cssi/SciSciGPT-SciSciNet huggingface dataset:
A stopping criterion for Markov chains when generating independent random graphs

Markov chains are convenient means of generating realizations of networks with a given (joint or otherwise) degree distribution, since they simply require a procedure for rewiring edges. The major challenge is to find the right number of steps to run such a chain, so that we generate truly independent samples. Theoretical bounds for mixing times of these Markov chains are too large to be practically useful. Practitioners have no useful guide for choosing the length, and tend to pick numbers fairly arbitrarily. We give a principled mathematical argument showing that it suffices for the length to be proportional to the number of desired number of edges. We also prescribe a method for choosing this proportionality constant. We run a series of experiments showing that the distributions of common graph properties converge in this time, providing empirical evidence for our claims.
Tensor-splitting properties of $n$-inverse pairs of operators

In this paper we study n-inverse pairs of operators on the tensor product of Banach spaces. In particular we show that an n-inverse pair of elementary tensors of operators on the tensor product of two Banach spaces can arise only from l- and m-inverse pairs of operators on the individual spaces. This gives a converse to a result of Duggal and Muller, and proves a conjecture of the second named author. Our proof uses techniques from algebraic geometry, which generalize to other relations among operators in a tensor product. We apply this theory to obtain results for n-symmetries in a tensor product as well.
The symmetric signature of a Witt space

Witt spaces are pseudomanifolds for which the middle-perversity intersection homology with rational coefficients is self-dual. We give a new construction of the symmetric signature for Witt spaces which is similar in spirit to the construction given by Miscenko for manifolds. Our construction has all of the expected properties, including invariance under stratified homotopy equivalence.
Distance to the line in the Heston model

Abstract The main object of study in the paper is the distance from a point to a line in the Riemannian manifold associated with the Heston model. We reduce the problem of computing such a distance to certain minimization problems for functions of one variable over finite intervals. One of the main ideas in this paper is to use a new system of coordinates in the Heston manifold and the level sets associated with this system. In the case of a vertical line, the formulas for the distance to the line are rather simple. For slanted lines, the formulas are more complicated, and a more subtle analysis of the level sets intersecting the given line is needed. We also find simple formulas for the Heston distance from a point to a level set. As a natural application, we use the formulas obtained in the present paper in the study of the small maturity limit of the implied volatility in the Heston model.
Foliations and rational connectedness in positive characteristic

In this paper, the technique of foliations in characteristic $p$ is used to investigate the difference between rational connectedness and separable rational connectedness in positive characteristic. The notion of being freely rationally connected is defined; a variety is freely rationally connected if a general pair of points can be connected by a free rational curve. It is proved that a freely rationally connected variety admits a finite purely inseparable morphism to a separably rationally connected variety. As an application, a generalized Graber-Harris-Starr type theorem in positive characteristic is proved; namely, if a family of varieties over a smooth curve has the property that its geometric generic fiber is normal and freely rationally connected, then it has a rational section after some Frobenius twisting. We also show that a freely rationally connected variety is simply connected.
Period lengths for iterated functions

Let Ωn be the nn-element set consisting of all functions that have {1, 2, 3,. . ., n} as both domain and codomain. Let T(f) be the order of f, i.e., the period of the sequence f, f(2), f(3), f(4). . . of compositional iterates. A closely related number, B(f) = the product of the lengths of the cycles of f, has previously been used as an approximation for T. This paper proves that the average values of these two quantities are quite different. The expected value of T is \[ \frac{1}{n^{n}}\sum\glimits_{f\in \Omega_{n}}{\bf T}(f)=\exp\biggl(k_{0}\sqrt[3]{\frac{n}{\log^{2}n}}\bigl(1+o(1)\bigr)\biggr), \] where k0 is a complicated but explicitly defined constant that is approximately 3.36. The expected value of B is much larger: \[ \frac{1}{n^{n}}\sum\glimits_{f\in \Omega_{n}}{\bf B}(f)=\exp\biggl(\frac{3}{2}\sqrt[3]{n}\bigl(1+o(1)\bigr)\biggr). \]
On the localization theorem for F-pure rings

Abstract In this article, we solve Grothendieck’s localization problem for a certain class of rings that arises from tight closure theory. It is quite essential to use the relative version of the Frobenius map (Radu–Andre morphism) to investigate the fibers of a flat ring homomorphism of characteristic p > 0 . We also give some interesting geometric applications of the localization problem.
A Pretrained DenseNet Encoder for Brain Tumor Segmentation

This article presents a convolutional neural network for the automatic segmentation of brain tumors in multimodal 3D MR images based on a U-net architecture. We evaluate the use of a densely connected convolutional network encoder (DenseNet) which was pretrained on the ImageNet data set. We detail two network architectures that can take into account multiple 3D images as inputs. This work aims to identify if a generic pretrained network can be used for very specific medical applications where the target data differ both in the number of spatial dimensions as well as in the number of inputs channels. Moreover in order to regularize this transfer learning task we only train the decoder part of the U-net architecture. We evaluate the effectiveness of the proposed approach on the BRATS 2018 segmentation challenge [1, 2, 3, 4, 5] where we obtained dice scores of 0.79, 0.90, 0.85 and 95% Hausdorff distance of 2.9 mm, 3.95 mm, and 6.48 mm for enhanced tumor core, whole tumor and tumor core respectively on the validation set. This scores degrades to 0.77, 0.88, 0.78 and 95% Hausdorff distance of 3.6 mm, 5.72 mm, and 5.83 mm on the testing set [1].
A representation theory perspective on simultaneous alignment and classification

Abstract Single particle cryo-electron microscopy (EM) is a method for determining the 3-D structure of macromolecules from many noisy 2-D projection images of individual macromolecules whose orientations and positions are random and unknown. The problem of orientation assignment for the images motivated work on multireference alignment. The recent non-unique games framework provides a representation theoretic approach to alignment over compact groups, and offers a convex relaxation with certificates of global optimality in some cases. One of the great opportunities in cryo-EM is studying heterogeneous samples, containing two or more distinct conformations of molecules. Taking advantage of this opportunity presents an algorithmic challenge: determining both the class and orientation of each particle. We generalize multireference alignment to a problem of alignment and classification, and propose to extend non-unique games to the problem of simultaneous alignment and classification with the goal of simultaneously classifying cryo-EM images and aligning them within their classes.
Monotonicity of quantum relative entropy and recoverability

The relative entropy is a principal measure of distinguishability in quantum information theory, with its most important property being that it is non-increasing with respect to noisy quantum operations. Here, we establish a remainder term for this inequality that quantifies how well one can recover from a loss of information by employing a rotated Petz recovery map. The main approach for proving this refinement is to combine the methods of [Fawzi and Renner, 2014] with the notion of a relative typical subspace from [Bjelakovic and Siegmund-Schultze, 2003]. Our paper constitutes partial progress towards a remainder term which features just the Petz recovery map (not a rotated Petz map), a conjecture which would have many consequences in quantum information theory. A well known result states that the monotonicity of relative entropy with respect to quantum operations is equivalent to each of the following inequalities: strong subadditivity of entropy, concavity of conditional entropy, joint convexity of relative entropy, and monotonicity of relative entropy with respect to partial trace. We show that this equivalence holds true for refinements of all these inequalities in terms of the Petz recovery map. So either all of these refinements are true or all are false.
A linear Cheeger inequality using eigenvector norms

The Cheeger constant, hG, is a measure of expansion within a graph. The classical Cheeger Inequality states: λ1/2 ≤ hG ≤ √ 2λ1 where λ1 is the first nontrivial eigenvalue of the normalized Laplacian matrix. Hence, hG is tightly controlled by λ1 to within a quadratic factor. We give an alternative Cheeger inequality where we consider the∞-norm of the corresponding eigenvector in addition to λ1. This inequality controls hG to within a linear factor of λ1 thereby providing an improvement to the previous quadratic bounds. An additional advantage of our result is that while the original Cheeger constant makes it clear that hG → 0 as λ1 → 0, our result shows that hG → 1/2 as λ1 → 1.
The distribution of special subsets of the Farey sequence

Abstract We will examine the subset F Q,p of Farey fractions of order Q consisting of those fractions whose denominators are not divisible by a fixed prime p. In particular, we will provide an asymptotic result on the distribution of H-tuples of consecutive fractions in F Q,p , as Q→∞.
Sparse Signal Detection With Compressive Measurements via Partial Support Set Estimation

In this paper, we consider the problem of sparse signal detection based on partial support set estimation with compressive measurements in a distributed network. Multiple nodes in the network are assumed to observe sparse signals, which share a common but unknown support. While in the traditional compressive sensing framework, the goal is to recover the complete sparse signal, in sparse signal detection, complete signal recovery may not be necessary to make a reliable detection decision. In particular, detection can be performed based on partially or inaccurately estimated signals, which requires less computational burden than that is required for complete signal recovery. To that end, we investigate the problem of sparse signal detection based on partially estimated support set. First, we discuss how to determine the minimum fraction of the support set to be known so that a desired detection performance is achieved in a centralized setting. Second, we develop two distributed algorithms for sparse signal detection when the raw compressed observations are not available at the central fusion center. In these algorithms, the final decision statistic is computed based on locally estimated partial support sets via orthogonal matching pursuit at individual nodes. The proposed distributed algorithms with less communication overhead are shown to provide comparable performance (sometimes better) to the centralized approach when the size of the estimated partial support set is very small.
Fictitious GAN: Training GANs with Historical Models

Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as a two-player zero-sum game between two neural networks. Here, we leverage this game theoretic view to study the convergence behavior of the training process. Inspired by the fictitious play learning process, a novel training method, referred to as Fictitious GAN, is introduced. Fictitious GAN trains the deep neural networks using a mixture of historical models. Specifically, the discriminator (resp. generator) is updated according to the best-response to the mixture outputs from a sequence of previously trained generators (resp. discriminators). It is shown that Fictitious GAN can effectively resolve some convergence issues that cannot be resolved by the standard training approach. It is proved that asymptotically the average of the generator outputs has the same distribution as the data samples.
AstroBlend: An Astrophysical Visualization Package for Blender

Abstract The rapid growth in scale and complexity of both computational and observational astrophysics over the past decade necessitates efficient and intuitive methods for examining and visualizing large datasets. Here, I present AstroBlend, an open-source Python library for use within the three dimensional modeling software, Blender. While Blender has been a popular open-source software among animators and visual effects artists, in recent years it has also become a tool for visualizing astrophysical datasets. AstroBlend combines the three dimensional capabilities of Blender with the analysis tools of the widely used astrophysical toolset, yt, to afford both computational and observational astrophysicists the ability to simultaneously analyze their data and create informative and appealing visualizations. The introduction of this package includes a description of features, work flow, and various example visualizations. A website — www.astroblend.com — has been developed which includes tutorials, and a gallery of example images and movies, along with links to downloadable data, three dimensional artistic models, and various other resources.
CH and the Moore–Mrowka problem

Abstract We solve an old question of Arhangelskii (the “Moore–Mrowka with CH problem”) by showing that the existence of a compact countably tight non-sequential space does not follow from the Continuum Hypothesis. Such spaces have long been known to exist under the stronger assumption ⋄.
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes

Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. However, most models do not take into account the fact that the effects of clutter interact with the foveated nature of the human visual system: visual clutter further from the fovea has an increasing detrimental influence on perception. Here, we introduce a new foveated clutter model to predict the detrimental effects in target search utilizing a forced fixation search task. We use Feature Congestion (Rosenholtz et al.) as our non foveated clutter model, and we stack a peripheral architecture on top of Feature Congestion for our foveated model. We introduce the Peripheral Integration Feature Congestion (PIFC) coefficient, as a fundamental ingredient of our model that modulates clutter as a non-linear gain contingent on eccentricity. We finally show that Foveated Feature Congestion (FFC) clutter scores (r(44) = −0.82 ± 0.04, p < 0.0001) correlate better with target detection (hit rate) than regular Feature Congestion (r(44) = −0.19 ± 0.13, p = 0.0774) in forced fixation search; and we extend foveation to other clutter models showing stronger correlations in all cases. Thus, our model allows us to enrich clutter perception research by computing fixation specific clutter maps. Code for building peripheral representations is available.
Enumerating cycles in the graph of overlapping permutations

Abstract The graph of overlapping permutations is a directed graph that is an analogue to the De Bruijn graph. It consists of vertices that are permutations of length n and edges that are permutations of length n + 1 in which an edge a 1 ⋯ a n + 1 would connect the standardization of a 1 ⋯ a n to the standardization of a 2 ⋯ a n + 1 . We examine properties of this graph to determine where directed cycles can exist, to count the number of directed 2 -cycles within the graph, and to enumerate the vertices that are contained within closed walks and directed cycles of more general lengths.
Padded Polynomials, Their Cousins, and Geometric Complexity Theory

We establish basic facts about the varieties of homogeneous polynomials divisible by powers of linear forms, and explain consequences for geometric complexity theory. This includes quadratic set-theoretic equations, a description of the ideal in terms of the kernel of a linear map that generalizes the Foulkes–Howe map, and an explicit description of the coordinate ring of the normalization. We also prove asymptotic injectivity of the Foulkes–Howe map.
The Kolakoski Sequence and Related Conjectures About Orbits

The Kolakoski sequence is the unique infinite sequence with values in {1, 2} and first two term 1, 2, … which equals the sequence of run-lengths of itself; we call this K(1, 2). Following Sing and ...
The Role of Data-Driven Priors in Multi-Agent Crowd Trajectory Estimation.

Resource constraints frequently complicate multi-agent planning problems. Existing algorithms for resource-constrained, multi-agent planning problems rely on the assumption that the constraints are deterministic. However, frequently resource constraints are themselves subject to uncertainty from external influences. Uncertainty about constraints is especially challenging when agents must execute in an environment where communication is unreliable, making on-line coordination difficult. In those cases, it is a significant challenge to find coordinated allocations at plan time depending on availability at run time. To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints. We show how to factorize resource limit uncertainty and use this to develop novel algorithms to plan policies for stochastic constraints. We evaluate the algorithms on a search-and-rescue problem and on a power-constrained planning domain where the resource constraints are decided by nature. We show that plans taking into account all potential realizations of the constraint obtain significantly better utility than planning for the expectation, while causing fewer constraint violations.
On the $p$-part of the Birch–Swinnerton-Dyer formula for multiplicative primes

Let $E/\mathbf{Q}$ be a semistable elliptic curve of analytic rank one, and let $p>3$ be a prime for which $E[p]$ is irreducible. In this note, following a slight modification of the methods of Jetchev-Skinner-Wan, we use Iwasawa theory to establish the $p$-part of the Birch and Swinnerton-Dyer formula for $E$. In particular, we extend the main result of loc.cit. to primes of multiplicative reduction.
Benchmarking numerical methods for lattice equations with the Toda lattice

Abstract We compare the performance of well-known numerical time-stepping methods that are widely used to compute solutions of the doubly-infinite Fermi–Pasta–Ulam–Tsingou (FPUT) lattice equations. The methods are benchmarked according to (1) their accuracy in capturing the soliton peaks and (2) in capturing highly-oscillatory parts of the solutions of the Toda lattice resulting from a variety of initial data. The numerical inverse scattering transform method is used to compute a reference solution with high accuracy. We find that benchmarking a numerical method on pure-soliton initial data can lead one to overestimate the accuracy of the method.
The AST/RO Survey of the Galactic Center Region

AST/RO is a 1.7m diameter submillimeter-wave telescope at the geographic South Pole. A key AST/RO project is the mapping of C I and CO J = 4 → 3 and J = 7 → 6 emission from the inner Milky Way (Martin et al. 2003). These data are released for general use. The Antarctic Submillimeter Telescope and Remote Observatory (AST/RO) is a 1.7 m diameter single-dish instrument which has been observing in the submillimeter-wave atmospheric windows for eight years (Stark et al. 2001, Stark 2003). Essential to AST/RO's capabilities is its location at Amundsen- Scott South Pole Station, an exceptionally cold, dry site which has unique lo- gistical opportunities and challenges. Observing time on AST/RO is available on a proposal basis. The distribution of molecular gas in the Galaxy is known from extensive and on-going surveys in CO and 13 CO J = 1 → 0 and J = 2 → 1; these are spectral lines which trace molecular gas. These lines alone do not, however, determine the excitation temperature, density, or cooling rate of that gas. Observations of C I and the mid-J lines of CO and 13 CO provide the missing information, showing a more complete picture of the thermodynamic state of the molecular gas, highlighting the active regions, and looking into the dense cores. AST/RO can measure the dominant cooling lines of molecular material in the interstellar medium: the 3 P1 → 3 P0 (492 GHz) and 3 P2 → 3 P1 (809 GHz) fine-structure lines of atomic carbon (CI) and the J = 4 → 3 (461 GHz) and J = 7 → 6 (807 GHz) rotational lines of carbon monoxide (CO). These measurements can then be modelled using the large velocity gradient (LVG) approximation, and the gas temperature and density thereby determined. Since the low-J states of CO are in local thermodynamic equilibrium (LTE) in almost all molecular gas, measurements of mid-J states are critical to achieving a model solution of the radiative transfer by breaking the degeneracy between beam filling factor and excitation temperature. Among the key AST/RO projects is mapping of the Galactic Center Region. Sky coverage as of 2002 is −1. ◦ 3 < l < 2 ◦ , −0 . ◦ 3 < b < 0 .
Tying Up Loose Strands: Defining Equations of the Strand Symmetric Model

The strand symmetric model is a phylogenetic model designed to reflect the symmetry inherent in the double-stranded structure of DNA. We show that the set of known phylogenetic invariants for the general strand symmetric model of the three leaf claw tree entirely defines the ideal. This knowledge allows one to determine the vanishing ideal of the general strand symmetric model of any trivalent tree. Our proof of the main result is computational. We use the fact that the Zariski closure of the strand symmetric model is the secant variety of a toric variety to compute the dimension of the variety. We then show that the known equations generate a prime ideal of the correct dimension using elimination theory.
Dynamic Tensor Recommender Systems

Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying coefficient model for temporal tensor factorization through a polynomial spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the spline coefficient estimator. The proposed method is applied to simulations and IRI marketing data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting.
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) online learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous carfollowing with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.
Coverage of an Environment Using Energy-Constrained Unmanned Aerial Vehicles

We study the problem of covering an environment using an Unmanned Aerial Vehicle (UAV) with limited battery capacity. We consider a scenario where the UAV can land on an Unmanned Ground Vehicle (UGV) and recharge the onboard battery. The UGV can also recharge the UAV while transporting the UAV to the next take-off site. We present an algorithm to solve a new variant of the area coverage problem that takes into account this symbiotic UAV and UGV system. The input consists of a set of boustrophedon cells — rectangular strips whose width is equal to the field-of-view of the sensor on the UAV. The goal is to find a tour for the UAV that visits and covers all cells in minimum time. This includes flight time for visiting and covering all cells, recharging time, as well as the take-off and landing times. We show how to reduce this problem to a known NP-hard problem, Generalized Traveling Salesperson Problem (GTSP). Given an optimal GTSP solver, our approach finds the optimal coverage paths for the UAV and UGV. We evaluate our algorithm through simulations and proof-of-concept experiments.
Boundary Observer for Congested Freeway Traffic State Estimation via Aw-Rascle-Zhang model

Abstract This paper develops boundary observer for estimation of congested freeway traffic states based on Aw-Rascle-Zhang(ARZ) partial differential equations (PDE) model. Traffic state estimation refers to acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to real-time traffic information. We propose a boundary observer design so that estimates of aggregated traffic states in a freeway segment are obtained simply from boundary measurement of flow and velocity. The macroscopic traffic dynamics is represented by the ARZ model, consisting of 2 × 2 coupled nonlinear hyperbolic PDEs for traffic density and velocity. Analysis of the linearized ARZ model leads to the study of a hetero-directional hyperbolic PDE model for congested traffic regime. Using spatial transformation and PDE backstepping method, we construct a boundary observer with a copy of the nonlinear plant and output injection of boundary measurement errors. The output injection gains are designed for the error system of the linearized ARZ model so that the exponential stability of error system in the L2 norm and finite-time convergence to zero are guaranteed. Simulations are conducted to validate the boundary observer design for nonlinear ARZ model without knowledge of initial conditions.
Entanglement-based quantum communication secured by nonlocal dispersion cancellation

Quantum key distribution (QKD) enables participants to exchange secret information over long distances with unconditional security. However, the performance of today's QKD systems is subject to hardware limitations, such as those of available nonclassical-light sources and single-photon detectors. By encoding photons in high-dimensional states, the rate of generating secure information under these technical constraints can be maximized. Here, we demonstrate a complete time-energy entanglement-based QKD system with proven security against the broad class of arbitrary collective attacks. The security of the system is based on nonlocal dispersion cancellation between two time-energy entangled photons. This resource-efficient QKD system is implemented at telecommunications wavelength, is suitable for optical fiber and free-space links, and is compatible with wavelength-division multiplexing.
Acceleration via Symplectic Discretization of High-Resolution Differential Equations

We study first-order optimization algorithms obtained by discretizing ordinary differential equations (ODEs) corresponding to Nesterov’s accelerated gradient methods (NAGs) and Polyak’s heavy-ball method. We consider three discretization schemes: symplectic Euler (S), explicit Euler (E) and implicit Euler (I) schemes. We show that the optimization algorithm generated by applying the symplectic scheme to a high-resolution ODE proposed by Shi et al. [2018] achieves the accelerated rate for minimizing both strongly convex function and convex function. On the other hand, the resulting algorithm either fails to achieve acceleration or is impractical when the scheme is implicit, the ODE is low-resolution, or the scheme is explicit.
The Page curve of Hawking radiation from semiclassical geometry

We consider a gravity theory coupled to matter, where the matter has a higher-dimensional holographic dual. In such a theory, finding quantum extremal surfaces becomes equivalent to finding the RT/HRT surfaces in the higher-dimensional theory. Using this we compute the entropy of Hawking radiation and argue that it follows the Page curve, as suggested by recent computations of the entropy and entanglement wedges for old black holes. The higher-dimensional geometry connects the radiation to the black hole interior in the spirit of ER=EPR. The black hole interior then becomes part of the entanglement wedge of the radiation. Inspired by this, we propose a new rule for computing the entropy of quantum systems entangled with gravitational systems which involves searching for “islands” in determining the entanglement wedge.
Search and Rescue Operations with Mesh Networked Robots

Efficient path planning and communication of multi-robot systems in the case of a search and rescue operation is a critical issue facing robotics disaster relief efforts. Ensuring all the nodes of a specialized robotic search team are within range, while also covering as much area as possible to guarantee efficient response time, is the goal of this paper. We propose a specialized search-and-rescue model based on a mesh network topology of aerial and ground robots. The proposed model is based on several methods. First, each robot determines its position relative to other robots within the system, using RSSI. Packets are then communicated to other robots in the system detailing important information regarding robot system status, status of the mission, and identification number. The results demonstrate the ability to determine multi-robot navigation with RSSI, allowing low computation costs and increased search-and-rescue time efficiency.
Light Ears: Information Leakage via Smart Lights

Modern Internet-enabled smart lights promise energy efficiency and many additional capabilities over traditional lamps. However, these connected lights also create a new attack surface, which can be maliciously used to violate users' privacy and security. In this paper, we design and evaluate novel attacks that take advantage of light emitted by modern smart bulbs, in order to infer users' private data and preferences. The first two attacks are designed to infer users' audio and video playback by a systematic observation and analysis of the multimedia-visualization functionality of smart light bulbs. The third attack utilizes the infrared capabilities of such smart light bulbs to create a covert-channel, which can be used as a gateway to exfiltrate user's private data out of their secured home or office network. A comprehensive evaluation of these attacks in various real-life settings confirms their feasibility and affirms the need for new privacy protection mechanisms.
Tissue-specific mouse mRNA isoform networks

Alternative Splicing produces multiple mRNA isoforms of genes which have important diverse roles such as regulation of gene expression, human heritable diseases, and response to environmental stresses. However, little has been done to assign functions at the mRNA isoform level. Functional networks, where the interactions are quantified by their probability of being involved in the same biological process are typically generated at the gene level. We use a diverse array of tissue-specific RNA-seq datasets and sequence information to train random forest models that predict the functional networks. Since there is no mRNA isoform-level gold standard, we use single isoform genes co-annotated to Gene Ontology biological process annotations, Kyoto Encyclopedia of Genes and Genomes pathways, BioCyc pathways and protein-protein interactions as functionally related (positive pair). To generate the non-functional pairs (negative pair), we use the Gene Ontology annotations tagged with "NOT" qualifier. We describe 17 Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION) following a leave-one-tissue-out strategy in addition to an organism level reference functional network for mouse. We validate our predictions by comparing its performance with previous methods, randomized positive and negative class labels, updated Gene Ontology annotations, and by literature evidence. We demonstrate the ability of our networks to reveal tissue-specific functional differences of the isoforms of the same genes. All scripts and data from TENSION are available at: https://doi.org/10.25380/iastate.c.4275191 .
Chernobyl Nuclear Disaster

On April 26, 1989, an explosion and fire at the Chernobyl nuclear power plant in Ukraine released an estimated 1.4 × 1018 Bq of ionizing radiation into the surrounding atmosphere. Acute health effects were noted in 237 highly exposed emergency workers, 134 were treated for acute radiation syndrome, and 28 died. An estimated 600 000 people were involved in the recovery and cleanup operations after the disaster and about 116 000 nearby residents were evacuated from the area, which became known as the Chernobyl exclusion zone. Areas of the countries surrounding the plant—Belarus, the Russian Federation, and Ukraine—are considered “contaminated” based on cesium-137 measurements. On the basis of these measurements, about 3% of the former Soviet Union is considered contaminated by cesium-137 densities greater than 37 kBq m−2. Radioactive material spread throughout the Northern Hemisphere, but radiation doses to populations outside Ukraine, Belarus, and the Russian Federation were relatively low and showed large differences depending on the occurrence of rainfall. Thyroid cancer in children exposed at a young age to Chernobyl is widely accepted as causally associated with the Chernobyl disaster. Other than childhood thyroid cancer, no other health effects have been considered related to Chernobyl exposure, but recently data on emergency workers may indicate some increase in morbidity and mortality caused by leukemia and solid organ cancer. Large areas of Ukraine, Belarus, and the Russian Federation are still excluded from agricultural uses, and other environmental uses are subject to strict controls. In the years ahead, a safer containment structure for the residual risk of the melted nuclear fuel still inside the quickly built “sarcophagus” or “shelter object” will need to be constructed. Chernobyl continues to serve as a model of international collaboration to assess and manage risks to human health and to the environment from the world's largest nuclear disaster. Keywords: Chernobyl; radioactivity; ionizing radiation; dose; acute radiation syndrome; cancer; leukemia; Ukraine; Russian Federation; Belarus; radionuclides; cesium; strontium; iodine; plutonium; americium; sarcophagus
Why Aren’t Students with Severe Disabilities Being Placed in General Education Classrooms: Examining the Relations Among Classroom Placement, Learner Outcomes, and Other Factors:

Placement decisions for students with severe disabilities have often been based less on the students’ unique learning needs but more on beliefs and presumptions about student learning, entrenched s...
Intermolecular Aminoallylation of Alkenes Using Allyl-Oxyphthalimide Derivatives: A Case Study in Radical Polarity Effects

A case study on the polarity effects of radical mediated intermolecular alkene aminoallylation is presented herein. This radical group transfer method pairs vinyl ethers with electronically deficient allyl-oxyphthalimide derivatives to give difunctionalized products while illustrating the guiding effects of polarity on this radical reactivity.
DOES A PERCEIVED CONNECTION TO A NEIGHBORHOOD REDUCE LONELINESS

This study investigated whether perceived neighborhood quality was associated with chronic loneliness for adults 60 and older in the United States. Although loneliness can be episodic and overcome, chronic loneliness has been identified as a social determinant of health. Utilizing ecological systems theory we hypothesized that higher levels of neighborhood social cohesiveness would be associated with lower odds of chronic loneliness. We postulated that the networks available to people in the proximal area where they live could provide social opportunities for reducing loneliness. This idea was consistent with prior findings indicating the salience of neighborhoods for retirees, but inconsistent with research indicating the importance of a confidant in reducing loneliness. Data from the 2008 and 2012 Health and Retirement Study Psychosocial Surveys were used (n = 3530). Loneliness was measured using the 3-item scale developed by Hughes and colleagues in 2004. Findings from unadjusted logistic regression indicated that loneliness was inversely related to neighborhood cohesion as measured by an index of the trustworthiness, friendliness and helpfulness of neighbors and cleanliness, occupancy, lack of graffiti, and sense of belonging in the area (OR = .73, p < .001). When demographic and health-related factors were entered into the model the odds of being lonely were significantly lower for those with higher ratings of social cohesion (OR = .83, p < .001). These findings were consistent with the idea that neighborhoods are an important social place for older persons and interventions at the neighborhood level may be more effective than individualized treatment plans.
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models

Ambiguous user queries in search engines result in the retrieval of documents that often span multiple topics. One potential solution is for the search engine to generate multiple refined queries, each of which relates to a subset of the documents spanning the same topic. A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents. We propose a new task of generating common question from multiple documents and present simple variant of an existing multi-source encoder-decoder framework, called the Multi-Source Question Generator (MSQG). We first train an RNN-based single encoder-decoder generator from (single document, question) pairs. At test time, given multiple documents, the Distribute step of our MSQG model predicts target word distributions for each document using the trained model. The Aggregate step aggregates these distributions to generate a common question. This simple yet effective strategy significantly outperforms several existing baseline models applied to the new task when evaluated using automated metrics and human judgments on the MS-MARCO-QA dataset.
Immersive Media and Their Future

Drawing on the deeply engaging characteristics of virtual reality (VR), augmented reality (AR), and mixed reality (MR), we refer to these three concepts collectively as “immersive media” (IM). IM are often thought of as new concepts, which may hold true in their integrated, holistic forms. However, the technological and psychological aspects of IM have long been explored, both in the media industry and in education. Through an analysis of the evolution of established media (EM), that is, cinema, TV, radio, and video games, we explore the extent to which past processes in EM development resemble the current trajectory of IM evolution. We infer that with time, IM are likely to join EM and, consequently, they should not be considered solely as a passing trend. An immediate practical implication is that educators should not neglect allocating resources and time to research the potential of IM technology for learning.
A Whittle Index Approach to Minimizing Functions of Age of Information

We consider a setting where multiple active sources send real-time updates over a single-hop wireless broadcast network to a monitoring station. Our goal is to design a scheduling policy that minimizes the time-average of general non-decreasing cost functions of Age of Information. We use a Whittle index based approach to find low complexity scheduling policies that have good performance, for reliable as well as unreliable channels. We prove that for a system with two sources, having possibly different cost functions and reliable channels, the Whittle index policy is exactly optimal. For reliable channels, we also derive structural properties of an optimal policy, that suggest that the performance of the Whittle index policy may be close to optimal in general. These results might also be of independent interest in the study of restless multi-armed bandit problems with similar underlying structure. Finally, we provide simulations comparing the Whittle index policy with optimal scheduling policies found using dynamic programming, which support our results.
Distinct neural mechanisms for the prosocial and rewarding properties of MDMA

The extensively abused recreational drug (±)3,4-methylenedioxymethamphetamine (MDMA) has shown promise as an adjunct to psychotherapy for treatment-resistant psychiatric disease. It is unknown, however, whether the mechanisms underlying its prosocial therapeutic effects and abuse potential are distinct. We modeled both the prosocial and nonsocial drug reward of MDMA in mice and investigated the mechanism of these processes using brain region-specific pharmacology, transgenic manipulations, electrophysiology, and in vivo calcium imaging. We demonstrate in mice that MDMA acting at the serotonin transporter within the nucleus accumbens is necessary and sufficient for MDMA's prosocial effect. MDMA's acute rewarding properties, in contrast, require dopaminergic signaling. MDMA's prosocial effect requires 5-HT1b receptor activation and is mimicked by d-fenfluramine, a selective serotonin-releasing compound. By dissociating the mechanisms of MDMA's prosocial effects from its addictive properties, we provide evidence for a conserved neuronal pathway, which can be leveraged to develop novel therapeutics with limited abuse liability.
Simultaneous Switching of Multiple GaN Transistors in a High-Speed Switch

A broadband travelling wave kicker operating with 80 MHz repetition rates is required for the new PIP-II accelerator at Fermilab. We present a technique to drive simultaneously four series-connected enhancement mode GaN-on-silicon power transistors by means of microwave photonics techniques. These four transistors are arranged into a high voltage and high repetition rate switch. Using multiple transistors in series is required to share switching losses. Using a photonic signal distribution system is required to achieve precise synchronization between transistors. We demonstrate 600 V arbitrary pulse generation into a 200 Ohm load with 2 ns rise/fall time. The arbitrary pulse widths can be adjusted from 4 ns to essentially DC.
Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals

We consider a wireless network with a base station serving multiple traffic streams to different destinations. Packets from each stream arrive to the base station according to a stochastic process and are enqueued in a separate (per stream) queue. The queueing discipline controls which packet within each queue is available for transmission. The base station decides, at every time t, which stream to serve to the corresponding destination. The goal of scheduling decisions is to keep the information at the destinations fresh. Information freshness is captured by the Age of Information (AoI) metric. In this paper, we derive a lower bound on the AoI performance achievable by any given network operating under any queueing discipline. Then, we consider three common queueing disciplines and develop both an Optimal Stationary Randomized policy and a Max-Weight policy under each discipline. Our approach allows us to evaluate the combined impact of the stochastic arrivals, queueing discipline and scheduling policy on AoI. We evaluate the AoI performance both analytically and using simulations. Numerical results show that the performance of the Max-Weight policy is close to the analytical lower bound.
Learning to Guide Random Search

We are interested in the optimization of a high-dimensional function when only function evaluations are possible. Although this derivative-free setting arises in many applications, existing methods suffer from high sample complexity since their sample complexity depend on problem dimensionality, in contrast to the dimensionality-independent rates of first-order methods. The recent success of deep learning methods suggests that many data modalities lie on low-dimensional manifolds that can be represented by deep nonlinear models. Based on this observation, we consider derivative-free optimization of functions defined on low-dimensional manifolds. We develop an online learning approach that learns this manifold while performing the optimization. In other words, we jointly learn the manifold and optimize the function. Our analysis suggests that the proposed method significantly reduces sample complexity. We empirically evaluate the presented method on continuous optimization benchmarks and high-dimensional continuous control problems. Our method achieves significantly lower sample complexity than Augmented Random Search and other derivative-free optimization algorithms.