cssi/SciSciGPT-SciSciNet huggingface dataset:
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation

This paper presents a new Graph Neural Network (GNN) type using feature-wise linear modulation (FiLM). Many standard GNN variants propagate information along the edges of a graph by computing “messages” based only on the representation of the source of each edge. In GNN-FiLM, the representation of the target node of an edge is additionally used to compute a transformation that can be applied to all incoming messages, allowing feature-wise modulation of the passed information. Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods. Hyperparameters for all methods were found using extensive search, yielding somewhat surprising results: differences between baseline models are smaller than reported in the literature. Nonetheless, GNN-FiLM outperforms baseline methods on a regression task on molecular graphs and performs competitively on other tasks.
DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering

Transformer-based QA models use input-wide self-attention -- i.e. across both the question and the input passage -- at all layers, causing them to be slow and memory-intensive. It turns out that we can get by without input-wide self-attention at all layers, especially in the lower layers. We introduce DeFormer, a decomposed transformer, which substitutes the full self-attention with question-wide and passage-wide self-attentions in the lower layers. This allows for question-independent processing of the input text representations, which in turn enables pre-computing passage representations reducing runtime compute drastically. Furthermore, because DeFormer is largely similar to the original model, we can initialize DeFormer with the pre-training weights of a standard transformer, and directly fine-tune on the target QA dataset. We show DeFormer versions of BERT and XLNet can be used to speed up QA by over 4.3x and with simple distillation-based losses they incur only a 1% drop in accuracy. We open source the code at https://github.com/StonyBrookNLP/deformer.
Provable Smoothness Guarantees for Black-Box Variational Inference

Black-box variational inference tries to approximate a complex target distribution though a gradient-based optimization of the parameters of a simpler distribution. Provable convergence guarantees require structural properties of the objective. This paper shows that for location-scale family approximations, if the target is M-Lipschitz smooth, then so is the objective, if the entropy is excluded. The key proof idea is to describe gradients in a certain inner-product space, thus permitting use of Bessel's inequality. This result gives insight into how to parameterize distributions, gives bounds the location of the optimal parameters, and is a key ingredient for convergence guarantees.
Analytical Estimation and Localization of Hardware Trojan Vulnerability in RTL Designs

Offshoring the proprietary Intellectual property (IP) has recently increased the threat of malicious logic insertion in the form of Hardware Trojan (HT). A potential and stealthy HT is triggered with nets that switch rarely during regular circuit operation. Detection of HT in the host design requires exhaustive simulation to activate the HT during pre- and post-silicon. Although the nets with variable switching probability less than a threshold are primarily chosen as a good candidate for Trojan triggering, there is no systematic fine-grained approach for earlier detection of rare nets from word-level measures of input signals. In this paper, we propose a high-level technique to estimate the nets with the rare activity of arithmetic modules from word-level information. Specifically, for a given module, we use the knowledge of internal construction of the architecture to detect “low activity” and “local regions” without resorting to expensive RTL and other low-level simulations. The presented heuristic method abstracts away from the low-level details of design and describes the rare activity of bits (modules) in a word (architecture) as a function of signal statistics. The resulting quick estimates of nets in rare regions allows a designer to develop a compact test generation algorithm without the knowledge of the bit-level activity. We determine the effect of different positions of the breakpoint in the input signal to calculate the accuracy of the approach. We conduct a set of experiments on six adder architectures and four multiplier architectures. The average error to calculate the rare nets between RTL simulation and estimated values are below 2% in all architectures.
ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance

Configuration space complexity makes the big-data software systems hard to configure well. Consider Hadoop, with over nine hundred parameters, developers often just use the default configurations provided with Hadoop distributions. The opportunity costs in lost performance are significant. Popular learning-based approaches to auto-tune software does not scale well for big-data systems because of the high cost of collecting training data. We present a new method based on a combination of Evolutionary Markov Chain Monte Carlo (EMCMC)} sampling and cost reduction techniques tofind better-performing configurations for big data systems. For cost reduction, we developed and experimentally tested and validated two approaches: using scaled-up big data jobs as proxies for the objective function for larger jobs and using a dynamic job similarity measure to infer that results obtained for one kind of big data problem will work well for similar problems. Our experimental results suggest that our approach promises to improve the performance of big data systems significantly and that it outperforms competing approaches based on random sampling, basic genetic algorithms (GA), and predictive model learning. Our experimental results support the conclusion that our approach strongly demonstrates the potential toimprove the performance of big data systems significantly and frugally.
Value and choice as separable and stable representations in orbitofrontal cortex

Value-based decision-making requires different variables-including offer value, choice, expected outcome, and recent history-at different times in the decision process. Orbitofrontal cortex (OFC) is implicated in value-based decision-making, but it is unclear how downstream circuits read out complex OFC responses into separate representations of the relevant variables to support distinct functions at specific times. We recorded from single OFC neurons while macaque monkeys made cost-benefit decisions. Using a novel analysis, we find separable neural dimensions that selectively represent the value, choice, and expected reward of the present and previous offers. The representations are generally stable during periods of behavioral relevance, then transition abruptly at key task events and between trials. Applying new statistical methods, we show that the sensitivity, specificity and stability of the representations are greater than expected from the population's low-level features-dimensionality and temporal smoothness-alone. The separability and stability suggest a mechanism-linear summation over static synaptic weights-by which downstream circuits can select for specific variables at specific times.
Did technology contribute to the housing boom? Evidence from MERS

Abstract We examine the effects of the Mortgage Electronic Registration System, or MERS, on mortgage origination volumes and foreclosure rates prior to the Great Recession. MERS was introduced in the late 1990s and significantly reduced the cost and time associated with secondary mortgage sales. Using novel data from the Massachusetts Registry of Deeds, we show that the introduction of MERS led to an expansion in mortgage credit supply that was primarily fueled by nonbank lenders originating mortgages to low-income borrowers. We also find that foreclosure rates were higher on these mortgages. Our paper provides a new explanation for the credit supply increases observed prior to the 2008 financial crisis and for the disproportionate supply increase observed in low-income areas.
On the Convergence of the Iterative Linear Exponential Quadratic Gaussian Algorithm to Stationary Points

A classical method for risk-sensitive nonlinear control is the iterative linear exponential quadratic Gaussian algorithm. We present its convergence analysis from a first-order optimization viewpoint. We identify the objective that the algorithm actually minimizes and we show how the addition of a proximal term guarantees convergence to a stationary point.
Double jump phase transition in a soliton cellular automaton

In this paper, we consider the soliton cellular automaton introduced in [Takahashi 1990] with a random initial configuration. We give multiple constructions of a Young diagram describing various statistics of the system in terms of familiar objects like birth-and-death chains and Galton-Watson forests. Using these ideas, we establish limit theorems showing that if the first $n$ boxes are occupied independently with probability $p\in(0,1)$, then the number of solitons is of order $n$ for all $p$, and the length of the longest soliton is of order $\log n$ for $p 1/2$. Additionally, we uncover a condensation phenomenon in the supercritical regime: For each fixed $j\geq 1$, the top $j$ soliton lengths have the same order as the longest for $p\leq 1/2$, whereas all but the longest have order at most $\log n$ for $p>1/2$. As an application, we obtain scaling limits for the lengths of the $k^{\text{th}}$ longest increasing and decreasing subsequences in a random stack-sortable permutation of length $n$ in terms of random walks and Brownian excursions.
Distributed Submodular Maximization with Parallel Execution

The submodular maximization problem is widely applicable in many engineering problems where objectives exhibit diminishing returns. While this problem is known to be NP-hard for certain subclasses of objective functions, there is a greedy algorithm which guarantees approximation at least 1/2 of the optimal solution. This greedy algorithm can be implemented with a set of agents, each making a decision sequentially based on the choices of all prior agents. In this paper, we consider a generalization of the greedy algorithm in which agents can make decisions in parallel, rather than strictly in sequence. In particular, we are interested in partitioning the agents, where a set of agents in the partition all make a decision simultaneously based on the choices of prior agents, so that the algorithm terminates in limited iterations. We provide bounds on the performance of this parallelized version of the greedy algorithm and show that dividing the agents evenly among the sets in the partition yields an optimal structure. It is shown that such optimal structures holds even under very relaxed information constraints. We additionally show that this optimal structure is still near-optimal, even when additional information (i.e., total curvature) is known about the objective function.
The motional Stark effect diagnostic reliably measures significant deviations in safety factor profile during DIII-D sawteeth

Motional Stark effect data acquired during large fast-ion stabilized sawteeth are critically reexamined. The safety factor at the sawtooth crash changes by Δ q ≃ 0.15, much more than any likely errors.
Bacterial motility enhances adhesion to oil droplets

Adhesion of bacteria to liquid-liquid interfaces can play a role in the biodegradation of dispersed hydrocarbons and in biochemical and bioprocess engineering. Whereas thermodynamic factors underpinning adhesion are well studied, the role of bacterial activity on adhesion is less explored. Here, we show that bacterial motility enhances adhesion to surfactant-decorated oil droplets dispersed in artificial sea water. Motile Halomonas titanicae adhered to hexadecane droplets stabilized with dioctyl sodium sulfosuccinate (DOSS) more rapidly and at greater surface densities compared to nonmotile H. titanicae, whose flagellar motion was arrested through addition of a proton uncoupler. Increasing the concentration of DOSS reduced the surface density of both motile and nonmotile bacteria as a result of the reduced interfacial tension.
Current state of nonlinear-type time–frequency analysis and applications to high-frequency biomedical signals

Abstract Motivated by analyzing complicated time series, nonlinear-type time–frequency analysis has become an active research topic in the past decades. Those developed tools have been applied to various problems. In this article, we review those developed tools and summarize their applications to high-frequency biomedical signals. They are applied to extract useful features from the signal or quantify its dynamical behavior for the subsequent statistical analysis.
Kernel Assisted Learning for Personalized Dose Finding

An individualized dose rule recommends a dose level within a continuous safe dose range based on patient level information such as physical conditions, genetic factors and medication histories. Traditionally, personalized dose finding process requires repeating clinical visits of the patient and frequent adjustments of the dosage. Thus the patient is constantly exposed to the risk of underdosing and overdosing during the process. Statistical methods for finding an optimal individualized dose rule can lower the costs and risks for patients. In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule. The proposed methodology can also be applied to all other continuous decision-making problems. Advantages of the proposed method include robustness to model misspecification and capability of providing statistical inference for the estimated parameters. In the simulation studies, we show that this method is capable of identifying the optimal individualized dose rule and produces favorable expected outcomes in the population. Finally, we illustrate our approach using data from a warfarin dosing study for thrombosis patients.
Wavelength and Polarization Dependence of Second Harmonic Responses from Gold Nanocrescent Arrays

In the developing field of nonlinear plasmonics, it is important to understand the fundamental relationship between properties of the localized surface plasmon resonance (LSPR) of metallic nanostru...
Markov Decision Processes For Multi-Objective Satellite Task Planning

This paper presents a semi-Markov decision process (SMDP) formulation of the satellite task scheduling problem. This formulation can consider multiple operational objectives simultaneously and plan transitions between distinct functional modes. We consider the problem of scheduling image collections, ground contacts, sun-pointed periods for battery recharging, and data recorder management for an agile, resource-constrained Earth-observing spacecraft. By considering multiple mission objectives simultaneously, the algorithm is able to find optimized task schedule that satisfies all operational constraints in a single planning step, thus reducing the operational complexity and number of steps involved in mission planning. We present two solution approaches based on forward search and Monte Carlo Tree search. We baseline against rule-based, graph search, and mixed-integer-linear programming approaches. The SMDP formulation is evaluated in both single-objective and multi-objective scenarios. The SMDP solution is found to perform comparably with the baseline methods at greatly increased speed in single-objective scenarios and greater schedule reward in multi-objective.
Intraoperative management of a hybrid extracorporeal membrane oxygenation circuit for lung transplantation.

Background The use of extracorporeal circulation (ECC) for intraoperative cardiopulmonary support during lung transplantation has been increasing in the recent years. Our group previously described a novel hybrid extracorporeal membrane oxygenation (ECMO) circuit for use in lung transplantation. Technique Our approach for intraoperative management of our novel hybrid ECMO circuit for lung transplantation is driven by two main goals: The first is to deliver management that ensures an appropriate balance between the native and ECMO cardiac outputs in order to provide a stable environment that promotes attenuation of ischemic-reperfusion injury during implantation. The second is to provide a stable hemodynamic environment that results in an appropriate global perfusion guided by multiple monitors and an organ systems-based approach during implantation. Comments Our novel technique for intraoperative management of this circuit during lung transplantation is described.
Implications of Cheap Oil for Emerging Markets

The COVID-19-triggered collapse in oil prices in March and April 2020 was the seventh, and by far the most severe, in a series of such collapses since 1970. This paper, first, compares this most recent collapse and its drivers with previous ones in an event study. It finds that it was associated with an exceptionally severe plunge in oil demand. Second, in a local projections model, this paper estimates the implications of demand- and supply-driven oil price collapses for growth in emerging markets and developing economies (EMDEs). The paper finds that steep oil price collapses were associated with significant and lasting output losses in energy-exporting EMDEs but no meaningful output gains in energy-importing EMDEs. These results are robust to multiple robustness checks.
BioSkills guide: Development and national validation of a tool for interpreting the vision and change core competencies

This article presents the BioSkills Guide, a resource enumerating program- and course-level learning outcomes aligned with the six core competencies of Vision and Change. The learning outcomes were...
Hierarchical community detection by recursive partitioning

The problem of community detection in networks is usually formulated as finding a single partition of the network into some “correct” number of communities. We argue that it is more interpretable a...
Fisher-Rao Regularized Transport Analysis of the Glymphatic System and Waste Drainage.

In this work, a unified representation of all the time-varying dynamics is accomplished with a Lagrangian framework for analyzing Fisher-Rao regularized dynamical optimal mass transport (OMT) derived flows. While formally equivalent to the Eulerian based Schrodinger bridge OMT regularization scheme, the Fisher-Rao approach allows a simple and interpretable methodology for studying the flows of interest in the present work. The advantage of the proposed Lagrangian technique is that the time-varying particle trajectories and attributes are displayed in a single visualization. This provides a natural capability to identify and distinguish flows under different conditions. The Lagrangian analysis applied to the glymphatic system (brain waste removal pathway associated with Alzheimer’s Disease) successfully captures known flows and distinguishes between flow patterns under two different anesthetics, providing deeper insights into altered states of waste drainage.
Smaller p-Values via Indirect Information

This article develops p-values for evaluating means of normal populations that make use of indirect or prior information. A p-value of this type is based on a biased frequentist hypothesis test tha...
Exploring and Predicting Transferability across NLP Tasks

Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. Can fine-tuning these models on tasks other than language modeling further improve performance? In this paper, we conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of problems (text classification, question answering, and sequence labeling). Our results show that transfer learning is more beneficial than previously thought, especially when target task data is scarce, and can improve performance even with low-data source tasks that differ substantially from the target task (e.g., part-of-speech tagging transfers well to the DROP QA dataset). We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task, and we validate their effectiveness in experiments controlled for source and target data size. Overall, our experiments reveal that factors such as data size, task and domain similarity, and task complexity all play a role in determining transferability.
Spectral Observations of Diffuse Far-Ultraviolet Emission from the Hot Phase of the Interstellar Medium with the Diffuse Ultraviolet Experiment

One of the keys to interpreting the character and evolution of interstellar matter in the Galaxy is understanding the distribution of the low-density hot (105-106 K) phase of the interstellar medium (ISM). This phase is much more difficult to observe than the cooler high-density components of the ISM because of its low density and lack of easily observable tracers. Because gas of this temperature emits mainly in the far-ultraviolet (FUV) (912-1800 A) and extreme-ultraviolet (EUV) (80-912 A), and (for gas hotter than 106 K) X-rays, observations in these bands can provide important constraints to the distribution of this gas. Because of interstellar opacity at EUV wavelengths, only FUV and X-ray observations can provide clues to the properties of hot gas from distant regions. We present results from a search for FUV emission from the diffuse ISM conducted with an orbital FUV spectrometer, DUVE, which was launched in 1992 July. The DUVE spectrometer, which covers the band from 950 to 1080 A with 3.2 A resolution, observed a region of low neutral hydrogen column density near the south Galactic pole for a total effective integration time of 1583 s. The only emission line detected was a geocoronal hydrogen line at 1025 A. We are able to place upper limits to several expected emission features that provide constraints on interstellar plasma parameters. We are also able to place limits on the continuum emission throughout the bandpass. We compare these limits and other diffuse observations with several models of the structure of the ISM and discuss the ramifications.
ISOTOPIC CONVERGENCE THEOREM

When approximating a space curve, it is natural to consider whether the knot type of the original curve is preserved in the approximant. This preservation is of strong contemporary interest in computer graphics and visualization. We establish a criterion to preserve knot type under approximation that relies upon pointwise convergence and convergence in total curvature.
The $h$–principle for broken Lefschetz fibrations

It is known that an arbitrary smooth, oriented 4–manifold admits the structure of what is called a broken Lefschetz fibration. Given a broken Lefschetz fibration, there are certain modifications, realized as homotopies of the fibration map, that enable one to construct infinitely many distinct fibrations of the same manifold. The aim of this paper is to prove that these modifications are sufficient to obtain every broken Lefschetz fibration in a given homotopy class of smooth maps. One notable application is that adding an additional “projection" move generates all broken Lefschetz fibrations, regardless of homotopy class. The paper ends with further applications and open problems.
Towards an effective particle string resolution of the cosmological constant problem

The Cosmological Constant Problem is re-examined from an effective field theory perspective. While the connection between gravity and particle physics has not been experimentally probed in the quantum regime, it is severely constrained by the successes of Standard Model quantum field theory at short distances, and classical General Relativity at large distances. At first sight, it appears that combining particle physics and gravity inevitably leads to an effective field theory below the weak scale which suffers from large radiative corrections to the cosmological constant. Consequently, this parameter must be very finely tuned to lie within the experimental bounds. An analog of just this type of predicament, and its resolution, are described in some detail using only familiar quantum field theory. The loop-hole abstracted from the analogy is the possibility of graviton ``compositeness'' at a scale less than 10−2 eV, which cuts off the large contributions to the cosmological constant from standard model physics. Experimentally, this would show up as a dramatic breakdown of Newton's Law in upcoming sub-centimeter tests of gravity. Currently, strings are the only known example of such compositeness. It is proposed that the gravitational sector comprises strings of very low tension, which couple to a stringy ``halo'' surrounding each point-like standard model particle.
State space distribution and dynamical flow for closed and open quantum systems

We present a general formalism for studying the effects of heterogeneity in open quantum systems. We develop this formalism in the state space of density operators, on which ensembles of quantum states can be conveniently represented by probability distributions. We describe how this representation reduces ambiguity in the definition of quantum ensembles by providing the ability to explicitly separate classical and quantum sources of probabilistic uncertainty. We then derive explicit equations of motion for state space distributions of both open and closed quantum systems and demonstrate that resulting dynamics take a fluid mechanical form analogous to a classical probability fluid on Hamiltonian phase space, thus enabling a straightforward quantum generalization of Liouville’s theorem. We illustrate the utility of our formalism by analyzing the dynamics of an open two-level system using the state-space formalism that is shown to be consistent with the derived analytical results.
PULSAR WIND NEBULAE WITH THICK TOROIDAL STRUCTURE

We investigate a class of pulsar wind nebulae that show synchrotron emission from a thick toroidal structure. The best studied such object is the small radio and X-ray nebula around the Vela pulsar, which can be interpreted as the result of interaction of a mildly supersonic inward flow with the recent pulsar wind. Such a flow near the center of a supernova remnant can be produced in a transient phase when the reverse shock reaches the center of the remnant. Other nebulae with a thick toroidal structure are G106.6+2.9 and G76.9+1.0. Their structure contrasts with young pulsar nebulae like the Crab Nebula and 3C 38, which show a more chaotic, filamentary structure in the synchrotron emission. In both situations, a torus-jet structure is present where the pulsar wind passes through a termination shock, indicating the flow is initially toroidal. We suggest that the difference is due to the Rayleigh-Taylor instability that operates when the outer boundary of the nebula is accelerating into freely expanding supernova ejecta. The instability gives rise to mixing in the Crab and related objects, but is not present in the nebulae with thick toroidal regions.
Noise-Based Logic Gates by Operations on the Reference System

We propose a new, low-complexity solution to realize multi-input-bit gates acting on exponentially large superpositions in noise-based logic processors. Two examples are shown, the NOT gate and the...
Synchronization of flexible sheets

When swimming in close proximity, some microorganisms such as spermatozoa synchronize their flagella. Previous work on swimming sheets showed that such synchronization requires a geometrical asymmetry in the flagellar waveforms. Here we inquire about a physical mechanism responsible for such symmetry breaking in nature. Using a two-dimensional model, we demonstrate that flexible sheets with symmetric internal forcing deform when interacting with each other via a thin fluid layer in such a way as to systematically break the overall waveform symmetry, thereby always evolving to an in-phase conformation where energy dissipation is minimized. This dynamics is shown to be mathematically equivalent to that obtained for prescribed waveforms in viscoelastic fluids, emphasizing the crucial role of elasticity in symmetry breaking and synchronization.
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics

We consider the fundamental problem of selecting $k$ out of $n$ random variables in a way that the expected highest or second-highest value is maximized. This question captures several applications where we have uncertainty about the quality of candidates (e.g. auction bids, search results) and have the capacity to explore only a small subset due to an exogenous constraint. For example, consider a second price auction where system constraints (e.g., costly retrieval or model computation) allow the participation of only $k$ out of $n$ bidders, and the goal is to optimize the expected efficiency (highest bid) or expected revenue (second highest bid). We study the case where we are given an explicit description of each random variable. We give a PTAS for the problem of maximizing the expected highest value. For the second-highest value, we prove a hardness result: assuming the Planted Clique Hypothesis, there is no constant factor approximation algorithm that runs in polynomial time. Surprisingly, under the assumption that each random variable has monotone hazard rate (MHR), a simple score-based algorithm, namely picking the $k$ random variables with the largest $1/\sqrt{k}$ top quantile value, is a constant approximation to the expected highest and second highest value, \emph{simultaneously}.
Adams operations on matrix factorizations

We define Adams operations on matrix factorizations, and we show these operations enjoy analogues of several key properties of the Adams operations on perfect complexes with support developed by Gillet-Soul\'e in their paper "Intersection Theory Using Adams Operations". As an application, we give a proof of a conjecture of Dao-Kurano concerning the vanishing of Hochster's theta invariant.
Period integrals of CY and general type complete intersections

We develop a global Poincare residue formula to study period integrals of families of complex manifolds. For any compact complex manifold X equipped with a linear system V ∗ of generically smooth CY hypersurfaces, the formula expresses period integrals in terms of a canonical global meromorphic top form on X. Two important ingredients of this construction are the notion of a CY principal bundle, and a classification of such rank one bundles. We also generalize the construction to CY and general type complete intersections. When X is an algebraic manifold having a sufficiently large automorphism group G and V ∗ is a linear representation of G, we construct a holonomic D-module that governs the period integrals. The construction is based in part on the theory of tautological systems we have developed in the paper Lian, Song and Yau ( arXiv:1105.2984v1 , 2011). The approach allows us to explicitly describe a Picard-Fuchs type system for complete intersection varieties of general types, as well as CY, in any Fano variety, and in a homogeneous space in particular. In addition, the approach provides a new perspective of old examples such as CY complete intersections in a toric variety or partial flag variety.
Separated Belyi maps

We construct Belyi maps having specified behavior at finitely many points. Specifically, for any curve C defined over Q-bar, and any disjoint finite subsets S, T in C(Q-bar), we construct a finite morphism f: C -> P^1 such that f ramifies at each point in S, the branch locus of f is {0,1, infty}, and f(T) is disjoint from {0,1, infty}. This refines a result of Mochizuki's. We also prove an analogous result over fields of positive characteristic, and in addition we analyze how many different Belyi maps f are required to imply the above conclusion for a single C and S and all sets T in C(Q-bar) \ S of prescribed cardinality.
VERY LARGE ARRAY MONITORING OF 1720 MHz OH MASERS TOWARD THE GALACTIC CENTER

We present the first variability study of the 1720 MHz OH masers located in the Galactic center. Most of these masers are associated with the interaction between the supernova remnant Sgr A East and the interstellar medium, but a few masers are associated with the circumnuclear disk (CND). The monitoring program covered five epochs and a timescale of 20-195 days, during which no masers disappeared and no new masers appeared. All masers have previously been detected in a single-epoch observation about one year prior to the start of the monitoring experiment, implying relatively stable conditions for the 1720 MHz OH masers. No extreme variability was detected. The masers associated with the northeastern interaction region between the supernova remnant and the +50 km s–1 molecular cloud show the highest level of variability. This can be explained with the +50 km s–1 molecular cloud being located behind the supernova remnant and with a region of high OH absorbing column density along the line of sight. Possibly, the supernova remnant provides additional turbulence to the gas in this region, through which the maser emission must travel. The masers in the southern interaction region are located on the outermost edge of Sgr A East, the line of sight of which is not covered by either absorbing OH gas or a supernova remnant, in agreement with the much lower variability level observed. Similarly, the masers associated with the CND show little variability, consistent with those arising through collisions between relatively large clumps of gas in the CND and no significant amount of turbulent gas along the line of sight.
Adiabatic isometric mapping algorithm for embedding 2-surfaces in Euclidean 3-space

Alexandrov proved that any simplicial complex homeomorphic to a sphere with strictly non-negative Gaussian curvature at each vertex can be isometrically embedded uniquely in as a convex polyhedron. Due to the nonconstructive nature of his proof, there have yet to be any algorithms, that we know of, that realizes the Alexandrov embedding in polynomial time. Following his proof, we developed the adiabatic isometric mapping (AIM) algorithm. AIM uses a guided adiabatic pull-back procedure on a given polyhedral metric to produce an embedding that approximates the unique Alexandrov polyhedron. Tests of AIM applied to two different polyhedral metrics suggests that its run time is sub cubic with respect to the number of vertices. Although Alexandrov's theorem specifically addresses the embedding of convex polyhedral metrics, we tested AIM on a broader class of polyhedral metrics that included regions of negative Gaussian curvature. One test was on a surface just outside the ergosphere of a Kerr black hole.
Comparative Design, Scaling, and Control of Appendages for Inertial Reorientation

This paper develops a comparative framework for the design of actuated inertial appendages for planar aerial reorientation. We define the inertial reorientation template, the simplest model of this behavior, and leverage its linear dynamics to reveal the design constraints linking a task with the body designs capable of completing it. As practicable inertial appendage designs lead to morphology that is generally more complex, we advance a notion of “anchoring,” whereby a judicious choice of physical design in concert with an appropriate control policy yields a system whose closed-loop dynamics are sufficiently captured by the template to permit all further designs to take place in its far simpler parameter space. This approach is effective and accurate over the diverse design spaces afforded by existing platforms, enabling a performance comparison through the shared task space. We analyze examples from the literature and find advantages to each body type, but conclude that tails provide the highest potential performance for reasonable designs. Thus motivated, we build a physical example by retrofitting a tail to a RHex robot and present empirical evidence of its efficacy.
Local-global principles for norm one tori over semi-global fields

Let K be a complete discretely valued field with the residue field $$\kappa $$ . Let F be the function field of a smooth, projective, geometrically integral curve over K and $$\mathscr {X}$$ be a regular proper model of F such that the reduced special fibre X is a union of regular curves with normal crossings. Suppose that the graph associated to $$\mathscr {X}$$ is a tree (e.g. $$F = K(t)$$ ). Let L/F be a Galois extension of degree n such that n is coprime to char $$(\kappa )$$ . Suppose that $$\kappa $$ is an algebraically closed field or a finite field containing a primitive $$n^\mathrm{th}$$ root of unity. Then we show that the local-global principle holds for the norm one torus associated to the extension L/F with respect to discrete valuations on F i.e. an element in $$F^{\times }$$ is a norm from the extension L/F if and only if it is a norm from the extensions $$L\otimes _F F_\nu /F_\nu $$ for all discrete valuations $$\nu $$ of F.
Giant Planet Formation by Disk Instability in Low Mass Disks

Forming giant planets by disk instability requires a gaseous disk that is massive enough to become gravitationally unstable and able to cool fast enough for self-gravitating clumps to form and survive. Models with simplified disk cooling have shown the critical importance of the ratio of the cooling to the orbital timescales. Uncertainties about the proper value of this ratio can be sidestepped by including radiative transfer. Three-dimensional radiative hydrodynamics models of a disk with a mass of 0.043 M ☉ from 4 to 20 AU in orbit around a 1 M ☉ protostar show that disk instabilities are considerably less successful in producing self-gravitating clumps than in a disk with twice this mass. The results are sensitive to the assumed initial outer disk (To ) temperatures. Models with To = 20 K are able to form a single self-gravitating clump, whereas models with To = 25 K form clumps that are not quite self-gravitating. These models imply that disk instability requires a disk with a mass of at least ~0.043 M ☉ inside 20 AU in order to form giant planets around solar-mass protostars with realistic disk cooling rates and outer-disk temperatures. Lower mass disks around solar-mass protostars must rely upon core accretion to form inner giant planets.
An adjustable-length cavity and Bose?Einstein condensate apparatus for multimode cavity QED

We present a novel cavity QED system in which a Bose?Einstein condensate (BEC) is trapped within a high-finesse optical cavity whose length may be adjusted to access both single-mode and multimode configurations. We demonstrate the coupling of an atomic ensemble to the cavity in both configurations and measure that the single-atom, -mode cooperativity exceeds unity. The atoms are confined either within an intracavity far-off-resonance optical dipole trap or a crossed optical dipole trap via transversely oriented lasers. Multimode cavity QED provides fully emergent and dynamical optical lattices for intracavity BECs, in that the process of atomic self-organization may be described as a continuous symmetry breaking phase transition resulting in the emergence of a compliant lattice with phonon-like excitations. Such systems will enable explorations of quantum soft matter, including superfluid smectics, superfluid glasses, and spin glasses as well as neuromorphic associative memory.
Cavity nonlinear optics with layered materials

Unprecedented material compatibility and ease of integration, in addition to the unique and diverse optoelectronic properties of layered materials have generated significant interest in their utilization in nanophotonic devices. While initial nanophotonic experiments primarily focused on light-sources, modulators, and detectors, recently researchers have demonstrated nonlinear optical devices using layered materials. In this paper, we review the current state of cavity-enhanced nonlinear optics with layered materials. Along with conventional nonlinear optics related to harmonic generation, we report on emerging directions of nonlinear optics, where the layered materials can potentially play a significant role.
Electrostatic theory for designing lossless negative permittivity metamaterials.

We develop an electrostatic theory for designing bulk composites with effective lossless negative permittivities. The theory and associated design procedure are validated by comparing their predictions with those of rigorous full-wave simulations. It is demonstrated that the excitation of the Frohlich mode (the first-order surface mode) of the constitutive nanoparticles plays a key role in achieving negative permittivities with compensated losses.
Initial Mass Function Variability (or Not) among Low-velocity Dispersion, Compact Stellar Systems

Analyses of strong gravitational lenses, galaxy-scale kinematics, and absorption line stellar population synthesis (SPS) have all concluded that the stellar initial mass function (IMF) varies within the massive early-type galaxy (ETG) population. However, the physical mechanism that drives variation in the IMF is an outstanding question. Here we use new SPS models to consider a diverse set of compact, low-velocity dispersion stellar systems: globular clusters (GCs), an ultra-compact dwarf (UCD), and the compact elliptical (cE) galaxy M32. We compare our results to massive ETGs and available dynamical measurements. We find that the GCs have stellar mass-to-light ratios (M/L) that are either consistent with a Kroupa IMF or are slightly bottom-light while the UCD and cE have mildly elevated M/L. The separation in derived IMFs for systems with similar metallicities and abundance patterns indicates that our SPS models can distinguish abundance and IMF effects. Variation among the sample in this paper is only $\sim 50\%$ in normalized M/L compared to the $\sim 4\times$ among the ETG sample. This suggests that metallicity is not the sole driver of IMF variability and additional parameters need to be considered.
Distinct degrees and their distribution in complex networks

We investigate a variety of statistical properties associated with the number of distinct degrees that exist in a typical network for various classes of networks. For a single realization of a network with N nodes that is drawn from an ensemble in which the number of nodes of degree k has an algebraic tail, Nk N=k for k 1, the number of distinct degrees grows as N 1= . Such an algebraic growth is also observed in scientic citation data. We also determine the N dependence of statistical quantities associated with the sparse, large-k range of the degree distribution, such as the location of the rst hole (where Nk = 0), the last doublet (two consecutive occupied degrees), triplet, dimer (Nk = 2), trimer, etc.
Ankle phenomenon in the cosmic ray energy spectrum

The author has suggested that the knee phenomenon in the cosmic ray energy spectrum at 3 PeV can be explained as a split between a radiation-dominated expansion and a matter-dominated expansion of an expanding heat bath. The model proposed in 1985, in fact, predicted that high energy cosmic rays are emitted from AGN, massive black holes, in agreement with recent data from the Pierre Auger Observatory. Similarly, the ankle phenomenon at 3 EeV is shown to be explained by a split between inflational expansion and ordinary material expansion of the expanding heat bath, not unlike that in the expansion of the universe. All the spectral indicies in the respective regions of the energy spectra agree with the theoretical calculation from the respective expansion rates. It is shown that the ankle energy is approximately equal to the threshold energy of cosmic ray production of an electron positron pair on the cmb photon.
Fundamentals of Power Allocation Strategies for Downlink Multi-User NOMA With Target Rates

For downlink multi-user non-orthogonal multiple access (NOMA) systems with successive interference cancellation (SIC) receivers, and a base-station not possessing the instantaneous channel gains, the fundamental relationship between the target rates and power allocation is investigated. It is proven that the total interference from signals not removed by SIC has a fundamental upper limit which is a function of the target rates, and the outage probability equals one when exceeding this limit. The concept of well-behaved power allocation strategies is defined, and its properties are proven to be derived solely based on the target rates. The existence of power allocation strategies that enable NOMA to outperform OMA in per-user outage probability is proven, and are always well-behaved for the case when the outage probability performance of NOMA and OMA are equal for all users. The proposed SIC decoding order is then shown to be the most energy efficient. The derivation of well-behaved power allocation strategies that have improved outage probability performance over OMA for each user is outlined. Simulations validate the theoretical results, demonstrating that NOMA systems can always outperform OMA systems in outage probability performance, without relying on the exact channel gains.
Artificial neural network in cosmic landscape

In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.
Experimental Setup for the Measurement of the Thermoelectric Power in Zero and Applied Magnetic Field

An experimental setup was developed for the measurement of the thermoelectric power (TEP, Seebeck coefficient) in the temperature range from 2 to 350 K and magnetic fields up to 140 kOe. The system was built to fit in a commercial cryostat and is versatile, accurate and automated; using two heaters and two thermometers increases the accuracy of the TEP measurement. High density data of temperature sweeps from 2 to 350 K can be acquired within 16 h and high density data of isothermal field sweeps from 0 to 140 kOe can be obtained within 2 h. Calibrations for the system have been performed on a platinum wire and Bi2Sr2CaCu2O8 + δ high-Tc superconductors. The measured TEP of phosphor bronze (voltage lead wire) turns out to be very small; the absolute TEP value of the phosphor bronze wire is much less than 0.5 µV K−1 below 80 K. For copper and platinum wires measured against the phosphor bronze wire, the agreement between measured results and the literature data is good. To demonstrate the applied magnetic field response of the system, we report measurements of the TEP on single crystal samples of LaAgSb2 and CeAgSb2 in fields up to 140 kOe.
Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting.