cssi/SciSciGPT-SciSciNet huggingface dataset:
Ramp Input Response of RC Tree Networks

Closed form expressions are presented to accurately describe the delay characteristics of RC tree networks. The Penfield-Rubinstein-Horowitz approach to estimating the step function response of RC trees has been extended to consider ramp inputs. This result improves timing accuracy by considering the shape of the input waveform driving each individual interconnect tree while maintaining computational simplicity for use in the automated timing analysis of complex VLSI circuits.
On High-Order Upwind Methods for Advection

Scheme III (piecewise linear) and V (piecewise parabolic) of Van Leer are shown to yield identical solutions provided the initial conditions are chosen in an appropriate manner. This result is counter intuitive since it is generally believed that piecewise linear and piecewise parabolic methods cannot produce the same solutions due to their different degrees of approximation. The result also shows a key connection between the approaches of discontinuous and continuous representations.
Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy

Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle platooning and by creating an avenue for influencing people's choice of routes. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies. Developing an algorithm to learn the preferences of the users, we formulate a planning optimization that chooses prices to maximize a social objective. We demonstrate the benefit of the proposed scheme by comparing the results to theoretical benchmarks which we show can be efficiently calculated.
Working memory gates visual input to primate prefrontal neurons

Visually guided behavior relies on the integration of sensory input and information held in working memory (WM). Yet it remains unclear how this is accomplished at the level of neural circuits. We studied the direct visual cortical inputs to neurons within a visuomotor area of prefrontal cortex in behaving monkeys. We show that the efficacy of visual input to prefrontal cortex is gated by information held in WM. Surprisingly, visual input to prefrontal neurons was found to target those with both visual and motor properties, rather than preferentially targeting other visual neurons. Furthermore, activity evoked from visual cortex was larger in magnitude, more synchronous, and more rapid, when monkeys remembered locations that matched the location of visual input. These results indicate that WM directly influences the circuitry that transforms visual input into visually guided behavior.
Adversarial Learning for Zero-Shot Stance Detection on Social Media

Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to topics not previously considered, highlighting future directions for zero-shot transfer.
Linear embedding of nonlinear dynamical systems and prospects for efficient quantum algorithms

The simulation of large nonlinear dynamical systems, including systems generated by discretization of hyperbolic partial differential equations, can be computationally demanding. Such systems are important in both fluid and kinetic computational plasma physics. This motivates exploring whether a future error-corrected quantum computer could perform these simulations more efficiently than any classical computer. We describe a method for mapping any finite nonlinear dynamical system to an infinite linear dynamical system (embedding) and detail three specific cases of this method that correspond to previously studied mappings. Then we explore an approach for approximating the resulting infinite linear system with finite linear systems (truncation). Using a number of qubits only logarithmic in the number of variables of the nonlinear system, a quantum computer could simulate truncated systems to approximate output quantities if the nonlinearity is sufficiently weak. Other aspects of the computational efficiency of the three detailed embedding strategies are also discussed.
SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval

We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many neural ranking methods that use dense vector nearest neighbor search, SPARTA learns a sparse representation that can be efficiently implemented as an Inverted Index. The resulting representation enables scalable neural retrieval that does not require expensive approximate vector search and leads to better performance than its dense counterpart. We validated our approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks. SPARTA achieves new state-of-the-art results across a variety of open-domain question answering tasks in both English and Chinese datasets, including open SQuAD, CMRC and etc. Analysis also confirms that the proposed method creates human interpretable representation and allows flexible control over the trade-off between performance and efficiency.
Vectorized secure evaluation of decision forests

As the demand for machine learning–based inference increases in tandem with concerns about privacy, there is a growing recognition of the need for secure machine learning, in which secret models can be used to classify private data without the model or data being leaked. Fully Homomorphic Encryption (FHE) allows arbitrary computation to be done over encrypted data, providing an attractive approach to providing such secure inference. While such computation is often orders of magnitude slower than its plaintext counterpart, the ability of FHE cryptosystems to do ciphertext packing—that is, encrypting an entire vector of plaintexts such that operations are evaluated elementwise on the vector—helps ameliorate this overhead, effectively creating a SIMD architecture where computation can be vectorized for more efficient evaluation. Most recent research in this area has targeted regular, easily vectorizable neural network models. Applying similar techniques to irregular ML models such as decision forests remains unexplored, due to their complex, hard-to-vectorize structures. In this paper we present COPSE, the first system that exploits ciphertext packing to perform decision-forest inference. COPSE consists of a staging compiler that automatically restructures and compiles decision forest models down to a new set of vectorizable primitives for secure inference. We find that COPSE’s compiled models outperform the state of the art across a range of decision forest models, often by more than an order of magnitude, while still scaling well.
RaSE: A Variable Screening Framework via Random Subspace Ensembles

Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing screening methods are designed to rank the predictors accor...
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments.

In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states. However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. Leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM addresses large-scale exploration problems while providing locally near-optimal coverage plans. We validate our proposed framework with high-fidelity dynamic simulations in diverse environments and on physical robots in Martian-analog lava tubes.
Optimization models for integrated biorefinery operations

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US’s cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system’s reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The sample average approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory’s biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution, increases utilization of the reactor and reduces operational costs.
Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently accurate, even after careful parameter tuning. This motivates the use of machine learning techniques to approximate the robot dynamics over a training set of state-control trajectories. The dynamics of many robots, including ground, aerial, and underwater vehicles, are described in terms of their SE(3) pose and generalized velocity, and satisfy conservation of energy principles. This paper proposes a Hamiltonian formulation over the SE(3) manifold of the structure of a neural ordinary differential equation (ODE) network to approximate the dynamics of a rigid body. In contrast to a black-box ODE network, our formulation guarantees total energy conservation by construction. We develop energy shaping and damping injection control for the learned, potentially under-actuated SE(3) Hamiltonian dynamics to enable a unified approach for stabilization and trajectory tracking with various platforms, including pendulum, rigid-body, and quadrotor systems.
Thermal Assessment of Paraffin Phase Change Material Mini-Packs on IceCube 3U CubeSat in Flight

Three paraffin phase change material (PCM) mini-packs were flown on the IceCube instrument in the International Space Station (ISS) orbit. They contained a total of 40.69 g of n-Hexadecane. In flight, from Day of Year (DOY) 250-255 in 2017, the IceCube instrument operation scheme was "Day-On Every Other Orbit." The instrument power-on time was approximately 45.6 minutes longer than the design power-on time. Its power-off time was 47 minutes longer than the design power-off time. Flight temperature telemetry data revealed that latent heat change of the paraffin PCM maintained the instrument temperatures at about 18 degrees Centigrade most of the time. It validated the functionality of the paraffin PCM mini-packs.
Connected sums of graded Artinian Gorenstein algebras and Lefschetz properties

Abstract In their paper [1] , H. Ananthnarayan, L. Avramov, and W.F. Moore introduced a connected sum construction for local Gorenstein rings A , B over a local Gorenstein ring T , which, in the graded Artinian case, can be viewed as an algebraic analogue of the topological construction of the same name. We give two alternative descriptions of this algebraic connected sum: the first uses algebraic analogues of Thom classes of vector bundles and Gysin homomorphisms , the second is in terms of Macaulay dual generators. We also investigate the extent to which the connected sum of A , B over an Artinian Gorenstein algebra T preserves the weak or strong Lefschetz property , thus providing new classes of rings which satisfy these properties.
Molecules and fossils tell distinct yet complementary stories of mammal diversification

Summary Reconstructing the tempo at which biodiversity arose is a fundamental goal of evolutionary biologists, yet the relative merits of evolutionary-rate estimates are debated based on whether they are derived from the fossil record or time-calibrated phylogenies (timetrees) of living species. Extinct lineages unsampled in timetrees are known to "pull" speciation rates downward, but the temporal scale at which this bias matters is unclear. To investigate this problem, we compare mammalian diversification-rate signatures in a credible set of molecular timetrees (n = 5,911 species, ∼70% from DNA) to those in fossil genus durations (n = 5,320). We use fossil extinction rates to correct or "push" the timetree-based (pulled) speciation-rate estimates, finding a surge of speciation during the Paleocene (∼66–56 million years ago, Ma) between the Cretaceous-Paleogene (K-Pg) boundary and the Paleocene-Eocene Thermal Maximum (PETM). However, about two-thirds of the K-Pg-to-PETM originating taxa did not leave modern descendants, indicating that this rate signature is likely undetectable from extant lineages alone. For groups without substantial fossil records, thankfully all is not lost. Pushed and pulled speciation rates converge starting ∼10 Ma and are equal at the present day when recent evolutionary processes can be estimated without bias using species-specific "tip" rates of speciation. Clade-wide moments of tip rates also enable enriched inference, as the skewness of tip rates is shown to approximate a clade's extent of past diversification-rate shifts. Molecular timetrees need fossil-correction to address deep-time questions, but they are sufficient for shallower time questions where extinctions are fewer.
Facilitating Meta-Theory Reasoning (Invited Paper)

Structural proof theory is praised for being a symbolic approach to reasoning and proofs, in which one can define schemas for reasoning steps and manipulate proofs as a mathematical structure. For this to be possible, proof systems must be designed as a set of rules such that proofs using those rules are correct by construction. Therefore, one must consider all ways these rules can interact and prove that they satisfy certain properties which makes them "well-behaved". This is called the meta-theory of a proof system. Meta-theory proofs typically involve many cases on structures with lots of symbols. The majority of cases are usually quite similar, and when a proof fails, it might be because of a sub-case on a very specific configuration of rules. Developing these proofs by hand is tedious and error-prone, and their combinatorial nature suggests they could be automated. There are various approaches on how to automate, either partially or completely, meta-theory proofs. In this paper, I will present some techniques that I have been involved in for facilitating meta-theory reasoning.
The weak converse of Zeckendorf’s theorem

By Zeckendorf’s Theorem, every positive integer is uniquely written as a sum of non-adjacent terms of the Fibonacci sequence, and its converse states that if a sequence in the positive integers has this property, it must be the Fibonacci sequence. If we instead consider the problem of finding a monotone sequence with such a property, we call it the weak converse of Zeckendorf’s theorem. In this paper, we first introduce a generalization of Zeckendorf conditions, and subsequently, Zeckendorf’s theorems and their weak converses for the general Zeckendorf conditions. We also extend the generalization and results to the real numbers in the interval (0, 1), and to p-adic integers.
Analysis of Attacker Behavior in Compromised Hosts During Command and Control

Traditional reactive approach of blacklisting botnets fails to adapt to the rapidly evolving landscape of cyberattacks. An automated and proactive approach to detect and block botnet hosts will immensely benefit the industry. Behavioral analysis of botnet is shown to be effective against a wide variety of attack types. Current works, however, focus solely on analyzing network traffic from and to the bots. In this work we take a different approach of analyzing the chain of commands input by attackers in a compromised host. We have deployed several honeypots to simulate Linux shells and allowed attackers access to the shells to collect a large dataset of commands. We have further developed an automated mechanism to analyze these data. For the automation we have developed a system called CYbersecurity information Exchange with Privacy (CYBEX-P). Finally, we have done a sequential analysis on the dataset to show that we can successfully predict attacker behavior from the shell commands without analyzing network traffic like previous works.
PIV Measurements of Supersonic Internally-Mixed Dual-Stream Jets

While externally mixed, or separate flow, nozzle systems are most common in high bypass-ratio aircraft, they are not as attractive for use in lower bypass-ratio systems and on aircraft that will fly supersonically. The noise of such propulsion systems is also dominated by jet noise, making the study and noise reduction of these exhaust systems very important, both for military aircraft and future civilian supersonic aircraft. This paper presents particle image velocimetry of internally mixed nozzle with different area ratios between core and bypass, and nozzles that are ideally expanded and convergent. Such configurations independently control the geometry of the internal mixing layer and of the external shock structure. These allow exploration of the impact of shocks on the turbulent mixing layers, the impact of bypass ratio on broadband shock noise and mixing noise, and the impact of temperature on the turbulent flow field. At the 2009 AIAA/CEAS Aeroacoustics Conference the authors presented data and analysis from a series of tests that looked at the acoustics of supersonic jets from internally mixed nozzles. In that paper the broadband shock and mixing noise components of the jet noise were independently manipulated by holding Mach number constant while varying bypass ratio and jet temperature. Significant portions of that analysis was predicated on assumptions regarding the flow fields of these jets, both shock structure and turbulence. In this paper we add to that analysis by presenting particle image velocimetry measurements of the flow fields of many of those jets. In addition, the turbulent velocity data documented here will be very useful for validation of computational flow codes that are being developed to design advanced nozzles for future aircraft.
Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task. In this paper, we first reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints as a sequential decision making problem in the discrete time domain. We, then, propose a decentralized deep reinforcement learning approach to solve the problem. More specifically, a value network is developed to encode the expected time to destination given the agent’s joint state (including the agent’s information, the nearby agents’ observable information, and the locations of the nearby GBSs). An SINR-prediction network is also designed, using accumulated SINR measurements obtained when interacting with the cellular network, to map the GBSs’ locations into the SINR levels in order to predict the UAV’s SINR. Numerical results show that with the value network and SINR-prediction network, real-time navigation for multi-UAVs can be efficiently performed in various environments with high success rate.
Adaptive Coding for Matrix Multiplication at Edge Networks

Edge computing is emerging as a new paradigm to allow processing data at the edge of the network, where data is typically generated and collected, by exploiting multiple devices at the edge collectively. However, exploiting the potential of edge computing is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication cost. In this paper, our focus is on characterizing the cost-benefit trade-offs of coded computation for practical edge computing systems, and develop an adaptive coded computation framework. In particular, we focus on matrix multiplication as a computationally intensive task, and develop an adaptive coding for matrix multiplication (ACM2) algorithm by taking into account the heterogeneous and time varying nature of edge devices. ACM2 dynamically selects the best coding policy by taking into account the computing time, storage requirements as well as successful decoding probability. We show that ACM2 improves the task completion delay significantly as compared to existing coded matrix multiplication algorithms.
Epitaxial Thin Films of a Chalcogenide Perovskite

Chalcogenide perovskites have emerged as a new class of electronic materials, but fundamental properties and applications of chalcogenide perovskites remain limited by the lack of high quality epitaxial thin films. We report epitaxial thin film growth of BaZrS3, a prototypical chalcogenide, by pulsed laser deposition. X-ray diffraction studies show that the films are strongly textured out of plane and have a clear in-plane epitaxial relationship with the substrate. Electron microscopy studies confirm the presence of epitaxy for the first few layers of the film at the interface, even though away from the interface the films are polycrystalline with a large number of extended defects suggesting the potential for further improvement in growth. X-Ray reflectivity and atomic force microscopy show smooth film surfaces and interfaces between the substrate and the film. The films show strong light absorption near the band edge and photoluminescence in the visible region. The photodetector devices show fast and efficient photo response with the highest ON/OFF ratio reported for BaZrS3 films thus far. Our study opens up opportunities to realize epitaxial thin films, heterostructures, and superlattices of chalcogenide perovskites to probe fundamental physical phenomena and the resultant electronic and photonic device technologies.
Perception vs. reality: The aviation noise complaint effect on home prices

Abstract Intercity air transportation has grown rapidly in recent decades and creates significant noise pollution that affects health. Previous research quantifies the losses that are capitalized into home values. Much research relies heavily on spatially restrictive noise contour plots to identify the house price discounts and determine economic damages. We break new ground by investigating whether residential noise complaints can offer insights on aircraft noise pollution and housing price impacts experienced by residents near Minneapolis-Saint-Paul International Airport outside of contour boundaries. Our findings indicate noise complaints are a reliable measure of residential noise annoyance and have a significant adverse effect on home prices extending nearly twice as far (10 km) as contours. Reevaluating economic damages based on our results indicates contour-based calculations severely underestimate aircraft-noise-pollution-induced losses incurred by homeowners and suggests $154 million of $167 million in post-abatement damages are borne by residents located outside the regulated Minneapolis contour area.
Quantum cohomology of flag varieties

We describe a construction of Gromov-Witten invariants for flag varieties and use it to give a presentation for the quantum cohomology ring, by extending the ideas used by Bertram in the case of Grassmannians. This provides a proof for the conjecture of Givental and Kim in [GK].
Reliability-based topology optimization using stochastic gradients

This paper addresses the computational challenges in reliability-based topology optimization (RBTO) of structures associated with the estimation of statistics of the objective and constraints using standard sampling methods. The aim is to overcome the accuracy issues of traditional methods that rely on approximating the limit-state function. Herein, we present a stochastic gradient-based approach, where we estimate the probability of failure at every few optimization iterations using an efficient sampling strategy. To estimate the gradients of the failure probability with respect to the design parameters, we apply Bayes’ rule wherein we assume a parametric exponential model for the probability density function of the design parameters conditioned on the failure. The design parameters and the parameters of this probability density function are updated using a stochastic gradient descent approach requiring only a small, e.g., $${\mathcal {O}}(1)$$ , number of random samples per iteration, thus, leading to considerable reduction of the computational cost as compared to standard RBTO techniques. We illustrate the proposed approach with a benchmark example that has an analytical solution as well as two widely used problems in structural topology optimization. These examples illustrate the efficacy of the approach in producing reliable designs.
Are Enhanced Index Funds Enhanced

One of the major trends in the mutual fund industry is the rising importance of passive investing. One of the responses of the investment community to this challenge has been the creation of enhanced return index funds. In this paper, we examine the performance of enhanced index funds and find that they outperform index funds when we analyze both pre-expense performance (management ability) and post-expense performance (investor returns). However, when we use any of several criteria that have been proposed for picking the best fund from among those following some index, index funds outperform enhanced index funds.
Extremal Optimization: Heuristics Via Co-Evolutionary Avalanches

An introduction to Extremal Optimization written for the Computer Simulation Column in ``Computing in Science and Engineering'' (CISE).
WfCommons: A framework for enabling scientific workflow research and development

Abstract Scientific workflows are a cornerstone of modern scientific computing. They are used to describe complex computational applications that require efficient and robust management of large volumes of data, which are typically stored/processed on heterogeneous, distributed resources. The workflow research and development community has employed a number of methods for the quantitative evaluation of existing and novel workflow algorithms and systems. In particular, a common approach is to simulate workflow executions. In previous works, we have presented a collection of tools that have been adopted by the community for conducting workflow research. Despite their popularity, they suffer from several shortcomings that prevent easy adoption, maintenance, and consistency with the evolving structures and computational requirements of production workflows. In this work, we present WfCommons , a framework that provides a collection of tools for analyzing workflow executions, for producing generators of synthetic workflows, and for simulating workflow executions. We demonstrate the realism of the generated synthetic workflows by comparing their simulated executions to real workflow executions. We also contrast these results with results obtained when using the previously available collection of tools. We find that the workflow generators that are automatically constructed by our framework not only generate representative same-scale workflows (i.e., with structures and task characteristics distributions that resemble those observed in real-world workflows), but also do so at scales larger than that of available real-world workflows. Finally, we conduct a case study to demonstrate the usefulness of our framework for estimating the energy consumption of large-scale workflow executions.
Surface science motivated by heating of trapped ions from the quantum ground state

For the past two and a half decades, anomalous heating of trapped ions from nearby electrode surfaces has continued to demonstrate unexpected results. Caused by electric-field noise, this heating of the ions' motional modes remains an obstacle for scalable quantum computation with trapped ions. One of the anomalous features of this electric-field noise is the reported nonmonotonic behavior in the heating rate when a trap is incrementally cleaned by ion bombardment. Motivated by this result, the present work reports on a surface analysis of a sample ion-trap electrode treated similarly with incremental doses of Ar$^+$ ion bombardment. Kelvin probe force microscopy and x-ray photoelectron spectroscopy were used to investigate how the work functions on the electrode surface vary depending on the residual contaminant coverage between each treatment. It is shown that the as-fabricated Au electrode is covered with a hydrocarbon film that is modified after the first treatment, resulting in work functions and core-level binding energies that resemble that of atomic-like carbon on Au. The change in the spatial distributions of work functions as the coverage changes with each treatment is apparently related to the nonmonotonic heating-rate behavior previously reported.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study

Recent advancements in open-domain question answering (ODQA), i.e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets. However, progress in QA over book stories (Book QA) lags behind despite its similar task formulation to ODQA. This work provides a comprehensive and quantitative analysis about the difficulty of Book QA: (1) We benchmark the research on the NarrativeQA dataset with extensive experiments with cutting-edge ODQA techniques. This quantifies the challenges Book QA poses, as well as advances the published state-of-the-art with a $\sim$7\% absolute improvement on Rouge-L. (2) We further analyze the detailed challenges in Book QA through human studies.\footnote{\url{ https://github.com/gorov/BookQA} .} Our findings indicate that the event-centric questions dominate this task, which exemplifies the inability of existing QA models to handle event-oriented scenarios.
Fast Approximation Algorithms for Bounded Degree and Crossing Spanning Tree Problems.

We develop fast approximation algorithms for the minimum-cost version of the Bounded-Degree MST problem (BD-MST) and its generalization the Crossing Spanning Tree problem (Crossing-ST). We solve the underlying LP to within a $(1+\epsilon)$ approximation factor in near-linear time via the multiplicative weight update (MWU) technique. This yields, in particular, a near-linear time algorithm that outputs an estimate $B$ such that $B \le B^* \le \lceil (1+\epsilon)B \rceil +1$ where $B^*$ is the minimum-degree of a spanning tree of a given graph. To round the fractional solution, in our main technical contribution, we describe a fast near-linear time implementation of swap-rounding in the spanning tree polytope of a graph. The fractional solution can also be used to sparsify the input graph that can in turn be used to speed up existing combinatorial algorithms. Together, these ideas lead to significantly faster approximation algorithms than known before for the two problems of interest. In addition, a fast algorithm for swap rounding in the graphic matroid is a generic tool that has other applications, including to TSP and submodular function maximization.
Enhancement for Robustness of Koopman Operator-based Data-driven Mobile Robotic Systems

Koopman operator theory has served as the basis to extract dynamics for nonlinear system modeling and control across settings, including non-holonomic mobile robot control. There is a growing interest in research to derive robustness (and/or safety) guarantees for systems the dynamics of which are extracted via the Koopman operator. In this paper, we propose a way to quantify the prediction error because of noisy measurements when the Koopman operator is approximated via Extended Dynamic Mode Decomposition. We further develop an enhanced robot control strategy to endow robustness to a class of data-driven (robotic) systems that rely on Koopman operator theory, and we show how part of the strategy can happen offline in an effort to make our algorithm capable of real-time implementation. We perform a parametric study to evaluate the (theoretical) performance of the algorithm using a Van der Pol oscillator, and conduct a series of simulated experiments in Gazebo using a non-holonomic wheeled robot.
blocks_3d: software for general 3d conformal blocks

We introduce the software blocks_3d for computing four-point conformal blocks of operators with arbitrary Lorentz representations in 3d CFTs. It uses Zamolodchikov-like recursion relations to numerically compute derivatives of blocks around a crossing-symmetric configuration. It is implemented as a heavily optimized, multi-threaded, C++ application. We give performance benchmarks for correlators containing scalars, fermions, and stress tensors. As an example application, we recompute bootstrap bounds on four-point functions of fermions and study whether a previously observed sharp jump can be explained using the “fake primary” effect. We conclude that the fake primary effect cannot fully explain the jump and the possible existence of a “dead-end” CFT near the jump merits further study.
The search for the farthest quasar: consequences for black hole growth and seed models

The quest for high-redshift quasars has led to a series of record-breaking sources, with the current record holder at $z=7.642$. Here, we show how future detections of $z>8$ quasars impact the constraints on the parameters for black hole growth and seed models. Using broad flat priors on the growth parameters (Eddington ratio $\,f_{\rm Edd}$, duty cycle ${\cal D}$, seed mass $M_{\rm \bullet, seed}$ and radiative efficiency $\epsilon$), we show that the large uncertainties in their determination decrease by a factor $\sim 5$ when a quasar's detection redshift goes from $z=9$ to $z=12$. In this high-redshift regime, $\epsilon$ tends to the lowest value allowed, and the distribution for $M_{\rm \bullet, seed}$ peaks well inside the heavy seed domain. Remarkably, two quasars detected at $z > 7$ with low accretion rates (J1243+0100 and J0313-1806) already tighten the available parameter space, requiring $M_{\rm \bullet, seed} > 10^{3.5} \,{\rm M_\odot}$ and $\epsilon < 0.1$. The radiative efficiency is a crucial unknown, with factor $\sim 2$ changes able to modify the predicted mass by $\sim 3$ orders of magnitude already at $z\sim 9$. The competing roles of inefficient accretion (decreasing $\epsilon$) and black hole spin-up (increasing $\epsilon$) significantly impact growth models. Finally, we suggest that yields currently predicted by upcoming quasar surveys (e.g., Euclid) will be instrumental for determining the most-likely seed mass regime. For example, assuming thin-disk accretion, a detection of a quasar with $M_\bullet \sim 10^{10} \,{\rm M_\odot}$ by $z\sim 9-10$ would exclude the entire parameter space available for light seeds and dramatically reduce the one for heavy seeds.
Direct-drive ocean wave-powered batch reverse osmosis

Abstract Ocean waves provide a clean, reliable source of energy making them a viable energy source for desalination, especially in coastal communities and island nations. However, large capital costs render current wave-powered desalination technologies economically infeasible. This work presents a configuration for ocean-wave-energy-powered batch reverse osmosis. The proposed system uses seawater as the working fluid in a hydro-mechanical coupling and replaces the reverse osmosis high-pressure pump with a hydraulic converter for direct-drive, allowing for minimal intermediary power conversions, which leads to fewer parts necessary for operation and higher efficiencies. The concept was analyzed with MATLAB and Simulink to model the transient energy dynamics of the wave energy converter, power take-off system, and desalination load. The coupling, incorporating energy recovery, could achieve an SEC and LCOW as low as 2.30 kWh/m3 and $1.96/m3, respectively, for different sea states and a second law efficiency of 0.461. The results of the model were validated at the sub-system level against existing literature on wave energy models and previous work completed on batch reverse osmosis models. This system is the first to combine these two technologies. SEC and LCOW values were validated by comparing to known and predicted values for various types of RO systems.
Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops

High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training. However, these labels are absent in many regions, and models trained in other regions on typical satellite features, such as those from optical sensors, often exhibit low performance when transferred. Here we explore the use of NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, combined with Sentinel-2 optical data, for crop type mapping. Using data from three major cropped regions (in China, France, and the United States) we first demonstrate that GEDI energy profiles are capable of reliably distinguishing maize, a crop typically above 2m in height, from crops like rice and soybean that are shorter. We further show that these GEDI profiles provide much more invariant features across geographies compared to spectral and phenological features detected by passive optical sensors. GEDI is able to distinguish maize from other crops within each region with accuracies higher than 84%, and able to transfer across regions with accuracies higher than 82% compared to 64% for transfer of optical features. Finally, we show that GEDI profiles can be used to generate training labels for models based on optical imagery from Sentinel-2, thereby enabling the creation of 10m wall-to-wall maps of tall versus short crops in label-scarce regions. As maize is the second most widely grown crop in the world and often the only tall crop grown within a landscape, we conclude that GEDI offers great promise for improving global crop type maps.
The Expert Witness: Lessons from the U.S. Experience

The first section of this paper explains why assessing the worth of expert testimony poses special epistemological difficulties. The second traces the history of the various rules and procedures by means of which the U.S. legal system has tried to ensure, or at least control, the quality of the expert testimony on which it so often relies—from the Frye Rule, the Federal Rules of Evidence, and the Daubert trilogy to recent constitutional cases regarding the appearance of forensic witnesses in court and experiments with court-appointed experts and scientific education for judges. The third and final section suggests some lessons to be learned from the limited success of these efforts, and explores what might be better strategies going forward.
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

Humans can easily segment moving objects without knowing what they are. That objectness could emerge from continuous visual observations motivates us to model grouping and movement concurrently from unlabeled videos. Our premise is that a video has different views of the same scene related by moving components, and the right region segmentation and region flow would allow mutual view synthesis which can be checked from the data itself without any external supervision. Our model starts with two separate pathways: an appearance pathway that outputs feature-based region segmentation for a single image, and a motion pathway that outputs motion features for a pair of images. It then binds them in a conjoint representation called segment flow that pools flow offsets over each region and provides a gross characterization of moving regions for the entire scene. By training the model to minimize view synthesis errors based on segment flow, our appearance and motion pathways learn region segmentation and flow estimation automatically without building them up from low-level edges or optical flows respectively. Our model demonstrates the surprising emergence of objectness in the appearance pathway, surpassing prior works on zero-shot object segmentation from an image, moving object segmentation from a video with unsupervised test-time adaptation, and semantic image segmentation by supervised fine-tuning. Our work is the first truly end-to-end zero-shot object segmentation from videos. It not only develops generic objectness for segmentation and tracking, but also outperforms prevalent image-based contrastive learning methods without augmentation engineering.
AdEle: An Adaptive Congestion-and-Energy-Aware Elevator Selection for Partially Connected 3D NoCs

By lowering the number of vertical connections in fully connected 3D networks-on-chip (NoCs), partially connected 3D NoCs (PC-3DNoCs) help alleviate reliability and fabrication issues. This paper proposes a novel, adaptive congestion- and energy-aware elevator-selection scheme called AdEle to improve the traffic distribution in PC-3DNoCs. AdEle employs an offline multi-objective simulated-annealing-based algorithm to find good elevator subsets and an online elevator selection policy to enhance elevator selection during routing. Compared to the state-of-the-art techniques under different real-application traffics and configuration scenarios, AdEle improves the network latency by 10.9% on average (up to 14.6%) with less than 6.9% energy consumption overhead.
Testing parton distribution functions with t -channel single-top-quark production

The production of single top-quarks in the t-channel at hadron colliders imposes strong analytic constraints on parton distribution functions (PDFs) through its double deeply inelastic scattering (DDIS) form. We exploit this to provide novel consistency checks between LO, NLO and NNLO PDF fits and propose to include it as a constraint in future PDF fits. Furthermore, while it is well-known that the b-quark PDF is highly sensitive to the b-quark mass, we show that the treatment of this systematic uncertainty is still incomplete, fragmented or outright missing at the moment. Consequently, we conclude that the b-quark mass uncertainty is the dominant but so far broadly neglected theory uncertainty for this process.
Ataxin-2 is essential for cytoskeletal dynamics and neurodevelopment in Drosophila.

Summary Ataxin-2 (Atx2) is a highly conserved RNA binding protein. Atx2 undergoes polyglutamine expansion leading to Amyotrophic Lateral Sclerosis (ALS) or Spinocerebellar Ataxia type 2 (SCA2). However, the physiological functions of Atx2 in neurons remain unknown. Here, using the powerful genetics of Drosophila, we show that Atx2 is essential for normal neuronal cytoskeletal dynamics and organelle trafficking. Upon neuron-specific Atx2 loss, the microtubule and actin networks were abnormally stabilized and cargo transport was drastically inhibited. Depletion of Atx2 caused multiple morphological defects in the nervous system of 3rd instar larvae. These include reduced brain size, impaired axon development and decreased dendrite outgrowth. Defects in the nervous system caused loss of the ability to crawl and lethality at the pupal stage. Taken together, these data mark Atx2 as a major regulator of cytoskeletal dynamics and denote Atx2 as an essential gene in neurodevelopment, as well as a neurodegenerative factor.
Robust control of quantum dynamics under input and parameter uncertainty

Despite significant progress in theoretical and laboratory quantum control, engineering quantum systems remains principally challenging due to manifestation of noise and uncertainties associated with the field and Hamiltonian parameters. In this paper, we extend and generalize the asymptotic quantum control robustness analysis method -- which provides more accurate estimates of quantum control objective moments than standard leading order techniques -- to diverse quantum observables, gates and moments thereof, and also introduce the Pontryagin Maximum Principle for quantum robust control. In addition, we present a Pareto optimization framework for achieving robust control via evolutionary open loop (model-based) and closed loop (model-free) approaches with the mechanisms of robustness and convergence described using asymptotic quantum control robustness analysis. In the open loop approach, a multiobjective genetic algorithm is used to obtain Pareto solutions in terms of the expectation and variance of the transition probability under Hamiltonian parameter uncertainty. The set of numerically determined solutions can then be used as a starting population for model-free learning control in a feedback loop. The closed loop approach utilizes real-coded genetic algorithm with adaptive exploration and exploitation operators in order to preserve solution diversity and dynamically optimize the transition probability in the presence of field noise. Together, these methods provide a foundation for high fidelity adaptive feedback control of quantum systems wherein open loop control predictions are iteratively improved based on data from closed loop experiments.
Ariosto’s Orlando Furioso: performance at the valois and bourbon courts

(2003). Ariosto’s Orlando Furioso: performance at the valois and bourbon courts. The Court Historian: Vol. 8, No. 2, pp. 177-187.
Overview of NASA's Supersonic Cruise Efficiency - Propulsion Research

The research in Supersonic Cruise Efficiency Propulsion (SCE-P) Technical Challenge area of NASA's Supersonics project is discussed. The research in SCE-P is being performed to enable efficient supersonic flight over land. Research elements in this area include: Advance Inlet Concepts, High Performance/Wider Operability Fan and Compressor, Advanced Nozzle Concepts, and Intelligent Sensors/Actuators. The research under each of these elements is briefly discussed.
Nyblade Receives 2012 Paul G. Silver Award for Outstanding Scientific Service: Citation

Andrew Nyblade is the first recipient of AGU's new Paul G. Silver Award for Outstanding Scientific Service. This award is given jointly by the Seismology, Geodesy, and Tectonophysics sections to a section member who has made outstanding contributions to these fields through mentoring of junior colleagues, leadership of community research initiatives, or other forms of unselfish collaboration in research. The award was named for the late Paul Silver as a tribute to the excellence and generosity of his scientific service and the importance of his research on mantle anisotropy, continental evolution, subduction zone dynamics, and earthquake source processes.