the Jurassic Shaqra Group and the Cretaceous Thamama Group, the boundary of which lies within the uppermost Tithonian. This interval includes at least five third-order sequence boundaries and maximum flooding zones. J60 and K40 are two regionally recognized sequence boundaries, and J50 and J100 represent two regionally established Jurassic maximum flooding surfaces. The Upper Jurassic succession includes shallow- to deep-marine carbonates of the Hanifa (Oxfordian) and Jubaila (Kimmeridgian) formations, and shallow-marine carbonates and evaporites of the Arab (Kimmeridgian) and Hith (Kimmeridgian to Tithonian) formations. The Upper Jurassic to Lower Cretaceous succession includes the shallow to moderately deep carbonates of the Sulaiy Formation (Tithonian to Berriasian) with the overlying Yamama Formation being of Cretaceous age (Valanginian). These formations were deposited in a period of increasing global temperature, during a transitional phase leading to greenhouse.
Adaptive Web Site Agents

We discuss the design and evaluation of a class of agents that we call adaptive web site agents. The goal of such an agent is to help a user find additional information at a particular web site, adapting its behavior in response to the actions of the individual user and the actions of other visitors to the web site. The agent recommends related documents to visitors and we show that these recommendations result in increased information read at the site. It integrates and coordinates among different reasons for making recommendations including user preference for subject area, similarity between documents, frequency of citation, frequency of access, and patterns of access by visitors to the web site. We argue that this information is best used not to change the structure or content of the web site but rather to change the behavior of an animated agent that assists the user.
Beaver dam influences on streamflow hydraulic properties and thermal regimes

Abstract Beaver dams alter channel hydraulics which in turn change the geomorphic templates of streams. Variability in geomorphic units, the building blocks of stream systems, and water temperature, critical to stream ecological function, define habitat heterogeneity and availability. While prior research has shown the impact of beaver dams on stream hydraulics, geomorphic template, or temperature, the connections or feedbacks between these habitat measures are not well understood. This has left questions regarding relationships between temperature variability at different spatial scales to hydraulic properties such as flow depth and velocity that are dependent on the geomorphology. We combine detailed predicted hydraulic properties, field-based maps with an additional classification scheme of geomorphic units, and detailed water temperature observations throughout a study reach to demonstrate the relationship between these factors at different spatial scales (reach, beaver dam complexes, and geomorphic units). Over a three-week, low flow period we found temperature to vary 2 °C between the upstream and downstream extents of the reach with a net warming of 1 °C during the day and a net cooling of 0.5 °C at night. At the beaver dam complex scale, net warming of 1.15 °C occurred during the day with variable cooling at night. Regardless of limited temperature changes at these larger scales, the temperature variability within a beaver dam complex reached up to 10.5 °C due to the diversity of geomorphic units. At the geomorphic unit scale, the highly altered flow velocity and depth distributions within primary geomorphic units provide an explanation of the temperature variability within the dam complex and insight regarding increases in habitat heterogeneity.
Physics of Superconducting Transition Temperatures

While superconductivity is a fascinating phenomenon with many remarkable qualitative features that are widely discussed, the practical issue of what determines the superconducting transition temperature, Tc, is often downplayed as being a “microscopic detail.” However, my friend, Ted Geballe, has relentlessly focussed on the physics of Tc, so in honor of his first century, I discuss some of the general physical considerations that govern Tc, both in “conventional” and “unconventional” superconductors.
The Hubble Deep Field

Great progress has been made in recent years in understanding the large-scale structure of the universe. Recall that it is only within this century that we have even come to realize the existence of other galaxies as separate entities. Until the 1920’s the universe did not extend beyond the Milky Way. Novae and Cepheid variables changed that picture, and for the past 75 years extragalactic astronomy has been one of the most active and fertile areas of science. The concept of an expanding universe and its beginning in a Big Bang all derive from the discovery of external galaxies. Distant galaxies are faint and have small angular sizes, therefore their study has remained the province of the largest telescopes. Before the launch of Hubble Space Telescope, ground-based telescopes had succeeded in detecting distant galaxies out to redshifts of z ~ 1, and in establishing certain of their characteristics. But, there was uncertainty as to how much further HST could push the study of distant galaxies given its modest 2.4m diameter mirror and the fact that the surface brightnesses of cosmologically distant objects decrease as (1 + z) 4 . For this reason, one of the early observations that was scheduled immediately after the first servicing mission of HST in December 1993 to repair spherical aberration was the re-imaging of the cluster of galaxies 0939+4713 at z = 0.4 that had been observed previously by Dressier et al. (1994) from both the ground and with the aberrated HST. The ten-orbit WFPC2 image demonstrated HST’s ability to resolve structure in distant galaxies, showing spiral and elliptical galaxies with a clarity approaching that achieved for the Coma cluster from the ground. Spirals are seen to be relatively abundant in 0939+4713, although they generally show an anomalous morphology.
The Proton Pump Inhibitor Omeprazole Does Not Promote Clostridioides difficile Colonization in a Murine Model.

ABSTRACT Proton pump inhibitor (PPI) use has been associated with microbiota alterations and susceptibility to Clostridioides difficile infections (CDIs) in humans. We assessed how PPI treatment alters the fecal microbiota and whether treatment promotes CDIs in a mouse model. Mice receiving a PPI treatment were gavaged with 40 mg of omeprazole per kg of body weight during a 7-day pretreatment phase, the day of C. difficile challenge, and the following 9 days. We found that mice treated with omeprazole were not colonized by C. difficile. When omeprazole treatment was combined with a single clindamycin treatment, one cage of mice remained resistant to C. difficile colonization, while the other cage was colonized. Treating mice with only clindamycin followed by challenge resulted in C. difficile colonization. 16S rRNA gene sequencing analysis revealed that omeprazole had minimal impact on the structure of the murine microbiota throughout the 16 days of omeprazole exposure. These results suggest that omeprazole treatment alone is not sufficient to disrupt microbiota resistance to C. difficile infection in mice that are normally resistant in the absence of antibiotic treatment. IMPORTANCE Antibiotics are the primary risk factor for Clostridioides difficile infections (CDIs), but other factors may also increase a person’s risk. In epidemiological studies, proton pump inhibitor (PPI) use has been associated with CDI incidence and recurrence. PPIs have also been associated with alterations in the human intestinal microbiota in observational and interventional studies. We evaluated the effects of the PPI omeprazole on the structure of the murine intestinal microbiota and its ability to disrupt colonization resistance to C. difficile. We found omeprazole treatment had minimal impact on the murine fecal microbiota and did not promote C. difficile colonization. Further studies are needed to determine whether other factors contribute to the association between PPIs and CDIs seen in humans or whether aspects of murine physiology may limit its utility to test these types of hypotheses.
Optical neutrality: invisibility without cloaking.

We show that it is possible to design an invisible wavelength-sized metal-dielectric metamaterial object without evoking cloaking. Our approach is an extension of the neutral inclusion concept by Zhou and Hu [Phys. Rev. E74, 026607 (2006)PLEEE81063-651X10.1103/PhysRevE.74.026607] to Mie scatterers. We demonstrate that an increase of metal fraction in the metamaterial leads to a transition from dielectric-like to metal-like scattering, which proceeds through invisibility or optical neutrality of the scatterer. Formally this is due to cancellation of multiple scattering orders, similarly to plasmonic cloaking introduced by Alu and Engheta [Phys. Rev. E72, 016623 (2005)PLEEE81063-651X10.1103/PhysRevE.72.016623], but without introduction of the separation of the scatterer into cloak and hidden regions.
Comment on “Blessings of Multiple Causes”

We are grateful to Wang and Blei (2019) (hereafter WB) for drawing attention to the important and increasingly popular project of using latent variable methods to control for unmeasured confounding...
Postoperative narcotic use in patients with ovarian cancer on an Enhanced Recovery After Surgery (ERAS) pathway.

Abstract Objectives To determine the impact of an ERAS pathway on post-discharge narcotic use for patients with ovarian cancer undergoing open surgery. Methods This was a retrospective cohort study of women who underwent open ovarian cancer surgeries in 2014 prior to ERAS (“pre-ERAS”) and in 2016/2018 after ERAS was instituted (“ERAS”). Patients taking chronic narcotics were excluded. A statewide prescription monitoring program was used to identify narcotic prescriptions filled in the three months after surgery. Quantity of narcotic medication is referenced in morphine milligram equivalents (MME). Results 42 pre-ERAS and 94 ERAS patients were included. The groups were similar in age, BMI, diabetes, tobacco use, mean number of prior abdominal/pelvic surgeries, and advanced stage disease. ERAS patients had a shorter hospital stay (6.7 days pre-ERAS vs 4.2 days ERAS, p = 0.003), used less narcotic in the 24 h prior to discharge (74.0 MME pre-ERAS vs 25.8 MME ERAS, p = 0.002), and filled prescriptions at time of discharge for less narcotic (519.9 MME pre-ERAS vs 339.7 MME ERAS, p = 0.011). After hospital discharge, ERAS patients filled fewer additional prescriptions (52.4% pre-ERAS, vs 29.4% ERAS, p = 0.012). In total, ERAS patients filled prescriptions for 55% fewer narcotics in the three months after surgery than the pre-ERAS group (1101.4 MME pre-ERAS vs 492.1 MME ERAS, p Conclusions Institution of an ERAS protocol appears to decrease the narcotic needs of patients in the three months after ovarian cancer surgery.
Eigenvalue rigidity for truncations of random unitary matrices

We consider the empirical eigenvalue distribution of an m × m principal submatrix of an n × n random unitary matrix distributed according to Haar measure. For n and m large with m n = α, the empiri...
Understanding Process Tracing

Process tracing is a fundamental tool of qualitative analysis. This method is often invoked by scholars who carry out within-case analysis based on qualitative data, yet frequently it is neither adequately understood nor rigorously applied. This deficit moti- vates this article, which offers a new framework for carrying out process tracing. The refor- mulation integrates discussions of process tracing and causal-process observations, gives greater attention to description as a key contribution, and emphasizes the causal sequence in which process-tracing observations can be situated. In the current period of major inno- vation in quantitative tools for causal inference, this reformulation is part of a wider, par- allel effort to achieve greater systematization of qualitative methods. A key point here is that these methods can add inferential leverage that is often lacking in quantitative anal- ysis. This article is accompanied by online teaching exercises, focused on four examples from American politics, two from comparative politics, three from international relations, and one from public health/epidemiology.
Noncommutative supergeometry and duality.

We introduce a notion of Q-algebra that can be considered as a generalization of the notion of Q-manifold (a supermanifold equipped with an odd vector field obeying {Q,Q} =0). We develop the theory of connections on modules over Q-algebras and prove a general duality theorem for gauge theories on such modules. This theorem contains as a simplest case SO(d,d,Z)-duality of gauge theories on noncommutative tori.
Emergence and Stability of Self-Evolved Cooperative Strategies using Stochastic Machines

To investigate the origin of cooperative behaviors, we developed an evolutionary model of sequential strategies and tested our model with computer simulations. The sequential strategies represented by stochastic machines were evaluated through games of Iterated Prisoner’s Dilemma (IPD) with other agents in the population, allowing co-evolution to occur. We expanded upon past works by proposing a novel mechanism to mutate stochastic Moore machines that enables a richer class of machines to be evolved. These machines were then subjected to various selection mechanisms and the resulting evolved strategies were analyzed. We found that cooperation can indeed emerge spontaneously in evolving populations playing iterated PD, specifically in the form of trigger strategies. In addition, we found that the resulting populations converged to evolutionarily stable states and were resilient towards mutation. In order to test the generalizability of our proposed mutation mechanism and simulation approach, we also evolved the machines to play other games such as Chicken, Stag Hunt, and Battle, and obtained strategies that perform as well as mixed strategies in Nash Equilibrium.
Bayesian Active Learning for Choice Models With Deep Gaussian Processes

This paper proposes an active learning algorithm and models which can gradually learn individual’s preference through pairwise comparisons. The active learning scheme aims at finding individual’s most preferred choice (e.g. an airline itinerary) with minimized number of pairwise comparisons. The pairwise comparisons are encoded into probabilistic models based on assumptions of choice models and deep Gaussian processes. More specifically, this paper develops two novel probabilistic models assuming correlated Gumbel noises and latent utility functions. One is based on shallow Gaussian priors and the other assumes deep Gaussian priors. In the active learning algorithm, the next-to-compare decision is determined by an original acquisition function. The proposed algorithm and models have been benchmarked using functions with multiple local optima and one public airline itinerary dataset. In both experiments, nests are designed to capture correlated Gumbel noises. The experiments indicate the effectiveness of our active learning algorithm and models. The deep Gaussian models are proven to find the best choice with a lower number of pairwise comparisons than the shallow one. In both experiments, deep Gaussian models outperform the shallow model. The shallow model is recommended when the choice set is large and less computational time is required (e.g. in one experiment, deep Gaussian models require approximately 60%-70% more time on average).
Quantum extensions of ordinary maps

We define a loop to be quantum nullhomotopic if and only if it admits a nonempty quantum set of extensions to the unit disk. We show that the canonical loop in the unit circle is not quantum nullhomotopic, but that every loop in the real projective plane is quantum nullhomotopic. Furthermore, we apply Kuiper's theorem to show that the canonical loop admits a continuous family of extensions to the unit disk that is indexed by an infinite quantum space. We obtain these results using a purely topological condition that we show to be equivalent to the existence of a quantum family of extensions of a given map.
Grade 3A follicular lymphoma can be effectively controlled with very low-dose radiation therapy

Very low-dose radiotherapy (4 Gy; VLDRT) is an effective treatment for indolent lymphomas and offers the advantage of a shorter course and improved side effect profile compared to full dose RT regi...
Model oxide-supported metal catalysts: energetics, particle thicknesses, chemisorption and catalytic properties

Many industrially important catalysts consist of late transition metal particles supported on the surfaces of oxide materials. Our studies of such systems using model catalysts consisting of metal films vapor deposited onto the surfaces of single-crystalline oxides are reviewed here. Systems studied include Cu on ZnO, Pt on ZnO, Au on TiO2 and Cu, Ag and Pb on MgO. A unique adsorption microcalorimeter was developed to measure directly the energetic stability of the metal atoms on the oxide surfaces and the adhesion energy at the metal/oxide interface, which clarify the structural and chemisorption properties of the ultrathin metal particles. The structure of the oxide surface and the metal particles was elucidated by low-energy electron diffraction (LEED), low-energy ion scattering spectroscopy (ISS), angular-resolved X-ray photoelectron spectroscopy (XPS) and X-ray photoelectron diffraction (XPD). The electronic character of the metal particles was revealed by XPS, Auger electron spectroscopy (AES), band-bending and work function measurements. Sintering rates were measured by temperature-programmed ion scattering spectroscopy (TPISS). The chemisorption properties of these particles and their catalytic reactivity were monitored by mass spectroscopy and temperature-programmed desorption (TPD).
Starshade formation flying I: optical sensing

A key challenge for starshades is formation flying. To successfully image exoplanets, the telescope boresight and starshade must be aligned to ∼1 m at separations of tens of thousands of kilometers. This challenge has two parts: first, the relative position of the starshade with respect to the telescope must be sensed; second, sensor measurements must be combined with a control law to keep the two spacecraft aligned in the presence of gravitational and other disturbances. In this work, we present an optical sensing approach using a pupil imaging camera in a 2.4-m telescope that can measure the relative spacecraft bearing to a few centimeters in 1 s, much faster than any relevant dynamical disturbances. A companion paper will describe how this sensor can be combined with a control law to keep the two spacecraft aligned with minimal interruptions to science observations.
A subtractive manufacturing constraint for level set topology optimization

We present a method for enforcing manufacturability constraints in generated parts such that they will be automatically ready for fabrication using a subtractive approach. We primarily target multi-axis CNC milling approaches but the method should generalize to other subtractive methods as well. To this end we take as user input: the radius of curvature of the tool bit, a coarse model of the tool head and optionally a set of milling directions. This allows us to enforce the following manufacturability conditions: (1) surface smoothness such that the radius of curvature of the part does not exceed the milling bit radius, (2) orientation such that every part of the surface to be milled is visible from at least one milling direction, (3) accessibility such that every surface patch can be reached by the tool bit without interference with the tool or head mount. We will show how to efficiently enforce the constraint during level set–based topology optimization modifying the advection velocity such that at each iteration the topology optimization maintains a descent optimization direction and does not violate any of the manufacturability conditions. This approach models the actual subtractive process by carving away material accessible to the machine at each iteration until a local optimum is achieved.
Data-driven Analysis of Regional Capacity Factors in a Large-Scale Power Market: A Perspective from Market Participants

A competitive wholesale electricity market consists of thousands of interacting market participants. Driven by the variations of fuels costs, system loads and weathers, these market participants compete actively and behave variously in the power market. Although electricity markets tend to become more transparent, a large amount of market information is still not publicly available to market participants. Hence, data-driven analysis based on public data is crucial for market participants to better understand and model large-scale power markets, and ultimately to perform better in power trading. While most of the previous researches related to the large-scale power markets are based on the synthetic networks, a data-driven approach utilizing the real power market data is proposed in this paper. First, the power plants' monthly net generation and capacity data are obtained from U.S. Energy Information Administration (EIA) and aggregated to figure out the monthly regional capacity factors which are used to characterize the market's regional behaviors for market participants. Then, the regional capacity factors are analyzed against the metered system loads and natural gas prices to study the generation behaviors in the power market. The analysis reveals the impacts of regional natural gas prices on capacity factors and the responses of generating behaviors to the system loads. The analysis results present the solid evidence and rational references for market participants to model and validate the large-scale power market in the future.
Network Design for Controllability Metrics

In this article, we consider the problem of tuning the edge weights of a networked system described by linear time-invariant dynamics. We assume that the topology of the underlying network is fixed, and that the set of feasible edge weights is a given polytope. In this setting, we first consider a feasibility problem consisting of tuning the edge weights such that certain controllability properties are satisfied. The particular controllability properties under consideration are 1) a lower bound on the smallest eigenvalue of the controllability Gramian and 2) an upper bound on the trace of the Gramian inverse. In both cases, the edge-tuning problem can be stated as a feasibility problem involving bilinear matrix equalities, which we approach using a sequence of convex relaxations. Furthermore, we also address a design problem consisting of finding edge weights able to satisfy the aforementioned controllability constraints while seeking to minimize a cost function of the edge weights, which we assume to be convex. Finally, we verify our results with numerical simulations over a number of random network realizations, as well as with an IEEE 14-bus power system topology.
A Machine Learning Solution for Beam Tracking in mmWave Systems

Utilizing millimeter-wave (mmWave) frequencies for wireless communication in mobile systems is challenging since it requires continuous tracking of the beam direction. Recently, beam tracking techniques based on channel sparsity and/or Kalman filter-based techniques were proposed where the solutions use assumptions regarding the environment and device mobility that may not hold in practical scenarios. In this paper, we explore a machine learning-based approach to track the angle of arrival (AoA) for specific paths in realistic scenarios. In particular, we use a recurrent neural network (R-NN) structure with a modified cost function to track the AoA. We propose methods to train the network in sequential data, and study the performance of our proposed solution in comparison to an extended Kalman filter based solution in a realistic mmWave scenario based on stochastic channel model from the QuaDRiGa framework. Results show that our proposed solution outperforms an extended Kalman filter-based method by reducing the AoA outage probability, and thus reducing the need for frequent beam search.
Digital Trade in MENA : Regulatory Readiness Assessment

A strong regulatory framework can provide essential tools for remote transactions and improve trust in digital trade. Yet, regulations can also introduce restrictions that hamper the conditions for digital markets. Based on a database of 20 Middle East and North Africa countries and 20 comparator countries around the world, this paper shows that the Middle East and North Africa region is falling behind in establishing a modern governance framework for the digital economy. The analysis focuses on a set of regulatory areas, including electronic documentation and signature, online consumer protection, data governance, cybersecurity, and intermediary liability regulations. It assesses each country's domestic regulatory framework in light of recent international trends and regulatory models. The study shows that regulation of digital markets in countries in the region is still in its infancy, being mostly governed by general laws that were not originally intended for the digital era. Some countries have tried to support an export-oriented information technology sector by keeping an updated regulatory framework. However, regulation in most countries in the region, regardless of their level of development, still features some major loopholes that can limit consumer trust in digital markets or reduce certainty -- and increase costs -- for digital businesses.
A visual analytics system for multi-model comparison on clinical data predictions

Abstract There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’ future status. Although some of these methods achieve high performance, challenges still exist in comparing and evaluating different models through their interpretable information. Such analytics can help clinicians improve evidence-based medical decision making. In this work, we develop a visual analytics system that compares multiple models’ prediction criteria and evaluates their consistency. With our system, users can generate knowledge on different models’ inner criteria and how confidently we can rely on each model’s prediction for a certain patient. Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
Incompleteness and jump hierarchies

This paper is an investigation of the relationship between Godel's second incompleteness theorem and the well-foundedness of jump hierarchies. It follows from a classic theorem of Spector's that the relation $\{(A,B) \in \mathbb{R}^2 : \mathcal{O}^A \leq_H B\}$ is well-founded. We provide an alternative proof of this fact that uses Godel's second incompleteness theorem instead of the theory of admissible ordinals. We then derive a semantic version of the second incompleteness theorem, originally due to Mummert and Simpson, from this result. Finally, we turn to the calculation of the ranks of reals in this well-founded relation. We prove that, for any $A\in\mathbb{R}$, if the rank of $A$ is $\alpha$, then $\omega_1^A$ is the $(1 + \alpha)^{\text{th}}$ admissible ordinal. It follows, assuming suitable large cardinal hypotheses, that, on a cone, the rank of $X$ is $\omega_1^X$.
Relationships among urban freeway accidents, traffic flow, weather, and lighting conditions

Linear and nonlinear multivariate statistical analyses are applied to determine how the types of accidents that occur on heavily used freeways in Southern California are related both to the flow of traffic and to weather and ambient lighting conditions. Traffic flow is measured in terms of time series of 30-s observations from inductive loop detectors in the vicinity of the accident prior to the time of its occurrence. Results indicate that the type of collision is strongly related to median traffic speed and to temporal variations in speed in the left and interior lanes. Hit-object collisions and collisions involving multiple vehicles that are associated with lane-change maneuvers are more likely to occur on wet roads, while rear-end collisions are more likely to occur on dry roads during daylight. Controlling for weather and lighting conditions, there is evidence that accident severity is influenced more by volume than by speed.
Proper, Stable Transfer Matrix Factorizations and Internal System Descriptions

The exact relations between coprime proper, stable factorizations of P(s) and coprime polynomial matrix factorizations are derived; and they directly lead to relations with internal descriptions of the plant in differential operator or state-space form. It is shown that obtaining any right, or left proper, stable coprime factorization is equivalent to state-feedback stabilization or to designing a full-order, full-state observer, respectively. Solving the Diophantine equation is shown to be equivalent to designing a full or reduced-order observer of a linear functional of the state and to designing a stable inverse system; and this suggests new computational methods to solve the Diophantine.
Chapter 6 – Irreversible Thermodynamics

A systematic analysis is provided to set up thermodynamic relations for irreversible processes. The study includes shock effects (not normally treated), the basic phenomenological equations relating fluxes and forces, steady-state conditions, and a large variety of different analyses for specific irreversible processes such as thermomolecular mechanical and electrokinetic processes, and detailed specifications of thermoelectric and thermomagnetic phenomena not normally treated in standard texts. All these processes involve departures from thermodynamic equilibrium.
Diophantine problems and p-adic period mappings

We give an alternative proof of Faltings’s theorem (Mordell’s conjecture): a curve of genus at least two over a number field has finitely many rational points. Our argument utilizes the set-up of Faltings’s original proof, but is in spirit closer to the methods of Chabauty and Kim: we replace the use of abelian varieties by a more detailed analysis of the variation of p-adic Galois representations in a family of algebraic varieties. The key inputs into this analysis are the comparison theorems of p-adic Hodge theory, and explicit topological computations of monodromy. By the same methods we show that, in sufficiently large dimension and degree, the set of hypersurfaces in projective space, with good reduction away from a fixed set of primes, is contained in a proper Zariski-closed subset of the moduli space of all hypersurfaces. This uses in an essential way the Ax–Schanuel property for period mappings, recently established by Bakker and Tsimerman.
Detecting malicious logins as graph anomalies

Abstract Authenticated lateral movement via compromised accounts is a common adversarial maneuver that is challenging to discover with signature- or rules-based intrusion detection systems. In this work a behavior-based approach to detecting malicious logins to novel systems indicative of lateral movement is presented, in which a user’s historical login activity is used to build a model of putative “normal” behavior. This historical login activity is represented as a collection of daily login graphs, which encode authentications among accessed systems with vertices representing computer systems and directed edges logins between them. We devise a method of local graph anomaly detection capable of identifying unusual vertices that indicate potentially malicious login events to the systems they represent. We test this capability on a group of highly-privileged accounts using real login data from an operational enterprise network. The method enjoys false positive rates significantly lower than those resulting from alerts based solely on login novelty, and is generally successful at detecting a wide variety of simulated adversarial login activity.
Generalized Metric Repair on Graphs

Many modern data analysis algorithms either assume or are considerably more efficient if the distances between the data points satisfy a metric. However, as real data sets are noisy, they often do not possess this fundamental property. For this reason, Gilbert and Jain [A. Gilbert and L. Jain, 2017] and Fan et al. [C. Fan et al., 2018] introduced the closely related sparse metric repair and metric violation distance problems. Given a matrix, representing all distances, the goal is to repair as few entries as possible to ensure they satisfy a metric. This problem was shown to be APX-hard, and an O(OPT^{1/3})-approximation was given, where OPT is the optimal solution size. In this paper, we generalize the problem, by describing distances by a possibly incomplete positively weighted graph, where again our goal is to find the smallest number of weight modifications so that they satisfy a metric. This natural generalization is more flexible as it takes into account different relationships among the data points. We demonstrate the inherent combinatorial structure of the problem, and give an approximation-preserving reduction from MULTICUT, which is hard to approximate within any constant factor assuming UGC. Conversely, we show that for any fixed constant ς, for the large class of ς-chordal graphs, the problem is fixed parameter tractable, answering an open question from previous work. Call a cycle broken if it contains an edge whose weight is larger than the sum of all its other edges, and call the amount of this difference its deficit. We present approximation algorithms, one depending on the maximum number of edges in a broken cycle, and one depending on the number of distinct deficit values, both quantities which may naturally be small. Finally, we give improved analysis of previous algorithms for complete graphs.
Is the Policy Gradient a Gradient

The policy gradient theorem describes the gradient of the expected discounted return with respect to an agent's policy parameters. However, most policy gradient methods drop the discount factor from the state distribution and therefore do not optimize the discounted objective. What do they optimize instead? This has been an open question for several years, and this lack of theoretical clarity has lead to an abundance of misstatements in the literature. We answer this question by proving that the update direction approximated by most methods is not the gradient of any function. Further, we argue that algorithms that follow this direction are not guaranteed to converge to a "reasonable'' fixed point by constructing a counterexample wherein the fixed point is globally pessimal with respect to both the discounted and undiscounted objectives. We motivate this work by surveying the literature and showing that there remains a widespread misunderstanding regarding discounted policy gradient methods, with errors present even in highly-cited papers published at top conferences.
Learning to Slide Unknown Objects with Differentiable Physics Simulations

We propose a new technique for pushing an unknown object from an initial configuration to a goal configuration with stability constraints. The proposed method leverages recent progress in differentiable physics models to learn unknown mechanical properties of pushed objects, such as their distributions of mass and coefficients of friction. The proposed learning technique computes the gradient of the distance between predicted poses of objects and their actual observed poses and utilizes that gradient to search for values of the mechanical properties that reduce the reality gap. The proposed approach is also utilized to optimize a policy to efficiently push an object toward the desired goal configuration. Experiments with real objects using a real robot to gather data show that the proposed approach can identify the mechanical properties of heterogeneous objects from a small number of pushing actions.
Chloroquine kills hair cells in zebrafish lateral line and murine cochlear cultures: Implications for ototoxicity.

Hearing and balance deficits have been reported during and following treatment with the antimalarial drug chloroquine. However, experimental work examining the direct actions of chloroquine on mechanoreceptive hair cells in common experimental models is lacking. This study examines the effects of chloroquine on hair cells using two common experimental models: the zebrafish lateral line and neonatal mouse cochlear cultures. Zebrafish larvae were exposed to varying concentrations of chloroquine phosphate or hydroxychloroquine for 1 h or 24 h, and hair cells assessed by antibody staining. A significant, dose-dependent reduction in the number of surviving hair cells was seen across conditions for both exposure periods. Hydroxychloroquine showed similar toxicity. In mouse cochlear cultures, chloroquine damage was specific to outer hair cells in tissue from the cochlear basal turn, consistent with susceptibility to other ototoxic agents. These findings suggest a need for future studies employing hearing and balance monitoring during exposure to chloroquine and related compounds, particularly with interest in these compounds as therapeutics against viral infections including coronavirus.
On the rim tori refinement of relative Gromov–Witten invariants

We construct Ionel–Parker’s proposed refinement of the standard relative Gromov–Witten invariants in terms of abelian covers of the symplectic divisor and discuss in what sense it gives rise to inv...
Surrogate Scoring Rules

Strictly proper scoring rules (SPSR) are incentive compatible for eliciting information about random variables from strategic agents when the principal can reward agents after the realization of the random variables. They also quantify the quality of elicited information, with more accurate predictions receiving higher scores in expectation. In this paper, we extend such scoring rules to settings where a principal elicits private probabilistic beliefs but only has access to agents' reports. We name our solution Surrogate Scoring Rules (SSR). SSR build on a bias correction step and an error rate estimation procedure for a reference answer defined using agents' reports. We show that, with a single bit of information about the prior distribution of the random variables, SSR in a multi-task setting recover SPSR in expectation, as if having access to the ground truth. Therefore, a salient feature of SSR is that they quantify the quality of information despite the lack of ground truth, just as SPSR do for the setting with ground truth. As a by-product, SSR induce dominant truthfulness in reporting. Our method is verified both theoretically and empirically using data collected from real human forecasters.
Distributed Renewable Power Generation and Implications for Capacity Investment and Electricity Prices

Renewable energy generation at the point of consumption (i.e., distributed generation) reduces consumer’s electricity expenditure, and eliminates the cost, complexity, and inefficiency associated with power transmission and distribution. In this paper, we address the problem of how a consumer should invest in distributed renewable generation to minimize the total expected cost of meeting his electricity demand. In contrast to the existing literature that focuses on grid-connected, large scale investments in renewable power generation in the wholesale electricity market, we address investment in stand-alone, distributed renewable energy by an individual consumer who participates in a regulated, retail electricity market. We formulate an infinite horizon, continuous-time model in which the utility moves first, and announces a retail electricity rate. Each consumer then acts strategically in deciding if, when, and how much distributed generation capacity to install. We find the subgame-perfect Nash equilibrium of this dynamic Stackelberg game by first deriving the consumer’s optimal investment time and the resulting optimal capacity of his installed distributed generation. Using those results, we quantify the ensuing cost savings to the consumer, which average over 22% across a range of model parameters. Next, we evaluate the impact of consumer’s investment in renewable energy on the revenue of his electric utility, and arrive at the structure of the pricing policy that maximizes that revenue. We quantify the revenue increase available to the utility from following this revenue-maximizing pricing when serving either a single consumer or multiple heterogeneous consumers, and find that it averages over 10% in our numerical studies.
On the Effects of Collision Avoidance on Emergent Swarm Behavior.

Swarms of autonomous agents, through their decentralized and robust nature, show great promise as a future solution to the myriad missions of business, military, and humanitarian relief. The diverse nature of mission sets creates the need for swarm algorithms to be deployed on a variety of hardware platforms. Certain swarm behaviors have been demonstrated on platforms where collisions between agents are harmless, but on many platforms collisions are prohibited since they would damage the agents involved. The available literature typically assumes that collisions can be avoided by adding a collision avoidance algorithm on top of an existing swarm behavior. Through an illustrative example in our experience replicating a particular behavior, we show that this can be difficult to achieve since the swarm behavior can be disrupted by the collision avoidance. We introduce metrics quantifying the level of disruption in our swarm behavior and propose a technique that is able to assist in tuning the collision avoidance algorithm such that the goal behavior is achieved as best as possible while collisions are avoided. We validate our results through simulation.
Costs of increasing oil and gas setbacks are initially modest but rise sharply

Abstract Spatial setback rules are a common form of oil and gas regulation worldwide - they require minimum distances between oil and gas operations and homes and other sensitive locations. While setbacks can reduce exposure to potential harms associated with oil and gas production, they can also cause substantial quantities of oil and gas resources to be unavailable for extraction. Using both theoretical modeling and spatial analysis with GIS tools on publicly available data, we determine oil and gas resource loss under different setback distances, focusing on Colorado counties as a case study. We show that increasing setbacks results in small resource loss for setbacks up to 1500 feet, but resource loss quickly increases with longer setbacks. Approximately $4.5 billion in annual resource revenues would be lost in Colorado under 2500-foot setbacks, a distance recently proposed in Colorado Proposition 112 and California AB 345.
BILITE: A Bayesian randomized phase II design for immunotherapy by jointly modeling the longitudinal immune response and time‐to‐event efficacy

Immunotherapy-treatments that target a patient's immune system-has attracted considerable attention in cancer research. Its recent success has led to generation of novel immunotherapeutic agents that need to be evaluated in clinical trials. Two unique features of immunotherapy are the immune response and the fact that some patients may achieve long-term durable response. In this article, we propose a two-arm Bayesian adaptive randomized phase II clinical trial design for immunotherapy that jointly models the longitudinal immune response and time-to-event efficacy (BILITE), with a fraction of patients assumed to be cured by the treatment. The longitudinal immune response is modeled using hierarchical nonlinear mixed-effects models with possibly different trajectory patterns for the cured and susceptible groups. Conditional on the immune response trajectory, the time-to-event efficacy data for patients in the susceptible group is modeled via a time-dependent Cox-type regression model. We quantify the desirability of the treatment using a utility function and propose a two-stage design to adaptively randomize patients to treatments and make treatment recommendations at the end of the trial. Simulation studies show that compared with a conventional design that ignores the immune response, BILITE yields superior operating characteristics in terms of the ability to identify promising agents and terminate the trial early for futility.
An Enzymatic TMPRSS2 Assay for Assessment of Clinical Candidates and Discovery of Inhibitors as Potential Treatment of COVID-19

SARS-CoV-2 is the viral pathogen causing the COVID19 global pandemic. Consequently, much research has gone into the development of preclinical assays for the discovery of new or repurposing of FDA-approved therapies. Preventing viral entry into a host cell would be an effective antiviral strategy. One mechanism for SARS-CoV-2 entry occurs when the spike protein on the surface of SARS-CoV-2 binds to an ACE2 receptor followed by cleavage at two cut sites ("priming") that causes a conformational change allowing for viral and host membrane fusion. TMPRSS2 has an extracellular protease domain capable of cleaving the spike protein to initiate membrane fusion. A validated inhibitor of TMPRSS2 protease activity would be a valuable tool for studying the impact TMPRSS2 has in viral entry and potentially be an effective antiviral therapeutic. To enable inhibitor discovery and profiling of FDA-approved therapeutics, we describe an assay for the biochemical screening of recombinant TMPRSS2 suitable for high throughput application. We demonstrate effectiveness to quantify inhibition down to subnanomolar concentrations by assessing the inhibition of camostat, nafamostat, and gabexate, clinically approved agents in Japan. Also, we profiled a camostat metabolite, FOY-251, and bromhexine hydrochloride, an FDA-approved mucolytic cough suppressant. The rank order potency for the compounds tested are nafamostat (IC50 = 0.27 nM), camostat (IC50 = 6.2 nM), FOY-251 (IC50 = 33.3 nM), and gabexate (IC50 = 130 nM). Bromhexine hydrochloride showed no inhibition of TMPRSS2. Further profiling of camostat, nafamostat, and gabexate against a panel of recombinant proteases provides insight into selectivity and potency.
Preference Learning and Demand Forecast

This paper develops a theory-based, cost-effective method to forecast actual product demand using inexpensive choice experiment data.
DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving Systems

The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles. We use numerous dashcam crash videos uploaded on the internet to extract valuable collision data and recreate the crash scenarios in a simulator. We tackle the problem of extracting 3D vehicle trajectories from videos recorded by an unknown and uncalibrated monocular camera source using a modular approach. A working architecture and demonstration videos along with the open-source implementation are provided with the paper.