cssi/SciSciGPT-SciSciNet huggingface dataset:
Improved Job sequencing Bounds from Decision Diagrams

We introduce a general method for relaxing decision diagrams that allows one to bound job sequencing problems by solving a Lagrangian dual problem on a relaxed diagram. We also provide guidelines for identifying problems for which this approach can result in useful bounds. These same guidelines can be applied to bounding deterministic dynamic programming problems in general, since decision diagrams rely on DP formulations. Computational tests show that Lagrangian relaxation on a decision diagram can yield very tight bounds for certain classes of hard job sequencing problems. For example, it proves for the first time that the best known solutions for Biskup-Feldman instances are within a small fraction of 1% of the optimal value, and sometimes optimal.
Pricing Uncertainty Induced by Climate Change

Geophysicists examine and document the repercussions for the earth’s climate induced by alternative emission scenarios and model specifications. Using simplified approximations, they produce tractable characterizations of the associated uncertainty. Meanwhile, economists write highly stylized damage functions to speculate about how climate change alters macroeconomic and growth opportunities. How can we assess both climate and emissions impacts, as well as uncertainty in the broadest sense, in social decision-making? We provide a framework for answering this question by embracing recent decision theory and tools from asset pricing, and we apply this structure with its interacting components to a revealing quantitative illustration.
Classification of neurons in the adult mouse cochlear nucleus: Linear discriminant analysis

The cochlear nucleus (CN) transforms the spike trains of spiral ganglion cells into a set of sensory representations that are essential for auditory discriminations and perception. These transformations require the coordinated activity of different classes of neurons that are embryologically derived from distinct sets of precursors. Decades of investigation have shown that the neurons of the CN are differentiated by their morphology, neurotransmitter receptors, ion channel expression and intrinsic excitability. In the present study we have used linear discriminant analysis (LDA) to perform an unbiased analysis of measures of the responses of CN neurons to current injections to objectively categorize cells on the basis of both morphology and physiology. Recordings were made from cells in brain slices from CBA/CaJ mice and a transgenic mouse line, NF107, crossed against the Ai32 line. For each cell, responses to current injections were analyzed for spike rate, spike shape, input resistance, resting membrane potential, membrane time constant, hyperpolarization-activated sag and time constant. Cells were filled with dye for morphological classification, and visually classified according to published accounts. The different morphological classes of cells were separated with the LDA. Ventral cochlear nucleus (VCN) bushy cells, planar multipolar (T-stellate) cells, and radiate multipolar (D-stellate) cells were in separate clusters and separate from all of the neurons from the dorsal cochlear nucleus (DCN). Within the DCN, the pyramidal cells and tuberculoventral cells were largely separated from a distinct cluster of cartwheel cells. principal axes, whereas VCN cells were in 3 clouds approximately orthogonal to this plane. VCN neurons from the two mouse strains overlapped but were slightly separated, indicating either a strain dependence or differences in slice preparation methods. We conclude that cochlear nucleus neurons can be objectively distinguished based on their intrinsic electrical properties, but such distinctions are still best aided by morphological identification.
Transcriptomic and functional analysis of the oosome, a unique form of germ plasm in the wasp Nasonia vitripennis

The oosome is the germline determinant in the wasp Nasonia vitripennis and is homologous to the polar granules of Drosophila. Despite a common evolutionary origin and developmental role, the oosome is morphologically quite distinct from polar granules. It is a solid sphere that migrates within the cytoplasm before budding out and forming pole cells. To gain an understanding of both the molecular basis of oosome development and the conserved essential features of germ plasm, we quantified and compared transcript levels between embryo fragments that contained the oosome and those that did not. The identity of the differentially localized transcripts indicated that Nasonia uses a distinct set of molecules to carry out conserved germ plasm functions. In addition, functional testing of a sample of localized transcripts revealed potentially novel mechanisms of ribonucleoprotein assembly and pole cell cellularization in the wasp. Our results demonstrate that the composition of germ plasm varies significantly within Holometabola, as very few mRNAs share localization to the oosome and polar granules. Some of this variability appears to be related to the unique properties of the oosome relative to the polar granules in Drosophila, and some may be related to differences in pole formation between species. This work will serve as the basis for further investigation into the patterns of germline determinant evolution among insects, the molecular basis of the unique properties of the oosome, and the incorporation of novel components into developmental networks.
Partial Identification of Answer Reviewing Effects in Multiple‐Choice Exams

Does reviewing previous answers during multiple-choice exams help examinees increase their final score? This article formalizes the question using a rigorous causal framework, the potential outcomes framework. Viewing examinees' reviewing status as a treatment and their final score as an outcome, the article first explains the challenges of identifying the causal effect of answer reviewing in regular exam-taking settings. In addition to the incapability of randomizing the treatment selection (reviewing status) and the lack of other information to make this selection process ignorable, the treatment variable itself is not fully known to researchers. Looking at examinees' answer sheet data, it is unclear whether an examinee who did not change his or her answer on a specific item reviewed it but retained the initial answer (treatment condition) or chose not to review it (control condition). Despite such challenges, however, the article develops partial identification strategies and shows that the sign of the answer reviewing effect can be reasonably inferred. By analyzing a statewide math assessment data set, the article finds that reviewing initial answers is generally beneficial for examinees.
Group velocity distribution and short-pulse dispersion in a disordered transverse Anderson localization optical waveguide

Abstract We investigate the group velocity distribution of waveguide modes in the presence of disorder. The results are based on extensive numerical simulations of disordered optical waveguides using statistical methods. We observe that the narrowest distribution of group velocities is obtained in the presence of a small amount of disorder; therefore, the modal dispersion of an optical pulse is minimized when there is only a slight disorder in the waveguide. The absence of disorder or the presence of a large amount of disorder can result in a large modal dispersion due to the broadening of the distribution of the group velocities. We devise a metric that can be applied to the mode group index probability-density-function and predict the optimal level of disorder that results in the lowest amount of modal dispersion for short pulse propagation. Our results are important for studying the propagation of optical pulses in the linear regime, e.g., for optical communications; and the nonlinear regime for high-power short-pulse propagation.
Fully Homomorphic NIZK and NIWI Proofs

In this work, we define and construct fully homomorphic non-interactive zero knowledge (FH-NIZK) and non-interactive witness-indistinguishable (FH-NIWI) proof systems.
Outage-Optimized Deployment of UAVs

We consider multiple unmanned aerial vehicles (UAVs) serving a density of ground terminals (GTs) as mobile base stations. The objective is to minimize the outage probability of GT-to-UAV transmissions. In this context, the optimal placement of UAVs under different UAV altitude constraints and GT densities is studied. First, using a random deployment argument, a general upper bound on the optimal outage probability is found for any density of GTs and any number of UAVs. Lower bounds on the performance of optimal deployments are also determined. The upper and lower bounds are combined to show that the optimal outage probability decays exponentially with the number of UAVs for GT densities with finite support. Next, the structure of optimal deployments are studied when the common altitude constraint is large. In this case, for a wide class of GT densities, it is shown that all UAVs should be placed to the same location in an optimal deployment. A design implication is that one can use a single multi-antenna UAV as opposed to multiple single-antenna UAVs without loss of optimality. Numerical optimization of UAV deployments are carried out using particle swarm optimization. Simulation results are also presented to confirm the analytical findings.
Host-Informed Expression of CRISPR Guide RNA for Genomic Engineering in Komagataella phaffii .

There is growing interest in the use of nonmodel microorganisms as hosts for biopharmaceutical manufacturing. These hosts require genomic engineering to meet clinically relevant product qualities and titers, but the adaptation of tools for editing genomes, such as CRISPR-Cas9, has been slow for poorly characterized hosts. Specifically, a lack of biochemical characterization of RNA polymerase III transcription has hindered reliable expression of guide RNAs in new hosts. Here, we present a sequencing-based strategy for the design of host-specific cassettes for modular, reliable, expression of guide RNAs. Using this strategy, we achieved up to 95% gene editing efficiency in the methylotrophic yeast Komagataella phaffii. We applied this approach for the rapid, multiplexed engineering of a complex phenotype, achieving humanized product glycosylation in two sequential steps of engineering. Reliable extension of simple gene editing tools to nonmodel manufacturing hosts will enable rapid engineering of manufacturing strains tuned for specific product profiles and potentially decrease the costs and timelines for process development.
Mapping the Born Rule to the Fractal Geometry of Quantum Paths

Here we show that there is an approximate mapping between multifractal theory and the Born rule of Quantum Mechanics. The derivation is based on the fractal geometry of quantum mechanical paths, which replicates the geometry of unrestricted random walks in d > 2 Euclidean dimensions.
Combining program analysis and statistical language model for code statement completion

Automatic code completion helps improve developers' productivity in their programming tasks. A program contains instructions expressed via code statements, which are considered as the basic units of program execution. In this paper, we introduce AutoSC, which combines program analysis and the principle of software naturalness to fill in a partially completed statement. AutoSC benefits from the strengths of both directions, in which the completed code statement is both frequent and valid. AutoSC is first trained on a large code corpus to derive the templates of candidate statements. Then, it uses program analysis to validate and concretize the templates into syntactically and type-valid candidate statements. Finally, these candidates are ranked by using a language model trained on the lexical form of the source code in the code corpus. Our empirical evaluation on the large datasets of real-world projects shows that AutoSC achieves 38.9--41.3% top-1 accuracy and 48.2--50.1% top-5 accuracy in statement completion. It also outperforms a state-of-the-art approach from 9X--69X in top-1 accuracy.
Search for L5 Earth Trojans with DECam

Most of the major planets in the Solar system support populations of co-orbiting bodies, known as Trojans, at their L4 and L5 Lagrange points. In contrast, Earth has only one known co-orbiting companion. This paper presents the results from a search for Earth Trojans (ETs) using the DECam instrument on the Blanco Telescope at CTIO. This search found no additional Trojans in spite of greater coverage compared to previous surveys of the L5 point. Therefore, the main result of this work is to place the most stringent constraints to date on the population of ETs. These constraints depend on assumptions regarding the underlying population properties, especially the slope of the magnitude distribution (which in turn depends on the size and albedo distributions of the objects). For standard assumptions, we calculate upper limits to a 90 per cent confidence limit on the L5 population of N_(ET) < 1 for magnitude H < 15.5, N_(ET) = 60–85 for H < 19.7, and N_(ET) = 97 for H = 20.4. This latter magnitude limit corresponds to Trojans ∼300 m in size for albedo 0.15. At H = 19.7, these upper limits are consistent with previous L4 ET constraints and significantly improve L5 constraints.
Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting thro...
On privacy preserving data release of linear dynamic networks

Abstract Distributed data sharing in dynamic networks is ubiquitous. It raises the concern that the private information of dynamic networks could be leaked when data receivers are malicious or communication channels are insecure. In this paper, we propose to intentionally perturb the inputs and outputs of a linear dynamic system to protect the privacy of target initial states and inputs from released outputs. We formulate the problem of perturbation design as an optimization problem which minimizes the cost caused by the added perturbations while maintaining system controllability and ensuring the privacy. We analyze the computational complexity of the formulated optimization problem. To minimize the l 0 and l 2 norms of the added perturbations, we derive their convex relaxations which can be efficiently solved. The efficacy of the proposed techniques is verified by a case study on a heating, ventilation, and air conditioning system.
Escaping from saddle points: Online stochastic gradient for tensor decomposition

We analyze stochastic gradient descent for optimizing non-convex functions. In many cases for non-convex functions the goal is to find a reasonable local minimum, and the main concern is that gradient updates are trapped in saddle points. In this paper we identify strict saddle property for non-convex problem that allows for efficient optimization. Using this property we show that from an arbitrary starting point, stochastic gradient descent converges to a local minimum in a polynomial number of iterations. To the best of our knowledge this is the first work that gives global convergence guarantees for stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points. Our analysis can be applied to orthogonal tensor decomposition, which is widely used in learning a rich class of latent variable models. We propose a new optimization formulation for the tensor decomposition problem that has strict saddle property. As a result we get the first online algorithm for orthogonal tensor decomposition with global convergence guarantee.
Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators

This paper describes various design considerations for deep neural networks that enable them to operate efficiently and accurately on processing-in-memory accelerators. We highlight important properties of these accelerators and the resulting design considerations using experiments conducted on various state-of-the- art deep neural networks with the large-scale ImageNet dataset.
Various Aspects of Whitham Times

We sketch some of the different roles played by Whitham times in connection with averaging, adiabatic invariants, soliton theory, Hamiltonian structures, topological field theory (TFT), Seiberg–Witten (SW) theory, isomonodromy problems, Hitchin systems, WDVV and Picard–Fuchs equations, renormalization, soft supersymmetry breaking, etc.
Analysis and Optimization of the Implicit Broadcasts in FPGA HLS to Improve Maximum Frequency

Designs generated by high-level synthesis (HLS) tools typically achieve a lower frequency compared to manual RTL designs. We study the timing issues in a diverse set of nine realistic HLS designs and observe that in most cases the frequency degradation is related to the signal broadcast structures. In this work, we classify the common broadcast types in HLS designs, including the data signal broadcast and two types of control signal broadcast: the pipeline control broadcast and the synchronization signal broadcast. We further identify several common limitations of the current HLS tools, which lead to improper handling of the broadcasts. First, the HLS delay model does not consider the extra delay caused by broadcasts, thus the scheduling results will be suboptimal. To solve the issue, we implement a set of comprehensive synthetic designs and benchmark the extra delay to calibrate the HLS delay model. Second, the HLS adopts back-pressure signals for pipeline control, which will lead to large broadcasts. Instead, we propose to use the skid-buffer-based pipeline control, where the back-pressure signal is removed, and an extra skid-buffer is used for flow-control. We use dynamic programming to minimize the area of the extra FIFO. Third, there exist redundant synchronizations among concurrent modules that may lead to huge broadcasts. We propose methods to identify and prune unnecessary synchronization signals. Our solutions boost the frequency of nine real-world HLS benchmarks by 53% on average and with marginal area and latency overhead. In some cases, the gain is more than 100 MHz.
Virtual Cell Clustering with Optimal Resource Allocation to Maximize Cellular System Capacity

This work presents a new optimization framework for cellular networks using neighborhood-based optimization. Under this optimization framework, resources are allocated within virtual cells encompassing several base-stations and the users within their coverage areas. We form the virtual cells using hierarchical clustering with a minimax linkage criterion given a particular number of such cells. Once the virtual cells are formed, we consider a single-user detection interference coordination model in which base-stations in a virtual cell jointly allocate the channels and power to users within the virtual cell. We propose two new schemes for solving this mixed integer NP- hard resource allocation problem. The first scheme transforms the problem into a continuous variables problem; the second scheme proposes a new channel allocation method and then alternately solves the channel allocation problem using this new method, and the power allocation problem. We evaluate the average system sum rate of these schemes for a variable number of virtual cells. These results quantify the sum-rate along a continuum of fully- centralized versus fully-distributed optimization for different clustering and resource allocation strategies. These results indicate that the penalty of fully-distributed optimization versus fully-centralized (cloud RAN) can be as high as 50%. However, if designed properly, a few base stations within a virtual cell using neighborhood- based optimization have almost the same performance as fully-centralized optimization.
Accurate Lindblad-form master equation for weakly damped quantum systems across all regimes

Realistic models of quantum systems must include dissipative interactions with a thermal environment. For weakly-damped systems, while the Lindblad-form Markovian master equation is invaluable for this task, it applies only when the frequencies of any subset of the system’s transitions are degenerate, or their differences are much greater than the transitions’ linewidths. Outside of these regimes the only available efficient description has been the Bloch–Redfield master equation, the efficacy of which has long been controversial due to its failure to guarantee the positivity of the density matrix. The ability to efficiently simulate weakly-damped systems across all regimes is becoming increasingly important, especially in quantum technologies. Here we solve this long-standing problem by deriving a Lindblad-form master equation for weakly-damped systems that is accurate for all regimes. We further show that when this master equation breaks down, so do all time-independent Markovian equations, including the B-R equation. We thus obtain a replacement for the B-R equation for thermal damping that is no less accurate, simpler in structure, completely positive, allows simulation by efficient quantum trajectory methods, and unifies the previous Lindblad master equations. We also show via exact simulations that the new master equation can describe systems in which slowly-varying transition frequencies cross each other during the evolution. System identification tools, developed in systems engineering, play an important role in our analysis. We expect these tools to prove useful in other areas of physics involving complex systems.
On the entropy of closed hypersurfaces and singular self-shrinkers

Self-shrinkers are the special solutions of mean curvature flow in $\mathbf{R}^{n+1}$ that evolve by shrinking homothetically; they serve as singularity models for the flow. The entropy of a hypersurface introduced by Colding–Minicozzi is a Lyapunov functional for the mean curvature flow, and is fundamental to their theory of generic mean curvature flow. In this paper we prove that a conjecture of Colding–Ilmanen–Minicozzi–White, namely that any closed hypersurface in $\mathbf{R}^{n+1}$ has entropy at least that of the round sphere, holds in any dimension $n$. This result had previously been established for the cases $n \leq 6$ by Bernstein–Wang using a carefully constructed weak flow. The main technical result of this paper is an extension of Colding–Minicozzi’s classification of entropy-stable self-shrinkers to the singular setting. In particular, we show that any entropystable self-shrinker whose singular set satisfies Wickramasekera’s α-structural hypothesis must be a round cylinder $\mathbf{S}^k (\sqrt{2k}) \times \mathbf{R}^{n-k}$.
Leveraging Pretrained Image Classifiers for Language-Based Segmentation

Current semantic segmentation models cannot easily generalize to new object classes unseen during train time: they require additional annotated images and retraining. We propose a novel segmentation model that injects visual priors into semantic segmentation architectures, allowing them to segment out new target labels without retraining. As visual priors, we use the activations of pretrained image classifiers, which provide noisy indications of the spatial location of both the target object and distractor objects in the scene. We leverage language semantics to obtain these activations for a target label unseen by the classifier. Further experiments show that the visual priors obtained via language semantics for both relevant and distracting objects are key to our performance
Risks associated with animal-assisted intervention programs: A literature review

The benefits of animal-assisted interventions (AAI), to utilize companion animals as an adjunctive treatment modality, is well-established and a burgeoning research field. However, few studies have evaluated the potential hazards of these programs, such as the potential for therapy animals to transfer hospital-associated pathogens between individuals and the hospital environment. Here we review the current literature on the possible risks of hospital-based AAI programs, including zoonotic pathogen transmission. We identified twenty-nine articles encompassing reviews of infection control guidelines and epidemiological studies on zoonotic pathogen prevalence in AAI. We observed substantial heterogeneity in infection control practices among hospital AAI programs. Few data confirmed pathogen transmission between therapy animals and patients. Given AAI's known benefits, we recommend that future research utilize a One Health framework to evaluate microbial dynamics among therapy animals, patients, and hospital environments. This framework may best promote safe practices to ensure the sustainability of these valuable AAI programs.
Modified point mass trajectory simulation for base-burn projectiles

Abstract : An addition to the Modified Point Mass Trajectory Model for Rocket- Assisted Projectiles is presented for the exterior ballistic simulation of base- bum projectiles. The addition models the change in aerodynamic base-drag based on the change in base pressure due to the base-bum motor's ejection of hot gas into the wake of the projectile. The mass flow rate of the remaining fuel of the base-bum motor is modeled as a function of the Instantaneous projectile spin rate and atmospheric air pressure. HAWK Doppler radar data collected at Yuma Proving Ground, Arizona, for the 155mm, MI 864 base-bum projectile have been used to verify the modeling approach for a variety of test conditions. Base-Bum Projectile, Trajectory Modeling, Doppler Radar, Aerodynamic Drag.
Frame Potentials and Orthogonal Vectors

An extension is given of a recent result of Glazyrin, showing that an orthonormal basis $\{ {e_{\text{t}}}\} _{{\text{t}} = 1}^d$ joined with the vectors $\{ {e_j}\} _{j = 1}^m$, where 1 ≤ m 1 ,...,x N } in ${\mathbb{S}^d}^{ - 1}$.
The value of hybrid interstitial tandem and ring applicators for organ at risk dose reduction in small volume cervical cancer.

Purpose Magnetic resonance imaging (MRI)-guided adaptive brachytherapy is the standard of care for cervical cancer. Hybrid intracavitary/interstitial applicators for bulky tumor (high-risk clinical target volume [HR-CTV] > 30 cc) dose escalation is recommended in the EMBRACE II trial. The value of hybrid applicators for smaller HR-CTV (< 30 cc) in organ at risk (OAR) sparing is less certain. Material and methods Twenty-seven patients with FIGO stage I-IVA cervical cancer treated with definitive chemoradiation and MRI-based brachytherapy using conventional tandem and ring (TR) applicators were re-planned using virtual needles. They were then summed with the external beam dose to evaluate target coverage and OAR dose using EQD2 summation. Target and OAR dose with/without hybrid applicator use were compared. Results Eighty-one percent had HR-CTV volumes < 30 cc, for which, hybrid TR applicators had significantly lower mean D2cc to all OARs without differences in target coverage. For HR-CTV < 30 cc, the bladder and rectal OAR goals per EMBRACE II were exceeded in significantly fewer patients with the hybrid TR applicators. No significant difference was found in the sigmoid D2cc dose goal. Conclusions In small volume tumors (< 30 cc), hybrid applicators may offer improved OAR sparing compared with conventional tandem and ring applicators, and may increase the proportion of patients meeting EMBRACE II OAR goals.
New Algorithms and Improved Guarantees for One-Bit Compressed Sensing on Manifolds

We study the problem of approximately recovering signals on a manifold from one-bit linear measurements drawn from either a Gaussian ensemble, partial circulant ensemble, or bounded orthonormal ensemble and quantized using Σ∆ or distributed noise shaping schemes. We assume we are given a Geometric Multi-Resolution Analysis, which approximates the manifold, and we propose a convex optimization algorithm for signal recovery. We prove an upper bound on the recovery error which outperforms prior works that use memoryless scalar quantization, requires a simpler analysis, and extends the class of measurements beyond Gaussians. Finally, we illustrate our results with numerical experiments.
Reduced Switching-Frequency Modulation Design for Model Predictive Control Based Modular Multilevel Converters

This paper proposes a novel switching algorithm for modular multilevel converters (MMCs) that significantly reduces the switching frequency while fulfilling all control objectives required for their proper operation. Unlike in the conventional capacitor voltage-balancing strategies, in addition to submodule (SM) capacitor voltages, the proposed algorithm considers previous switching statuses during sorting. The algorithm is applied to a seven-level back-to-back MMC-HVDC system and tested under various operating conditions. Significant reduction in the switching frequency with trivial impacts on submodule capacitor voltages are observed.
Duet at TREC 2019 Deep Learning Track

This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019. For the document retrieval task, we adapt the Duet model to ingest a “multiple field” view of documents—we refer to the new architecture as Duet with Multiple Fields (DuetMF). A second submission combines the DuetMF model with other neural and traditional relevance estimators in a learning-to-rank framework and achieves improved performance over the DuetMF baseline. For the passage retrieval task, we submit a single run based on an ensemble of eight Duet models.
The Reissner Fiber Is Highly Dynamic In Vivo and Controls Morphogenesis of the Spine

Summary Cerebrospinal fluid (CSF) physiology is important for the development and homeostasis of the central nervous system, and its disruption has been linked to scoliosis in zebrafish [ 1 , 2 ]. Suspended in the CSF is an extracellular structure called the Reissner fiber, which extends from the brain through the central canal of the spinal cord. Zebrafish scospondin-null mutants are unable to assemble a Reissner fiber and fail to form a straight body axis during embryonic development [ 3 ]. Here, we describe hypomorphic missense mutations of scospondin, which allow Reissner fiber assembly and extension of a straight axis. However, during larval development, these mutants display progressive Reissner fiber disassembly, which is concomitant with the emergence of axial curvatures and scoliosis in adult animals. Using a scospondin-GFP knockin zebrafish line, we demonstrate several dynamic properties of the Reissner fiber in vivo, including embryonic fiber assembly, the continuous rostral to caudal movement of the fiber within the brain and central canal, and subcommissural organ (SCO)-spondin-GFP protein secretion from the floor plate to merge with the fiber. Finally, we show that disassembly of the Reissner fiber is also associated with the progression of axial curvatures in distinct scoliosis mutant zebrafish models. Together, these data demonstrate a critical role for the Reissner fiber for the maintenance of a straight body axis and spine morphogenesis in adult zebrafish. Our study establishes a framework for future investigations to address the cellular effectors responsible for Reissner-fiber-dependent regulation of axial morphology. Video Abstract Download : Download video (26MB)
AdaPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems

Model-free policy learning has enabled good performance on complex tasks that were previously intractable with traditional control techniques. However, this comes at the cost of requiring a perfectly accurate model for training. This is infeasible due to the very high sample complexity of model-free methods preventing training on the target system. This renders such methods unsuitable for physical systems. Model mismatch due to dynamics parameter differences and unmodeled dynamics error may cause suboptimal or unsafe behavior upon direct transfer. We introduce the Adaptive Policy Transfer for Stochastic Dynamics (AdaPT) algorithm that achieves provably safe and robust, dynamically-feasible zero-shot transfer of RL-policies to new domains with dynamics error. AdaPT combines the strengths of offline policy learning in a black-box source simulator with online tube-based MPC to attenuate bounded dynamics mismatch between the source and target dynamics. AdaPT allows online transfer of policies, trained solely in a simulation offline, to a family of unknown targets without fine-tuning. We also formally show that (i) AdaPT guarantees bounded state and control deviation through state-action tubes under relatively weak technical assumptions and, (ii) AdaPT results in a bounded loss of reward accumulation relative to a policy trained and evaluated in the source environment. We evaluate AdaPT on 2 continuous, non-holonomic simulated dynamical systems with 4 different disturbance models, and find that AdaPT performs between 50 and \(300\%\) better on mean reward accrual than direct policy transfer.
Media, Education and Anti-Americanism in the Muslim World

Recent surveys in the United States and the Muslim world show widespread misinformation about the events of September 11, 2001. Using data from 9 predominantly Muslim countries, we study how such beliefs depend on exposure to news media and levels of education. Standard economic theory would predict that increased access to information should cause beliefs to converge. More recent models of biased belief formation suggest that this result might hinge critically on who is providing the information. Consistent with the latter, we find that overall intensity of media use and level of education have at best a weak correlation with beliefs, while particular information sources have strong and divergent effects. Compared to those with little media exposure or schooling, individuals watching Arab news channels or educated in schools with little Western influence are less likely to agree that the September 11 attacks were carried out by Arab terrorists. Those exposed to media or education from Western sources are more likely to agree. Belief that the attacks were morally justified and general attitudes toward the US are also strongly correlated with source of information. These findings survive controls for demographic characteristics and are robust to identifying media effects using cross-country variation in language.
CHAPTER 4 – Data Models

Publisher Summary This chapter discusses common data models, providing the link among the physical remote sensing models, the sensor models, and image processing algorithms with examples. The connection among radiation, sensor models, and data models are also discussed and explored by simulation. The chapter concludes the following points: (1) the spectral statistics of remote-sensing image data are influenced by the topography in the scene and the topographic effect tends to correlate the data among spectral bands along a straight line through the origin of the reflectance scattergram; (2) the spectral statistics of remote-sensing image data are also influenced by the sensor's spectral passband locations and widths and noise characteristics; and random, spatially uncorrelated sensor noise increases the within-class variance of all surface materials equally; (3) the spatial and spectral statistics of remote-sensing image data are influenced by the sensor's spatial response function, which increases the spatial correlation length, reduces within-class variance, and creates mixed spectral vectors.
Trade shocks and macroeconomic fluctuations in Africa

This paper examines the role of external shocks in explaining macroeconomic fluctuations in African countries. We construct a quantitative, stochastic, dynamic, multi-sector equilibrium model of a small open economy calibrated to represent a typical African economy. In our framework, external shocks consist of trade shocks, modeled as fluctuations in the prices of exported primary commodities, imported capital goods and intermediate inputs, and a financial shock, modeled as fluctuations in the world real interest rate. Our results indicate that while trade shocks account for roughly 45 percent of economic fluctuations in aggregate output, financial shocks play only a minor role. We also find that adverse trade shocks induce prolonged recessions.
Chapter 8 – Leaves and Fluxes

Publisher Summary This chapter considers the transpiration of water and the photosynthetic fixation of carbon dioxide, with emphasis on the parts of the pathway and the overall rates for leaves. The driving forces for such fluxes are differences in CO2 and H2O concentrations or mole fractions. Resistances were first used to describe gas fluxes quantitatively for leaves by Henry Brown and Fergusson Escombe in 1900. Resistance networks were developed to specify which parts of the pathway are most limiting for photosynthesis or transpiration. Recently, the use of two different forms of conductance has become more popular, especially for describing transpiration, and the chapter considers both forms. It is seen that greater stomatal opening may be an advantage for photosynthesis but can result in excessive transpiration. Consequently, a benefit/cost index, such as the amount of CO2 fixed per unit of water lost, can be important for evaluating ecological aspects of gas exchange. The chapter indicated how the flux densities of water vapor and CO2 could be measured for a leaf. Although the discussion of gas fluxes is referred to leaves, it is also applicable to stems, flower petals, and other plant parts.
Optimal hold functions for sampled data regulation

Abstract This paper investigates the use of Generalized Sampled Data Hold Function Control (GSHF) to optimize quadratic measures of performance in sampled data control loops. The idea of GSHF control is to use sampled data feedback, but consider the hold function as a design parameter. Explicit solutions are given for the GSHF versions of the optimal LQ and LQG regulators. The questions of existence, uniqueness and computation of optimal hold functions are treated. An example is presented.
Oil Futures Prices in a Production Economy with Investment Constraints

We document a new stylized fact, that the relationship between the volatility of oil futures prices and the slope of the forward curve is nonmonotone and has a V-shape. This pattern cannot be generated by standard models that emphasize storage. We develop an equilibrium model of oil production in which investment is irreversible and capacity constrained. Investment constraints affect firms’ investment decisions and imply that the supply elasticity changes over time. Since demand shocks must be absorbed by changes in prices or changes in supply, time-varying supply elasticity results in time-varying volatility of futures prices. Estimating this model, we show it is quantitatively consistent with the V-shape relationship between the volatility of futures prices and the slope of the forward curve. IN RECENT YEARS COMMODITY MARKETS have experienced dramatic growth in trad
The Japanese trade balance: Recent history and future prospects

Abstract The rise in the yen in the early 1990s was held out as a `solution' to the persistent Japanese trade surplus. I argued, instead, that the sharp rise in the yen through mid-1993 would lead at most to a modest decline in net exports. This forecast, made in late 1993, was remarkably accurate, despite a subsequent fall in the dollar to 80 yen. It reflects a resilient feature of trade data known to most empirical researchers: that relative price movements account for only a fraction of observed movements in the balance of trade or current account.
On examples of supersingular and rationally chain connected threefolds

Abstract In this work, we show that for a certain class of threefolds in positive characteristics, rational-chain-connectivity is equivalent to supersingularity. The same result is known for K3 surfaces with elliptic fibrations. And there are examples of threefolds that are both supersingular and rationally chain connected.
USING HOME MAINTENANCE AND REPAIRS TO SMOOTH VARIABLE EARNINGS

Abstract Recent research documents a significant increase in U.S. transitory income variance over the past 25 years. An emerging literature explores the role of durables in the household's attempt to smooth consumption over these movements in transitory income. This paper examines the degree to which homeowners adjust their home maintenance decisions in order to offset transitory income fluctuations. American Housing Survey data show that home maintenance expenditures are economically significant, amounting to nearly $2,100 per year. We find a statistically significant positive elasticity of maintenance expenditures to estimated transitory income changes. However, the results suggest that adjusting home maintenance expenditures plays a relatively minor role in the household's overall consumption smoothing strategy. In terms of actual dollars, deferred home maintenance offsets on average from 1 to 7 cents of each dollar of transitory income loss.
Benefits of control, managerial ownership, and the stock returns of acquiring firms

This article examines how the benefits to managers of corporate control affect the relationship between managerial ownership and the stock returns of acquiring firms. At low levels of managerial ownership, agency costs of equity (such as perquisite consumption) reduce acquirer returns. At high levels of managerial ownership, managers enjoy nonassignable private benefits of control that they would lose if they sold their ownership stake. These benefits of control are increasing in the managerial ownership stake. Examining mergers between 1985 and 1991, we find evidence of a nonmonotonic relationship between the returns earned by acquirers and their managerial ownership level.
Individual Behaviors and Substance Use: The Role of Price

I discuss economic approaches to the demand for harmfully addictive substances with an emphasis on the role of money prices. First, I examine trends in the real prices and in the prevalence of the use of cigarettes, alcohol, cocaine, heroin, and marijuana in the U.S.A. Then I present estimates of time-series demand functions. Next, I discuss how economists have modified their traditional model of consumer behaviour to incorporate the addictive aspects of illegal substances. I conclude with implications for tax policy and for the lively and contentious debate concerning the legalization of marijuana, cocaine, and heroin.
Generalizing Lieb's Concavity Theorem via operator interpolation

Abstract We introduce the notion of k-trace and use interpolation of operators to prove the joint concavity of the function ( A , B ) ↦ Tr k [ ( B q 2 K ⁎ A p K B q 2 ) s ] 1 k , which generalizes Lieb's concavity theorem and its following results from trace to a class of homogeneous functions Tr k [ ⋅ ] 1 k . Here Tr k [ A ] denotes the k th elementary symmetric polynomial of the eigenvalues of A. This result gives an alternative proof for the concavity of A ↦ Tr k [ exp ⁡ ( H + log ⁡ A ) ] 1 k that was obtained and used in a recent work to derive expectation estimates and tail bounds on partial spectral sums of random matrices.
Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder

Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functional MRI (fMRI) characterizes brain connectivity and may be used to inform diagnoses and democratize medicine. However, successful construction of predictive models, such as deep learning models, from fMRI requires addressing key choices about the model's architecture, including the number of layers and number of neurons per layer. Meanwhile, deriving functional connectivity (FC) features from fMRI requires choosing an atlas with an appropriate level of granularity. Once an accurate diagnostic model has been built, it is vital to determine which features are predictive of ASD and if similar features are learned across atlas granularity levels. Identifying new important features extends our understanding of the biological underpinnings of ASD, while identifying features that corroborate past findings and extend across atlas levels instills model confidence. To identify aptly suited architectural configurations, probability distributions of the configurations of high versus low performing models are compared. To determine the effect of atlas granularity, connectivity features are derived from atlases with 3 levels of granularity and important features are ranked with permutation feature importance. Results show the highest performing models use between 2–4 hidden layers and 16–64 neurons per layer, granularity dependent. Connectivity features identified as important across all 3 atlas granularity levels include FC to the supplementary motor gyrus and language association cortex, regions whose abnormal development are associated with deficits in social and sensory processing common in ASD. Importantly, the cerebellum, often not included in functional analyses, is also identified as a region whose abnormal connectivity is highly predictive of ASD. Results of this study identify important regions to include in future studies of ASD, help assist in the selection of network architectures, and help identify appropriate levels of granularity to facilitate the development of accurate diagnostic models of ASD.
Theoretically-Efficient and Practical Parallel DBSCAN

The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for DBSCAN in Euclidean space that take O(nlog n) work for two dimensions, sub-quadratic work for three or more dimensions, and can be computed approximately in linear work for any constant number of dimensions. However, existing parallel DBSCAN algorithms require quadratic work in the worst case. This paper bridges the gap between theory and practice of parallel DBSCAN by presenting new parallel algorithms for Euclidean exact DBSCAN and approximate DBSCAN that match the work bounds of their sequential counterparts, and are highly parallel (polylogarithmic depth). We present implementations of our algorithms along with optimizations that improve their practical performance. We perform a comprehensive experimental evaluation of our algorithms on a variety of datasets and parameter settings. Our experiments on a 36-core machine with two-way hyper-threading show that our implementations outperform existing parallel implementations by up to several orders of magnitude, and achieve speedups of up to 33x over the best sequential algorithms.