cssi/SciSciGPT-SciSciNet huggingface dataset:
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term. In this paper, we propose to learn a particle-based simulator for complex control tasks. Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within. This enables the model to quickly adapt to new environments of unknown dynamics within a few observations. We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world. Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.
Deep convolutional neural networks for eigenvalue problems in mechanics

We show that deep convolutional neural networks (CNN) can massively outperform traditional densely-connected neural networks (both deep or shallow) in predicting eigenvalue problems in mechanics. In this sense, we strike out in a new direction in mechanics computations with strongly predictive NNs whose success depends not only on architectures being deep, but also being fundamentally different from the widely-used to date. We consider a model problem: predicting the eigenvalues of 1-D and 2-D phononic crystals. For the 1-D case, the optimal CNN architecture reaches $98\%$ accuracy level on unseen data when trained with just 20,000 samples, compared to $85\%$ accuracy even with $100,000$ samples for the typical network of choice in mechanics research. We show that, with relatively high data-efficiency, CNNs have the capability to generalize well and automatically learn deep symmetry operations, easily extending to higher dimensions and our 2D case. Most importantly, we show how CNNs can naturally represent mechanical material tensors, with its convolution kernels serving as local receptive fields, which is a natural representation of mechanical response. Strategies proposed are applicable to other mechanics' problems and may, in the future, be used to sidestep cumbersome algorithms with purely data-driven approaches based upon modern deep architectures.
Holomorphic maps from the complex unit ball to Type IV classical domains

Abstract We prove rigidity results for holomorphic proper maps from the complex unit ball to the Type IV bounded symmetric domain when the codimension is small. In addition, a classification result is established in the codimension one case.
Distribution of Resonances in Scattering by Thin Barriers

This thesis contains a detailed study of the rates of wave decay for scattering by thin barriers. Thin barriers are systems in which, except for a narrow region, waves do not interact. This type of behavior is observed in physical systems including concert halls and quantum corrals. A quantum corral is constructed by configuring individual atoms or molecules to form a barrier which partially confines electrons to its interior. Here, the atoms produce a potential which plays the role of the thin barrier. In the setting of concert halls, the walls play the role of the barrier and produce partial confinement of sound waves. Rather than studying systems with a finite width interaction region, we imagine that the interaction region is reduced to a single hypersurface in R^d by taking a limit of barriers whose width is decreasing and intensity is increasing.Specifically, we are interested in wave equations with delta and delta' potentials on a hypersurface. These operators are used as models for leaky quantum graphs and quantum corrals.
A subgroup formula for f-invariant entropy

We study a measure entropy for finitely generated free group actions called f-invariant entropy. The f-invariant entropy was developed by L. Bowen and is essentially a special case of his measure entropy theory for actions of sofic groups. In this paper we relate the f-invariant entropy of a finitely generated free group action to the f-invariant entropy of the restricted action of a subgroup. We show that the ratio of these entropies equals the index of the subgroup. This generalizes a well-known formula for the Kolmogorov-Sinai entropy of amenable group actions. We then extend the definition of f-invariant entropy to actions of finitely generated virtually free groups. We also obtain a numerical virtual measure conjugacy invariant for actions of finitely generated virtually free groups.
Spectral Action in Betti Geometric Langlands

Let X be a smooth projective curve, G a reductive group, and BunG(X) the moduli of G-bundles on X. For each point of X, the Satake category acts by Hecke modifications on sheaves on BunG(X). We show that, for sheaves with nilpotent singular support, the action is locally constant with respect to the point of X. This equips sheaves with nilpotent singular support with a module structure over perfect complexes on the Betti moduli LocG∨ (X) of dual group local systems. In particular, we establish the “automorphic to Galois” direction in the Betti Geometric Langlands correspondence—to each indecomposable automorphic sheaf, we attach a dual group local system—and define the Betti version of V. Lafforgue’s excursion operators.
Free subgroups acting properly discontinuously

Abstract Given an action of a group G on a topological space X , we establish a necessary and sufficient condition for the existence of a free subgroup F of rank 2 of G acting properly discontinuously on at least one nonempty, open, F -invariant subspace of X . In the case of a discrete topology (group action on a set X ), the condition simply detects free subgroups of rank 2 acting freely on some orbit.
Effective field theory for hydrodynamics: Wess-Zumino term and anomalies in two spacetime dimensions

We develop the formalism that incorporates quantum anomalies in the effective field theory of nondissipative fluids. We consider the effect of adding a Wess-Zumino-like term to the low-energy effective action to account for anomalies. In this paper we restrict to two spacetime dimensions. We find modifications to the constitutive relations for the current and the stress-energy tensor, and, more interestingly, half a new propagating mode (one-and-a-halfth sound): a left- or right-moving wave with propagation speed that goes to zero with the anomaly coefficient. Unlike for the chiral magnetic wave in four dimensions, this mode propagates even in the absence of external fields. We check our results against a more standard, purely hydrodynamical derivation. Unitarity of the effective field theory suggests an upper bound on the anomaly coefficient in hydrodynamics.
Very true pseudo-BCK algebras

In this paper, we introduce the very true operators on pseudo-BCK algebras and we study their properties. We prove that the composition of two very true operators is a very true operator if and only if they commute. Moreover, given a very true bounded pseudo-BCK algebra (A, v), we define the pseudo- $$\hbox {BCK}_{vt,st}$$ algebra by adding two truth-depressing hedge operators associated with v. We also define the very true deductive systems and the very true homomorphisms, and we investigate their properties. Also, given a normal v-deductive system H of a very true pseudo-BCK algebra (A, v) we construct a very true operator on the quotient pseudo-BCK algebra A / H. Some particular properties are proved for the case of very true operators on classes of pseudo-BCK algebras such as pseudo-BCK(pP) algebras, $$\hbox {FL}_w$$ -algebras and pseudo-MTL algebras.
Learning to Generate Compositional Color Descriptions

The production of color language is essential for grounded language generation. Color descriptions have many challenging properties: they can be vague, compositionally complex, and denotationally rich. We present an effective approach to generating color descriptions using recurrent neural networks and a Fourier-transformed color representation. Our model outperforms previous work on a conditional language modeling task over a large corpus of naturalistic color descriptions. In addition, probing the model's output reveals that it can accurately produce not only basic color terms but also descriptors with non-convex denotations ("greenish"), bare modifiers ("bright", "dull"), and compositional phrases ("faded teal") not seen in training.
Noncommutative solenoids and the Gromov-Hausdorff propinquity

We prove that noncommutative solenoids are limits, in the sense of the Gromov-Hausdorff propinquity, of quantum tori. From this observation, we prove that noncommutative solenoids can be approximated by finite dimensional quantum compact metric spaces, and that they form a continuous family of quantum compact metric spaces over the space of multipliers of the solenoid, properly metrized.
Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists

In this paper, we study follower demographics of Donald Trump and Hillary Clinton, the two leading candidates in the 2016 U.S. presidential race. We build a unique dataset US2016, which includes the number of followers for each candidate from September 17, 2015 to December 22, 2015. US2016 also includes the geographical location of these followers, the number of their own followers and, very importantly, the profile image of each follower. We use individuals’ number of followers and profile images to analyze four dimensions of follower demographics: social status, gender, race and age. Our study shows that in terms of social influence, the Trumpists are more polarized than the Clintonists: they tend to have either a lot of influence or little influence. We also find that compared with the Clintonists, the Trumpists are more likely to be either very young or very old. Our study finds no gender affinity effect for Clinton in the Twitter sphere, but we do find that the Clintonists are more racially diverse.
Gesture based Human-Swarm Interactions for Formation Control using interpreters

Abstract We propose a novel Human-Swarm Interaction (HSI) framework which enables the user to control a swarm’s shape and formation. The user commands the swarm utilizing just arm gestures and motions which are recorded by an off-the-shelf wearable armband. We propose a novel interpreter system, which acts as an intermediary between the user and the swarm to simplify the user’s role in the interaction. The interpreter takes high level input drawn using gestures by the user, and translates it into low level swarm control commands. This interpreter employs machine learning, Kalman filtering and optimal control techniques to translate the user input into swarm control parameters. A notion of Human Interpretable dynamics is introduced, which is used by the interpreter for planning as well as to provide feedback to the user. The dynamics of the swarm are controlled using a novel decentralized formation controller based on distributed linear iterations and dynamic average consensus. The framework is demonstrated theoretically as well as experimentally in a 2D environment, with a human controlling a swarm of simulated robots in real time.
Pursuit on a graph under partial information from sensors

We consider a class of pursuit-evasion problems where an evader enters a directed acyclic graph and attempts to reach one of the terminal nodes. A pursuer enters the graph at a later time and attempts to capture the evader before it reaches a terminal node. The pursuer can only obtain information about the evader's path via sensors located at each node in the graph; the sensor measurements are either green or red (indicating whether or not the evader has passed through that node). We first show that it is NP-hard to determine whether the pursuer can enter with some nonzero delay and still be guaranteed to capture the evader. We further show that it is NP-hard to approximate (within any constant factor) the largest delay at which the pursuer can enter and still guarantee capture. Finally, we provide a linear-time algorithm to compute the maximum pursuer delay for a class of node-sweeping policies on bounded-degree tree networks.
Crime in Urban Areas: A Data Mining Perspective

Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. Traditional urban crime research focused on leveraging demographic data, which is insufficient to capture the complexity and dynamics of urban crimes. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources as well as rich environmental and social information. The availability of big urban data provides unprecedented opportunities, which enable us to conduct advanced urban crime research. Meanwhile, environmental and social crime theories from criminology provide better understandings about the behaviors of offenders and complex patterns of crime in urban. They can not only help bridge the gap from what we have (big urban data) to what we want to understand about urban crime (urban crime analysis); but also guide us to build computational models for crime. In this article, we give an overview to key theories from criminology, summarize crime analysis on urban data, review state-of-the-art algorithms for various types of computational crime tasks and discuss some appealing research directions that can bring the urban crime research into a new frontier.
Moore-Gibson-Thompson equation with memory, part II: general decay of energy

Abstract We study a temporally third order (Moore–Gibson–Thompson) equation with a memory term. Previously it was known that, in non-critical regime, the global solutions exist and the energy functionals decay to zero. More precisely, it is known that the energy has exponential decay if the memory kernel decays exponentially. The current work is a generalization of the previous one (Part I) in that it allows the memory kernel to be more general and shows that the energy decays the same way as the memory kernel does, exponentially or not.
Extreme Adaptation for Personalized Neural Machine Translation.

Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-size-fits-all models. In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation. Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.
Analytic approximation of matrix functions and dual extremal functions

We study the question of the existence of a dual extremal function for a bounded matrix function on the unit circle in connection with the problem of approximation by analytic matrix functions. We characterize the class of matrix functions, for which a dual extremal function exists in terms of the existence of a maximizing vector of the corresponding Hankel operator and in terms of certain special factorizations that involve thematic matrix functions.
Locality-Aware Laplacian Mesh Smoothing

In this paper, we propose a novel reordering scheme to improve the performance of a Laplacian Mesh Smoothing (LMS). While the Laplacian smoothing algorithm is well optimized and studied, we show how a simple reordering of the vertices of the mesh can greatly improve the execution time of the smoothing algorithm. The idea of our reordering is based on (i) the postulate that cache misses are a very time consuming part of the execution of LMS, and (ii) the study of the reuse distance patterns of various executions of the LMS algorithm. Our reordering algorithm is very simple but allows for huge performance improvement. We ran it on a Westmere-EX platform and obtained a speedup of 75 on 32 cores compared to the single core execution without reordering, and a gain in execution of 32% on 32 cores compared to state of the art reordering. Finally, we show that we leave little room for a better ordering by reducing the L2 and L3 cache misses to a bare minimum.
A Sensing Contribution-Based Two-Layer Game for Channel Selection and Spectrum Access in Cognitive Radio Ad-hoc Networks

In cognitive radio (CR) networks, the secondary users (SUs) sense the spectrum licensed to the primary users (PUs) to identify and possibly transmit over temporarily unoccupied channels. Cooperative sensing was proposed to improve the sensing accuracy, but in heterogeneous scenarios, SUs do not contribute equally to the cooperative sensing result because they experience different received PU signal quality at their sensors. In this paper, a two-layer cooperative game is developed for distributed sensing and access in multichannel CR ad hoc networks, where the SUs’ transmission opportunities are commensurate with their sensing contributions, thus fostering cooperation and eliminating free-riders. Numerical results show that the proposed two-layer game is computationally efficient and outperforms previously investigated collaborative sensing and spectrum access approaches in heterogeneous multichannel CR scenarios in terms of energy efficiency, throughput, SU fairness, and complexity. Moreover, it is demonstrated that this game is robust to changes in the network topology and the number of SUs. Finally, a new physical-layer approach is proposed to distribute the network-level miss-detection constraints fairly among the interfering SUs for guaranteed PU protection and demonstrate the performance advantages of the AND-rule combining of spectrum sensing results for heterogeneous SUs.
Image-Based Prognostics Using Penalized Tensor Regression

ABSTRACTThis article proposes a new methodology to predict and update the residual useful lifetime of a system using a sequence of degradation images. The methodology integrates tensor linear algeb...
On the sample complexity of learning graphical games

We analyze the sample complexity of learning graphical games from purely behavioral data. We assume that we can only observe the players' joint actions and not their payoffs. We analyze the sufficient and necessary number of samples for the correct recovery of the set of pure-strategy Nash equilibria (PSNE) of the true game. Our analysis focuses on directed graphs with n nodes and at most k parents per node. Sparse graphs correspond to k ∊ O(1) with respect to n, while dense graphs correspond to k ∊ O(n). By using VC dimension arguments, we show that if the number of samples is greater than O(kn log2 n) for sparse graphs or O(n2 log n) for dense graphs, then maximum likelihood estimation correctly recovers the PSNE with high probability. By using information-theoretic arguments, we show that if the number of samples is less than Q(kn log2 n) for sparse graphs or Q(n2 log n) for dense graphs, then any conceivable method fails to recover the PSNE with arbitrary probability.
Learning with incremental iterative regularization

Within a statistical learning setting, we propose and study an iterative regularization algorithm for least squares defined by an incremental gradient method. In particular, we show that, if all other parameters are fixed a priori, the number of passes over the data (epochs) acts as a regularization parameter, and prove strong universal consistency, i.e. almost sure convergence of the risk, as well as sharp finite sample bounds for the iterates. Our results are a step towards understanding the effect of multiple epochs in stochastic gradient techniques in machine learning and rely on integrating statistical and optimization results.
Lorentz space estimates for the Ginzburg–Landau energy

Abstract In this paper we prove novel lower bounds for the Ginzburg–Landau energy with or without magnetic field. These bounds rely on an improvement of the “vortex-balls construction” estimates by extracting a new positive term in the energy lower bounds. This extra term can be conveniently estimated through a Lorentz space norm, on which it thus provides an upper bound. The Lorentz space L 2 , ∞ we use is critical with respect to the expected vortex profiles and can serve to estimate the total number of vortices and get improved convergence results.
Structured Inference Networks for Nonlinear State Space Models

Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks. Our learning algorithm simultaneously learns a compiled inference network and the generative model, leveraging a structured variational approximation parameterized by recurrent neural networks to mimic the posterior distribution. We apply the learning algorithm to both synthetic and real-world datasets, demonstrating its scalability and versatility. We find that using the structured approximation to the posterior results in models with significantly higher held-out likelihood.
Authentic Research in the Classroom for Teachers and Students

With the advent of research-grade robotic telescopes (and professional archives) coupled with the wide availability of the Internet in schools, getting high-quality data in the classroom has become much easier than ever before. Robotic telescopes (and archives) have revolutionized what is possible to accomplish in the confines of a high school classroom. Especially in the context of new science standards in the US, schools need to be moving towards more project-based learning and incorporating more authentic scientific inquiry, so demand for programs such as this is only expected to grow. This contribution highlights a few of the programs that incorporate authentic research in the classroom, via teachers and/or students. I also point out some recurring themes among these programs and suggest a funnel as a way to think about the ‘ecosystem’ of projects getting astronomical data into the hands of teachers, students, and the public.
Identifying and Characterizing Sybils in the Tor Network

Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practical tools and methods to expo ...
Optimality of Simple Layered Superposition Coding in the 3 User MISO BC With Finite Precision CSIT

We study the $K=3$ user multiple input single output (MISO) broadcast channel (BC) with $M=3$ antennas at the transmitter and 1 antenna at each receiver, from the generalized degrees of freedom (GDoF) perspective, under the assumption that the channel state information at the transmitter (CSIT) is limited to the finite precision. In particular, our goal is to identify a parameter regime where a simple layered superposition (SLS) coding scheme achieves the entire GDoF region. With $\alpha _{ij}$ representing the channel strength parameter for the link from the $j^{th}$ antenna of the transmitter to the $i^{th}$ receiver, we prove that the SLS is GDoF optimal without the need for time-sharing if $\max (\alpha _{ki},\alpha _{im})\leq \alpha _{ii}$ and $\alpha _{ki}+\alpha _{im}\le \alpha _{ii}+\alpha _{km}$ for all $i,k\in [{3}],m\in [M]$ . The GDoF region under this condition is a convex polyhedron. The result generalizes to arbitrary $M\geq 3$ .
Exploring Gender Bias in Nursing Evaluations of Emergency Medicine Residents.

OBJECTIVES Nursing evaluations are an important component of residents' professional development as nurses are present for interactions with patients and nonphysician providers. Despite this, there has been few prior studies on the benefits, harms, or effectiveness of using nursing evaluations to help guide emergency medicine residents' development. We hypothesized that gender bias exists in nursing evaluations and that female residents, compared to their male counterparts, would receive more negative feedback on the perception of their interpersonal communication skills. METHODS Data were drawn from nursing evaluations of residents between March 2013 and April 2016. All comments were coded if they contained words falling into four main categories: standout, ability, grindstone, and interpersonal. This methodology and the list of words that guided coding were based on the work of prior scholars. Names and gendered pronouns were obscured and each comment was manually reviewed and coded for valence (positive, neutral, negative) and strength (certain or tentative) by at least two members of the research team. Following the qualitative coding, quantitative analysis was performed to test for differences. To evaluate whether any measurable differences in ability between male and female residents existed, we compiled and compared American Board of Emergency Medicine in-training examination scores and relevant milestone evaluations between female and male residents from the same period in which the residents were evaluated by nursing staff. RESULTS Of 1,112 nursing evaluations, 30% contained comments. Chi-square tests on the distribution of valence (positive, neutral, or negative) indicated statistically significant differences in ability and grindstone categories based on the gender of the resident. A total of 51% of ability comments about female residents were negative compared to 20% of those about male residents (χ2 = 11.83, p < 0.01). A total of 57% of grindstone comments about female residents were negative as opposed 24% of those about male residents (χ2 = 6.03, p < 0.01). CONCLUSIONS Our findings demonstrate that, despite the lack of difference in ability or competence as measured by in-service examination scores and milestone evaluations, nurses evaluate female residents lower in their abilities and work ethic compared to male residents.
How to Record Quantum Queries, and Applications to Quantum Indifferentiability

The quantum random oracle model (QROM) has become the standard model in which to prove the post-quantum security of random-oracle-based constructions. Unfortunately, none of the known proof techniques allow the reduction to record information about the adversary’s queries, a crucial feature of many classical ROM proofs, including all proofs of indifferentiability for hash function domain extension.
Time-dependent study of anisotropy in Rayleigh-Taylor instability induced turbulent flows with a variety of density ratios

This study focuses on understanding the time-dependent anisotropy, mixing, scaling of flows induced by Rayleigh-Taylor instability, and complementing the late-time snapshots reported by Cabot and Zhou [“Statistical measurements of scaling and anisotropy of turbulent flows induced by Rayleigh-Taylor instability,” Phys. Fluids 25, 015107 (2013)]. In particular, we utilize three large datasets with different Atwood numbers (density ratios) from well resolved direct numerical simulations at a moderate Reynolds number with the goal of determining the degree of departure of this inhomogeneous flow from that of homogeneous, isotropic turbulence. Three key time-dependent statistical measurements are considered in detail to delineate the role played by the acceleration. First, a number of directional length scales in this anisotropic turbulence are inspected. Second, the relationship among the outer-scale, the turbulence length, and the Taylor-microscale based Reynolds numbers is also clarified. Finally, the normalized dissipation rate is employed to inspect the distinctive features of the flow in the inhomogeneous direction parallel to gravity and in the homogeneous perpendicular directions.This study focuses on understanding the time-dependent anisotropy, mixing, scaling of flows induced by Rayleigh-Taylor instability, and complementing the late-time snapshots reported by Cabot and Zhou [“Statistical measurements of scaling and anisotropy of turbulent flows induced by Rayleigh-Taylor instability,” Phys. Fluids 25, 015107 (2013)]. In particular, we utilize three large datasets with different Atwood numbers (density ratios) from well resolved direct numerical simulations at a moderate Reynolds number with the goal of determining the degree of departure of this inhomogeneous flow from that of homogeneous, isotropic turbulence. Three key time-dependent statistical measurements are considered in detail to delineate the role played by the acceleration. First, a number of directional length scales in this anisotropic turbulence are inspected. Second, the relationship among the outer-scale, the turbulence length, and the Taylor-microscale based Reynolds numbers is also clarified. Finally, the norma...
Pseudogene Associated Recurrent Gene Fusion in Prostate Cancer

Abstract We present the functional characterization of a pseudogene associated recurrent gene fusion in prostate cancer. The fusion gene KLK4-KLKP1 is formed by the fusion of the protein coding gene KLK4 with the noncoding pseudogene KLKP1. Screening of a cohort of 659 patients (380 Caucasian American; 250 African American, and 29 patients from other races) revealed that the KLK4-KLKP1 is expressed in about 32% of prostate cancer patients. Correlative analysis with other ETS gene fusions and SPINK1 revealed a concomitant expression pattern of KLK4-KLKP1 with ERG and a mutually exclusive expression pattern with SPINK1, ETV1, ETV4, and ETV5. Development of an antibody specific to KLK4-KLKP1 fusion protein confirmed the expression of the full-length KLK4-KLKP1 protein in prostate tissues. The in vitro and in vivo functional assays to study the oncogenic properties of KLK4-KLKP1 confirmed its role in cell proliferation, cell invasion, intravasation, and tumor formation. Presence of strong ERG and AR binding sites located at the fusion junction in KLK4-KLKP1 suggests that the fusion gene is regulated by ERG and AR. Correlative analysis of clinical data showed an association of KLK4-KLKP1 with lower preoperative PSA values and in young men (
Robust computation with rhythmic spike patterns

Information coding by precise timing of spikes can be faster and more energy efficient than traditional rate coding. However, spike-timing codes are often brittle, which has limited their use in theoretical neuroscience and computing applications. Here, we propose a type of attractor neural network in complex state space and show how it can be leveraged to construct spiking neural networks with robust computational properties through a phase-to-timing mapping. Building on Hebbian neural associative memories, like Hopfield networks, we first propose threshold phasor associative memory (TPAM) networks. Complex phasor patterns whose components can assume continuous-valued phase angles and binary magnitudes can be stored and retrieved as stable fixed points in the network dynamics. TPAM achieves high memory capacity when storing sparse phasor patterns, and we derive the energy function that governs its fixed-point attractor dynamics. Second, we construct 2 spiking neural networks to approximate the complex algebraic computations in TPAM, a reductionist model with resonate-and-fire neurons and a biologically plausible network of integrate-and-fire neurons with synaptic delays and recurrently connected inhibitory interneurons. The fixed points of TPAM correspond to stable periodic states of precisely timed spiking activity that are robust to perturbation. The link established between rhythmic firing patterns and complex attractor dynamics has implications for the interpretation of spike patterns seen in neuroscience and can serve as a framework for computation in emerging neuromorphic devices.
Predicting the Politics of an Image Using Webly Supervised Data

The news media shape public opinion, and often, the visual bias they contain is evident for human observers. This bias can be inferred from how different media sources portray different subjects or topics. In this paper, we model visual political bias in contemporary media sources at scale, using webly supervised data. We collect a dataset of over one million unique images and associated news articles from left- and right-leaning news sources, and develop a method to predict the image's political leaning. This problem is particularly challenging because of the enormous intra-class visual and semantic diversity of our data. We propose a two-stage method to tackle this problem. In the first stage, the model is forced to learn relevant visual concepts that, when joined with document embeddings computed from articles paired with the images, enable the model to predict bias. In the second stage, we remove the requirement of the text domain and train a visual classifier from the features of the former model. We show this two-stage approach facilitates learning and outperforms several strong baselines. We also present extensive qualitative results demonstrating the nuances of the data.
Solving graph compression via optimal transport

We propose a new approach to graph compression by appeal to optimal transport. The transport problem is seeded with prior information about node importance, attributes, and edges in the graph. The transport formulation can be setup for either directed or undirected graphs, and its dual characterization is cast in terms of distributions over the nodes. The compression pertains to the support of node distributions and makes the problem challenging to solve directly. To this end, we introduce Boolean relaxations and specify conditions under which these relaxations are exact. The relaxations admit algorithms with provably fast convergence. Moreover, we provide an exact O(d log d) algorithm for the subproblem of projecting a d-dimensional vector to transformed simplex constraints. Our method outperforms state-of-the-art compression methods on graph classification.
A Requiem for “Blame It on Beijing” interpreting rotating global current account surpluses

Abstract Global current account imbalances have reappeared, although the extent and distribution of these imbalances are noticeably different from those experienced in the middle of the last decade. What does that recurrence mean for our understanding of the origin and nature of such imbalances? Will imbalances persist over time? Informed by empirical estimates of the determinants of current account imbalances encompassing the period after the global recession, we find that – as before – the observable manifestations of the factors driving the global saving glut have limited explanatory power for the time series variation in imbalances. Fiscal factors determine imbalances, and have accounted for a noticeable share of the recent variation in imbalances, including in the U.S. and Germany. For advanced economies, the financial component of the current account has been playing an increasing role in determining the movements of the account. Examining observable policy actions, it is clear that net official flows have been associated with some share of imbalances, although tracing out the motivations for intervention is difficult. Looking forward, it is clear that policy can influence global imbalances, although some component of the U.S. deficit will likely remain given the U.S. role in generating safe assets.
Spinning radiation from topological insulator

We show that thermal radiation from a topological insulator carries a nonzero average spin angular momentum.
Beyond Trace Reconstruction: Population Recovery from the Deletion Channel

Population recovery is the problem of learning an unknown distribution over an unknown set of n-bit strings, given access to independent draws from the distribution that have been independently corrupted according to some noise channel. Recent work has intensively studied such problems both for the {bit-flip} noise channel and for the erasure noise channel. In this paper we initiate the study of population recovery under the deletion channel, in which each bit b is independently deleted with some fixed probability and the surviving bits are concatenated and transmitted. This is a far more challenging noise model than bit-flip~noise or erasure noise; indeed, even the simplest case in which the population is of size 1 (corresponding to a trivial probability distribution supported on a single string) corresponds to the trace reconstruction problem, which is a challenging problem that has received much recent attention. In this work we give algorithms and lower bounds for population recovery under the deletion channel when the population size is some value l > 1. As our main sample complexity upper bound, we show that for any population size l = o(log n / log log n), a population of l strings from {o,1}^n can be learned under deletion channel noise using 2^n^1/2+o(1) samples. On the lower bound side, we show that at least n^Ω (l) samples are required to perform population recovery under the deletion channel when the population size is l, for all l ≤ n^1/2-e. Our upper bounds are obtained via a robust multivariate generalization of a polynomial-based analysis, due to Krasikov and Roddity [KR97], of how the k-deck of a bit-string uniquely identifies the string; this is a very different approach from recent algorithms for trace reconstruction (the l=1 case). Our lower bounds build on moment-matching results of Roos[Roos:00] and Daskalakis and Papadimitriou[DP15].
Evidence for positive priming of leaf litter decomposition by contact with eutrophic pond sediments

Organic matter processing controls the flow of carbon and nutrients through ecosystems. Heterotrophic metabolism within ponds is supported by both terrestrial leaf litter and autochthonous production. We investigated the potential for the priming of leaf litter decomposition in small ponds using microcosms. We incubated senescent tulip poplar (Liriodendron tulipifera) leaf discs in the dark for 130 days either in contact with eutrophic pond sediments or isolated from sediment contact. Leaves that had been in contact with the sediments were significantly less tough and lost more carbon mass following the incubation than leaves that were not in contact with the sediments, indicating that they were decomposing faster. We calculated a positive priming effect of the sediments of 42% and 77% based on the change in toughness and C mass loss, respectively. We further found that leaf discs that were in contact with the sediments had significantly less fungal biomass, measured as ergosterol mass, and less leaf-derived N in fungal biomass than the leaf discs isolated from the sediments. These results indicate that the presence of the more labile organic matter of the sediments alters the rate of organic matter mineralization and the cycling of nitrogen and carbon.
Analysis of two transmission eigenvalue problems with a coated boundary condition

In this paper, we investigate two transmission eigenvalue problems associated with the scattering of a media with a coated boundary. In recent years, there has been a lot of interest in studying th...
A novel Tmem119-tdTomato reporter mouse model for studying microglia in the central nervous system.

Abstract Microglia are resident immune cells of the central nervous system (CNS). The exact role of microglia in CNS disorders is not clear due to lack of tools to discriminate between microglia and infiltrating myeloid cells. Here, we present a novel reporter mouse model targeting a microglia-specific marker, TMEM119, for studying microglia in health and disease. By placing a reporter cassette (GSG-3xFlag-P2A-tdTomato) between the coding sequence of exon 2 and 3′UTR of the Tmem119 gene using CRISPR/Cas9 technology, we generated a Tmem119-tdTomato knock-in mouse strain. Gene expression assay showed no difference of endogenous Tmem119 in the CNS of Tmem119tdTomato/+ relative to wild-type mice. The cells expressing tdTomato were recognized by immunofluorescence staining using commercially available anti-TMEM119 antibodies. Additionally, immunofluorescence and flow cytometry techniques revealed that tdTomato+ cells are detected throughout the CNS, but not in peripheral tissues of Tmem119tdTomato/+ mice. Aging does not influence TMEM119 expression as tdTomato+ cells were detectable in the CNS of older mice (300 and 540 days old). Further immunofluorescence characterization shows that tdTomato+ cells colocalize with Iba1+ cells in the brain, but not with neurons, astrocytes or oligodendrocytes. Moreover, flow cytometry analysis of brain tissues of adult mice demonstrates that the majority of microglia CD45loCD11b+ cells (96.3%) are tdTomato-positive; and a minority of infiltrating CD45hiCD11b+ myeloid cells (5.3%) are also tdTomato-positive, which we further characterized and found that tdTomato expression is in part of choroid plexus macrophages but not in meningeal and perivascular macrophages. Functionally, using an acute injury model, we measured time-lapse activation of tdTomato-labeled microglia by transcranial two-photon microscopy in live Tmem119tdTomato/+ mice. Taken together, the Tmem119-tdTomato reporter mouse model is a valuable tool to specifically study the role of microglia in health and disease.
Superintegrability of Generalized Toda Models on Symmetric Spaces

In this paper we prove superintegrability of Hamiltonian systems generated by functions on $K\backslash G/K$, restriced to a symplectic leaf of the Poisson variety $G/K$, where $G$ is a simple Lie group with the standard Poisson Lie structure, $K$ is the subgroup of fixed points with respect to the Cartan involution.
Rescuing the attentional performance of rats with cholinergic losses by the M1 positive allosteric modulator TAK-071.

Loss of basal forebrain cholinergic neurons contributes to the severity of the cognitive decline in age-related dementia and, in patients with Parkinson’s disease (PD), to impairments in gait and balance and the resulting risks for falls. Contrasting with the extensive evidence indicating an essential role of cholinergic activity in mediating cognitive, specifically attentional abilities, treatment with conventional acetylcholinesterase inhibitors (AChEIs) has not fulfilled the promise of efficacy of pro-cholinergic treatments. Here, we investigated the potential usefulness of a muscarinic M1 positive allosteric modulator (PAM) in an animal model of cholinergic loss-induced impairments in attentional performance. Given evidence indicating that fast, transient cholinergic signaling mediates the detection of cues in attentional contexts, we hypothesized that a M1 PAM amplifies such transient signaling and thereby rescues attentional performance. Rats performed an operant sustained attention task (SAT), including in the presence of a distractor (dSAT) and during a post-distractor (post-dSAT) period. The post-dSAT period served to assess the capacity for recovering performance following a disruptive event. Basal forebrain infusions of the cholino-specific immunotoxin 192 IgG-saporin impaired SAT performance, and greater cholinergic losses predicted lower post-dSAT performance. Administration of TAK-071 (0.1, 0.3 mg/kg, p.o., administered over 6-day blocks) improved the performance of all rats during the post-dSAT period (main effect of dose). Drug-induced improvement of post-dSAT performance was relatively greater in lesioned rats, irrespective of sex, but also manifested in female control rats. TAK-071 primarily improved perceptual sensitivity (d’) in lesioned rats and facilitated the adoption of a more liberal response bias (B˝D) in all female rats. These findings suggest that TAK-071 may benefit the attentional performance of patients with partial cholinergic losses and specifically in situations that tax top-down, or goal-driven, attentional control.
Harmonic equiangular tight frames comprised of regular simplices

Abstract An equiangular tight frame (ETF) is a sequence of unit-norm vectors in a Euclidean space whose coherence achieves equality in the Welch bound, and thus yields an optimal packing in a projective space. A regular simplex is a simple type of ETF in which the number of vectors is one more than the dimension of the underlying space. More sophisticated examples include harmonic ETFs which equate to difference sets in finite abelian groups. Recently, it was shown that some harmonic ETFs are comprised of regular simplices. In this paper, we continue the investigation into these special harmonic ETFs. We begin by characterizing when the subspaces that are spanned by the ETF's regular simplices form an equi-isoclinic tight fusion frame (EITFF), which is a type of optimal packing in a Grassmannian space. We shall see that every difference set that produces an EITFF in this way also yields a complex circulant conference matrix. Next, we consider a subclass of these difference sets that can be factored in terms of a smaller difference set and a relative difference set. It turns out that these relative difference sets lend themselves to a second, related and yet distinct, construction of complex circulant conference matrices. Finally, we provide explicit infinite families of ETFs to which this theory applies.
Auctioning electricity under deep renewable integration using a penalty for shortfall

Abstract We propose a mechanism for the ISO to allocate and sell renewable generation in a two-stage market to a number of strategic and flexible consumers. To offer an incentive to participate in the auction, the renewable generator promises to pay the cost associated with any shortfall in generation due to the uncertain nature of the traded resource. The ISO’s goal is to devise an auction mechanism that efficiently allocates electricity among consumers, while eliciting the true value functions from them. We analyze the resulting mechanism to show that it is incentive compatible in dominant strategy, individually rational, and budget-balanced. This mechanism guarantees the generator a positive expected profit.
Nanocalorimetry and ab initio study of ternary elements in CuZr-based shape memory alloy

Abstract We present a computational-experimental study on the ternary alloying effect of CuZr-based shape memory alloy. The transformation behavior, including crystallization, martensite-austenite transformation temperature and hysteresis of Cu-Zr-X (X = Ni, Co, Hf) thin-film samples were investigated by nanocalorimetry. We used ab initio simulations to determine the B2-Cm transformation pathway, evaluate the lattice parameters, the relative phase stability, and the twin boundary energy as a function of composition. Experimental results show that alloying with Ni or Co reduces the hysteresis of the martensitic transformation, while Hf increases it. These observations are in agreement with the trend of the middle eigenvalue of the martensitic transformation matrix. The energy difference between the pure phases obtained from simulations suggests that both Co and Ni stabilize martensite against austenite. However, experiments show that Co decreases transformation temperature, while Ni increases it. We attribute this observation to the larger twin boundary energy and strain energy in the Co-containing alloy. Our results indicate that ab initio simulations are a helpful tool in the development of new shape memory alloys, provided the energy terms associated with the fine twin structure of the martensite are taken into account.