cssi/SciSciGPT-SciSciNet huggingface dataset:
Concentration of measure for radial distributions and consequences for statistical modeling

Abstract Motivated by problems in high-dimensional statistics such as mixture modeling for classification and clustering, we consider the behavior of radial densities as the dimension increases. We establish a form of concentration of measure, and even a convergence in distribution, under additional assumptions. This extends the well-known behavior of the normal distribution (its concentration around the sphere of radius square-root of the dimension) to other radial densities. We draw some possible consequences for statistical modeling in high-dimensions, including a possible universality property of Gaussian mixtures.
Enhanced object detection via fusion with prior beliefs from image classification

In this paper, we introduce a novel fusion method that can enhance object detection performance by fusing decisions from two different types of computer vision tasks: object detection and image classification. In the proposed work, the class label of an image obtained from the image classification task is viewed as prior knowledge about existence or non-existence of certain objects. The prior knowledge is then fused with the decisions of object detection to improve detection accuracy by mitigating false positives of an object detector that are strongly contradicted with the prior knowledge. A recently introduced novel fusion approach called dynamic belief fusion (DBF) is used to fuse the detector output with the classification prior. Experimental results show that the detection performance of all the detection algorithms used in the proposed work is improved on benchmark datasets via the proposed fusion framework.
Stochastic canonical heights

Abstract We construct height functions defined stochastically on projective varieties equipped with endomorphisms, and we prove that these functions satisfy analogs of the usual properties of canonical heights. Moreover, we give a dynamical interpretation of the kernel of these stochastic height functions, and in the case of the projective line, we relate the size of this kernel to the Julia sets of the original maps. Finally, as an application, we establish the finiteness of some generalized Zsigmondy sets over global fields.
MEAN REVERSION TRADING WITH SEQUENTIAL DEADLINES AND TRANSACTION COSTS

We study the optimal timing strategies for trading a mean-reverting price process with a finite deadline to enter and a separate finite deadline to exit the market. The price process is modeled by ...
Inflexibility, Weil-Petersson distance, and volumes of fibered 3-manifolds

A recent preprint of S. Kojima and G. McShane [KM] observes a beautiful explicit connection between Teichmuller translation distance and hyperbolic volume. It relies on a key estimate which we supply here: using geometric inflexibility of hyperbolic 3-manifolds, we show that for S a closed surface, and ψ ∈ Mod(S) pseudo-Anosov, the double iteration Q(ψ−n(X),ψn(X)) has convex core volume differing from 2nvol(Mψ ) by a uniform additive constant, where Mψ is the hyperbolic mapping torus for ψ . We combine this estimate with work of Schlenker, and a branched covering argument to obtain an explicit lower bound on Weil-Petersson translation distance of a pseudo-Anosov ψ ∈Mod(S) for general compact S of genus g with n boundary components: we have vol(Mψ )≤ 3 √ π/2(2g−2+n)‖ψ‖WP. This gives the first explicit estimates on the Weil-Petersson systoles of moduli space, of the minimal distance between nodal surfaces in the completion of Teichmuller space, and explicit lower bounds to the Weil-Petersson diameter of the moduli space via [CP]. In the process, we recover the estimates of [KM] on Teichmuller translation distance via a Cauchy-Schwarz estimate (see [Lin]).
Intersective polynomials and the primes

Abstract Intersective polynomials are polynomials in Z [ x ] having roots every modulus. For example, P 1 ( n ) = n 2 and P 2 ( n ) = n 2 − 1 are intersective polynomials, but P 3 ( n ) = n 2 + 1 is not. The purpose of this note is to deduce, using results of Green and Tao (2006) [8] and Lucier (2006) [16] , that for any intersective polynomial h, inside any subset of positive relative density of the primes, we can find distinct primes p 1 , p 2 such that p 1 − p 2 = h ( n ) for some integer n. Such a conclusion also holds in the Chen primes (where by a Chen prime we mean a prime number p such that p + 2 is the product of at most 2 primes).
Flexible toggles and symmetric invertible asynchronous elementary cellular automata

Abstract A sequential dynamical system (SDS) consists of a graph G with vertices v 1 , … , v n , a state set A , a collection of “vertex functions” { f v i } i = 1 n , and a permutation π ∈ S n that specifies how to compose these functions to yield the SDS map [ G , { f v i } i = 1 n , π ] : A n → A n . In this paper, we study symmetric invertible SDS defined over the cycle graph C n using the set of states F 2 . These are, in other words, asynchronous elementary cellular automata (ECA) defined using ECA rules 150 and 105. Each of these SDS defines a group action on the set F 2 n of n -bit binary vectors. Because the SDS maps are products of involutions, this relates to generalized toggle groups, which Striker recently introduced. In this paper, we further generalize the notion of a generalized toggle group to that of a flexible toggle group; the SDS maps we consider are examples of Coxeter elements of flexible toggle groups. Our main result is the complete classification of the dynamics of symmetric invertible SDS defined over cycle graphs using the set of states F 2 and the identity update order π = 123 ⋯ n . More precisely, if T denotes the SDS map of such an SDS, then we obtain an explicit formula for | Per r ( T ) | , the number of periodic points of T of period r , for every positive integer r . It turns out that if we fix r and vary n and T , then | Per r ( T ) | only takes at most three nonzero values.
Looping Directions and Integrals of Eigenfunctions over Submanifolds

Let $$(M,\,g)$$ be a compact n-dimensional Riemannian manifold without boundary and $$e_\lambda $$ be an $$L^2$$ -normalized eigenfunction of the Laplace–Beltrami operator with respect to the metric g, i.e., $$\begin{aligned} -\Delta _g e_\lambda = \lambda ^2 e_\lambda \quad \text {and} \quad \left\| e_\lambda \right\| _{L^2(M)} = 1. \end{aligned}$$ Let $$\varSigma $$ be a d-dimensional submanifold and $$\mathrm{d}\mu $$ a smooth, compactly supported measure on $$\varSigma .$$ It is well known (e.g., proved by Zelditch, Commun Partial Differ Equ 17(1–2):221–260, 1992 in far greater generality) that $$\begin{aligned} \int _\varSigma e_\lambda \, \mathrm{d}\mu = O\left( \lambda ^\frac{n-d-1}{2}\right) . \end{aligned}$$ We show this bound improves to $$o\left( \lambda ^\frac{n-d-1}{2}\right) $$ provided the set of looping directions, $$\begin{aligned} {{\mathcal {L}}}_{\varSigma } = \{ (x,\,\xi ) \in \mathrm{SN}^*\varSigma : \varPhi _t(x,\,\xi ) \in \mathrm{SN}^*\varSigma \text { for some } t > 0 \} \end{aligned}$$ has measure zero as a subset of $$\mathrm{SN}^*\varSigma ,$$ where here $$\varPhi _t$$ is the geodesic flow on the cosphere bundle $$S^*M$$ and $$\mathrm{SN}^*\varSigma $$ is the unit conormal bundle over $$\varSigma .$$
Throughput Region of Spatially Correlated Interference Packet Networks

In multi-user wireless packet networks, interference, typically modeled as packet collision, is the throughput bottleneck. Users become aware of the interference pattern via feedback and use this information for contention resolution and packet retransmission. Conventional random access protocols interrupt communication to resolve contention, which reduces network throughput and increases latency and power consumption. In this paper, we take a different approach, and we develop opportunistic random access protocols rather than pursuing conventional methods. We allow wireless nodes to communicate without interruption and to observe the interference pattern. We then use this interference pattern knowledge and channel statistics to counter the negative impact of interference. We prove the optimality of our protocols using an extremal rank-ratio inequality. An important part of our contributions is the integration of spatial correlation in our assumptions and results. We identify spatial correlation regimes in which inherently outdated feedback becomes as good as idealized instantaneous feedback and correlation regimes in which feedback does not provide any throughput gain. To better illustrate the results, and as an intermediate step, we characterize the capacity region of finite-field spatially correlated interference channels with delayed channel state information at the transmitters.
Guided cost learning: deep inverse optimal control via policy optimization

Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems. Our method addresses two key challenges in inverse optimal control: first, the need for informative features and effective regularization to impose structure on the cost, and second, the difficulty of learning the cost function under unknown dynamics for high-dimensional continuous systems. To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering. To address the latter challenge, we formulate an efficient sample-based approximation for MaxEnt IOC. We evaluate our method on a series of simulated tasks and real-world robotic manipulation problems, demonstrating substantial improvement over prior methods both in terms of task complexity and sample efficiency.
Long Range Topological Order, the Chiral Condensate, and the Berry Connection in QCD

Topological insulators are substances which are bulk insulators but which carry current via special “topologically protected” edge states. The understanding of long range topological order in these systems is built around the idea of a Berry connection, which is a gauge connection obtained from the phase of the electron wave function transported over momentum space rather than coordinate space. The phase of a closed Wilson loop of the Berry connection around the Brillouin zone defines a topological order parameter which labels discrete flux vacua. The conducting states are surface modes on the domain walls between discrete vacua. Evidence from large-Nc chiral dynamics, holographic QCD, and Monte Carlo observations has pointed to a picture of the QCD vacuum that is very similar to that of a topological insulator, with discrete quasivacua labelled by θ angles that differ by mod 2π . In this picture, the domain walls are membranes of Chern-Simons charge, and the quark condensate consists of surface modes on these membranes, which are delocalized and thus support the long range propagation of Goldstone pions. The Berry phase in QED2 describes charge polarization of fermion-antifermion pairs, while in 4D QCD it describes the polarization of Chern-Simons membranes.
Solar activity studies using microwave imaging observations

We report on the status of solar cycle 24 based on polar prominence eruptions (PEs) and microwave brightness enhancement (MBE) information obtained by the Nobeyama radioheliograph. The north polar region of the Sun had near-zero field strength for more than three years (2012–2015) and ended only in September 2015 as indicated by the presence of polar PEs and the lack of MBE. The zero-polar-field condition in the south started only around 2013, but it ended by June 2014. Thus the asymmetry in the times of polarity reversal switched between cycle 23 and 24. The polar MBE is a good proxy for the polar magnetic field strength as indicated by the high degree of correlation between the two. The cross-correlation between the high- and low-latitude MBEs is significant for a lag of ∼5.5 to 7.3 years, suggesting that the polar field of one cycle indicates the sunspot number of the next cycle in agreement with the Babcock-Leighton mechanism of solar cycles. The extended period of near-zero field in the north-polar region should result in a weak and delayed sunspot activity in the northern hemisphere in cycle 25.
Explicit computation of the Chern character forms

We propose a method for explicit computation of the Chern character form of a holomorphic Hermitian vector bundle (E, h) over a complex manifold X in a local holomorphic frame. First, we use the descent equations arising in the double complex of (p, q)-forms on X and find the explicit degree decomposition of the Chern–Simons form \({\mathrm {cs}}_{k}\) associated to the Chern character form \({\mathrm {ch}}_{k}\) of (E, h). Second, we introduce the so-called ascent equations that start from the \((2k-1,0)\) component of \({\mathrm {cs}}_{k}\), and use the Cholesky decomposition of the Hermitian metric h to represent the Chern–Simons form, modulo d-exact forms, as a \(\partial \)-exact form. This yields a formula for the Bott–Chern form \({\mathrm {bc}}_{k}\) of type \((k-1,k-1)\) such that \(\displaystyle {{\mathrm {ch}}_{k}=\frac{\sqrt{-1}}{2\pi }\bar{\partial }\partial {\mathrm {bc}}_{k}}\). Explicit computation is presented for the cases \(k=2\) and 3.
An elementary derivation of the Chinese restaurant process from Sethuraman’s stick-breaking process

Abstract The usual proof that Sethuraman’s stick-breaking process gives rise to the Chinese restaurant process is indirect, relying on abstract properties of the Dirichlet process. This note provides a direct elementary proof, without using any measure theory.
Regularity criterion for the 3D Hall-magneto-hydrodynamics

Abstract This paper studies the regularity problem for the 3D incompressible resistive viscous Hall-magneto-hydrodynamic (Hall-MHD) system. The Kolmogorov 41 phenomenological theory of turbulence [14] predicts that there exists a critical wavenumber above which the high frequency part is dominated by the dissipation term in the fluid equation. Inspired by this idea, we apply an approach of splitting the wavenumber combined with an estimate of the energy flux to obtain a new regularity criterion. The regularity condition presented here is weaker than conditions in the existing criteria (Prodi–Serrin type criteria) for the 3D Hall-MHD system.
Notes On Super Riemann Surfaces And Their Moduli

These are notes on the theory of super Riemann surfaces and their moduli spaces, aiming to collect results that are useful for a better understanding of superstring perturbation theory in the RNS formalism.
A note on knot surgery

In this paper we clarify an issue in the knot surgery construction of Fintushel and Stern. Using knot surgery, they construct an infinite number of smooth structures on 4-manifolds satisfying certain conditions, but they do not explicitly work out the circumstances under which two manifolds that arise from their construction will fail to be diffeomorphic on the grounds of Seiberg–Witten theory. This paper fills in that gap.
1997 October Event(s) in the Crab Pulsar

In October 1997 daily monitoring observations of the Crab pulsar at 327 MHz and 610 MHz with an 85ft telescope in Green Bank, WV showed a jump in the dispersion by 0.12 cm^{-3} pc. Pulses were seen simultaneously at both old and new dispersions for a period of days. In the months before this event faint ghost emission, a replica of the pulse, was detected with a nearly frequency independent delay that quadratically diminished to zero. There was also a curious shift in the phase, a slowdown, at all frequencies at the time of the dispersion jump. I attribute most of these phenomena to the perturbing optics of a plasma prism that is located in the filamentary interface between the synchrotron nebula and the supernova ejecta and which crosses the line of sight over a period of months. The required density, scale length and velocity are reasonable given detailed HST and previous observations of these filaments.
Bounding Multiple Unicasts through Index Coding and Locally Repairable Codes

We establish a duality result between linear index coding and Locally Repairable Codes (LRCs). Specifically, we show that a natural extension of LRCs we call Generalized Locally Repairable Codes (GLCRs) are exactly dual to linear index codes. In a GLRC, every node is decodable from a specific set of other nodes and these sets induce a recoverability directed graph. We show that the dual linear subspace of a GLRC is a solution to an index coding instance where the side information graph is this GLRC recoverability graph. We show that the GLRC rate is equivalent to the complementary index coding rate, i.e. the number of transmissions saved by coding. Our second result uses this duality to establish a new upper bound for the multiple unicast network coding problem. In multiple unicast network coding, we are given a directed acyclic graph and r sources that want to send independent messages to r corresponding destinations. Our new upper bound is efficiently computable and relies on a strong approximation result for complementary index coding. We believe that our bound could lead to a logarithmic approximation factor for multiple unicast network coding if a plausible connection we state is verified.
A non-Archimedean Ohsawa–Takegoshi extension theorem

We prove an Ohsawa–Takegoshi-type extension theorem on the Berkovich closed unit disc over certain non-Archimedean fields. As an application, we establish a non-Archimedean analogue of Demailly’s regularization theorem for quasisubharmonic functions on the Berkovich unit disc.
Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved \(F_2\) score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.
Open partial isometries and positivity in operator spaces

We study positivity in C*-modules and operator spaces using open tripotents, and an ordered version of the `noncommutative Shilov boundary'. Because of their independent interest, we also systematically study open tripotents and their properties.
Persistence of Gaussian processes: non-summable correlations

Suppose the auto-correlations of real-valued, centered Gaussian process $$Z(\cdot )$$ are non-negative and decay as $$\rho (|s-t|)$$ for some $$\rho (\cdot )$$ regularly varying at infinity of order $$-\alpha \in [-1,0)$$ . With $$I_\rho (t)=\int _0^t \rho (s)ds$$ its primitive, we show that the persistence probabilities decay rate of $$ -\log \mathbb {P}(\sup _{t \in [0,T]}\{Z(t)\}<0)$$ is precisely of order $$(T/I_\rho (T)) \log I_\rho (T)$$ , thereby closing the gap between the lower and upper bounds of Newell and Rosenblatt (Ann. Math. Stat. 33:1306–1313, 1962), which stood as such for over fifty years. We demonstrate its usefulness by sharpening recent results of Sakagawa (Adv. Appl. Probab. 47:146–163, 2015) about the dependence on d of such persistence decay for the Langevin dynamics of certain $$\nabla \phi $$ -interface models on $$\mathbb {Z}^d$$ .
Stein’s method for steady-state diffusion approximations: An introduction through the Erlang-A and Erlang-C models

This paper provides an introduction to the Stein method framework in the context of steady-state diffusion approximations. The framework consists of three components: the Poisson equation and gradient bounds, generator coupling, and moment bounds. Working in the setting of the Erlang-A and Erlang-C models, we prove that both Wasserstein and Kolmogorov distances between the stationary distribution of a normalized customer count process, and that of an appropriately defined diffusion process decrease at a rate of 1/R, where R is the offered load. Futhermore, these error bounds are universal, valid in any load condition from lightly loaded to heavily loaded.
Prime étale groupoid algebras with applications to inverse semigroup and Leavitt path algebras

Abstract In this paper we give some sufficient and some necessary conditions for an etale groupoid algebra to be a prime ring. As an application we recover the known primeness results for inverse semigroup algebras and Leavitt path algebras. It turns out that primeness of the algebra is connected with the dynamical property of topological transitivity of the groupoid. We obtain analogous results for semiprimeness.
Maximum Size of a Family of Pairwise Graph-Different Permutations

Two permutations of the vertices of a graph $G$ are called $G$-different if there exists an index $i$ such that $i$-th entry of the two permutations form an edge in $G$. We bound or determine the maximum size of a family of pairwise $G$-different permutations for various graphs $G$. We show that for all balanced bipartite graphs $G$ of order $n$ with minimum degree $n/2 - o(n)$, the maximum number of pairwise $G$-different permutations of the vertices of $G$ is $2^{(1-o(1))n}$. We also present examples of bipartite graphs $G$ with maximum degree $O(\log n)$ that have this property. We explore the problem of bounding the maximum size of a family of pairwise graph-different permutations when an unlimited number of disjoint vertices is added to a given graph. We determine this exact value for the graph of 2 disjoint edges, and present some asymptotic bounds relating to this value for graphs consisting of the union of $n/2$ disjoint edges.
Upper bounds on the cardinality of higher sumsets

Let A and B be finite sets in a commutative group. We bound |A+hB| in terms of |A|, |A+B| and h. We provide a submultiplicative upper bound that improves on the existing bound of Imre Ruzsa by inserting a factor that decreases with h.
Dropout Model Evaluation in MOOCs.

The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model performance which goes beyond the state-of-the-practice in the community to analyze both algorithms and feature extraction methods from raw data. We apply this method to a series of algorithms and feature sets derived from a large sample of Massive Open Online Courses (MOOCs). While a complete comparison of all potential modeling approaches is beyond the scope of this paper, we show that this approach reveals a large gap in dropout prediction performance between forum-, assignment-, and clickstream-based feature extraction methods, where the latter is significantly better than the former two, which are in turn indistinguishable from one another. This work has methodological implications for evaluating predictive or AI-based models of student success, and practical implications for the design and targeting of at-risk student models and interventions.
Quasiconvexity in the Heisenberg group

We show that if A is a closed subset of the Heisenberg group whose vertical projections are nowhere dense, then the complement of A is quasiconvex. In particular, closed sets which are null sets for the cc-Hausdorff 3-measure have quasiconvex complements. Conversely, we exhibit a compact totally disconnected set of Hausdorff dimension three whose complement is not quasiconvex.
Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms

Recent work from the reinforcement learning community has shown that Evolution Strategies are a fast and scalable alternative to other reinforcement learning methods. In this paper we show that Evolution Strategies are a special case of model-based stochastic search methods. This class of algorithms has nice asymptotic convergence properties and known convergence rates. We show how these methods can be used to solve both cooperative and competitive multiagent problems in an efficient manner. We demonstrate the effectiveness of this approach on two complex multi-agent UAV swarm combat scenarios: where a team of fixed wing aircraft must attack a well-defended base, and where two teams of agents go head to head to defeat each other T.
Ghirlanda–Guerra identities and ultrametricity: An elementary proof in the discrete case

Abstract In this Note we give another proof of the fact that a random overlap array, which satisfies the Ghirlanda–Guerra identities and whose elements take values in a finite set, is ultrametric with probability one. The new proof bypasses random change of density invariance principles for directing measures of such arrays and, in addition to the Dovbysh–Sudakov representation, is based only on elementary algebraic consequences of the Ghirlanda–Guerra identities.
Noncommutative discriminants via Poisson primes

Abstract We present a general method for computing discriminants of noncommutative algebras. It builds a connection with Poisson geometry and expresses the discriminants as products of Poisson primes. The method is applicable to algebras obtained by specialization from families, such as quantum algebras at roots of unity. It is illustrated with the specializations of the algebras of quantum matrices at roots of unity and more generally all quantum Schubert cell algebras.
Neutrally expandable models of arithmetic

A subset of a model of ${\sf PA}$ is called neutral if it does not change the $\mathrm{dcl}$ relation. A model with undefinable neutral classes is called neutrally expandable. We study the existence and non-existence of neutral sets in various models of ${\sf PA}$. We show that cofinal extensions of prime models are neutrally expandable, and $\omega_1$-like neutrally expandable models exist, while no recursively saturated model is neutrally expandable. We also show that neutrality is not a first-order property. In the last section, we study a local version of neutral expandability.
Controlling organization and forces in active matter through optically defined boundaries

Living systems are capable of locomotion, reconfiguration and replication. To perform these tasks, cells spatiotemporally coordinate the interactions of force-generating, ‘active’ molecules that create and manipulate non-equilibrium structures and force fields of up to millimetre length scales. Experimental active-matter systems of biological or synthetic molecules are capable of spontaneously organizing into structures and generating global flows. However, these experimental systems lack the spatiotemporal control found in cells, limiting their utility for studying non-equilibrium phenomena and bioinspired engineering. Here we uncover non-equilibrium phenomena and principles of boundary-mediated control by optically modulating structures and fluid flow in an engineered system of active biomolecules. Our system consists of purified microtubules and light-activatable motor proteins that crosslink and organize the microtubules into distinct structures upon illumination. We develop basic operations—defined as sets of light patterns—to create, move and merge the microtubule structures. By combining these operations, we create microtubule networks that span several hundred micrometres in length and contract at speeds up to an order of magnitude higher than the speed of an individual motor protein. We manipulate these contractile networks to generate and sculpt persistent fluid flows. The principles of boundary-mediated control that we uncover may be used to study emergent cellular structures and forces and to develop programmable active-matter devices.
Online Antenna Tuning in Heterogeneous Cellular Networks With Deep Reinforcement Learning

We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The interactions between the cells, most notably due to their coupled interference render this optimization prohibitively complex. Utilizing a single agent reinforcement learning (RL) algorithm for this optimization becomes quite suboptimum despite its scalability, whereas multi-agent RL algorithms yield better solutions at the expense of scalability. Hence, we propose a two-step compromise algorithm. Specifically, a multi-agent mean field RL algorithm is first utilized in the offline phase so as to transfer information as features for the second (online) phase single agent RL algorithm, which employs a deep neural network to learn users locations. This two-step approach is a practical solution for real deployments, which should automatically adapt to environmental changes in the network. Our results illustrate that the proposed algorithm approaches the performance of the multi-agent RL, which requires millions of trials, with hundreds of online trials, assuming relatively low environmental dynamics, and performs much better than a single agent RL. Furthermore, the proposed algorithm is compact and implementable, and empirically appears to provide a performance guarantee regardless of the amount of environmental dynamics.
Civilians, Control, and Collaboration during Civil Conflict

What affects civilian collaboration with armed actors during civil war? While theory and evidence confirm that harm by armed actors influences when and with whom civilians collaborate, we argue that collaboration is also a function of civilians’ perceptions of armed actors’ efforts to minimize collateral casualties. We test this argument using a series of nationwide surveys of Afghan civilians conducted quarterly between 2013-2015. Our data record civilian willingness to report roadside bombs to government authorities and perceptions of government and Taliban efforts to minimize civilian harm. Civilians are less (more) willing to collaborate with the government when they perceive the government (Taliban) carelessly using force, even after accounting for political sentiment, local security conditions, and a range of additional confounding factors. Moreover, our evidence suggests that perceived carelessness in the rival’s area of control influences collaboration. We discuss how these empirical results inform broader literatures on collaboration, conquest, occupation, and control.
Motion Scaling Solutions for Improved Performance in High Delay Surgical Teleoperation

Robotic teleoperation brings great potential for advances within the field of surgery. The ability of a surgeon to reach patient remotely opens exciting opportunities. Early experience with telerobotic surgery has been interesting, but the clinical feasibility remains out of reach, largely due to the deleterious effects of communication delays. Teleoperation tasks are significantly impacted by unavoidable signal latency, which directly results in slower operations, less precision in movements, and increased human errors. Introducing significant changes to the surgical workflow, for example by introducing semi-automation or self-correction, present too significant a technological and ethical burden for commercial surgical robotic systems to adopt. In this paper, we present three simple and intuitive motion scaling solutions to combat teleoperated robotic systems under delay and help improve operator accuracy. Motion scaling offers potentially improved user performance and reduction in errors with minimal change to the underlying teleoperation architecture. To validate the use of motion scaling as a performance enhancer in telesurgery, we conducted a user study with 17 participants, and our results show that the proposed solutions do indeed reduce the error rate when operating under high delay.
Revisiting MJO, Kelvin waves, and El Niño relationships using a simple ocean model

Mechanisms governing interactions between the Madden–Julian Oscillation (MJO), Kelvin wave activity, and El Nino development are reexamined using the oceanic component of the Zebiak–Cane (ZCocn) model of the Pacific basin. Prescribed wind stress from a free run of the super-parameterized Community Climate System Model version 4 (SP-CCSM4) is used to force ZCocn and the simulated El Nino events are analyzed with respect to their relationship with the MJO wind forcing. Composites of El Nino events strongly influenced by the MJO show the earlier onset of a flattened, El Nino-like state of the thermocline. In contrast, the composites of El Nino events not influenced by the MJO winds show a later onset, and are dominated by a transient-like thermocline along with periods of upwelling Kelvin wave activity. Sensitivity experiments performed to identify whether modifying MJO wind stress and oceanic Kelvin wave activity influences these features show that although MJO contributes to the development of these features, it is not necessarily the primary driver. The relative phasing between MJO and oceanic Kelvin wave activity seems to be the most important factor governing the influence of MJO on El Nino. When in phase and collocated with Kelvin wave activity, MJO westerly wind stress contributes to the amplification of preexisting downwelling Kelvin waves, leading to earlier onset and greater strength of the resulting El Nino events. The out-of-phase interactions between MJO and oceanic Kelvin waves explain the observed lack of influence of MJO onto some El Nino events.
Prediction of many-electron wavefunctions using atomic potentials: extended basis sets and molecular dissociation

A one-electron Schrodinger equation based on special one-electron potentials for atoms is shown to exist that produces orbitals for an arbitrary molecule that are sufficiently accurate to be used without modification to construct single- and multi-determinant wavefunctions. The exact Hamiltonian is used to calculate the energy variationally and to generate configuration interaction expansions. Earlier work on equilibrium geometries is extended to larger basis sets and molecular dissociation. For a test set of molecules representing different bonding environments, a single set of invariant atomic potentials gives wavefunctions with energies that deviate from configuration interaction energies based on SCF orbitals by less than 0.04 eV per bond or valence electron pair. On a single diagonalization of the Fock matrix, the corresponding errors are reduced 0.01 eV. Atomization energies are also in good agreement with CI values based on canonical SCF orbitals. Configuration interaction applications to single bond dissociations of water and glycine, and multiple bond dissociations of ethylene and oxygen produce dissociation energy curves in close agreement with CI calculations based on canonical SCF orbitals for the entire range of internuclear distances.
Aurora A–Selective Inhibitor LY3295668 Leads to Dominant Mitotic Arrest, Apoptosis in Cancer Cells, and Shows Potent Preclinical Antitumor Efficacy

Although Aurora A, B, and C kinases share high sequence similarity, especially within the kinase domain, they function distinctly in cell-cycle progression. Aurora A depletion primarily leads to mitotic spindle formation defects and consequently prometaphase arrest, whereas Aurora B/C inactivation primarily induces polyploidy from cytokinesis failure. Aurora B/C inactivation phenotypes are also epistatic to those of Aurora A, such that the concomitant inactivation of Aurora A and B, or all Aurora isoforms by nonisoform-selective Aurora inhibitors, demonstrates the Aurora B/C-dominant cytokinesis failure and polyploidy phenotypes. Several Aurora inhibitors are in clinical trials for T/B-cell lymphoma, multiple myeloma, leukemia, lung, and breast cancers. Here, we describe an Aurora A-selective inhibitor, LY3295668, which potently inhibits Aurora autophosphorylation and its kinase activity in vitro and in vivo, persistently arrests cancer cells in mitosis, and induces more profound apoptosis than Aurora B or Aurora A/B dual inhibitors without Aurora B inhibition-associated cytokinesis failure and aneuploidy. LY3295668 inhibits the growth of a broad panel of cancer cell lines, including small-cell lung and breast cancer cells. It demonstrates significant efficacy in small-cell lung cancer xenograft and patient-derived tumor preclinical models as a single agent and in combination with standard-of-care agents. LY3295668, as a highly Aurora A-selective inhibitor, may represent a preferred approach to the current pan-Aurora inhibitors as a cancer therapeutic agent.
Stop the Presses? Moving From Strategic Silence to Strategic Amplification in a Networked Media Ecosystem:

In a media ecosystem besieged with misinformation and polarizing rhetoric, what the news media chooses not to cover can be as significant as what they do cover. In this article, we examine the hist...
Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.

Motivation Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. Supplementary information Supplementary data are available at Bioinformatics online.
Performance peer groups in CEO compensation contracts

We take advantage of comprehensive panel data available as a result of the 2006 SEC disclosure rules on relative performance evaluation (RPE) to (i) better understand how firms choose performance peer groups used in CEO RPE contracts and (ii) to investigate the causal impact of mandatory disclosure on the peer selection process. We find that while firms for the most part choose performance peers to better identify their CEOs’ impact on firms’ performance, they also tend to select underperforming peers. Dynamically, we find that peers that are added and retained every year are weaker than ones that were not chosen. These findings suggest managers may have some influence on the choice of performance peers. Lastly, using a quasi-natural experiment we find that the enhanced disclosure did not affect the tendency of firms to select underperforming peers.
Common Belief in Choquet Rationality and Ambiguity Attitudes - Extended Abstract.

We consider finite games in strategic form with Choquet expected utility. Using the notion of (unambiguously) believed, we define Choquet rationalizability and characterize it by Choquet rationality and common beliefs in Choquet rationality in the universal capacity type space in a purely measurable setting. We also show that Choquet rationalizability is equivalent to iterative elimination of strictly dominated actions (not in the original game but) in an extended game. This allows for computation of Choquet rationalizable actions without the need to first compute Choquet integrals. Choquet expected utility allows us to investigate common belief in ambiguity love/aversion. We show that ambiguity love/aversion leads to smaller/larger Choquet rationalizable sets of action profiles.
Discovering the Computational Relevance of Brain Network Organization

Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connectivity in cognition: network coding (encoding/decoding) models. These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data. The success of these models supports the possibility that localized neural functions mechanistically emerge (are computed) from distributed activity flow processes that are specified primarily by connectivity patterns.
An Improved Method to Produce Clinical-Scale Natural Killer Cells from Human Pluripotent Stem Cells

Human natural killer (NK) cell-based adoptive anticancer immunotherapy has gained intense interest with many clinical trials actively recruiting patients to treat a variety of both hematological malignancies and solid tumors. Most of these trials use primary NK cells isolated either from peripheral blood (PB-NK cells) or umbilical cord blood (UCB-NK cells), though these sources require NK cell collection for each patient leading to donor variability and heterogeneity in the NK cell populations. In contrast, NK cells derived human embryonic stem cells (hESC-NK cells) or induced pluripotent stem cells (hiPSC-NK cells) provide more homogeneous cell populations that can be grown at clinical scale, and genetically engineered if needed. These characteristics make hESC-/iPSC-derived NK cells an ideal cell population for developing standardized, "off-the-shelf" immunotherapy products. Additionally, production of NK cells from undifferentiated human pluripotent stem cells enables studies to better define pathways that regulate human NK cell development and function. Our group previously has established a stromal-free, two-stage culture system to derive NK cells from hESC/hiPSC in vitro followed by clinical-scale expansion of these cells using interleukin (IL)-21 expressing artificial antigen-presenting cells. However, prior to differentiation, this method requires single-cell adaptation of hESCs/hiPSCs which takes months. Recently we optimized this method by adapting the mouse embryonic fibroblast-dependent hESC/hiPSC to feeder-free culture conditions. These feeder-free hESCs/hiPSCs are directly used to form embryoid body (EB) to generate hemato-endothelial precursor cells. This new method produces mature, functional NK cells with higher efficiency to enable rapid production of an essentially unlimited number of homogenous NK cells that can be used for standardized, targeted immunotherapy for the treatment of refractory cancers and infectious diseases.
Time-Domain Data Fusion Using Weighted Evidence and Dempster-Shafer Combination Rule: Application in Object Classification.

To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps( t s ).
Practical method to reclassify Web of Science articles into unique subject categories and broad disciplines

Classification of bibliographic items into subjects and disciplines in large databases is essential for many quantitative science studies. The Web of Science classification of journals into approxi...
Historical Foundations and Enduring Fundamentals of American Religious Freedom

The eighteenth-century American founders believed that religion is special and deserves special constitutional protection, and that all peaceable faiths must be drawn into the constitutional proces...
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models

Probabilistic topic modeling is a common first step in crosslingual tasks to enable knowledge transfer and extract multilingual features. Although many multilingual topic models have been developed...
AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses.

Many sequence-to-sequence dialogue models tend to generate safe, uninformative responses. There have been various useful efforts on trying to eliminate them. However, these approaches either improve decoding algorithms during inference, rely on hand-crafted features, or employ complex models. In our work, we build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. Specifically, we start with a simple yet effective automatic metric, AvgOut, which calculates the average output probability distribution of all time steps on the decoder side during training. This metric directly estimates which tokens are more likely to be generated, thus making it a faithful evaluation of the model diversity (i.e., for diverse models, the token probabilities should be more evenly distributed rather than peaked at a few dull tokens). We then leverage this novel metric to propose three models that promote diversity without losing relevance. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch; the second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level; the third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal. Moreover, we experiment with a hybrid model by combining the loss terms of MinAvgOut and RL. All four models outperform their base LSTM-RNN model on both diversity and relevance by a large margin, and are comparable to or better than competitive baselines (also verified via human evaluation). Moreover, our approaches are orthogonal to the base model, making them applicable as an add-on to other emerging better dialogue models in the future.